diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 3ac19a0a..331426a3 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -7,6 +7,10 @@ assignees: '' --- +**Read Troubleshoot** + +[x] I admit that I have read the [Troubleshoot](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md) before making this issue. + **Describe the problem** A clear and concise description of what the bug is. diff --git a/.gitignore b/.gitignore index dcbea2e0..05ce1df8 100644 --- a/.gitignore +++ b/.gitignore @@ -7,17 +7,17 @@ __pycache__ *.patch *.backup *.corrupted +*.partial +*.onnx sorted_styles.json +/input +/cache /language/default.json -lena.png -lena_result.png -lena_test.py +/test_imgs config.txt config_modification_tutorial.txt user_path_config.txt user_path_config-deprecated.txt -build_chb.py -experiment.py /modules/*.png /repositories /venv diff --git a/args_manager.py b/args_manager.py index 38b182be..d063a5e1 100644 --- a/args_manager.py +++ b/args_manager.py @@ -1,33 +1,38 @@ -from fcbh.options import enable_args_parsing -enable_args_parsing(False) -import fcbh.cli_args as fcbh_cli +import ldm_patched.modules.args_parser as args_parser -fcbh_cli.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.") -fcbh_cli.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.") +args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.") +args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.") -fcbh_cli.parser.add_argument("--language", type=str, default='default', - help="Translate UI using json files in [language] folder. " +args_parser.parser.add_argument("--language", type=str, default='default', + help="Translate UI using json files in [language] folder. " "For example, [--language example] will use [language/example.json] for translation.") # For example, https://github.com/lllyasviel/Fooocus/issues/849 -fcbh_cli.parser.add_argument("--enable-smart-memory", action="store_true", - help="Force loading models to vram when the unload can be avoided. " +args_parser.parser.add_argument("--disable-offload-from-vram", action="store_true", + help="Force loading models to vram when the unload can be avoided. " "Some Mac users may need this.") -fcbh_cli.parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None) -fcbh_cli.parser.add_argument("--disable-image-log", action='store_true', - help="Prevent writing images and logs to hard drive.") +args_parser.parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None) +args_parser.parser.add_argument("--disable-image-log", action='store_true', + help="Prevent writing images and logs to hard drive.") -fcbh_cli.parser.set_defaults( +args_parser.parser.add_argument("--disable-analytics", action='store_true', + help="Disables analytics for Gradio", default=False) + +args_parser.parser.set_defaults( disable_cuda_malloc=True, - auto_launch=True, + in_browser=True, port=None ) -fcbh_cli.args = fcbh_cli.parser.parse_args() +args_parser.args = args_parser.parser.parse_args() # (Disable by default because of issues like https://github.com/lllyasviel/Fooocus/issues/724) -fcbh_cli.args.disable_smart_memory = not fcbh_cli.args.enable_smart_memory +args_parser.args.always_offload_from_vram = not args_parser.args.disable_offload_from_vram -args = fcbh_cli.args +if args_parser.args.disable_analytics: + import os + os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" + +args = args_parser.args diff --git a/backend/doc b/backend/doc deleted file mode 100644 index 26cbc8f7..00000000 --- a/backend/doc +++ /dev/null @@ -1,5 +0,0 @@ -# Fooocus' Comfy Backend Headless (FCBH) - -This is a Comfy Backend from StabilityAI. This pre-complied backend makes it easier for people who have trouble using pygit2. - -FCBH is maintained by Fooocus's reviewing upon StabilityAI's changes. diff --git a/backend/headless/LICENSE b/backend/headless/LICENSE deleted file mode 100644 index f288702d..00000000 --- a/backend/headless/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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It is safest -to attach them to the start of each source file to most effectively -state the exclusion of warranty; and each file should have at least -the "copyright" line and a pointer to where the full notice is found. - - - Copyright (C) - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. 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If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. diff --git a/backend/headless/fcbh/cli_args.py b/backend/headless/fcbh/cli_args.py deleted file mode 100644 index 1d5b584b..00000000 --- a/backend/headless/fcbh/cli_args.py +++ /dev/null @@ -1,118 +0,0 @@ -import argparse -import enum -import fcbh.options - -class EnumAction(argparse.Action): - """ - Argparse action for handling Enums - """ - def __init__(self, **kwargs): - # Pop off the type value - enum_type = kwargs.pop("type", None) - - # Ensure an Enum subclass is provided - if enum_type is None: - raise ValueError("type must be assigned an Enum when using EnumAction") - if not issubclass(enum_type, enum.Enum): - raise TypeError("type must be an Enum when using EnumAction") - - # Generate choices from the Enum - choices = tuple(e.value for e in enum_type) - kwargs.setdefault("choices", choices) - kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") - - super(EnumAction, self).__init__(**kwargs) - - self._enum = enum_type - - def __call__(self, parser, namespace, values, option_string=None): - # Convert value back into an Enum - value = self._enum(values) - setattr(namespace, self.dest, value) - - -parser = argparse.ArgumentParser() - -parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") -parser.add_argument("--port", type=int, default=8188, help="Set the listen port.") -parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") -parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.") - -parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.") -parser.add_argument("--output-directory", type=str, default=None, help="Set the fcbh_backend output directory.") -parser.add_argument("--temp-directory", type=str, default=None, help="Set the fcbh_backend temp directory (default is in the fcbh_backend directory).") -parser.add_argument("--input-directory", type=str, default=None, help="Set the fcbh_backend input directory.") -parser.add_argument("--auto-launch", action="store_true", help="Automatically launch fcbh_backend in the default browser.") -parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.") -parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.") -cm_group = parser.add_mutually_exclusive_group() -cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") -cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") - -parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.") - -fp_group = parser.add_mutually_exclusive_group() -fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).") -fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.") - -parser.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.") - -fpvae_group = parser.add_mutually_exclusive_group() -fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") -fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.") -fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.") - -fpte_group = parser.add_mutually_exclusive_group() -fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).") -fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).") -fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.") -fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") - - -parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") - -parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.") - -class LatentPreviewMethod(enum.Enum): - NoPreviews = "none" - Auto = "auto" - Latent2RGB = "latent2rgb" - TAESD = "taesd" - -parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction) - -attn_group = parser.add_mutually_exclusive_group() -attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") -attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.") -attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.") - -parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") - -vram_group = parser.add_mutually_exclusive_group() -vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") -vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") -vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.") -vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.") -vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.") -vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") - - -parser.add_argument("--disable-smart-memory", action="store_true", help="Force fcbh_backend to agressively offload to regular ram instead of keeping models in vram when it can.") - - -parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") -parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") -parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") - -parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") - -if fcbh.options.args_parsing: - args = parser.parse_args() -else: - args = parser.parse_args([]) - -if args.windows_standalone_build: - args.auto_launch = True - -if args.disable_auto_launch: - args.auto_launch = False diff --git a/backend/headless/fcbh/ops.py b/backend/headless/fcbh/ops.py deleted file mode 100644 index 6b870a22..00000000 --- a/backend/headless/fcbh/ops.py +++ /dev/null @@ -1,40 +0,0 @@ -import torch -from contextlib import contextmanager - -class Linear(torch.nn.Linear): - def reset_parameters(self): - return None - -class Conv2d(torch.nn.Conv2d): - def reset_parameters(self): - return None - -class Conv3d(torch.nn.Conv3d): - def reset_parameters(self): - return None - -def conv_nd(dims, *args, **kwargs): - if dims == 2: - return Conv2d(*args, **kwargs) - elif dims == 3: - return Conv3d(*args, **kwargs) - else: - raise ValueError(f"unsupported dimensions: {dims}") - -@contextmanager -def use_fcbh_ops(device=None, dtype=None): # Kind of an ugly hack but I can't think of a better way - old_torch_nn_linear = torch.nn.Linear - force_device = device - force_dtype = dtype - def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None): - if force_device is not None: - device = force_device - if force_dtype is not None: - dtype = force_dtype - return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype) - - torch.nn.Linear = linear_with_dtype - try: - yield - finally: - torch.nn.Linear = old_torch_nn_linear diff --git a/entry_with_update.py b/entry_with_update.py index 5c027863..4b66ac2d 100644 --- a/entry_with_update.py +++ b/entry_with_update.py @@ -37,7 +37,7 @@ try: repo.reset(local_branch.target, pygit2.GIT_RESET_HARD) print("Fast-forward merge") elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL: - print("Update failed - Did you modified any file?") + print("Update failed - Did you modify any file?") except Exception as e: print('Update failed.') print(str(e)) diff --git a/expansion_experiments.py b/experiments_expansion.py similarity index 100% rename from expansion_experiments.py rename to experiments_expansion.py diff --git a/face_experiments.py b/experiments_face.py similarity index 72% rename from face_experiments.py rename to experiments_face.py index 5c0a3e58..3b4909fa 100644 --- a/face_experiments.py +++ b/experiments_face.py @@ -1,5 +1,5 @@ import cv2 -import fooocus_extras.face_crop as cropper +import extras.face_crop as cropper img = cv2.imread('lena.png') diff --git a/experiments_interrogate.py b/experiments_interrogate.py new file mode 100644 index 00000000..16639d62 --- /dev/null +++ b/experiments_interrogate.py @@ -0,0 +1,8 @@ +import cv2 +from extras.interrogate import default_interrogator as default_interrogator_photo +from extras.wd14tagger import default_interrogator as default_interrogator_anime + +img = cv2.imread('./test_imgs/red_box.jpg')[:, :, ::-1].copy() +print(default_interrogator_photo(img)) +img = cv2.imread('./test_imgs/miku.jpg')[:, :, ::-1].copy() +print(default_interrogator_anime(img)) diff --git a/extras/BLIP/configs/bert_config.json b/extras/BLIP/configs/bert_config.json new file mode 100644 index 00000000..3ef38aab --- /dev/null +++ b/extras/BLIP/configs/bert_config.json @@ -0,0 +1,21 @@ +{ + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "type_vocab_size": 2, + "vocab_size": 30522, + "encoder_width": 768, + "add_cross_attention": true +} diff --git a/extras/BLIP/configs/caption_coco.yaml b/extras/BLIP/configs/caption_coco.yaml new file mode 100644 index 00000000..42eab703 --- /dev/null +++ b/extras/BLIP/configs/caption_coco.yaml @@ -0,0 +1,33 @@ +image_root: '/export/share/datasets/vision/coco/images/' +ann_root: 'annotation' +coco_gt_root: 'annotation/coco_gt' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' + +# size of vit model; base or large +vit: 'base' +vit_grad_ckpt: False +vit_ckpt_layer: 0 +batch_size: 32 +init_lr: 1e-5 + +# vit: 'large' +# vit_grad_ckpt: True +# vit_ckpt_layer: 5 +# batch_size: 16 +# init_lr: 2e-6 + +image_size: 384 + +# generation configs +max_length: 20 +min_length: 5 +num_beams: 3 +prompt: 'a picture of ' + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 5 + diff --git a/extras/BLIP/configs/med_config.json b/extras/BLIP/configs/med_config.json new file mode 100644 index 00000000..0ffad0a6 --- /dev/null +++ b/extras/BLIP/configs/med_config.json @@ -0,0 +1,21 @@ +{ + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "type_vocab_size": 2, + "vocab_size": 30524, + "encoder_width": 768, + "add_cross_attention": true +} diff --git a/extras/BLIP/configs/nlvr.yaml b/extras/BLIP/configs/nlvr.yaml new file mode 100644 index 00000000..2d1122aa --- /dev/null +++ b/extras/BLIP/configs/nlvr.yaml @@ -0,0 +1,21 @@ +image_root: '/export/share/datasets/vision/NLVR2/' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth' + +#size of vit model; base or large +vit: 'base' +batch_size_train: 16 +batch_size_test: 64 +vit_grad_ckpt: False +vit_ckpt_layer: 0 +max_epoch: 15 + +image_size: 384 + +# optimizer +weight_decay: 0.05 +init_lr: 3e-5 +min_lr: 0 + diff --git a/extras/BLIP/configs/nocaps.yaml b/extras/BLIP/configs/nocaps.yaml new file mode 100644 index 00000000..90281358 --- /dev/null +++ b/extras/BLIP/configs/nocaps.yaml @@ -0,0 +1,15 @@ +image_root: '/export/share/datasets/vision/nocaps/' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' + +vit: 'base' +batch_size: 32 + +image_size: 384 + +max_length: 20 +min_length: 5 +num_beams: 3 +prompt: 'a picture of ' \ No newline at end of file diff --git a/extras/BLIP/configs/pretrain.yaml b/extras/BLIP/configs/pretrain.yaml new file mode 100644 index 00000000..02355ee0 --- /dev/null +++ b/extras/BLIP/configs/pretrain.yaml @@ -0,0 +1,27 @@ +train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json', + '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json', + ] +laion_path: '' + +# size of vit model; base or large +vit: 'base' +vit_grad_ckpt: False +vit_ckpt_layer: 0 + +image_size: 224 +batch_size: 75 + +queue_size: 57600 +alpha: 0.4 + +# optimizer +weight_decay: 0.05 +init_lr: 3e-4 +min_lr: 1e-6 +warmup_lr: 1e-6 +lr_decay_rate: 0.9 +max_epoch: 20 +warmup_steps: 3000 + + + diff --git a/extras/BLIP/configs/retrieval_coco.yaml b/extras/BLIP/configs/retrieval_coco.yaml new file mode 100644 index 00000000..a8569e9b --- /dev/null +++ b/extras/BLIP/configs/retrieval_coco.yaml @@ -0,0 +1,34 @@ +image_root: '/export/share/datasets/vision/coco/images/' +ann_root: 'annotation' +dataset: 'coco' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' + +# size of vit model; base or large + +vit: 'base' +batch_size_train: 32 +batch_size_test: 64 +vit_grad_ckpt: True +vit_ckpt_layer: 4 +init_lr: 1e-5 + +# vit: 'large' +# batch_size_train: 16 +# batch_size_test: 32 +# vit_grad_ckpt: True +# vit_ckpt_layer: 12 +# init_lr: 5e-6 + +image_size: 384 +queue_size: 57600 +alpha: 0.4 +k_test: 256 +negative_all_rank: True + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 6 + diff --git a/extras/BLIP/configs/retrieval_flickr.yaml b/extras/BLIP/configs/retrieval_flickr.yaml new file mode 100644 index 00000000..d75ea4ee --- /dev/null +++ b/extras/BLIP/configs/retrieval_flickr.yaml @@ -0,0 +1,34 @@ +image_root: '/export/share/datasets/vision/flickr30k/' +ann_root: 'annotation' +dataset: 'flickr' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth' + +# size of vit model; base or large + +vit: 'base' +batch_size_train: 32 +batch_size_test: 64 +vit_grad_ckpt: True +vit_ckpt_layer: 4 +init_lr: 1e-5 + +# vit: 'large' +# batch_size_train: 16 +# batch_size_test: 32 +# vit_grad_ckpt: True +# vit_ckpt_layer: 10 +# init_lr: 5e-6 + +image_size: 384 +queue_size: 57600 +alpha: 0.4 +k_test: 128 +negative_all_rank: False + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 6 + diff --git a/extras/BLIP/configs/retrieval_msrvtt.yaml b/extras/BLIP/configs/retrieval_msrvtt.yaml new file mode 100644 index 00000000..395f6254 --- /dev/null +++ b/extras/BLIP/configs/retrieval_msrvtt.yaml @@ -0,0 +1,12 @@ +video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' + +# size of vit model; base or large +vit: 'base' +batch_size: 64 +k_test: 128 +image_size: 384 +num_frm_test: 8 \ No newline at end of file diff --git a/extras/BLIP/configs/vqa.yaml b/extras/BLIP/configs/vqa.yaml new file mode 100644 index 00000000..74327e6d --- /dev/null +++ b/extras/BLIP/configs/vqa.yaml @@ -0,0 +1,25 @@ +vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/ +vg_root: '/export/share/datasets/vision/visual-genome/' #followed by image/ +train_files: ['vqa_train','vqa_val','vg_qa'] +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth' + +# size of vit model; base or large +vit: 'base' +batch_size_train: 16 +batch_size_test: 32 +vit_grad_ckpt: False +vit_ckpt_layer: 0 +init_lr: 2e-5 + +image_size: 480 + +k_test: 128 +inference: 'rank' + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 10 \ No newline at end of file diff --git a/extras/BLIP/models/bert_tokenizer/config.json b/extras/BLIP/models/bert_tokenizer/config.json new file mode 100644 index 00000000..45a2321a --- /dev/null +++ b/extras/BLIP/models/bert_tokenizer/config.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "BertForMaskedLM" + ], + "attention_probs_dropout_prob": 0.1, + "gradient_checkpointing": false, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "position_embedding_type": "absolute", + "transformers_version": "4.6.0.dev0", + "type_vocab_size": 2, + "use_cache": true, + "vocab_size": 30522 +} diff --git a/extras/BLIP/models/bert_tokenizer/tokenizer.json b/extras/BLIP/models/bert_tokenizer/tokenizer.json new file mode 100644 index 00000000..949a6f01 --- /dev/null +++ b/extras/BLIP/models/bert_tokenizer/tokenizer.json @@ -0,0 +1 @@ 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b/extras/BLIP/models/blip.py new file mode 100644 index 00000000..a2566331 --- /dev/null +++ b/extras/BLIP/models/blip.py @@ -0,0 +1,239 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li +''' +import warnings +warnings.filterwarnings("ignore") + +from extras.BLIP.models.vit import VisionTransformer, interpolate_pos_embed +from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel +from transformers import BertTokenizer + +import torch +from torch import nn +import torch.nn.functional as F + +import os +from urllib.parse import urlparse +from timm.models.hub import download_cached_file + +class BLIP_Base(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 224, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + + def forward(self, image, caption, mode): + + assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal" + text = self.tokenizer(caption, return_tensors="pt").to(image.device) + + if mode=='image': + # return image features + image_embeds = self.visual_encoder(image) + return image_embeds + + elif mode=='text': + # return text features + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + return text_output.last_hidden_state + + elif mode=='multimodal': + # return multimodel features + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + text.input_ids[:,0] = self.tokenizer.enc_token_id + output = self.text_encoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + return output.last_hidden_state + + + +class BLIP_Decoder(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 384, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + prompt = 'a picture of ', + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_decoder = BertLMHeadModel(config=med_config) + + self.prompt = prompt + self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1 + + + def forward(self, image, caption): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) + + text.input_ids[:,0] = self.tokenizer.bos_token_id + + decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100) + decoder_targets[:,:self.prompt_length] = -100 + + decoder_output = self.text_decoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + labels = decoder_targets, + return_dict = True, + ) + loss_lm = decoder_output.loss + + return loss_lm + + def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0): + image_embeds = self.visual_encoder(image) + + if not sample: + image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) + + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts} + + prompt = [self.prompt] * image.size(0) + input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) + input_ids[:,0] = self.tokenizer.bos_token_id + input_ids = input_ids[:, :-1] + + if sample: + #nucleus sampling + outputs = self.text_decoder.generate(input_ids=input_ids, + max_length=max_length, + min_length=min_length, + do_sample=True, + top_p=top_p, + num_return_sequences=1, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + repetition_penalty=1.1, + **model_kwargs) + else: + #beam search + outputs = self.text_decoder.generate(input_ids=input_ids, + max_length=max_length, + min_length=min_length, + num_beams=num_beams, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + repetition_penalty=repetition_penalty, + **model_kwargs) + + captions = [] + for output in outputs: + caption = self.tokenizer.decode(output, skip_special_tokens=True) + captions.append(caption[len(self.prompt):]) + return captions + + +def blip_decoder(pretrained='',**kwargs): + model = BLIP_Decoder(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + assert(len(msg.missing_keys)==0) + return model + +def blip_feature_extractor(pretrained='',**kwargs): + model = BLIP_Base(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + assert(len(msg.missing_keys)==0) + return model + +def init_tokenizer(): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "bert_tokenizer") + tokenizer = BertTokenizer.from_pretrained(tokenizer_path) + tokenizer.add_special_tokens({'bos_token':'[DEC]'}) + tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) + tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] + return tokenizer + + +def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): + + assert vit in ['base', 'large'], "vit parameter must be base or large" + if vit=='base': + vision_width = 768 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, + num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0 or drop_path_rate + ) + elif vit=='large': + vision_width = 1024 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, + num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0.1 or drop_path_rate + ) + return visual_encoder, vision_width + +def is_url(url_or_filename): + parsed = urlparse(url_or_filename) + return parsed.scheme in ("http", "https") + +def load_checkpoint(model,url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + + state_dict = checkpoint['model'] + + state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) + if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): + state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], + model.visual_encoder_m) + for key in model.state_dict().keys(): + if key in state_dict.keys(): + if state_dict[key].shape!=model.state_dict()[key].shape: + del state_dict[key] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + diff --git a/extras/BLIP/models/blip_itm.py b/extras/BLIP/models/blip_itm.py new file mode 100644 index 00000000..6f4da821 --- /dev/null +++ b/extras/BLIP/models/blip_itm.py @@ -0,0 +1,76 @@ +from extras.BLIP.models.med import BertConfig, BertModel +from transformers import BertTokenizer + +import torch +from torch import nn +import torch.nn.functional as F + +from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint + +class BLIP_ITM(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 384, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + embed_dim = 256, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + text_width = self.text_encoder.config.hidden_size + + self.vision_proj = nn.Linear(vision_width, embed_dim) + self.text_proj = nn.Linear(text_width, embed_dim) + + self.itm_head = nn.Linear(text_width, 2) + + + def forward(self, image, caption, match_head='itm'): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, + return_tensors="pt").to(image.device) + + + if match_head=='itm': + output = self.text_encoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + itm_output = self.itm_head(output.last_hidden_state[:,0,:]) + return itm_output + + elif match_head=='itc': + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) + text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) + + sim = image_feat @ text_feat.t() + return sim + + +def blip_itm(pretrained='',**kwargs): + model = BLIP_ITM(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + assert(len(msg.missing_keys)==0) + return model + \ No newline at end of file diff --git a/extras/BLIP/models/blip_nlvr.py b/extras/BLIP/models/blip_nlvr.py new file mode 100644 index 00000000..0eb9eaa6 --- /dev/null +++ b/extras/BLIP/models/blip_nlvr.py @@ -0,0 +1,105 @@ +from extras.BLIP.models.med import BertConfig +from extras.BLIP.models.nlvr_encoder import BertModel +from extras.BLIP.models.vit import interpolate_pos_embed +from extras.BLIP.models.blip import create_vit, init_tokenizer, is_url + +from timm.models.hub import download_cached_file + +import torch +from torch import nn +import torch.nn.functional as F +from transformers import BertTokenizer +import numpy as np +import os + + +class BLIP_NLVR(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 480, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + self.cls_head = nn.Sequential( + nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size), + nn.ReLU(), + nn.Linear(self.text_encoder.config.hidden_size, 2) + ) + + def forward(self, image, text, targets, train=True): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0)) + + text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device) + text.input_ids[:,0] = self.tokenizer.enc_token_id + + output = self.text_encoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = [image0_embeds,image1_embeds], + encoder_attention_mask = [image_atts[:image0_embeds.size(0)], + image_atts[image0_embeds.size(0):]], + return_dict = True, + ) + hidden_state = output.last_hidden_state[:,0,:] + prediction = self.cls_head(hidden_state) + + if train: + loss = F.cross_entropy(prediction, targets) + return loss + else: + return prediction + +def blip_nlvr(pretrained='',**kwargs): + model = BLIP_NLVR(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + print("missing keys:") + print(msg.missing_keys) + return model + + +def load_checkpoint(model,url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + state_dict = checkpoint['model'] + + state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) + + for key in list(state_dict.keys()): + if 'crossattention.self.' in key: + new_key0 = key.replace('self','self0') + new_key1 = key.replace('self','self1') + state_dict[new_key0] = state_dict[key] + state_dict[new_key1] = state_dict[key] + elif 'crossattention.output.dense.' in key: + new_key0 = key.replace('dense','dense0') + new_key1 = key.replace('dense','dense1') + state_dict[new_key0] = state_dict[key] + state_dict[new_key1] = state_dict[key] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + \ No newline at end of file diff --git a/extras/BLIP/models/blip_pretrain.py b/extras/BLIP/models/blip_pretrain.py new file mode 100644 index 00000000..9b8a3a47 --- /dev/null +++ b/extras/BLIP/models/blip_pretrain.py @@ -0,0 +1,339 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li +''' +from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel +from transformers import BertTokenizer +import transformers +transformers.logging.set_verbosity_error() + +import torch +from torch import nn +import torch.nn.functional as F + +from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint + +class BLIP_Pretrain(nn.Module): + def __init__(self, + med_config = 'configs/bert_config.json', + image_size = 224, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + embed_dim = 256, + queue_size = 57600, + momentum = 0.995, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0) + + if vit=='base': + checkpoint = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth", + map_location="cpu", check_hash=True) + state_dict = checkpoint["model"] + msg = self.visual_encoder.load_state_dict(state_dict,strict=False) + elif vit=='large': + from timm.models.helpers import load_custom_pretrained + from timm.models.vision_transformer import default_cfgs + load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k']) + + self.tokenizer = init_tokenizer() + encoder_config = BertConfig.from_json_file(med_config) + encoder_config.encoder_width = vision_width + self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False) + self.text_encoder.resize_token_embeddings(len(self.tokenizer)) + + text_width = self.text_encoder.config.hidden_size + + self.vision_proj = nn.Linear(vision_width, embed_dim) + self.text_proj = nn.Linear(text_width, embed_dim) + + self.itm_head = nn.Linear(text_width, 2) + + # create momentum encoders + self.visual_encoder_m, vision_width = create_vit(vit,image_size) + self.vision_proj_m = nn.Linear(vision_width, embed_dim) + self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False) + self.text_proj_m = nn.Linear(text_width, embed_dim) + + self.model_pairs = [[self.visual_encoder,self.visual_encoder_m], + [self.vision_proj,self.vision_proj_m], + [self.text_encoder,self.text_encoder_m], + [self.text_proj,self.text_proj_m], + ] + self.copy_params() + + # create the queue + self.register_buffer("image_queue", torch.randn(embed_dim, queue_size)) + self.register_buffer("text_queue", torch.randn(embed_dim, queue_size)) + self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long)) + + self.image_queue = nn.functional.normalize(self.image_queue, dim=0) + self.text_queue = nn.functional.normalize(self.text_queue, dim=0) + + self.queue_size = queue_size + self.momentum = momentum + self.temp = nn.Parameter(0.07*torch.ones([])) + + # create the decoder + decoder_config = BertConfig.from_json_file(med_config) + decoder_config.encoder_width = vision_width + self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config) + self.text_decoder.resize_token_embeddings(len(self.tokenizer)) + tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention') + + + def forward(self, image, caption, alpha): + with torch.no_grad(): + self.temp.clamp_(0.001,0.5) + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) + + text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30, + return_tensors="pt").to(image.device) + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) + + # get momentum features + with torch.no_grad(): + self._momentum_update() + image_embeds_m = self.visual_encoder_m(image) + image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1) + image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1) + + text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) + text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1) + + sim_i2t_m = image_feat_m @ text_feat_all / self.temp + sim_t2i_m = text_feat_m @ image_feat_all / self.temp + + sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device) + sim_targets.fill_diagonal_(1) + + sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets + sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets + + sim_i2t = image_feat @ text_feat_all / self.temp + sim_t2i = text_feat @ image_feat_all / self.temp + + loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean() + loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() + + loss_ita = (loss_i2t+loss_t2i)/2 + + self._dequeue_and_enqueue(image_feat_m, text_feat_m) + + ###============== Image-text Matching ===================### + encoder_input_ids = text.input_ids.clone() + encoder_input_ids[:,0] = self.tokenizer.enc_token_id + + # forward the positve image-text pair + bs = image.size(0) + output_pos = self.text_encoder(encoder_input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + with torch.no_grad(): + weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4 + weights_t2i.fill_diagonal_(0) + weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4 + weights_i2t.fill_diagonal_(0) + + # select a negative image for each text + image_embeds_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_t2i[b], 1).item() + image_embeds_neg.append(image_embeds[neg_idx]) + image_embeds_neg = torch.stack(image_embeds_neg,dim=0) + + # select a negative text for each image + text_ids_neg = [] + text_atts_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_i2t[b], 1).item() + text_ids_neg.append(encoder_input_ids[neg_idx]) + text_atts_neg.append(text.attention_mask[neg_idx]) + + text_ids_neg = torch.stack(text_ids_neg,dim=0) + text_atts_neg = torch.stack(text_atts_neg,dim=0) + + text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0) + text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0) + + image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0) + image_atts_all = torch.cat([image_atts,image_atts],dim=0) + + output_neg = self.text_encoder(text_ids_all, + attention_mask = text_atts_all, + encoder_hidden_states = image_embeds_all, + encoder_attention_mask = image_atts_all, + return_dict = True, + ) + + vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0) + vl_output = self.itm_head(vl_embeddings) + + itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)], + dim=0).to(image.device) + loss_itm = F.cross_entropy(vl_output, itm_labels) + + ##================= LM ========================## + decoder_input_ids = text.input_ids.clone() + decoder_input_ids[:,0] = self.tokenizer.bos_token_id + decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100) + + decoder_output = self.text_decoder(decoder_input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + labels = decoder_targets, + return_dict = True, + ) + + loss_lm = decoder_output.loss + return loss_ita, loss_itm, loss_lm + + + + @torch.no_grad() + def copy_params(self): + for model_pair in self.model_pairs: + for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): + param_m.data.copy_(param.data) # initialize + param_m.requires_grad = False # not update by gradient + + + @torch.no_grad() + def _momentum_update(self): + for model_pair in self.model_pairs: + for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): + param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum) + + + @torch.no_grad() + def _dequeue_and_enqueue(self, image_feat, text_feat): + # gather keys before updating queue + image_feats = concat_all_gather(image_feat) + text_feats = concat_all_gather(text_feat) + + batch_size = image_feats.shape[0] + + ptr = int(self.queue_ptr) + assert self.queue_size % batch_size == 0 # for simplicity + + # replace the keys at ptr (dequeue and enqueue) + self.image_queue[:, ptr:ptr + batch_size] = image_feats.T + self.text_queue[:, ptr:ptr + batch_size] = text_feats.T + ptr = (ptr + batch_size) % self.queue_size # move pointer + + self.queue_ptr[0] = ptr + + +def blip_pretrain(**kwargs): + model = BLIP_Pretrain(**kwargs) + return model + + +@torch.no_grad() +def concat_all_gather(tensor): + """ + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + tensors_gather = [torch.ones_like(tensor) + for _ in range(torch.distributed.get_world_size())] + torch.distributed.all_gather(tensors_gather, tensor, async_op=False) + + output = torch.cat(tensors_gather, dim=0) + return output + + +from typing import List +def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str): + uninitialized_encoder_weights: List[str] = [] + if decoder.__class__ != encoder.__class__: + print( + f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized." + ) + + def tie_encoder_to_decoder_recursively( + decoder_pointer: nn.Module, + encoder_pointer: nn.Module, + module_name: str, + uninitialized_encoder_weights: List[str], + skip_key: str, + depth=0, + ): + assert isinstance(decoder_pointer, nn.Module) and isinstance( + encoder_pointer, nn.Module + ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module" + if hasattr(decoder_pointer, "weight") and skip_key not in module_name: + assert hasattr(encoder_pointer, "weight") + encoder_pointer.weight = decoder_pointer.weight + if hasattr(decoder_pointer, "bias"): + assert hasattr(encoder_pointer, "bias") + encoder_pointer.bias = decoder_pointer.bias + print(module_name+' is tied') + return + + encoder_modules = encoder_pointer._modules + decoder_modules = decoder_pointer._modules + if len(decoder_modules) > 0: + assert ( + len(encoder_modules) > 0 + ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}" + + all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()]) + encoder_layer_pos = 0 + for name, module in decoder_modules.items(): + if name.isdigit(): + encoder_name = str(int(name) + encoder_layer_pos) + decoder_name = name + if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len( + encoder_modules + ) != len(decoder_modules): + # this can happen if the name corresponds to the position in a list module list of layers + # in this case the decoder has added a cross-attention that the encoder does not have + # thus skip this step and subtract one layer pos from encoder + encoder_layer_pos -= 1 + continue + elif name not in encoder_modules: + continue + elif depth > 500: + raise ValueError( + "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model." + ) + else: + decoder_name = encoder_name = name + tie_encoder_to_decoder_recursively( + decoder_modules[decoder_name], + encoder_modules[encoder_name], + module_name + "/" + name, + uninitialized_encoder_weights, + skip_key, + depth=depth + 1, + ) + all_encoder_weights.remove(module_name + "/" + encoder_name) + + uninitialized_encoder_weights += list(all_encoder_weights) + + # tie weights recursively + tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key) diff --git a/extras/BLIP/models/blip_retrieval.py b/extras/BLIP/models/blip_retrieval.py new file mode 100644 index 00000000..09493586 --- /dev/null +++ b/extras/BLIP/models/blip_retrieval.py @@ -0,0 +1,319 @@ +from extras.BLIP.models.med import BertConfig, BertModel +from transformers import BertTokenizer + +import torch +from torch import nn +import torch.nn.functional as F + +from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint + +class BLIP_Retrieval(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 384, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + embed_dim = 256, + queue_size = 57600, + momentum = 0.995, + negative_all_rank = False, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + text_width = self.text_encoder.config.hidden_size + + self.vision_proj = nn.Linear(vision_width, embed_dim) + self.text_proj = nn.Linear(text_width, embed_dim) + + self.itm_head = nn.Linear(text_width, 2) + + # create momentum encoders + self.visual_encoder_m, vision_width = create_vit(vit,image_size) + self.vision_proj_m = nn.Linear(vision_width, embed_dim) + self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False) + self.text_proj_m = nn.Linear(text_width, embed_dim) + + self.model_pairs = [[self.visual_encoder,self.visual_encoder_m], + [self.vision_proj,self.vision_proj_m], + [self.text_encoder,self.text_encoder_m], + [self.text_proj,self.text_proj_m], + ] + self.copy_params() + + # create the queue + self.register_buffer("image_queue", torch.randn(embed_dim, queue_size)) + self.register_buffer("text_queue", torch.randn(embed_dim, queue_size)) + self.register_buffer("idx_queue", torch.full((1,queue_size),-100)) + self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long)) + + self.image_queue = nn.functional.normalize(self.image_queue, dim=0) + self.text_queue = nn.functional.normalize(self.text_queue, dim=0) + + self.queue_size = queue_size + self.momentum = momentum + self.temp = nn.Parameter(0.07*torch.ones([])) + + self.negative_all_rank = negative_all_rank + + + def forward(self, image, caption, alpha, idx): + with torch.no_grad(): + self.temp.clamp_(0.001,0.5) + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) + + text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, + return_tensors="pt").to(image.device) + + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) + + ###============== Image-text Contrastive Learning ===================### + idx = idx.view(-1,1) + idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1) + pos_idx = torch.eq(idx, idx_all).float() + sim_targets = pos_idx / pos_idx.sum(1,keepdim=True) + + # get momentum features + with torch.no_grad(): + self._momentum_update() + image_embeds_m = self.visual_encoder_m(image) + image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1) + image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1) + + text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) + text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1) + + sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp + sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp + + sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets + sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets + + sim_i2t = image_feat @ text_feat_m_all / self.temp + sim_t2i = text_feat @ image_feat_m_all / self.temp + + loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean() + loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() + + loss_ita = (loss_i2t+loss_t2i)/2 + + idxs = concat_all_gather(idx) + self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs) + + ###============== Image-text Matching ===================### + encoder_input_ids = text.input_ids.clone() + encoder_input_ids[:,0] = self.tokenizer.enc_token_id + + # forward the positve image-text pair + bs = image.size(0) + output_pos = self.text_encoder(encoder_input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + + + if self.negative_all_rank: + # compute sample similarity + with torch.no_grad(): + mask = torch.eq(idx, idxs.t()) + + image_feat_world = concat_all_gather(image_feat) + text_feat_world = concat_all_gather(text_feat) + + sim_i2t = image_feat @ text_feat_world.t() / self.temp + sim_t2i = text_feat @ image_feat_world.t() / self.temp + + weights_i2t = F.softmax(sim_i2t,dim=1) + weights_i2t.masked_fill_(mask, 0) + + weights_t2i = F.softmax(sim_t2i,dim=1) + weights_t2i.masked_fill_(mask, 0) + + image_embeds_world = all_gather_with_grad(image_embeds) + + # select a negative image (from all ranks) for each text + image_embeds_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_t2i[b], 1).item() + image_embeds_neg.append(image_embeds_world[neg_idx]) + image_embeds_neg = torch.stack(image_embeds_neg,dim=0) + + # select a negative text (from all ranks) for each image + input_ids_world = concat_all_gather(encoder_input_ids) + att_mask_world = concat_all_gather(text.attention_mask) + + text_ids_neg = [] + text_atts_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_i2t[b], 1).item() + text_ids_neg.append(input_ids_world[neg_idx]) + text_atts_neg.append(att_mask_world[neg_idx]) + + else: + with torch.no_grad(): + mask = torch.eq(idx, idx.t()) + + sim_i2t = image_feat @ text_feat.t() / self.temp + sim_t2i = text_feat @ image_feat.t() / self.temp + + weights_i2t = F.softmax(sim_i2t,dim=1) + weights_i2t.masked_fill_(mask, 0) + + weights_t2i = F.softmax(sim_t2i,dim=1) + weights_t2i.masked_fill_(mask, 0) + + # select a negative image (from same rank) for each text + image_embeds_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_t2i[b], 1).item() + image_embeds_neg.append(image_embeds[neg_idx]) + image_embeds_neg = torch.stack(image_embeds_neg,dim=0) + + # select a negative text (from same rank) for each image + text_ids_neg = [] + text_atts_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_i2t[b], 1).item() + text_ids_neg.append(encoder_input_ids[neg_idx]) + text_atts_neg.append(text.attention_mask[neg_idx]) + + text_ids_neg = torch.stack(text_ids_neg,dim=0) + text_atts_neg = torch.stack(text_atts_neg,dim=0) + + text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0) + text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0) + + image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0) + image_atts_all = torch.cat([image_atts,image_atts],dim=0) + + output_neg = self.text_encoder(text_ids_all, + attention_mask = text_atts_all, + encoder_hidden_states = image_embeds_all, + encoder_attention_mask = image_atts_all, + return_dict = True, + ) + + + vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0) + vl_output = self.itm_head(vl_embeddings) + + itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)], + dim=0).to(image.device) + loss_itm = F.cross_entropy(vl_output, itm_labels) + + return loss_ita, loss_itm + + + @torch.no_grad() + def copy_params(self): + for model_pair in self.model_pairs: + for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): + param_m.data.copy_(param.data) # initialize + param_m.requires_grad = False # not update by gradient + + + @torch.no_grad() + def _momentum_update(self): + for model_pair in self.model_pairs: + for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): + param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum) + + + @torch.no_grad() + def _dequeue_and_enqueue(self, image_feat, text_feat, idxs): + # gather keys before updating queue + image_feats = concat_all_gather(image_feat) + text_feats = concat_all_gather(text_feat) + + + batch_size = image_feats.shape[0] + + ptr = int(self.ptr_queue) + assert self.queue_size % batch_size == 0 # for simplicity + + # replace the keys at ptr (dequeue and enqueue) + self.image_queue[:, ptr:ptr + batch_size] = image_feats.T + self.text_queue[:, ptr:ptr + batch_size] = text_feats.T + self.idx_queue[:, ptr:ptr + batch_size] = idxs.T + ptr = (ptr + batch_size) % self.queue_size # move pointer + + self.ptr_queue[0] = ptr + + +def blip_retrieval(pretrained='',**kwargs): + model = BLIP_Retrieval(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + print("missing keys:") + print(msg.missing_keys) + return model + + +@torch.no_grad() +def concat_all_gather(tensor): + """ + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + tensors_gather = [torch.ones_like(tensor) + for _ in range(torch.distributed.get_world_size())] + torch.distributed.all_gather(tensors_gather, tensor, async_op=False) + + output = torch.cat(tensors_gather, dim=0) + return output + + +class GatherLayer(torch.autograd.Function): + """ + Gather tensors from all workers with support for backward propagation: + This implementation does not cut the gradients as torch.distributed.all_gather does. + """ + + @staticmethod + def forward(ctx, x): + output = [torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())] + torch.distributed.all_gather(output, x) + return tuple(output) + + @staticmethod + def backward(ctx, *grads): + all_gradients = torch.stack(grads) + torch.distributed.all_reduce(all_gradients) + return all_gradients[torch.distributed.get_rank()] + + +def all_gather_with_grad(tensors): + """ + Performs all_gather operation on the provided tensors. + Graph remains connected for backward grad computation. + """ + # Queue the gathered tensors + world_size = torch.distributed.get_world_size() + # There is no need for reduction in the single-proc case + if world_size == 1: + return tensors + + tensor_all = GatherLayer.apply(tensors) + + return torch.cat(tensor_all, dim=0) diff --git a/extras/BLIP/models/blip_vqa.py b/extras/BLIP/models/blip_vqa.py new file mode 100644 index 00000000..99928a8b --- /dev/null +++ b/extras/BLIP/models/blip_vqa.py @@ -0,0 +1,186 @@ +from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel +from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint + +import torch +from torch import nn +import torch.nn.functional as F +from transformers import BertTokenizer +import numpy as np + +class BLIP_VQA(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 480, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) + self.tokenizer = init_tokenizer() + + encoder_config = BertConfig.from_json_file(med_config) + encoder_config.encoder_width = vision_width + self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) + + decoder_config = BertConfig.from_json_file(med_config) + self.text_decoder = BertLMHeadModel(config=decoder_config) + + + def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, + return_tensors="pt").to(image.device) + question.input_ids[:,0] = self.tokenizer.enc_token_id + + if train: + ''' + n: number of answers for each question + weights: weight for each answer + ''' + answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) + answer.input_ids[:,0] = self.tokenizer.bos_token_id + answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100) + + question_output = self.text_encoder(question.input_ids, + attention_mask = question.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True) + + question_states = [] + question_atts = [] + for b, n in enumerate(n): + question_states += [question_output.last_hidden_state[b]]*n + question_atts += [question.attention_mask[b]]*n + question_states = torch.stack(question_states,0) + question_atts = torch.stack(question_atts,0) + + answer_output = self.text_decoder(answer.input_ids, + attention_mask = answer.attention_mask, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + labels = answer_targets, + return_dict = True, + reduction = 'none', + ) + + loss = weights * answer_output.loss + loss = loss.sum()/image.size(0) + + return loss + + + else: + question_output = self.text_encoder(question.input_ids, + attention_mask = question.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True) + + if inference=='generate': + num_beams = 3 + question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) + question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) + model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} + + bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) + + outputs = self.text_decoder.generate(input_ids=bos_ids, + max_length=10, + min_length=1, + num_beams=num_beams, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + **model_kwargs) + + answers = [] + for output in outputs: + answer = self.tokenizer.decode(output, skip_special_tokens=True) + answers.append(answer) + return answers + + elif inference=='rank': + max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, + answer.input_ids, answer.attention_mask, k_test) + return max_ids + + + + def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k): + + num_ques = question_states.size(0) + start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token + + start_output = self.text_decoder(start_ids, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + return_dict = True, + reduction = 'none') + logits = start_output.logits[:,0,:] # first token's logit + + # topk_probs: top-k probability + # topk_ids: [num_question, k] + answer_first_token = answer_ids[:,1] + prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) + topk_probs, topk_ids = prob_first_token.topk(k,dim=1) + + # answer input: [num_question*k, answer_len] + input_ids = [] + input_atts = [] + for b, topk_id in enumerate(topk_ids): + input_ids.append(answer_ids.index_select(dim=0, index=topk_id)) + input_atts.append(answer_atts.index_select(dim=0, index=topk_id)) + input_ids = torch.cat(input_ids,dim=0) + input_atts = torch.cat(input_atts,dim=0) + + targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100) + + # repeat encoder's output for top-k answers + question_states = tile(question_states, 0, k) + question_atts = tile(question_atts, 0, k) + + output = self.text_decoder(input_ids, + attention_mask = input_atts, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + labels = targets_ids, + return_dict = True, + reduction = 'none') + + log_probs_sum = -output.loss + log_probs_sum = log_probs_sum.view(num_ques,k) + + max_topk_ids = log_probs_sum.argmax(dim=1) + max_ids = topk_ids[max_topk_ids>=0,max_topk_ids] + + return max_ids + + +def blip_vqa(pretrained='',**kwargs): + model = BLIP_VQA(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) +# assert(len(msg.missing_keys)==0) + return model + + +def tile(x, dim, n_tile): + init_dim = x.size(dim) + repeat_idx = [1] * x.dim() + repeat_idx[dim] = n_tile + x = x.repeat(*(repeat_idx)) + order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])) + return torch.index_select(x, dim, order_index.to(x.device)) + + \ No newline at end of file diff --git a/extras/BLIP/models/med.py b/extras/BLIP/models/med.py new file mode 100644 index 00000000..7b00a354 --- /dev/null +++ b/extras/BLIP/models/med.py @@ -0,0 +1,955 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on huggingface code base + * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert +''' + +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +from torch import Tensor, device, dtype, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss +import torch.nn.functional as F + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + + +logger = logging.get_logger(__name__) + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode=='multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warn( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """ Initialize the weights """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + + def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + causal_mask = torch.cat( + [ + torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, + ) + + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + device = input_ids.device + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = inputs_embeds.device + elif encoder_embeds is not None: + input_shape = encoder_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = encoder_embeds.device + else: + raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, + device, is_decoder) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + else: + embedding_output = encoder_embeds + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + + +class BertLMHeadModel(BertPreTrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=True, + reduction='mean', + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are + ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + Returns: + Example:: + >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig + >>> import torch + >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') + >>> config = BertConfig.from_pretrained("bert-base-cased") + >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> prediction_logits = outputs.logits + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if labels is not None: + use_cache = False + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + mode=mode, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + if return_logits: + return prediction_scores[:, :-1, :].contiguous() + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + if reduction=='none': + lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): + input_shape = input_ids.shape + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_shape) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "past_key_values": past, + "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), + "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), + "is_decoder": True, + } + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past diff --git a/extras/BLIP/models/nlvr_encoder.py b/extras/BLIP/models/nlvr_encoder.py new file mode 100644 index 00000000..1946bb4a --- /dev/null +++ b/extras/BLIP/models/nlvr_encoder.py @@ -0,0 +1,843 @@ +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +from torch import Tensor, device, dtype, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss +import torch.nn.functional as F + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + + +logger = logging.get_logger(__name__) + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config, twin=False, merge=False): + super().__init__() + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + if twin: + self.dense0 = nn.Linear(config.hidden_size, config.hidden_size) + self.dense1 = nn.Linear(config.hidden_size, config.hidden_size) + else: + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if merge: + self.act = ACT2FN[config.hidden_act] + self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size) + self.merge = True + else: + self.merge = False + + def forward(self, hidden_states, input_tensor): + if type(hidden_states) == list: + hidden_states0 = self.dense0(hidden_states[0]) + hidden_states1 = self.dense1(hidden_states[1]) + if self.merge: + #hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1))) + hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1)) + else: + hidden_states = (hidden_states0+hidden_states1)/2 + else: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False, layer_num=-1): + super().__init__() + if is_cross_attention: + self.self0 = BertSelfAttention(config, is_cross_attention) + self.self1 = BertSelfAttention(config, is_cross_attention) + else: + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6)) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + if type(encoder_hidden_states)==list: + self_outputs0 = self.self0( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states[0], + encoder_attention_mask[0], + past_key_value, + output_attentions, + ) + self_outputs1 = self.self1( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states[1], + encoder_attention_mask[1], + past_key_value, + output_attentions, + ) + attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states) + + outputs = (attention_output,) + self_outputs0[1:] # add attentions if we output them + else: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode=='multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warn( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """ Initialize the weights """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + + def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + causal_mask = torch.cat( + [ + torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, + ) + + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + device = input_ids.device + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = inputs_embeds.device + elif encoder_embeds is not None: + input_shape = encoder_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = encoder_embeds.device + else: + raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, + device, is_decoder) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + else: + embedding_output = encoder_embeds + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + diff --git a/extras/BLIP/models/vit.py b/extras/BLIP/models/vit.py new file mode 100644 index 00000000..91c0adad --- /dev/null +++ b/extras/BLIP/models/vit.py @@ -0,0 +1,308 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on timm code base + * https://github.com/rwightman/pytorch-image-models/tree/master/timm +''' + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial + +from timm.models.vision_transformer import _cfg, PatchEmbed +from timm.models.registry import register_model +from timm.models.layers import trunc_normal_, DropPath +from timm.models.helpers import named_apply, adapt_input_conv + + +def checkpoint_wrapper(x): + return x + + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.attn_gradients = None + self.attention_map = None + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def forward(self, x, register_hook=False): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + if register_hook: + self.save_attention_map(attn) + attn.register_hook(self.save_attn_gradients) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if use_grad_checkpointing: + self.attn = checkpoint_wrapper(self.attn) + self.mlp = checkpoint_wrapper(self.mlp) + + def forward(self, x, register_hook=False): + x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - + https://arxiv.org/abs/2010.11929 + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, + use_grad_checkpointing=False, ckpt_layer=0): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + qk_scale (float): override default qk scale of head_dim ** -0.5 if set + representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + norm_layer: (nn.Module): normalization layer + """ + super().__init__() + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) + ) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward(self, x, register_blk=-1): + B = x.shape[0] + x = self.patch_embed(x) + + cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + x = x + self.pos_embed[:,:x.size(1),:] + x = self.pos_drop(x) + + for i,blk in enumerate(self.blocks): + x = blk(x, register_blk==i) + x = self.norm(x) + + return x + + @torch.jit.ignore() + def load_pretrained(self, checkpoint_path, prefix=''): + _load_weights(self, checkpoint_path, prefix) + + +@torch.no_grad() +def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): + """ Load weights from .npz checkpoints for official Google Brain Flax implementation + """ + import numpy as np + + def _n2p(w, t=True): + if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: + w = w.flatten() + if t: + if w.ndim == 4: + w = w.transpose([3, 2, 0, 1]) + elif w.ndim == 3: + w = w.transpose([2, 0, 1]) + elif w.ndim == 2: + w = w.transpose([1, 0]) + return torch.from_numpy(w) + + w = np.load(checkpoint_path) + if not prefix and 'opt/target/embedding/kernel' in w: + prefix = 'opt/target/' + + if hasattr(model.patch_embed, 'backbone'): + # hybrid + backbone = model.patch_embed.backbone + stem_only = not hasattr(backbone, 'stem') + stem = backbone if stem_only else backbone.stem + stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) + stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) + stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) + if not stem_only: + for i, stage in enumerate(backbone.stages): + for j, block in enumerate(stage.blocks): + bp = f'{prefix}block{i + 1}/unit{j + 1}/' + for r in range(3): + getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) + getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) + getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) + if block.downsample is not None: + block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) + block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) + block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) + embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) + else: + embed_conv_w = adapt_input_conv( + model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) + model.patch_embed.proj.weight.copy_(embed_conv_w) + model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) + model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) + pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) + if pos_embed_w.shape != model.pos_embed.shape: + pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights + pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) + model.pos_embed.copy_(pos_embed_w) + model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) + model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) +# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: +# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) +# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) +# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: +# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) +# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) + for i, block in enumerate(model.blocks.children()): + block_prefix = f'{prefix}Transformer/encoderblock_{i}/' + mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' + block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) + block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) + block.attn.qkv.weight.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) + block.attn.qkv.bias.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) + block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) + block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) + for r in range(2): + getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) + getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) + block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) + block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) + + +def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): + # interpolate position embedding + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = visual_encoder.patch_embed.num_patches + num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + + if orig_size!=new_size: + # class_token and dist_token are kept unchanged + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) + + return new_pos_embed + else: + return pos_embed_checkpoint \ No newline at end of file diff --git a/modules/expansion.py b/extras/expansion.py similarity index 97% rename from modules/expansion.py rename to extras/expansion.py index 357c139e..c1b59b8a 100644 --- a/modules/expansion.py +++ b/extras/expansion.py @@ -8,12 +8,12 @@ import os import torch import math -import fcbh.model_management as model_management +import ldm_patched.modules.model_management as model_management from transformers.generation.logits_process import LogitsProcessorList from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed from modules.config import path_fooocus_expansion -from fcbh.model_patcher import ModelPatcher +from ldm_patched.modules.model_patcher import ModelPatcher # limitation of np.random.seed(), called from transformers.set_seed() diff --git a/fooocus_extras/face_crop.py b/extras/face_crop.py similarity index 93% rename from fooocus_extras/face_crop.py rename to extras/face_crop.py index 8f1e54e4..d4da7e81 100644 --- a/fooocus_extras/face_crop.py +++ b/extras/face_crop.py @@ -25,11 +25,11 @@ def crop_image(img_rgb): global faceRestoreHelper if faceRestoreHelper is None: - from fooocus_extras.facexlib.utils.face_restoration_helper import FaceRestoreHelper + from extras.facexlib.utils.face_restoration_helper import FaceRestoreHelper faceRestoreHelper = FaceRestoreHelper( upscale_factor=1, model_rootpath=modules.config.path_controlnet, - device='cpu' # use cpu is safer since we are out of fcbh management + device='cpu' # use cpu is safer since we are out of memory management ) faceRestoreHelper.clean_all() diff --git a/fooocus_extras/facexlib/detection/__init__.py b/extras/facexlib/detection/__init__.py similarity index 95% rename from fooocus_extras/facexlib/detection/__init__.py rename to extras/facexlib/detection/__init__.py index 67fac30b..4e52fd74 100644 --- a/fooocus_extras/facexlib/detection/__init__.py +++ b/extras/facexlib/detection/__init__.py @@ -1,7 +1,7 @@ import torch from copy import deepcopy -from fooocus_extras.facexlib.utils import load_file_from_url +from extras.facexlib.utils import load_file_from_url from .retinaface import RetinaFace diff --git a/fooocus_extras/facexlib/detection/align_trans.py b/extras/facexlib/detection/align_trans.py similarity index 100% rename from fooocus_extras/facexlib/detection/align_trans.py rename to extras/facexlib/detection/align_trans.py diff --git a/fooocus_extras/facexlib/detection/matlab_cp2tform.py b/extras/facexlib/detection/matlab_cp2tform.py similarity index 100% rename from fooocus_extras/facexlib/detection/matlab_cp2tform.py rename to extras/facexlib/detection/matlab_cp2tform.py diff --git a/fooocus_extras/facexlib/detection/retinaface.py b/extras/facexlib/detection/retinaface.py similarity index 97% rename from fooocus_extras/facexlib/detection/retinaface.py rename to extras/facexlib/detection/retinaface.py index 26f220a8..5e0b4f0a 100644 --- a/fooocus_extras/facexlib/detection/retinaface.py +++ b/extras/facexlib/detection/retinaface.py @@ -6,9 +6,9 @@ import torch.nn.functional as F from PIL import Image from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter -from fooocus_extras.facexlib.detection.align_trans import get_reference_facial_points, warp_and_crop_face -from fooocus_extras.facexlib.detection.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head -from fooocus_extras.facexlib.detection.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm, +from extras.facexlib.detection.align_trans import get_reference_facial_points, warp_and_crop_face +from extras.facexlib.detection.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head +from extras.facexlib.detection.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm, py_cpu_nms) diff --git a/fooocus_extras/facexlib/detection/retinaface_net.py b/extras/facexlib/detection/retinaface_net.py similarity index 100% rename from fooocus_extras/facexlib/detection/retinaface_net.py rename to extras/facexlib/detection/retinaface_net.py diff --git a/fooocus_extras/facexlib/detection/retinaface_utils.py b/extras/facexlib/detection/retinaface_utils.py similarity index 100% rename from fooocus_extras/facexlib/detection/retinaface_utils.py rename to extras/facexlib/detection/retinaface_utils.py diff --git a/fooocus_extras/facexlib/parsing/__init__.py b/extras/facexlib/parsing/__init__.py similarity index 94% rename from fooocus_extras/facexlib/parsing/__init__.py rename to extras/facexlib/parsing/__init__.py index 22c687c2..8b4758bd 100644 --- a/fooocus_extras/facexlib/parsing/__init__.py +++ b/extras/facexlib/parsing/__init__.py @@ -1,6 +1,6 @@ import torch -from fooocus_extras.facexlib.utils import load_file_from_url +from extras.facexlib.utils import load_file_from_url from .bisenet import BiSeNet from .parsenet import ParseNet diff --git a/fooocus_extras/facexlib/parsing/bisenet.py b/extras/facexlib/parsing/bisenet.py similarity index 100% rename from fooocus_extras/facexlib/parsing/bisenet.py rename to extras/facexlib/parsing/bisenet.py diff --git a/fooocus_extras/facexlib/parsing/parsenet.py b/extras/facexlib/parsing/parsenet.py similarity index 100% rename from fooocus_extras/facexlib/parsing/parsenet.py rename to extras/facexlib/parsing/parsenet.py diff --git a/fooocus_extras/facexlib/parsing/resnet.py b/extras/facexlib/parsing/resnet.py similarity index 100% rename from fooocus_extras/facexlib/parsing/resnet.py rename to extras/facexlib/parsing/resnet.py diff --git a/fooocus_extras/facexlib/utils/__init__.py b/extras/facexlib/utils/__init__.py similarity index 100% rename from fooocus_extras/facexlib/utils/__init__.py rename to extras/facexlib/utils/__init__.py diff --git a/fooocus_extras/facexlib/utils/face_restoration_helper.py b/extras/facexlib/utils/face_restoration_helper.py similarity index 98% rename from fooocus_extras/facexlib/utils/face_restoration_helper.py rename to extras/facexlib/utils/face_restoration_helper.py index 9570ef3d..2a39361a 100644 --- a/fooocus_extras/facexlib/utils/face_restoration_helper.py +++ b/extras/facexlib/utils/face_restoration_helper.py @@ -4,9 +4,9 @@ import os import torch from torchvision.transforms.functional import normalize -from fooocus_extras.facexlib.detection import init_detection_model -from fooocus_extras.facexlib.parsing import init_parsing_model -from fooocus_extras.facexlib.utils.misc import img2tensor, imwrite +from extras.facexlib.detection import init_detection_model +from extras.facexlib.parsing import init_parsing_model +from extras.facexlib.utils.misc import img2tensor, imwrite def get_largest_face(det_faces, h, w): diff --git a/fooocus_extras/facexlib/utils/face_utils.py b/extras/facexlib/utils/face_utils.py similarity index 97% rename from fooocus_extras/facexlib/utils/face_utils.py rename to extras/facexlib/utils/face_utils.py index c7c785f4..0bbe43c8 100644 --- a/fooocus_extras/facexlib/utils/face_utils.py +++ b/extras/facexlib/utils/face_utils.py @@ -211,9 +211,9 @@ def paste_face_back(img, face, inverse_affine): if __name__ == '__main__': import os - from fooocus_extras.facexlib.detection import init_detection_model - from fooocus_extras.facexlib.utils.face_restoration_helper import get_largest_face - from fooocus_extras.facexlib.visualization import visualize_detection + from extras.facexlib.detection import init_detection_model + from extras.facexlib.utils.face_restoration_helper import get_largest_face + from extras.facexlib.visualization import visualize_detection img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png' img_name = os.splitext(os.path.basename(img_path))[0] diff --git a/fooocus_extras/facexlib/utils/misc.py b/extras/facexlib/utils/misc.py similarity index 100% rename from fooocus_extras/facexlib/utils/misc.py rename to extras/facexlib/utils/misc.py diff --git a/extras/interrogate.py b/extras/interrogate.py new file mode 100644 index 00000000..410d685f --- /dev/null +++ b/extras/interrogate.py @@ -0,0 +1,63 @@ +import os +import torch +import ldm_patched.modules.model_management as model_management + +from torchvision import transforms +from torchvision.transforms.functional import InterpolationMode +from modules.model_loader import load_file_from_url +from modules.config import path_clip_vision +from ldm_patched.modules.model_patcher import ModelPatcher +from extras.BLIP.models.blip import blip_decoder + + +blip_image_eval_size = 384 +blip_repo_root = os.path.join(os.path.dirname(__file__), 'BLIP') + + +class Interrogator: + def __init__(self): + self.blip_model = None + self.load_device = torch.device('cpu') + self.offload_device = torch.device('cpu') + self.dtype = torch.float32 + + @torch.no_grad() + @torch.inference_mode() + def interrogate(self, img_rgb): + if self.blip_model is None: + filename = load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/model_base_caption_capfilt_large.pth', + model_dir=path_clip_vision, + file_name='model_base_caption_capfilt_large.pth', + ) + + model = blip_decoder(pretrained=filename, image_size=blip_image_eval_size, vit='base', + med_config=os.path.join(blip_repo_root, "configs", "med_config.json")) + model.eval() + + self.load_device = model_management.text_encoder_device() + self.offload_device = model_management.text_encoder_offload_device() + self.dtype = torch.float32 + + model.to(self.offload_device) + + if model_management.should_use_fp16(device=self.load_device): + model.half() + self.dtype = torch.float16 + + self.blip_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device) + + model_management.load_model_gpu(self.blip_model) + + gpu_image = transforms.Compose([ + transforms.ToTensor(), + transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), + transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) + ])(img_rgb).unsqueeze(0).to(device=self.load_device, dtype=self.dtype) + + caption = self.blip_model.model.generate(gpu_image, sample=True, num_beams=1, max_length=75)[0] + + return caption + + +default_interrogator = Interrogator().interrogate diff --git a/fooocus_extras/ip_adapter.py b/extras/ip_adapter.py similarity index 91% rename from fooocus_extras/ip_adapter.py rename to extras/ip_adapter.py index 2961a650..a145b68d 100644 --- a/fooocus_extras/ip_adapter.py +++ b/extras/ip_adapter.py @@ -1,12 +1,12 @@ import torch -import fcbh.clip_vision +import ldm_patched.modules.clip_vision import safetensors.torch as sf -import fcbh.model_management as model_management +import ldm_patched.modules.model_management as model_management import contextlib -import fcbh.ldm.modules.attention as attention +import ldm_patched.ldm.modules.attention as attention -from fooocus_extras.resampler import Resampler -from fcbh.model_patcher import ModelPatcher +from extras.resampler import Resampler +from ldm_patched.modules.model_patcher import ModelPatcher from modules.core import numpy_to_pytorch @@ -82,7 +82,7 @@ class IPAdapterModel(torch.nn.Module): self.ip_layers.load_state_dict_ordered(state_dict["ip_adapter"]) -clip_vision: fcbh.clip_vision.ClipVisionModel = None +clip_vision: ldm_patched.modules.clip_vision.ClipVisionModel = None ip_negative: torch.Tensor = None ip_adapters: dict = {} @@ -91,7 +91,7 @@ def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path): global clip_vision, ip_negative, ip_adapters if clip_vision is None and isinstance(clip_vision_path, str): - clip_vision = fcbh.clip_vision.load(clip_vision_path) + clip_vision = ldm_patched.modules.clip_vision.load(clip_vision_path) if ip_negative is None and isinstance(ip_negative_path, str): ip_negative = sf.load_file(ip_negative_path)['data'] @@ -165,7 +165,7 @@ def preprocess(img, ip_adapter_path): global ip_adapters entry = ip_adapters[ip_adapter_path] - fcbh.model_management.load_model_gpu(clip_vision.patcher) + ldm_patched.modules.model_management.load_model_gpu(clip_vision.patcher) pixel_values = clip_preprocess(numpy_to_pytorch(img).to(clip_vision.load_device)) if clip_vision.dtype != torch.float32: @@ -173,25 +173,20 @@ def preprocess(img, ip_adapter_path): else: precision_scope = lambda a, b: contextlib.nullcontext(a) - with precision_scope(fcbh.model_management.get_autocast_device(clip_vision.load_device), torch.float32): - outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True) + with precision_scope(ldm_patched.modules.model_management.get_autocast_device(clip_vision.load_device), torch.float32): + outputs = clip_vision.model(pixel_values=pixel_values, intermediate_output=-2) ip_adapter = entry['ip_adapter'] ip_layers = entry['ip_layers'] image_proj_model = entry['image_proj_model'] ip_unconds = entry['ip_unconds'] - if ip_adapter.plus: - cond = outputs.hidden_states[-2] - else: - cond = outputs.image_embeds + cond = outputs[1].to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) - cond = cond.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) - - fcbh.model_management.load_model_gpu(image_proj_model) + ldm_patched.modules.model_management.load_model_gpu(image_proj_model) cond = image_proj_model.model(cond).to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) - fcbh.model_management.load_model_gpu(ip_layers) + ldm_patched.modules.model_management.load_model_gpu(ip_layers) if ip_unconds is None: uncond = ip_negative.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) diff --git a/fooocus_extras/preprocessors.py b/extras/preprocessors.py similarity index 100% rename from fooocus_extras/preprocessors.py rename to extras/preprocessors.py diff --git a/fooocus_extras/resampler.py b/extras/resampler.py similarity index 100% rename from fooocus_extras/resampler.py rename to extras/resampler.py diff --git a/fooocus_extras/vae_interpose.py b/extras/vae_interpose.py similarity index 88% rename from fooocus_extras/vae_interpose.py rename to extras/vae_interpose.py index 93e7e68f..72fb09a4 100644 --- a/fooocus_extras/vae_interpose.py +++ b/extras/vae_interpose.py @@ -4,9 +4,9 @@ import os import torch import safetensors.torch as sf import torch.nn as nn -import fcbh.model_management +import ldm_patched.modules.model_management -from fcbh.model_patcher import ModelPatcher +from ldm_patched.modules.model_patcher import ModelPatcher from modules.config import path_vae_approx @@ -76,17 +76,17 @@ def parse(x): model.eval() sd = sf.load_file(vae_approx_filename) model.load_state_dict(sd) - fp16 = fcbh.model_management.should_use_fp16() + fp16 = ldm_patched.modules.model_management.should_use_fp16() if fp16: model = model.half() vae_approx_model = ModelPatcher( model=model, - load_device=fcbh.model_management.get_torch_device(), + load_device=ldm_patched.modules.model_management.get_torch_device(), offload_device=torch.device('cpu') ) vae_approx_model.dtype = torch.float16 if fp16 else torch.float32 - fcbh.model_management.load_model_gpu(vae_approx_model) + ldm_patched.modules.model_management.load_model_gpu(vae_approx_model) x = x_origin.to(device=vae_approx_model.load_device, dtype=vae_approx_model.dtype) x = vae_approx_model.model(x).to(x_origin) diff --git a/extras/wd14tagger.py b/extras/wd14tagger.py new file mode 100644 index 00000000..368c13df --- /dev/null +++ b/extras/wd14tagger.py @@ -0,0 +1,98 @@ +# https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags +# https://github.com/pythongosssss/ComfyUI-WD14-Tagger/blob/main/wd14tagger.py + +# { +# "wd-v1-4-moat-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2", +# "wd-v1-4-convnextv2-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2", +# "wd-v1-4-convnext-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2", +# "wd-v1-4-convnext-tagger": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger", +# "wd-v1-4-vit-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2" +# } + + +import numpy as np +import csv +import onnxruntime as ort + +from PIL import Image +from onnxruntime import InferenceSession +from modules.config import path_clip_vision +from modules.model_loader import load_file_from_url + + +global_model = None +global_csv = None + + +def default_interrogator(image_rgb, threshold=0.35, character_threshold=0.85, exclude_tags=""): + global global_model, global_csv + + model_name = "wd-v1-4-moat-tagger-v2" + + model_onnx_filename = load_file_from_url( + url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.onnx', + model_dir=path_clip_vision, + file_name=f'{model_name}.onnx', + ) + + model_csv_filename = load_file_from_url( + url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.csv', + model_dir=path_clip_vision, + file_name=f'{model_name}.csv', + ) + + if global_model is not None: + model = global_model + else: + model = InferenceSession(model_onnx_filename, providers=ort.get_available_providers()) + global_model = model + + input = model.get_inputs()[0] + height = input.shape[1] + + image = Image.fromarray(image_rgb) # RGB + ratio = float(height)/max(image.size) + new_size = tuple([int(x*ratio) for x in image.size]) + image = image.resize(new_size, Image.LANCZOS) + square = Image.new("RGB", (height, height), (255, 255, 255)) + square.paste(image, ((height-new_size[0])//2, (height-new_size[1])//2)) + + image = np.array(square).astype(np.float32) + image = image[:, :, ::-1] # RGB -> BGR + image = np.expand_dims(image, 0) + + if global_csv is not None: + csv_lines = global_csv + else: + csv_lines = [] + with open(model_csv_filename) as f: + reader = csv.reader(f) + next(reader) + for row in reader: + csv_lines.append(row) + global_csv = csv_lines + + tags = [] + general_index = None + character_index = None + for line_num, row in enumerate(csv_lines): + if general_index is None and row[2] == "0": + general_index = line_num + elif character_index is None and row[2] == "4": + character_index = line_num + tags.append(row[1]) + + label_name = model.get_outputs()[0].name + probs = model.run([label_name], {input.name: image})[0] + + result = list(zip(tags, probs[0])) + + general = [item for item in result[general_index:character_index] if item[1] > threshold] + character = [item for item in result[character_index:] if item[1] > character_threshold] + + all = character + general + remove = [s.strip() for s in exclude_tags.lower().split(",")] + all = [tag for tag in all if tag[0] not in remove] + + res = ", ".join((item[0].replace("(", "\\(").replace(")", "\\)") for item in all)).replace('_', ' ') + return res diff --git a/fooocus_version.py b/fooocus_version.py index 0a0bc34e..efcfe020 100644 --- a/fooocus_version.py +++ b/fooocus_version.py @@ -1 +1 @@ -version = '2.1.824' +version = '2.1.839' diff --git a/javascript/script.js b/javascript/script.js index 05a8c590..593d8c36 100644 --- a/javascript/script.js +++ b/javascript/script.js @@ -125,18 +125,23 @@ document.addEventListener("DOMContentLoaded", function() { * Add a ctrl+enter as a shortcut to start a generation */ document.addEventListener('keydown', function(e) { - var handled = false; - if (e.key !== undefined) { - if ((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true; - } else if (e.keyCode !== undefined) { - if ((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true; - } - if (handled) { - var button = gradioApp().querySelector('button[id=generate_button]'); - if (button) { - button.click(); + const isModifierKey = (e.metaKey || e.ctrlKey || e.altKey); + const isEnterKey = (e.key == "Enter" || e.keyCode == 13); + + if(isModifierKey && isEnterKey) { + const generateButton = gradioApp().querySelector('button:not(.hidden)[id=generate_button]'); + if (generateButton) { + generateButton.click(); + e.preventDefault(); + return; + } + + const stopButton = gradioApp().querySelector('button:not(.hidden)[id=stop_button]') + if(stopButton) { + stopButton.click(); + e.preventDefault(); + return; } - e.preventDefault(); } }); diff --git a/launch.py b/launch.py index de23994d..e98045f6 100644 --- a/launch.py +++ b/launch.py @@ -1,17 +1,19 @@ import os import sys - +import ssl print('[System ARGV] ' + str(sys.argv)) root = os.path.dirname(os.path.abspath(__file__)) -backend_path = os.path.join(root, 'backend', 'headless') -sys.path += [root, backend_path] - +sys.path.append(root) os.chdir(root) + os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" +os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0" os.environ["GRADIO_SERVER_PORT"] = "7865" +ssl._create_default_https_context = ssl._create_unverified_context + import platform import fooocus_version @@ -87,19 +89,19 @@ def download_models(): return -def ini_fcbh_args(): +def ini_args(): from args_manager import args return args prepare_environment() build_launcher() -args = ini_fcbh_args() +args = ini_args() -if args.cuda_device is not None: - os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device) - print("Set device to:", args.cuda_device) +if args.gpu_device_id is not None: + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id) + print("Set device to:", args.gpu_device_id) download_models() diff --git a/backend/headless/nodes.py b/ldm_patched/contrib/external.py similarity index 85% rename from backend/headless/nodes.py rename to ldm_patched/contrib/external.py index e702eae3..e20b08c5 100644 --- a/backend/headless/nodes.py +++ b/ldm_patched/contrib/external.py @@ -1,3 +1,5 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + import torch import os @@ -14,31 +16,31 @@ from PIL.PngImagePlugin import PngInfo import numpy as np import safetensors.torch -sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "fcbh")) +pass # sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "ldm_patched")) -import fcbh.diffusers_load -import fcbh.samplers -import fcbh.sample -import fcbh.sd -import fcbh.utils -import fcbh.controlnet +import ldm_patched.modules.diffusers_load +import ldm_patched.modules.samplers +import ldm_patched.modules.sample +import ldm_patched.modules.sd +import ldm_patched.modules.utils +import ldm_patched.modules.controlnet -import fcbh.clip_vision +import ldm_patched.modules.clip_vision -import fcbh.model_management -from fcbh.cli_args import args +import ldm_patched.modules.model_management +from ldm_patched.modules.args_parser import args import importlib -import folder_paths -import latent_preview +import ldm_patched.utils.path_utils +import ldm_patched.utils.latent_visualization def before_node_execution(): - fcbh.model_management.throw_exception_if_processing_interrupted() + ldm_patched.modules.model_management.throw_exception_if_processing_interrupted() def interrupt_processing(value=True): - fcbh.model_management.interrupt_current_processing(value) + ldm_patched.modules.model_management.interrupt_current_processing(value) MAX_RESOLUTION=8192 @@ -361,12 +363,12 @@ class VAEEncodeForInpaint: class SaveLatent: def __init__(self): - self.output_dir = folder_paths.get_output_directory() + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT", ), - "filename_prefix": ("STRING", {"default": "latents/fcbh_backend"})}, + "filename_prefix": ("STRING", {"default": "latents/ldm_patched"})}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () @@ -376,8 +378,8 @@ class SaveLatent: CATEGORY = "_for_testing" - def save(self, samples, filename_prefix="fcbh_backend", prompt=None, extra_pnginfo=None): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + def save(self, samples, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir) # support save metadata for latent sharing prompt_info = "" @@ -385,7 +387,7 @@ class SaveLatent: prompt_info = json.dumps(prompt) metadata = None - if not args.disable_metadata: + if not args.disable_server_info: metadata = {"prompt": prompt_info} if extra_pnginfo is not None: for x in extra_pnginfo: @@ -406,14 +408,14 @@ class SaveLatent: output["latent_tensor"] = samples["samples"] output["latent_format_version_0"] = torch.tensor([]) - fcbh.utils.save_torch_file(output, file, metadata=metadata) + ldm_patched.modules.utils.save_torch_file(output, file, metadata=metadata) return { "ui": { "latents": results } } class LoadLatent: @classmethod def INPUT_TYPES(s): - input_dir = folder_paths.get_input_directory() + input_dir = ldm_patched.utils.path_utils.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] return {"required": {"latent": [sorted(files), ]}, } @@ -423,7 +425,7 @@ class LoadLatent: FUNCTION = "load" def load(self, latent): - latent_path = folder_paths.get_annotated_filepath(latent) + latent_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent) latent = safetensors.torch.load_file(latent_path, device="cpu") multiplier = 1.0 if "latent_format_version_0" not in latent: @@ -433,7 +435,7 @@ class LoadLatent: @classmethod def IS_CHANGED(s, latent): - image_path = folder_paths.get_annotated_filepath(latent) + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) @@ -441,7 +443,7 @@ class LoadLatent: @classmethod def VALIDATE_INPUTS(s, latent): - if not folder_paths.exists_annotated_filepath(latent): + if not ldm_patched.utils.path_utils.exists_annotated_filepath(latent): return "Invalid latent file: {}".format(latent) return True @@ -449,22 +451,22 @@ class LoadLatent: class CheckpointLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ), - "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}} + return {"required": { "config_name": (ldm_patched.utils.path_utils.get_filename_list("configs"), ), + "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), )}} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "advanced/loaders" def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): - config_path = folder_paths.get_full_path("configs", config_name) - ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) - return fcbh.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + config_path = ldm_patched.utils.path_utils.get_full_path("configs", config_name) + ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) + return ldm_patched.modules.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) class CheckpointLoaderSimple: @classmethod def INPUT_TYPES(s): - return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" @@ -472,15 +474,15 @@ class CheckpointLoaderSimple: CATEGORY = "loaders" def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): - ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) - out = fcbh.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) + out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return out[:3] class DiffusersLoader: @classmethod def INPUT_TYPES(cls): paths = [] - for search_path in folder_paths.get_folder_paths("diffusers"): + for search_path in ldm_patched.utils.path_utils.get_folder_paths("diffusers"): if os.path.exists(search_path): for root, subdir, files in os.walk(search_path, followlinks=True): if "model_index.json" in files: @@ -493,20 +495,20 @@ class DiffusersLoader: CATEGORY = "advanced/loaders/deprecated" def load_checkpoint(self, model_path, output_vae=True, output_clip=True): - for search_path in folder_paths.get_folder_paths("diffusers"): + for search_path in ldm_patched.utils.path_utils.get_folder_paths("diffusers"): if os.path.exists(search_path): path = os.path.join(search_path, model_path) if os.path.exists(path): model_path = path break - return fcbh.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return ldm_patched.modules.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) class unCLIPCheckpointLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") FUNCTION = "load_checkpoint" @@ -514,8 +516,8 @@ class unCLIPCheckpointLoader: CATEGORY = "loaders" def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): - ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) - out = fcbh.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) + out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return out class CLIPSetLastLayer: @@ -542,7 +544,7 @@ class LoraLoader: def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip": ("CLIP", ), - "lora_name": (folder_paths.get_filename_list("loras"), ), + "lora_name": (ldm_patched.utils.path_utils.get_filename_list("loras"), ), "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), }} @@ -555,7 +557,7 @@ class LoraLoader: if strength_model == 0 and strength_clip == 0: return (model, clip) - lora_path = folder_paths.get_full_path("loras", lora_name) + lora_path = ldm_patched.utils.path_utils.get_full_path("loras", lora_name) lora = None if self.loaded_lora is not None: if self.loaded_lora[0] == lora_path: @@ -566,17 +568,30 @@ class LoraLoader: del temp if lora is None: - lora = fcbh.utils.load_torch_file(lora_path, safe_load=True) + lora = ldm_patched.modules.utils.load_torch_file(lora_path, safe_load=True) self.loaded_lora = (lora_path, lora) - model_lora, clip_lora = fcbh.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) + model_lora, clip_lora = ldm_patched.modules.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) return (model_lora, clip_lora) +class LoraLoaderModelOnly(LoraLoader): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "lora_name": (ldm_patched.utils.path_utils.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_lora_model_only" + + def load_lora_model_only(self, model, lora_name, strength_model): + return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) + class VAELoader: @staticmethod def vae_list(): - vaes = folder_paths.get_filename_list("vae") - approx_vaes = folder_paths.get_filename_list("vae_approx") + vaes = ldm_patched.utils.path_utils.get_filename_list("vae") + approx_vaes = ldm_patched.utils.path_utils.get_filename_list("vae_approx") sdxl_taesd_enc = False sdxl_taesd_dec = False sd1_taesd_enc = False @@ -600,16 +615,16 @@ class VAELoader: @staticmethod def load_taesd(name): sd = {} - approx_vaes = folder_paths.get_filename_list("vae_approx") + approx_vaes = ldm_patched.utils.path_utils.get_filename_list("vae_approx") encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) - enc = fcbh.utils.load_torch_file(folder_paths.get_full_path("vae_approx", encoder)) + enc = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path("vae_approx", encoder)) for k in enc: sd["taesd_encoder.{}".format(k)] = enc[k] - dec = fcbh.utils.load_torch_file(folder_paths.get_full_path("vae_approx", decoder)) + dec = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path("vae_approx", decoder)) for k in dec: sd["taesd_decoder.{}".format(k)] = dec[k] @@ -632,15 +647,15 @@ class VAELoader: if vae_name in ["taesd", "taesdxl"]: sd = self.load_taesd(vae_name) else: - vae_path = folder_paths.get_full_path("vae", vae_name) - sd = fcbh.utils.load_torch_file(vae_path) - vae = fcbh.sd.VAE(sd=sd) + vae_path = ldm_patched.utils.path_utils.get_full_path("vae", vae_name) + sd = ldm_patched.modules.utils.load_torch_file(vae_path) + vae = ldm_patched.modules.sd.VAE(sd=sd) return (vae,) class ControlNetLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + return {"required": { "control_net_name": (ldm_patched.utils.path_utils.get_filename_list("controlnet"), )}} RETURN_TYPES = ("CONTROL_NET",) FUNCTION = "load_controlnet" @@ -648,15 +663,15 @@ class ControlNetLoader: CATEGORY = "loaders" def load_controlnet(self, control_net_name): - controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) - controlnet = fcbh.controlnet.load_controlnet(controlnet_path) + controlnet_path = ldm_patched.utils.path_utils.get_full_path("controlnet", control_net_name) + controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path) return (controlnet,) class DiffControlNetLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), - "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + "control_net_name": (ldm_patched.utils.path_utils.get_filename_list("controlnet"), )}} RETURN_TYPES = ("CONTROL_NET",) FUNCTION = "load_controlnet" @@ -664,8 +679,8 @@ class DiffControlNetLoader: CATEGORY = "loaders" def load_controlnet(self, model, control_net_name): - controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) - controlnet = fcbh.controlnet.load_controlnet(controlnet_path, model) + controlnet_path = ldm_patched.utils.path_utils.get_full_path("controlnet", control_net_name) + controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path, model) return (controlnet,) @@ -749,7 +764,7 @@ class ControlNetApplyAdvanced: class UNETLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ), + return {"required": { "unet_name": (ldm_patched.utils.path_utils.get_filename_list("unet"), ), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_unet" @@ -757,14 +772,14 @@ class UNETLoader: CATEGORY = "advanced/loaders" def load_unet(self, unet_name): - unet_path = folder_paths.get_full_path("unet", unet_name) - model = fcbh.sd.load_unet(unet_path) + unet_path = ldm_patched.utils.path_utils.get_full_path("unet", unet_name) + model = ldm_patched.modules.sd.load_unet(unet_path) return (model,) class CLIPLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ), + return {"required": { "clip_name": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" @@ -772,14 +787,14 @@ class CLIPLoader: CATEGORY = "advanced/loaders" def load_clip(self, clip_name): - clip_path = folder_paths.get_full_path("clip", clip_name) - clip = fcbh.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings")) + clip_path = ldm_patched.utils.path_utils.get_full_path("clip", clip_name) + clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return (clip,) class DualCLIPLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), + return {"required": { "clip_name1": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), "clip_name2": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" @@ -787,15 +802,15 @@ class DualCLIPLoader: CATEGORY = "advanced/loaders" def load_clip(self, clip_name1, clip_name2): - clip_path1 = folder_paths.get_full_path("clip", clip_name1) - clip_path2 = folder_paths.get_full_path("clip", clip_name2) - clip = fcbh.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings")) + clip_path1 = ldm_patched.utils.path_utils.get_full_path("clip", clip_name1) + clip_path2 = ldm_patched.utils.path_utils.get_full_path("clip", clip_name2) + clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return (clip,) class CLIPVisionLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ), + return {"required": { "clip_name": (ldm_patched.utils.path_utils.get_filename_list("clip_vision"), ), }} RETURN_TYPES = ("CLIP_VISION",) FUNCTION = "load_clip" @@ -803,8 +818,8 @@ class CLIPVisionLoader: CATEGORY = "loaders" def load_clip(self, clip_name): - clip_path = folder_paths.get_full_path("clip_vision", clip_name) - clip_vision = fcbh.clip_vision.load(clip_path) + clip_path = ldm_patched.utils.path_utils.get_full_path("clip_vision", clip_name) + clip_vision = ldm_patched.modules.clip_vision.load(clip_path) return (clip_vision,) class CLIPVisionEncode: @@ -825,7 +840,7 @@ class CLIPVisionEncode: class StyleModelLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}} + return {"required": { "style_model_name": (ldm_patched.utils.path_utils.get_filename_list("style_models"), )}} RETURN_TYPES = ("STYLE_MODEL",) FUNCTION = "load_style_model" @@ -833,8 +848,8 @@ class StyleModelLoader: CATEGORY = "loaders" def load_style_model(self, style_model_name): - style_model_path = folder_paths.get_full_path("style_models", style_model_name) - style_model = fcbh.sd.load_style_model(style_model_path) + style_model_path = ldm_patched.utils.path_utils.get_full_path("style_models", style_model_name) + style_model = ldm_patched.modules.sd.load_style_model(style_model_path) return (style_model,) @@ -890,7 +905,7 @@ class unCLIPConditioning: class GLIGENLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}} + return {"required": { "gligen_name": (ldm_patched.utils.path_utils.get_filename_list("gligen"), )}} RETURN_TYPES = ("GLIGEN",) FUNCTION = "load_gligen" @@ -898,8 +913,8 @@ class GLIGENLoader: CATEGORY = "loaders" def load_gligen(self, gligen_name): - gligen_path = folder_paths.get_full_path("gligen", gligen_name) - gligen = fcbh.sd.load_gligen(gligen_path) + gligen_path = ldm_patched.utils.path_utils.get_full_path("gligen", gligen_name) + gligen = ldm_patched.modules.sd.load_gligen(gligen_path) return (gligen,) class GLIGENTextBoxApply: @@ -934,8 +949,8 @@ class GLIGENTextBoxApply: return (c, ) class EmptyLatentImage: - def __init__(self, device="cpu"): - self.device = device + def __init__(self): + self.device = ldm_patched.modules.model_management.intermediate_device() @classmethod def INPUT_TYPES(s): @@ -948,7 +963,7 @@ class EmptyLatentImage: CATEGORY = "latent" def generate(self, width, height, batch_size=1): - latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device) return ({"samples":latent}, ) @@ -1041,7 +1056,7 @@ class LatentUpscale: width = max(64, width) height = max(64, height) - s["samples"] = fcbh.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) + s["samples"] = ldm_patched.modules.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) return (s,) class LatentUpscaleBy: @@ -1060,7 +1075,7 @@ class LatentUpscaleBy: s = samples.copy() width = round(samples["samples"].shape[3] * scale_by) height = round(samples["samples"].shape[2] * scale_by) - s["samples"] = fcbh.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") + s["samples"] = ldm_patched.modules.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") return (s,) class LatentRotate: @@ -1176,7 +1191,7 @@ class LatentBlend: if samples1.shape != samples2.shape: samples2.permute(0, 3, 1, 2) - samples2 = fcbh.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') + samples2 = ldm_patched.modules.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') samples2.permute(0, 2, 3, 1) samples_blended = self.blend_mode(samples1, samples2, blend_mode) @@ -1245,15 +1260,15 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None - noise = fcbh.sample.prepare_noise(latent_image, seed, batch_inds) + noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] - callback = latent_preview.prepare_callback(model, steps) - disable_pbar = not fcbh.utils.PROGRESS_BAR_ENABLED - samples = fcbh.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + callback = ldm_patched.utils.latent_visualization.prepare_callback(model, steps) + disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED + samples = ldm_patched.modules.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) out = latent.copy() @@ -1268,8 +1283,8 @@ class KSampler: "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "sampler_name": (fcbh.samplers.KSampler.SAMPLERS, ), - "scheduler": (fcbh.samplers.KSampler.SCHEDULERS, ), + "sampler_name": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ), + "scheduler": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "latent_image": ("LATENT", ), @@ -1294,8 +1309,8 @@ class KSamplerAdvanced: "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "sampler_name": (fcbh.samplers.KSampler.SAMPLERS, ), - "scheduler": (fcbh.samplers.KSampler.SCHEDULERS, ), + "sampler_name": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ), + "scheduler": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "latent_image": ("LATENT", ), @@ -1321,15 +1336,16 @@ class KSamplerAdvanced: class SaveImage: def __init__(self): - self.output_dir = folder_paths.get_output_directory() + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() self.type = "output" self.prefix_append = "" + self.compress_level = 4 @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), - "filename_prefix": ("STRING", {"default": "fcbh_backend"})}, + "filename_prefix": ("STRING", {"default": "ldm_patched"})}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } @@ -1340,15 +1356,15 @@ class SaveImage: CATEGORY = "image" - def save_images(self, images, filename_prefix="fcbh_backend", prompt=None, extra_pnginfo=None): + def save_images(self, images, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) metadata = None - if not args.disable_metadata: + if not args.disable_server_info: metadata = PngInfo() if prompt is not None: metadata.add_text("prompt", json.dumps(prompt)) @@ -1357,7 +1373,7 @@ class SaveImage: metadata.add_text(x, json.dumps(extra_pnginfo[x])) file = f"{filename}_{counter:05}_.png" - img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4) + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) results.append({ "filename": file, "subfolder": subfolder, @@ -1369,9 +1385,10 @@ class SaveImage: class PreviewImage(SaveImage): def __init__(self): - self.output_dir = folder_paths.get_temp_directory() + self.output_dir = ldm_patched.utils.path_utils.get_temp_directory() self.type = "temp" self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 1 @classmethod def INPUT_TYPES(s): @@ -1383,7 +1400,7 @@ class PreviewImage(SaveImage): class LoadImage: @classmethod def INPUT_TYPES(s): - input_dir = folder_paths.get_input_directory() + input_dir = ldm_patched.utils.path_utils.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] return {"required": {"image": (sorted(files), {"image_upload": True})}, @@ -1394,7 +1411,7 @@ class LoadImage: RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "load_image" def load_image(self, image): - image_path = folder_paths.get_annotated_filepath(image) + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) i = Image.open(image_path) i = ImageOps.exif_transpose(i) image = i.convert("RGB") @@ -1409,7 +1426,7 @@ class LoadImage: @classmethod def IS_CHANGED(s, image): - image_path = folder_paths.get_annotated_filepath(image) + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) @@ -1417,7 +1434,7 @@ class LoadImage: @classmethod def VALIDATE_INPUTS(s, image): - if not folder_paths.exists_annotated_filepath(image): + if not ldm_patched.utils.path_utils.exists_annotated_filepath(image): return "Invalid image file: {}".format(image) return True @@ -1426,7 +1443,7 @@ class LoadImageMask: _color_channels = ["alpha", "red", "green", "blue"] @classmethod def INPUT_TYPES(s): - input_dir = folder_paths.get_input_directory() + input_dir = ldm_patched.utils.path_utils.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] return {"required": {"image": (sorted(files), {"image_upload": True}), @@ -1438,7 +1455,7 @@ class LoadImageMask: RETURN_TYPES = ("MASK",) FUNCTION = "load_image" def load_image(self, image, channel): - image_path = folder_paths.get_annotated_filepath(image) + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) i = Image.open(image_path) i = ImageOps.exif_transpose(i) if i.getbands() != ("R", "G", "B", "A"): @@ -1456,7 +1473,7 @@ class LoadImageMask: @classmethod def IS_CHANGED(s, image, channel): - image_path = folder_paths.get_annotated_filepath(image) + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) @@ -1464,7 +1481,7 @@ class LoadImageMask: @classmethod def VALIDATE_INPUTS(s, image, channel): - if not folder_paths.exists_annotated_filepath(image): + if not ldm_patched.utils.path_utils.exists_annotated_filepath(image): return "Invalid image file: {}".format(image) if channel not in s._color_channels: @@ -1498,7 +1515,7 @@ class ImageScale: elif height == 0: height = max(1, round(samples.shape[2] * width / samples.shape[3])) - s = fcbh.utils.common_upscale(samples, width, height, upscale_method, crop) + s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, crop) s = s.movedim(1,-1) return (s,) @@ -1518,7 +1535,7 @@ class ImageScaleBy: samples = image.movedim(-1,1) width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) - s = fcbh.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = s.movedim(1,-1) return (s,) @@ -1550,7 +1567,7 @@ class ImageBatch: def batch(self, image1, image2): if image1.shape[1:] != image2.shape[1:]: - image2 = fcbh.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) + image2 = ldm_patched.modules.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) s = torch.cat((image1, image2), dim=0) return (s,) @@ -1703,6 +1720,7 @@ NODE_CLASS_MAPPINGS = { "ConditioningZeroOut": ConditioningZeroOut, "ConditioningSetTimestepRange": ConditioningSetTimestepRange, + "LoraLoaderModelOnly": LoraLoaderModelOnly, } NODE_DISPLAY_NAME_MAPPINGS = { @@ -1805,10 +1823,10 @@ def load_custom_node(module_path, ignore=set()): def load_custom_nodes(): base_node_names = set(NODE_CLASS_MAPPINGS.keys()) - node_paths = folder_paths.get_folder_paths("custom_nodes") + node_paths = ldm_patched.utils.path_utils.get_folder_paths("custom_nodes") node_import_times = [] for custom_node_path in node_paths: - possible_modules = os.listdir(custom_node_path) + possible_modules = os.listdir(os.path.realpath(custom_node_path)) if "__pycache__" in possible_modules: possible_modules.remove("__pycache__") @@ -1831,7 +1849,7 @@ def load_custom_nodes(): print() def init_custom_nodes(): - extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "fcbh_extras") + extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "ldm_patched_extras") extras_files = [ "nodes_latent.py", "nodes_hypernetwork.py", @@ -1850,6 +1868,8 @@ def init_custom_nodes(): "nodes_model_advanced.py", "nodes_model_downscale.py", "nodes_images.py", + "nodes_video_model.py", + "nodes_sag.py", ] for node_file in extras_files: diff --git a/backend/headless/fcbh_extras/nodes_canny.py b/ldm_patched/contrib/external_canny.py similarity index 96% rename from backend/headless/fcbh_extras/nodes_canny.py rename to ldm_patched/contrib/external_canny.py index 86aa7de8..42c22210 100644 --- a/backend/headless/fcbh_extras/nodes_canny.py +++ b/ldm_patched/contrib/external_canny.py @@ -1,9 +1,11 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + #From https://github.com/kornia/kornia import math import torch import torch.nn.functional as F -import fcbh.model_management +import ldm_patched.modules.model_management def get_canny_nms_kernel(device=None, dtype=None): """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression.""" @@ -290,8 +292,8 @@ class Canny: CATEGORY = "image/preprocessors" def detect_edge(self, image, low_threshold, high_threshold): - output = canny(image.to(fcbh.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) - img_out = output[1].cpu().repeat(1, 3, 1, 1).movedim(1, -1) + output = canny(image.to(ldm_patched.modules.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) + img_out = output[1].to(ldm_patched.modules.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) return (img_out,) NODE_CLASS_MAPPINGS = { diff --git a/backend/headless/fcbh_extras/nodes_clip_sdxl.py b/ldm_patched/contrib/external_clip_sdxl.py similarity index 95% rename from backend/headless/fcbh_extras/nodes_clip_sdxl.py rename to ldm_patched/contrib/external_clip_sdxl.py index dcf8859f..230321a8 100644 --- a/backend/headless/fcbh_extras/nodes_clip_sdxl.py +++ b/ldm_patched/contrib/external_clip_sdxl.py @@ -1,5 +1,7 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + import torch -from nodes import MAX_RESOLUTION +from ldm_patched.contrib.external import MAX_RESOLUTION class CLIPTextEncodeSDXLRefiner: @classmethod diff --git a/backend/headless/fcbh_extras/nodes_compositing.py b/ldm_patched/contrib/external_compositing.py similarity index 92% rename from backend/headless/fcbh_extras/nodes_compositing.py rename to ldm_patched/contrib/external_compositing.py index 37fe60dc..0cf91d9a 100644 --- a/backend/headless/fcbh_extras/nodes_compositing.py +++ b/ldm_patched/contrib/external_compositing.py @@ -1,6 +1,8 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + import numpy as np import torch -import fcbh.utils +import ldm_patched.modules.utils from enum import Enum def resize_mask(mask, shape): @@ -122,15 +124,15 @@ class PorterDuffImageComposite: if dst_alpha.shape[:2] != dst_image.shape[:2]: upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2) - upscale_output = fcbh.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') + upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) if src_image.shape != dst_image.shape: upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2) - upscale_output = fcbh.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') + upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0) if src_alpha.shape != dst_alpha.shape: upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2) - upscale_output = fcbh.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center') + upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center') src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode]) diff --git a/backend/headless/fcbh_extras/nodes_custom_sampler.py b/ldm_patched/contrib/external_custom_sampler.py similarity index 81% rename from backend/headless/fcbh_extras/nodes_custom_sampler.py rename to ldm_patched/contrib/external_custom_sampler.py index 2881f31e..9413a58f 100644 --- a/backend/headless/fcbh_extras/nodes_custom_sampler.py +++ b/ldm_patched/contrib/external_custom_sampler.py @@ -1,9 +1,11 @@ -import fcbh.samplers -import fcbh.sample -from fcbh.k_diffusion import sampling as k_diffusion_sampling -import latent_preview +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.modules.samplers +import ldm_patched.modules.sample +from ldm_patched.k_diffusion import sampling as k_diffusion_sampling +import ldm_patched.utils.latent_visualization import torch -import fcbh.utils +import ldm_patched.modules.utils class BasicScheduler: @@ -11,7 +13,7 @@ class BasicScheduler: def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), - "scheduler": (fcbh.samplers.SCHEDULER_NAMES, ), + "scheduler": (ldm_patched.modules.samplers.SCHEDULER_NAMES, ), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), } } @@ -21,7 +23,7 @@ class BasicScheduler: FUNCTION = "get_sigmas" def get_sigmas(self, model, scheduler, steps): - sigmas = fcbh.samplers.calculate_sigmas_scheduler(model.model, scheduler, steps).cpu() + sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, steps).cpu() return (sigmas, ) @@ -81,6 +83,25 @@ class PolyexponentialScheduler: sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) return (sigmas, ) +class SDTurboScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "steps": ("INT", {"default": 1, "min": 1, "max": 10}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, steps): + timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[:steps] + sigmas = model.model.model_sampling.sigma(timesteps) + sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) + return (sigmas, ) + class VPScheduler: @classmethod def INPUT_TYPES(s): @@ -140,7 +161,7 @@ class KSamplerSelect: @classmethod def INPUT_TYPES(s): return {"required": - {"sampler_name": (fcbh.samplers.SAMPLER_NAMES, ), + {"sampler_name": (ldm_patched.modules.samplers.SAMPLER_NAMES, ), } } RETURN_TYPES = ("SAMPLER",) @@ -149,7 +170,7 @@ class KSamplerSelect: FUNCTION = "get_sampler" def get_sampler(self, sampler_name): - sampler = fcbh.samplers.sampler_object(sampler_name) + sampler = ldm_patched.modules.samplers.sampler_object(sampler_name) return (sampler, ) class SamplerDPMPP_2M_SDE: @@ -172,7 +193,7 @@ class SamplerDPMPP_2M_SDE: sampler_name = "dpmpp_2m_sde" else: sampler_name = "dpmpp_2m_sde_gpu" - sampler = fcbh.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) + sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) return (sampler, ) @@ -196,7 +217,7 @@ class SamplerDPMPP_SDE: sampler_name = "dpmpp_sde" else: sampler_name = "dpmpp_sde_gpu" - sampler = fcbh.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) + sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) return (sampler, ) class SamplerCustom: @@ -229,17 +250,17 @@ class SamplerCustom: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None - noise = fcbh.sample.prepare_noise(latent_image, noise_seed, batch_inds) + noise = ldm_patched.modules.sample.prepare_noise(latent_image, noise_seed, batch_inds) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] x0_output = {} - callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) + callback = ldm_patched.utils.latent_visualization.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) - disable_pbar = not fcbh.utils.PROGRESS_BAR_ENABLED - samples = fcbh.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed) + disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED + samples = ldm_patched.modules.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed) out = latent.copy() out["samples"] = samples @@ -257,6 +278,7 @@ NODE_CLASS_MAPPINGS = { "ExponentialScheduler": ExponentialScheduler, "PolyexponentialScheduler": PolyexponentialScheduler, "VPScheduler": VPScheduler, + "SDTurboScheduler": SDTurboScheduler, "KSamplerSelect": KSamplerSelect, "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, "SamplerDPMPP_SDE": SamplerDPMPP_SDE, diff --git a/backend/headless/fcbh_extras/nodes_freelunch.py b/ldm_patched/contrib/external_freelunch.py similarity index 98% rename from backend/headless/fcbh_extras/nodes_freelunch.py rename to ldm_patched/contrib/external_freelunch.py index 7512b841..f8dd5a44 100644 --- a/backend/headless/fcbh_extras/nodes_freelunch.py +++ b/ldm_patched/contrib/external_freelunch.py @@ -1,3 +1,5 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + #code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) import torch diff --git a/backend/headless/fcbh_extras/nodes_hypernetwork.py b/ldm_patched/contrib/external_hypernetwork.py similarity index 90% rename from backend/headless/fcbh_extras/nodes_hypernetwork.py rename to ldm_patched/contrib/external_hypernetwork.py index 4bad51d1..17aaacb0 100644 --- a/backend/headless/fcbh_extras/nodes_hypernetwork.py +++ b/ldm_patched/contrib/external_hypernetwork.py @@ -1,9 +1,11 @@ -import fcbh.utils -import folder_paths +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.modules.utils +import ldm_patched.utils.path_utils import torch def load_hypernetwork_patch(path, strength): - sd = fcbh.utils.load_torch_file(path, safe_load=True) + sd = ldm_patched.modules.utils.load_torch_file(path, safe_load=True) activation_func = sd.get('activation_func', 'linear') is_layer_norm = sd.get('is_layer_norm', False) use_dropout = sd.get('use_dropout', False) @@ -97,7 +99,7 @@ class HypernetworkLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), - "hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ), + "hypernetwork_name": (ldm_patched.utils.path_utils.get_filename_list("hypernetworks"), ), "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) @@ -106,7 +108,7 @@ class HypernetworkLoader: CATEGORY = "loaders" def load_hypernetwork(self, model, hypernetwork_name, strength): - hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name) + hypernetwork_path = ldm_patched.utils.path_utils.get_full_path("hypernetworks", hypernetwork_name) model_hypernetwork = model.clone() patch = load_hypernetwork_patch(hypernetwork_path, strength) if patch is not None: diff --git a/backend/headless/fcbh_extras/nodes_hypertile.py b/ldm_patched/contrib/external_hypertile.py similarity index 90% rename from backend/headless/fcbh_extras/nodes_hypertile.py rename to ldm_patched/contrib/external_hypertile.py index 0d7d4c95..45f7c3ea 100644 --- a/backend/headless/fcbh_extras/nodes_hypertile.py +++ b/ldm_patched/contrib/external_hypertile.py @@ -1,10 +1,13 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + #Taken from: https://github.com/tfernd/HyperTile/ import math from einops import rearrange -import random +# Use torch rng for consistency across generations +from torch import randint -def random_divisor(value: int, min_value: int, /, max_options: int = 1, counter = 0) -> int: +def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: min_value = min(min_value, value) # All big divisors of value (inclusive) @@ -12,8 +15,10 @@ def random_divisor(value: int, min_value: int, /, max_options: int = 1, counter ns = [value // i for i in divisors[:max_options]] # has at least 1 element - random.seed(counter) - idx = random.randint(0, len(ns) - 1) + if len(ns) - 1 > 0: + idx = randint(low=0, high=len(ns) - 1, size=(1,)).item() + else: + idx = 0 return ns[idx] @@ -42,7 +47,6 @@ class HyperTile: latent_tile_size = max(32, tile_size) // 8 self.temp = None - self.counter = 1 def hypertile_in(q, k, v, extra_options): if q.shape[-1] in apply_to: @@ -53,10 +57,8 @@ class HyperTile: h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) factor = 2**((q.shape[-1] // model_channels) - 1) if scale_depth else 1 - nh = random_divisor(h, latent_tile_size * factor, swap_size, self.counter) - self.counter += 1 - nw = random_divisor(w, latent_tile_size * factor, swap_size, self.counter) - self.counter += 1 + nh = random_divisor(h, latent_tile_size * factor, swap_size) + nw = random_divisor(w, latent_tile_size * factor, swap_size) if nh * nw > 1: q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) diff --git a/backend/headless/fcbh_extras/nodes_images.py b/ldm_patched/contrib/external_images.py similarity index 57% rename from backend/headless/fcbh_extras/nodes_images.py rename to ldm_patched/contrib/external_images.py index 549b28a8..3dbb3e3b 100644 --- a/backend/headless/fcbh_extras/nodes_images.py +++ b/ldm_patched/contrib/external_images.py @@ -1,13 +1,17 @@ -import nodes -import folder_paths -from fcbh.cli_args import args +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.contrib.external +import ldm_patched.utils.path_utils +from ldm_patched.modules.args_parser import args from PIL import Image +from PIL.PngImagePlugin import PngInfo + import numpy as np import json import os -MAX_RESOLUTION = nodes.MAX_RESOLUTION +MAX_RESOLUTION = ldm_patched.contrib.external.MAX_RESOLUTION class ImageCrop: @classmethod @@ -48,7 +52,7 @@ class RepeatImageBatch: class SaveAnimatedWEBP: def __init__(self): - self.output_dir = folder_paths.get_output_directory() + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() self.type = "output" self.prefix_append = "" @@ -57,7 +61,7 @@ class SaveAnimatedWEBP: def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), - "filename_prefix": ("STRING", {"default": "fcbh_backend"}), + "filename_prefix": ("STRING", {"default": "ldm_patched"}), "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), "lossless": ("BOOLEAN", {"default": True}), "quality": ("INT", {"default": 80, "min": 0, "max": 100}), @@ -75,9 +79,9 @@ class SaveAnimatedWEBP: CATEGORY = "_for_testing" def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None): - method = self.methods.get(method, "aoeu") + method = self.methods.get(method) filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() pil_images = [] for image in images: @@ -85,9 +89,8 @@ class SaveAnimatedWEBP: img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pil_images.append(img) - metadata = None - if not args.disable_metadata: - metadata = pil_images[0].getexif() + metadata = pil_images[0].getexif() + if not args.disable_server_info: if prompt is not None: metadata[0x0110] = "prompt:{}".format(json.dumps(prompt)) if extra_pnginfo is not None: @@ -113,8 +116,62 @@ class SaveAnimatedWEBP: animated = num_frames != 1 return { "ui": { "images": results, "animated": (animated,) } } +class SaveAnimatedPNG: + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ldm_patched"}), + "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + "compress_level": ("INT", {"default": 4, "min": 0, "max": 9}) + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "_for_testing" + + def save_images(self, images, fps, compress_level, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + pil_images = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + + metadata = None + if not args.disable_server_info: + metadata = PngInfo() + if prompt is not None: + metadata.add(b"ldm_patched", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add(b"ldm_patched", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True) + + file = f"{filename}_{counter:05}_.png" + pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:]) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + return { "ui": { "images": results, "animated": (True,)} } + NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, "SaveAnimatedWEBP": SaveAnimatedWEBP, + "SaveAnimatedPNG": SaveAnimatedPNG, } diff --git a/backend/headless/fcbh_extras/nodes_latent.py b/ldm_patched/contrib/external_latent.py similarity index 90% rename from backend/headless/fcbh_extras/nodes_latent.py rename to ldm_patched/contrib/external_latent.py index 2dbad6c5..e2364b88 100644 --- a/backend/headless/fcbh_extras/nodes_latent.py +++ b/ldm_patched/contrib/external_latent.py @@ -1,12 +1,14 @@ -import fcbh.utils +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.modules.utils import torch def reshape_latent_to(target_shape, latent): if latent.shape[1:] != target_shape[1:]: latent.movedim(1, -1) - latent = fcbh.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") + latent = ldm_patched.modules.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") latent.movedim(-1, 1) - return fcbh.utils.repeat_to_batch_size(latent, target_shape[0]) + return ldm_patched.modules.utils.repeat_to_batch_size(latent, target_shape[0]) class LatentAdd: diff --git a/backend/headless/fcbh_extras/nodes_mask.py b/ldm_patched/contrib/external_mask.py similarity index 97% rename from backend/headless/fcbh_extras/nodes_mask.py rename to ldm_patched/contrib/external_mask.py index 73786470..ab1da4c6 100644 --- a/backend/headless/fcbh_extras/nodes_mask.py +++ b/ldm_patched/contrib/external_mask.py @@ -1,15 +1,17 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + import numpy as np import scipy.ndimage import torch -import fcbh.utils +import ldm_patched.modules.utils -from nodes import MAX_RESOLUTION +from ldm_patched.contrib.external import MAX_RESOLUTION def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): if resize_source: source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") - source = fcbh.utils.repeat_to_batch_size(source, destination.shape[0]) + source = ldm_patched.modules.utils.repeat_to_batch_size(source, destination.shape[0]) x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) @@ -22,7 +24,7 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou else: mask = mask.clone() mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") - mask = fcbh.utils.repeat_to_batch_size(mask, source.shape[0]) + mask = ldm_patched.modules.utils.repeat_to_batch_size(mask, source.shape[0]) # calculate the bounds of the source that will be overlapping the destination # this prevents the source trying to overwrite latent pixels that are out of bounds diff --git a/backend/headless/fcbh_extras/nodes_model_advanced.py b/ldm_patched/contrib/external_model_advanced.py similarity index 67% rename from backend/headless/fcbh_extras/nodes_model_advanced.py rename to ldm_patched/contrib/external_model_advanced.py index 5eedb78e..4ebd9dbf 100644 --- a/backend/headless/fcbh_extras/nodes_model_advanced.py +++ b/ldm_patched/contrib/external_model_advanced.py @@ -1,9 +1,11 @@ -import folder_paths -import fcbh.sd -import fcbh.model_sampling +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.utils.path_utils +import ldm_patched.modules.sd +import ldm_patched.modules.model_sampling import torch -class LCM(fcbh.model_sampling.EPS): +class LCM(ldm_patched.modules.model_sampling.EPS): def calculate_denoised(self, sigma, model_output, model_input): timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) @@ -17,7 +19,9 @@ class LCM(fcbh.model_sampling.EPS): return c_out * x0 + c_skip * model_input -class ModelSamplingDiscreteLCM(torch.nn.Module): +class ModelSamplingDiscreteDistilled(torch.nn.Module): + original_timesteps = 50 + def __init__(self): super().__init__() self.sigma_data = 1.0 @@ -29,13 +33,12 @@ class ModelSamplingDiscreteLCM(torch.nn.Module): alphas = 1.0 - betas alphas_cumprod = torch.cumprod(alphas, dim=0) - original_timesteps = 50 - self.skip_steps = timesteps // original_timesteps + self.skip_steps = timesteps // self.original_timesteps - alphas_cumprod_valid = torch.zeros((original_timesteps), dtype=torch.float32) - for x in range(original_timesteps): - alphas_cumprod_valid[original_timesteps - 1 - x] = alphas_cumprod[timesteps - 1 - x * self.skip_steps] + alphas_cumprod_valid = torch.zeros((self.original_timesteps), dtype=torch.float32) + for x in range(self.original_timesteps): + alphas_cumprod_valid[self.original_timesteps - 1 - x] = alphas_cumprod[timesteps - 1 - x * self.skip_steps] sigmas = ((1 - alphas_cumprod_valid) / alphas_cumprod_valid) ** 0.5 self.set_sigmas(sigmas) @@ -55,15 +58,15 @@ class ModelSamplingDiscreteLCM(torch.nn.Module): def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] - return dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1) + return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device) def sigma(self, timestep): - t = torch.clamp(((timestep - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) + t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] - return log_sigma.exp() + return log_sigma.exp().to(timestep.device) def percent_to_sigma(self, percent): if percent <= 0.0: @@ -109,14 +112,14 @@ class ModelSamplingDiscrete: def patch(self, model, sampling, zsnr): m = model.clone() - sampling_base = fcbh.model_sampling.ModelSamplingDiscrete + sampling_base = ldm_patched.modules.model_sampling.ModelSamplingDiscrete if sampling == "eps": - sampling_type = fcbh.model_sampling.EPS + sampling_type = ldm_patched.modules.model_sampling.EPS elif sampling == "v_prediction": - sampling_type = fcbh.model_sampling.V_PREDICTION + sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION elif sampling == "lcm": sampling_type = LCM - sampling_base = ModelSamplingDiscreteLCM + sampling_base = ModelSamplingDiscreteDistilled class ModelSamplingAdvanced(sampling_base, sampling_type): pass @@ -128,6 +131,36 @@ class ModelSamplingDiscrete: m.add_object_patch("model_sampling", model_sampling) return (m, ) +class ModelSamplingContinuousEDM: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["v_prediction", "eps"],), + "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, sigma_max, sigma_min): + m = model.clone() + + if sampling == "eps": + sampling_type = ldm_patched.modules.model_sampling.EPS + elif sampling == "v_prediction": + sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION + + class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced() + model_sampling.set_sigma_range(sigma_min, sigma_max) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + class RescaleCFG: @classmethod def INPUT_TYPES(s): @@ -169,5 +202,6 @@ class RescaleCFG: NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, + "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, "RescaleCFG": RescaleCFG, } diff --git a/backend/headless/fcbh_extras/nodes_model_downscale.py b/ldm_patched/contrib/external_model_downscale.py similarity index 84% rename from backend/headless/fcbh_extras/nodes_model_downscale.py rename to ldm_patched/contrib/external_model_downscale.py index 28f68935..4f1da54d 100644 --- a/backend/headless/fcbh_extras/nodes_model_downscale.py +++ b/ldm_patched/contrib/external_model_downscale.py @@ -1,5 +1,7 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + import torch -import fcbh.utils +import ldm_patched.modules.utils class PatchModelAddDownscale: upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] @@ -27,12 +29,12 @@ class PatchModelAddDownscale: if transformer_options["block"][1] == block_number: sigma = transformer_options["sigmas"][0].item() if sigma <= sigma_start and sigma >= sigma_end: - h = fcbh.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") + h = ldm_patched.modules.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") return h def output_block_patch(h, hsp, transformer_options): if h.shape[2] != hsp.shape[2]: - h = fcbh.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") + h = ldm_patched.modules.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") return h, hsp m = model.clone() diff --git a/backend/headless/fcbh_extras/nodes_model_merging.py b/ldm_patched/contrib/external_model_merging.py similarity index 81% rename from backend/headless/fcbh_extras/nodes_model_merging.py rename to ldm_patched/contrib/external_model_merging.py index 63432ab6..c0cf9afd 100644 --- a/backend/headless/fcbh_extras/nodes_model_merging.py +++ b/ldm_patched/contrib/external_model_merging.py @@ -1,13 +1,15 @@ -import fcbh.sd -import fcbh.utils -import fcbh.model_base -import fcbh.model_management +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py -import folder_paths +import ldm_patched.modules.sd +import ldm_patched.modules.utils +import ldm_patched.modules.model_base +import ldm_patched.modules.model_management + +import ldm_patched.utils.path_utils import json import os -from fcbh.cli_args import args +from ldm_patched.modules.args_parser import args class ModelMergeSimple: @classmethod @@ -121,14 +123,14 @@ class ModelMergeBlocks: class CheckpointSave: def __init__(self): - self.output_dir = folder_paths.get_output_directory() + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip": ("CLIP",), "vae": ("VAE",), - "filename_prefix": ("STRING", {"default": "checkpoints/fcbh_backend"}),}, + "filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" @@ -137,7 +139,7 @@ class CheckpointSave: CATEGORY = "advanced/model_merging" def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) @@ -145,9 +147,9 @@ class CheckpointSave: metadata = {} enable_modelspec = True - if isinstance(model.model, fcbh.model_base.SDXL): + if isinstance(model.model, ldm_patched.modules.model_base.SDXL): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" - elif isinstance(model.model, fcbh.model_base.SDXLRefiner): + elif isinstance(model.model, ldm_patched.modules.model_base.SDXLRefiner): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" else: enable_modelspec = False @@ -162,12 +164,12 @@ class CheckpointSave: # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h", # "v2-inpainting" - if model.model.model_type == fcbh.model_base.ModelType.EPS: + if model.model.model_type == ldm_patched.modules.model_base.ModelType.EPS: metadata["modelspec.predict_key"] = "epsilon" - elif model.model.model_type == fcbh.model_base.ModelType.V_PREDICTION: + elif model.model.model_type == ldm_patched.modules.model_base.ModelType.V_PREDICTION: metadata["modelspec.predict_key"] = "v" - if not args.disable_metadata: + if not args.disable_server_info: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: @@ -176,17 +178,17 @@ class CheckpointSave: output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - fcbh.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata) + ldm_patched.modules.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata) return {} class CLIPSave: def __init__(self): - self.output_dir = folder_paths.get_output_directory() + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP",), - "filename_prefix": ("STRING", {"default": "clip/fcbh_backend"}),}, + "filename_prefix": ("STRING", {"default": "clip/ldm_patched"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" @@ -200,13 +202,13 @@ class CLIPSave: prompt_info = json.dumps(prompt) metadata = {} - if not args.disable_metadata: + if not args.disable_server_info: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) - fcbh.model_management.load_models_gpu([clip.load_model()]) + ldm_patched.modules.model_management.load_models_gpu([clip.load_model()]) clip_sd = clip.get_sd() for prefix in ["clip_l.", "clip_g.", ""]: @@ -225,24 +227,24 @@ class CLIPSave: replace_prefix[prefix] = "" replace_prefix["transformer."] = "" - full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir) + full_output_folder, filename, counter, subfolder, filename_prefix_ = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix_, self.output_dir) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - current_clip_sd = fcbh.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) + current_clip_sd = ldm_patched.modules.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) - fcbh.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) + ldm_patched.modules.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) return {} class VAESave: def __init__(self): - self.output_dir = folder_paths.get_output_directory() + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "vae": ("VAE",), - "filename_prefix": ("STRING", {"default": "vae/fcbh_backend_vae"}),}, + "filename_prefix": ("STRING", {"default": "vae/ldm_patched_vae"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" @@ -251,13 +253,13 @@ class VAESave: CATEGORY = "advanced/model_merging" def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} - if not args.disable_metadata: + if not args.disable_server_info: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: @@ -266,7 +268,7 @@ class VAESave: output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - fcbh.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) + ldm_patched.modules.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) return {} NODE_CLASS_MAPPINGS = { diff --git a/backend/headless/fcbh_extras/nodes_post_processing.py b/ldm_patched/contrib/external_post_processing.py similarity index 95% rename from backend/headless/fcbh_extras/nodes_post_processing.py rename to ldm_patched/contrib/external_post_processing.py index a546ceb4..432c53fb 100644 --- a/backend/headless/fcbh_extras/nodes_post_processing.py +++ b/ldm_patched/contrib/external_post_processing.py @@ -1,10 +1,12 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + import numpy as np import torch import torch.nn.functional as F from PIL import Image import math -import fcbh.utils +import ldm_patched.modules.utils class Blend: @@ -35,7 +37,7 @@ class Blend: def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): if image1.shape != image2.shape: image2 = image2.permute(0, 3, 1, 2) - image2 = fcbh.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') + image2 = ldm_patched.modules.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') image2 = image2.permute(0, 2, 3, 1) blended_image = self.blend_mode(image1, image2, blend_mode) @@ -226,7 +228,7 @@ class Sharpen: batch_size, height, width, channels = image.shape kernel_size = sharpen_radius * 2 + 1 - kernel = gaussian_kernel(kernel_size, sigma) * -(alpha*10) + kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) center = kernel_size // 2 kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) @@ -262,7 +264,7 @@ class ImageScaleToTotalPixels: width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) - s = fcbh.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = s.movedim(1,-1) return (s,) diff --git a/backend/headless/fcbh_extras/nodes_rebatch.py b/ldm_patched/contrib/external_rebatch.py similarity index 98% rename from backend/headless/fcbh_extras/nodes_rebatch.py rename to ldm_patched/contrib/external_rebatch.py index 88a4ebe2..607c7feb 100644 --- a/backend/headless/fcbh_extras/nodes_rebatch.py +++ b/ldm_patched/contrib/external_rebatch.py @@ -1,3 +1,5 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + import torch class LatentRebatch: diff --git a/ldm_patched/contrib/external_sag.py b/ldm_patched/contrib/external_sag.py new file mode 100644 index 00000000..59d1890c --- /dev/null +++ b/ldm_patched/contrib/external_sag.py @@ -0,0 +1,174 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +from torch import einsum +import torch.nn.functional as F +import math + +from einops import rearrange, repeat +import os +from ldm_patched.ldm.modules.attention import optimized_attention, _ATTN_PRECISION +import ldm_patched.modules.samplers + +# from ldm_patched.modules/ldm/modules/attention.py +# but modified to return attention scores as well as output +def attention_basic_with_sim(q, k, v, heads, mask=None): + b, _, dim_head = q.shape + dim_head //= heads + scale = dim_head ** -0.5 + + h = heads + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + # force cast to fp32 to avoid overflowing + if _ATTN_PRECISION =="fp32": + with torch.autocast(enabled=False, device_type = 'cuda'): + q, k = q.float(), k.float() + sim = einsum('b i d, b j d -> b i j', q, k) * scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * scale + + del q, k + + if mask is not None: + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return (out, sim) + +def create_blur_map(x0, attn, sigma=3.0, threshold=1.0): + # reshape and GAP the attention map + _, hw1, hw2 = attn.shape + b, _, lh, lw = x0.shape + attn = attn.reshape(b, -1, hw1, hw2) + # Global Average Pool + mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold + ratio = round(math.sqrt(lh * lw / hw1)) + mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)] + + # Reshape + mask = ( + mask.reshape(b, *mid_shape) + .unsqueeze(1) + .type(attn.dtype) + ) + # Upsample + mask = F.interpolate(mask, (lh, lw)) + + blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma) + blurred = blurred * mask + x0 * (1 - mask) + return blurred + +def gaussian_blur_2d(img, kernel_size, sigma): + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + + x_kernel = pdf / pdf.sum() + x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) + + kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) + kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) + + padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] + + img = F.pad(img, padding, mode="reflect") + img = F.conv2d(img, kernel2d, groups=img.shape[-3]) + return img + +class SelfAttentionGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.1}), + "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, scale, blur_sigma): + m = model.clone() + + attn_scores = None + mid_block_shape = None + + # TODO: make this work properly with chunked batches + # currently, we can only save the attn from one UNet call + def attn_and_record(q, k, v, extra_options): + nonlocal attn_scores + # if uncond, save the attention scores + heads = extra_options["n_heads"] + cond_or_uncond = extra_options["cond_or_uncond"] + b = q.shape[0] // len(cond_or_uncond) + if 1 in cond_or_uncond: + uncond_index = cond_or_uncond.index(1) + # do the entire attention operation, but save the attention scores to attn_scores + (out, sim) = attention_basic_with_sim(q, k, v, heads=heads) + # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn] + n_slices = heads * b + attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] + return out + else: + return optimized_attention(q, k, v, heads=heads) + + def post_cfg_function(args): + nonlocal attn_scores + nonlocal mid_block_shape + uncond_attn = attn_scores + + sag_scale = scale + sag_sigma = blur_sigma + sag_threshold = 1.0 + model = args["model"] + uncond_pred = args["uncond_denoised"] + uncond = args["uncond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"] + x = args["input"] + + # create the adversarially blurred image + degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) + degraded_noised = degraded + x - uncond_pred + # call into the UNet + (sag, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options) + return cfg_result + (degraded - sag) * sag_scale + + m.set_model_sampler_post_cfg_function(post_cfg_function) + + # from diffusers: + # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch + m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) + + return (m, ) + +NODE_CLASS_MAPPINGS = { + "SelfAttentionGuidance": SelfAttentionGuidance, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SelfAttentionGuidance": "Self-Attention Guidance", +} diff --git a/backend/headless/fcbh_extras/nodes_tomesd.py b/ldm_patched/contrib/external_tomesd.py similarity index 98% rename from backend/headless/fcbh_extras/nodes_tomesd.py rename to ldm_patched/contrib/external_tomesd.py index df048506..b01d6910 100644 --- a/backend/headless/fcbh_extras/nodes_tomesd.py +++ b/ldm_patched/contrib/external_tomesd.py @@ -1,3 +1,5 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + #Taken from: https://github.com/dbolya/tomesd import torch diff --git a/backend/headless/fcbh_extras/nodes_upscale_model.py b/ldm_patched/contrib/external_upscale_model.py similarity index 58% rename from backend/headless/fcbh_extras/nodes_upscale_model.py rename to ldm_patched/contrib/external_upscale_model.py index daeea6e6..31d102f0 100644 --- a/backend/headless/fcbh_extras/nodes_upscale_model.py +++ b/ldm_patched/contrib/external_upscale_model.py @@ -1,14 +1,16 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + import os -from fcbh_extras.chainner_models import model_loading -from fcbh import model_management +from ldm_patched.pfn import model_loading +from ldm_patched.modules import model_management import torch -import fcbh.utils -import folder_paths +import ldm_patched.modules.utils +import ldm_patched.utils.path_utils class UpscaleModelLoader: @classmethod def INPUT_TYPES(s): - return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ), + return {"required": { "model_name": (ldm_patched.utils.path_utils.get_filename_list("upscale_models"), ), }} RETURN_TYPES = ("UPSCALE_MODEL",) FUNCTION = "load_model" @@ -16,10 +18,10 @@ class UpscaleModelLoader: CATEGORY = "loaders" def load_model(self, model_name): - model_path = folder_paths.get_full_path("upscale_models", model_name) - sd = fcbh.utils.load_torch_file(model_path, safe_load=True) + model_path = ldm_patched.utils.path_utils.get_full_path("upscale_models", model_name) + sd = ldm_patched.modules.utils.load_torch_file(model_path, safe_load=True) if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: - sd = fcbh.utils.state_dict_prefix_replace(sd, {"module.":""}) + sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"module.":""}) out = model_loading.load_state_dict(sd).eval() return (out, ) @@ -47,9 +49,9 @@ class ImageUpscaleWithModel: oom = True while oom: try: - steps = in_img.shape[0] * fcbh.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) - pbar = fcbh.utils.ProgressBar(steps) - s = fcbh.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) + steps = in_img.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) + pbar = ldm_patched.modules.utils.ProgressBar(steps) + s = ldm_patched.modules.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) oom = False except model_management.OOM_EXCEPTION as e: tile //= 2 diff --git a/ldm_patched/contrib/external_video_model.py b/ldm_patched/contrib/external_video_model.py new file mode 100644 index 00000000..4504528a --- /dev/null +++ b/ldm_patched/contrib/external_video_model.py @@ -0,0 +1,91 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.contrib.external +import torch +import ldm_patched.modules.utils +import ldm_patched.modules.sd +import ldm_patched.utils.path_utils + + +class ImageOnlyCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders/video_models" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) + out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) + return (out[0], out[3], out[2]) + + +class SVD_img2vid_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 1024, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 576, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), + "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), + "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), + "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + if augmentation_level > 0: + encode_pixels += torch.randn_like(pixels) * augmentation_level + t = vae.encode(encode_pixels) + positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([video_frames, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + +class VideoLinearCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "sampling/video_models" + + def patch(self, model, min_cfg): + def linear_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + + scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1)) + return uncond + scale * (cond - uncond) + + m = model.clone() + m.set_model_sampler_cfg_function(linear_cfg) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, + "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, + "VideoLinearCFGGuidance": VideoLinearCFGGuidance, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)", +} diff --git a/backend/headless/fcbh/cldm/cldm.py b/ldm_patched/controlnet/cldm.py similarity index 90% rename from backend/headless/fcbh/cldm/cldm.py rename to ldm_patched/controlnet/cldm.py index d464a462..82265ef9 100644 --- a/backend/headless/fcbh/cldm/cldm.py +++ b/ldm_patched/controlnet/cldm.py @@ -5,15 +5,15 @@ import torch import torch as th import torch.nn as nn -from ..ldm.modules.diffusionmodules.util import ( +from ldm_patched.ldm.modules.diffusionmodules.util import ( zero_module, timestep_embedding, ) -from ..ldm.modules.attention import SpatialTransformer -from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample -from ..ldm.util import exists -import fcbh.ops +from ldm_patched.ldm.modules.attention import SpatialTransformer +from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample +from ldm_patched.ldm.util import exists +import ldm_patched.modules.ops class ControlledUnetModel(UNetModel): #implemented in the ldm unet @@ -53,7 +53,8 @@ class ControlNet(nn.Module): transformer_depth_middle=None, transformer_depth_output=None, device=None, - operations=fcbh.ops, + operations=ldm_patched.modules.ops.disable_weight_init, + **kwargs, ): super().__init__() assert use_spatial_transformer == True, "use_spatial_transformer has to be true" @@ -140,24 +141,24 @@ class ControlNet(nn.Module): ) ] ) - self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations)]) + self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) self.input_hint_block = TimestepEmbedSequential( - operations.conv_nd(dims, hint_channels, 16, 3, padding=1), + operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), - operations.conv_nd(dims, 16, 16, 3, padding=1), + operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), - operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2), + operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), - operations.conv_nd(dims, 32, 32, 3, padding=1), + operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), - operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2), + operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), - operations.conv_nd(dims, 96, 96, 3, padding=1), + operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), - operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2), + operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), - zero_module(operations.conv_nd(dims, 256, model_channels, 3, padding=1)) + operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) self._feature_size = model_channels @@ -205,7 +206,7 @@ class ControlNet(nn.Module): ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) - self.zero_convs.append(self.make_zero_conv(ch, operations=operations)) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: @@ -233,7 +234,7 @@ class ControlNet(nn.Module): ) ch = out_ch input_block_chans.append(ch) - self.zero_convs.append(self.make_zero_conv(ch, operations=operations)) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) ds *= 2 self._feature_size += ch @@ -275,14 +276,14 @@ class ControlNet(nn.Module): operations=operations )] self.middle_block = TimestepEmbedSequential(*mid_block) - self.middle_block_out = self.make_zero_conv(ch, operations=operations) + self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) self._feature_size += ch - def make_zero_conv(self, channels, operations=None): - return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0))) + def make_zero_conv(self, channels, operations=None, dtype=None, device=None): + return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) def forward(self, x, hint, timesteps, context, y=None, **kwargs): - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype) + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) guided_hint = self.input_hint_block(hint, emb, context) @@ -294,7 +295,7 @@ class ControlNet(nn.Module): assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) - h = x.type(self.dtype) + h = x for module, zero_conv in zip(self.input_blocks, self.zero_convs): if guided_hint is not None: h = module(h, emb, context) diff --git a/backend/headless/fcbh/k_diffusion/sampling.py b/ldm_patched/k_diffusion/sampling.py similarity index 100% rename from backend/headless/fcbh/k_diffusion/sampling.py rename to ldm_patched/k_diffusion/sampling.py diff --git a/backend/headless/fcbh/k_diffusion/utils.py b/ldm_patched/k_diffusion/utils.py similarity index 100% rename from backend/headless/fcbh/k_diffusion/utils.py rename to ldm_patched/k_diffusion/utils.py diff --git a/backend/headless/fcbh/ldm/models/autoencoder.py b/ldm_patched/ldm/models/autoencoder.py similarity index 94% rename from backend/headless/fcbh/ldm/models/autoencoder.py rename to ldm_patched/ldm/models/autoencoder.py index fc6932d2..14224ad3 100644 --- a/backend/headless/fcbh/ldm/models/autoencoder.py +++ b/ldm_patched/ldm/models/autoencoder.py @@ -4,10 +4,10 @@ import torch.nn.functional as F from contextlib import contextmanager from typing import Any, Dict, List, Optional, Tuple, Union -from fcbh.ldm.modules.distributions.distributions import DiagonalGaussianDistribution +from ldm_patched.ldm.modules.distributions.distributions import DiagonalGaussianDistribution -from fcbh.ldm.util import instantiate_from_config -from fcbh.ldm.modules.ema import LitEma +from ldm_patched.ldm.util import instantiate_from_config +from ldm_patched.ldm.modules.ema import LitEma class DiagonalGaussianRegularizer(torch.nn.Module): def __init__(self, sample: bool = True): @@ -152,11 +152,11 @@ class AutoencodingEngineLegacy(AutoencodingEngine): ddconfig = kwargs.pop("ddconfig") super().__init__( encoder_config={ - "target": "fcbh.ldm.modules.diffusionmodules.model.Encoder", + "target": "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", "params": ddconfig, }, decoder_config={ - "target": "fcbh.ldm.modules.diffusionmodules.model.Decoder", + "target": "ldm_patched.ldm.modules.diffusionmodules.model.Decoder", "params": ddconfig, }, **kwargs, @@ -220,7 +220,7 @@ class AutoencoderKL(AutoencodingEngineLegacy): super().__init__( regularizer_config={ "target": ( - "fcbh.ldm.models.autoencoder.DiagonalGaussianRegularizer" + "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer" ) }, **kwargs, diff --git a/backend/headless/fcbh/ldm/modules/attention.py b/ldm_patched/ldm/modules/attention.py similarity index 60% rename from backend/headless/fcbh/ldm/modules/attention.py rename to ldm_patched/ldm/modules/attention.py index c0383556..f4579bac 100644 --- a/backend/headless/fcbh/ldm/modules/attention.py +++ b/ldm_patched/ldm/modules/attention.py @@ -5,21 +5,24 @@ import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat from typing import Optional, Any +from functools import partial -from .diffusionmodules.util import checkpoint + +from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding from .sub_quadratic_attention import efficient_dot_product_attention -from fcbh import model_management +from ldm_patched.modules import model_management if model_management.xformers_enabled(): import xformers import xformers.ops -from fcbh.cli_args import args -import fcbh.ops +from ldm_patched.modules.args_parser import args +import ldm_patched.modules.ops +ops = ldm_patched.modules.ops.disable_weight_init # CrossAttn precision handling -if args.dont_upcast_attention: +if args.disable_attention_upcast: print("disabling upcasting of attention") _ATTN_PRECISION = "fp16" else: @@ -53,7 +56,7 @@ def init_(tensor): # feedforward class GEGLU(nn.Module): - def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=fcbh.ops): + def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): super().__init__() self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) @@ -63,7 +66,7 @@ class GEGLU(nn.Module): class FeedForward(nn.Module): - def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=fcbh.ops): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) @@ -81,16 +84,6 @@ class FeedForward(nn.Module): def forward(self, x): return self.net(x) - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) @@ -120,10 +113,13 @@ def attention_basic(q, k, v, heads, mask=None): del q, k if exists(mask): - mask = rearrange(mask, 'b ... -> b (...)') - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) + if mask.dtype == torch.bool: + mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + else: + sim += mask # attention, what we cannot get enough of sim = sim.softmax(dim=-1) @@ -276,9 +272,20 @@ def attention_split(q, k, v, heads, mask=None): ) return r1 +BROKEN_XFORMERS = False +try: + x_vers = xformers.__version__ + #I think 0.0.23 is also broken (q with bs bigger than 65535 gives CUDA error) + BROKEN_XFORMERS = x_vers.startswith("0.0.21") or x_vers.startswith("0.0.22") or x_vers.startswith("0.0.23") +except: + pass + def attention_xformers(q, k, v, heads, mask=None): b, _, dim_head = q.shape dim_head //= heads + if BROKEN_XFORMERS: + if b * heads > 65535: + return attention_pytorch(q, k, v, heads, mask) q, k, v = map( lambda t: t.unsqueeze(3) @@ -327,18 +334,30 @@ elif model_management.pytorch_attention_enabled(): print("Using pytorch cross attention") optimized_attention = attention_pytorch else: - if args.use_split_cross_attention: + if args.attention_split: print("Using split optimization for cross attention") optimized_attention = attention_split else: - print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention") + print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --attention-split") optimized_attention = attention_sub_quad if model_management.pytorch_attention_enabled(): optimized_attention_masked = attention_pytorch +def optimized_attention_for_device(device, mask=False): + if device == torch.device("cpu"): #TODO + if model_management.pytorch_attention_enabled(): + return attention_pytorch + else: + return attention_basic + if mask: + return optimized_attention_masked + + return optimized_attention + + class CrossAttention(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=fcbh.ops): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) @@ -370,53 +389,72 @@ class CrossAttention(nn.Module): class BasicTransformerBlock(nn.Module): - def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, - disable_self_attn=False, dtype=None, device=None, operations=fcbh.ops): + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, + disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops): super().__init__() + + self.ff_in = ff_in or inner_dim is not None + if inner_dim is None: + inner_dim = dim + + self.is_res = inner_dim == dim + + if self.ff_in: + self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) + self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + self.disable_self_attn = disable_self_attn - self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn - self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) - self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, - heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none - self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device) - self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device) - self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device) + self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + + if disable_temporal_crossattention: + if switch_temporal_ca_to_sa: + raise ValueError + else: + self.attn2 = None + else: + context_dim_attn2 = None + if not switch_temporal_ca_to_sa: + context_dim_attn2 = context_dim + + self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, + heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none + self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + + self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.checkpoint = checkpoint self.n_heads = n_heads self.d_head = d_head + self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa def forward(self, x, context=None, transformer_options={}): return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint) def _forward(self, x, context=None, transformer_options={}): extra_options = {} - block = None - block_index = 0 - if "current_index" in transformer_options: - extra_options["transformer_index"] = transformer_options["current_index"] - if "block_index" in transformer_options: - block_index = transformer_options["block_index"] - extra_options["block_index"] = block_index - if "original_shape" in transformer_options: - extra_options["original_shape"] = transformer_options["original_shape"] - if "block" in transformer_options: - block = transformer_options["block"] - extra_options["block"] = block - if "cond_or_uncond" in transformer_options: - extra_options["cond_or_uncond"] = transformer_options["cond_or_uncond"] - if "patches" in transformer_options: - transformer_patches = transformer_options["patches"] - else: - transformer_patches = {} + block = transformer_options.get("block", None) + block_index = transformer_options.get("block_index", 0) + transformer_patches = {} + transformer_patches_replace = {} + + for k in transformer_options: + if k == "patches": + transformer_patches = transformer_options[k] + elif k == "patches_replace": + transformer_patches_replace = transformer_options[k] + else: + extra_options[k] = transformer_options[k] extra_options["n_heads"] = self.n_heads extra_options["dim_head"] = self.d_head - if "patches_replace" in transformer_options: - transformer_patches_replace = transformer_options["patches_replace"] - else: - transformer_patches_replace = {} + if self.ff_in: + x_skip = x + x = self.ff_in(self.norm_in(x)) + if self.is_res: + x += x_skip n = self.norm1(x) if self.disable_self_attn: @@ -465,31 +503,34 @@ class BasicTransformerBlock(nn.Module): for p in patch: x = p(x, extra_options) - n = self.norm2(x) - - context_attn2 = context - value_attn2 = None - if "attn2_patch" in transformer_patches: - patch = transformer_patches["attn2_patch"] - value_attn2 = context_attn2 - for p in patch: - n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) - - attn2_replace_patch = transformer_patches_replace.get("attn2", {}) - block_attn2 = transformer_block - if block_attn2 not in attn2_replace_patch: - block_attn2 = block - - if block_attn2 in attn2_replace_patch: - if value_attn2 is None: + if self.attn2 is not None: + n = self.norm2(x) + if self.switch_temporal_ca_to_sa: + context_attn2 = n + else: + context_attn2 = context + value_attn2 = None + if "attn2_patch" in transformer_patches: + patch = transformer_patches["attn2_patch"] value_attn2 = context_attn2 - n = self.attn2.to_q(n) - context_attn2 = self.attn2.to_k(context_attn2) - value_attn2 = self.attn2.to_v(value_attn2) - n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) - n = self.attn2.to_out(n) - else: - n = self.attn2(n, context=context_attn2, value=value_attn2) + for p in patch: + n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) + + attn2_replace_patch = transformer_patches_replace.get("attn2", {}) + block_attn2 = transformer_block + if block_attn2 not in attn2_replace_patch: + block_attn2 = block + + if block_attn2 in attn2_replace_patch: + if value_attn2 is None: + value_attn2 = context_attn2 + n = self.attn2.to_q(n) + context_attn2 = self.attn2.to_k(context_attn2) + value_attn2 = self.attn2.to_v(value_attn2) + n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) + n = self.attn2.to_out(n) + else: + n = self.attn2(n, context=context_attn2, value=value_attn2) if "attn2_output_patch" in transformer_patches: patch = transformer_patches["attn2_output_patch"] @@ -497,7 +538,12 @@ class BasicTransformerBlock(nn.Module): n = p(n, extra_options) x += n - x = self.ff(self.norm3(x)) + x + if self.is_res: + x_skip = x + x = self.ff(self.norm3(x)) + if self.is_res: + x += x_skip + return x @@ -513,13 +559,13 @@ class SpatialTransformer(nn.Module): def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, disable_self_attn=False, use_linear=False, - use_checkpoint=True, dtype=None, device=None, operations=fcbh.ops): + use_checkpoint=True, dtype=None, device=None, operations=ops): super().__init__() if exists(context_dim) and not isinstance(context_dim, list): context_dim = [context_dim] * depth self.in_channels = in_channels inner_dim = n_heads * d_head - self.norm = Normalize(in_channels, dtype=dtype, device=device) + self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) if not use_linear: self.proj_in = operations.Conv2d(in_channels, inner_dim, @@ -565,3 +611,164 @@ class SpatialTransformer(nn.Module): x = self.proj_out(x) return x + x_in + +class SpatialVideoTransformer(SpatialTransformer): + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + use_linear=False, + context_dim=None, + use_spatial_context=False, + timesteps=None, + merge_strategy: str = "fixed", + merge_factor: float = 0.5, + time_context_dim=None, + ff_in=False, + checkpoint=False, + time_depth=1, + disable_self_attn=False, + disable_temporal_crossattention=False, + max_time_embed_period: int = 10000, + dtype=None, device=None, operations=ops + ): + super().__init__( + in_channels, + n_heads, + d_head, + depth=depth, + dropout=dropout, + use_checkpoint=checkpoint, + context_dim=context_dim, + use_linear=use_linear, + disable_self_attn=disable_self_attn, + dtype=dtype, device=device, operations=operations + ) + self.time_depth = time_depth + self.depth = depth + self.max_time_embed_period = max_time_embed_period + + time_mix_d_head = d_head + n_time_mix_heads = n_heads + + time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads) + + inner_dim = n_heads * d_head + if use_spatial_context: + time_context_dim = context_dim + + self.time_stack = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_time_mix_heads, + time_mix_d_head, + dropout=dropout, + context_dim=time_context_dim, + # timesteps=timesteps, + checkpoint=checkpoint, + ff_in=ff_in, + inner_dim=time_mix_inner_dim, + disable_self_attn=disable_self_attn, + disable_temporal_crossattention=disable_temporal_crossattention, + dtype=dtype, device=device, operations=operations + ) + for _ in range(self.depth) + ] + ) + + assert len(self.time_stack) == len(self.transformer_blocks) + + self.use_spatial_context = use_spatial_context + self.in_channels = in_channels + + time_embed_dim = self.in_channels * 4 + self.time_pos_embed = nn.Sequential( + operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device), + ) + + self.time_mixer = AlphaBlender( + alpha=merge_factor, merge_strategy=merge_strategy + ) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + time_context: Optional[torch.Tensor] = None, + timesteps: Optional[int] = None, + image_only_indicator: Optional[torch.Tensor] = None, + transformer_options={} + ) -> torch.Tensor: + _, _, h, w = x.shape + x_in = x + spatial_context = None + if exists(context): + spatial_context = context + + if self.use_spatial_context: + assert ( + context.ndim == 3 + ), f"n dims of spatial context should be 3 but are {context.ndim}" + + if time_context is None: + time_context = context + time_context_first_timestep = time_context[::timesteps] + time_context = repeat( + time_context_first_timestep, "b ... -> (b n) ...", n=h * w + ) + elif time_context is not None and not self.use_spatial_context: + time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w) + if time_context.ndim == 2: + time_context = rearrange(time_context, "b c -> b 1 c") + + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c") + if self.use_linear: + x = self.proj_in(x) + + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype) + emb = self.time_pos_embed(t_emb) + emb = emb[:, None, :] + + for it_, (block, mix_block) in enumerate( + zip(self.transformer_blocks, self.time_stack) + ): + transformer_options["block_index"] = it_ + x = block( + x, + context=spatial_context, + transformer_options=transformer_options, + ) + + x_mix = x + x_mix = x_mix + emb + + B, S, C = x_mix.shape + x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) + x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options + x_mix = rearrange( + x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps + ) + + x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + if not self.use_linear: + x = self.proj_out(x) + out = x + x_in + return out + + diff --git a/backend/headless/fcbh/ldm/modules/diffusionmodules/__init__.py b/ldm_patched/ldm/modules/diffusionmodules/__init__.py similarity index 100% rename from backend/headless/fcbh/ldm/modules/diffusionmodules/__init__.py rename to ldm_patched/ldm/modules/diffusionmodules/__init__.py diff --git a/backend/headless/fcbh/ldm/modules/diffusionmodules/model.py b/ldm_patched/ldm/modules/diffusionmodules/model.py similarity index 95% rename from backend/headless/fcbh/ldm/modules/diffusionmodules/model.py rename to ldm_patched/ldm/modules/diffusionmodules/model.py index e0c4440b..9c898639 100644 --- a/backend/headless/fcbh/ldm/modules/diffusionmodules/model.py +++ b/ldm_patched/ldm/modules/diffusionmodules/model.py @@ -6,8 +6,9 @@ import numpy as np from einops import rearrange from typing import Optional, Any -from fcbh import model_management -import fcbh.ops +from ldm_patched.modules import model_management +import ldm_patched.modules.ops +ops = ldm_patched.modules.ops.disable_weight_init if model_management.xformers_enabled_vae(): import xformers @@ -48,7 +49,7 @@ class Upsample(nn.Module): super().__init__() self.with_conv = with_conv if self.with_conv: - self.conv = fcbh.ops.Conv2d(in_channels, + self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, @@ -78,7 +79,7 @@ class Downsample(nn.Module): self.with_conv = with_conv if self.with_conv: # no asymmetric padding in torch conv, must do it ourselves - self.conv = fcbh.ops.Conv2d(in_channels, + self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, @@ -105,30 +106,30 @@ class ResnetBlock(nn.Module): self.swish = torch.nn.SiLU(inplace=True) self.norm1 = Normalize(in_channels) - self.conv1 = fcbh.ops.Conv2d(in_channels, + self.conv1 = ops.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels > 0: - self.temb_proj = fcbh.ops.Linear(temb_channels, + self.temb_proj = ops.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels) self.dropout = torch.nn.Dropout(dropout, inplace=True) - self.conv2 = fcbh.ops.Conv2d(out_channels, + self.conv2 = ops.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if self.in_channels != self.out_channels: if self.use_conv_shortcut: - self.conv_shortcut = fcbh.ops.Conv2d(in_channels, + self.conv_shortcut = ops.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: - self.nin_shortcut = fcbh.ops.Conv2d(in_channels, + self.nin_shortcut = ops.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, @@ -245,22 +246,22 @@ class AttnBlock(nn.Module): self.in_channels = in_channels self.norm = Normalize(in_channels) - self.q = fcbh.ops.Conv2d(in_channels, + self.q = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) - self.k = fcbh.ops.Conv2d(in_channels, + self.k = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) - self.v = fcbh.ops.Conv2d(in_channels, + self.v = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) - self.proj_out = fcbh.ops.Conv2d(in_channels, + self.proj_out = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, @@ -312,14 +313,14 @@ class Model(nn.Module): # timestep embedding self.temb = nn.Module() self.temb.dense = nn.ModuleList([ - fcbh.ops.Linear(self.ch, + ops.Linear(self.ch, self.temb_ch), - fcbh.ops.Linear(self.temb_ch, + ops.Linear(self.temb_ch, self.temb_ch), ]) # downsampling - self.conv_in = fcbh.ops.Conv2d(in_channels, + self.conv_in = ops.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, @@ -388,7 +389,7 @@ class Model(nn.Module): # end self.norm_out = Normalize(block_in) - self.conv_out = fcbh.ops.Conv2d(block_in, + self.conv_out = ops.Conv2d(block_in, out_ch, kernel_size=3, stride=1, @@ -461,7 +462,7 @@ class Encoder(nn.Module): self.in_channels = in_channels # downsampling - self.conv_in = fcbh.ops.Conv2d(in_channels, + self.conv_in = ops.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, @@ -506,7 +507,7 @@ class Encoder(nn.Module): # end self.norm_out = Normalize(block_in) - self.conv_out = fcbh.ops.Conv2d(block_in, + self.conv_out = ops.Conv2d(block_in, 2*z_channels if double_z else z_channels, kernel_size=3, stride=1, @@ -541,7 +542,7 @@ class Decoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, - conv_out_op=fcbh.ops.Conv2d, + conv_out_op=ops.Conv2d, resnet_op=ResnetBlock, attn_op=AttnBlock, **ignorekwargs): @@ -565,7 +566,7 @@ class Decoder(nn.Module): self.z_shape, np.prod(self.z_shape))) # z to block_in - self.conv_in = fcbh.ops.Conv2d(z_channels, + self.conv_in = ops.Conv2d(z_channels, block_in, kernel_size=3, stride=1, diff --git a/backend/headless/fcbh/ldm/modules/diffusionmodules/openaimodel.py b/ldm_patched/ldm/modules/diffusionmodules/openaimodel.py similarity index 66% rename from backend/headless/fcbh/ldm/modules/diffusionmodules/openaimodel.py rename to ldm_patched/ldm/modules/diffusionmodules/openaimodel.py index 1c6ebd3d..e5784f28 100644 --- a/backend/headless/fcbh/ldm/modules/diffusionmodules/openaimodel.py +++ b/ldm_patched/ldm/modules/diffusionmodules/openaimodel.py @@ -5,17 +5,20 @@ import numpy as np import torch as th import torch.nn as nn import torch.nn.functional as F +from einops import rearrange +from functools import partial from .util import ( checkpoint, avg_pool_nd, zero_module, - normalization, timestep_embedding, + AlphaBlender, ) -from ..attention import SpatialTransformer -from fcbh.ldm.util import exists -import fcbh.ops +from ..attention import SpatialTransformer, SpatialVideoTransformer, default +from ldm_patched.ldm.util import exists +import ldm_patched.modules.ops +ops = ldm_patched.modules.ops.disable_weight_init class TimestepBlock(nn.Module): """ @@ -28,15 +31,21 @@ class TimestepBlock(nn.Module): Apply the module to `x` given `emb` timestep embeddings. """ -#This is needed because accelerate makes a copy of transformer_options which breaks "current_index" -def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None): +#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index" +def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): for layer in ts: - if isinstance(layer, TimestepBlock): + if isinstance(layer, VideoResBlock): + x = layer(x, emb, num_video_frames, image_only_indicator) + elif isinstance(layer, TimestepBlock): x = layer(x, emb) + elif isinstance(layer, SpatialVideoTransformer): + x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options) + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 elif isinstance(layer, SpatialTransformer): x = layer(x, context, transformer_options) - if "current_index" in transformer_options: - transformer_options["current_index"] += 1 + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 elif isinstance(layer, Upsample): x = layer(x, output_shape=output_shape) else: @@ -61,7 +70,7 @@ class Upsample(nn.Module): upsampling occurs in the inner-two dimensions. """ - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=fcbh.ops): + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels @@ -97,7 +106,7 @@ class Downsample(nn.Module): downsampling occurs in the inner-two dimensions. """ - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=fcbh.ops): + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels @@ -145,9 +154,12 @@ class ResBlock(TimestepBlock): use_checkpoint=False, up=False, down=False, + kernel_size=3, + exchange_temb_dims=False, + skip_t_emb=False, dtype=None, device=None, - operations=fcbh.ops + operations=ops ): super().__init__() self.channels = channels @@ -157,11 +169,17 @@ class ResBlock(TimestepBlock): self.use_conv = use_conv self.use_checkpoint = use_checkpoint self.use_scale_shift_norm = use_scale_shift_norm + self.exchange_temb_dims = exchange_temb_dims + + if isinstance(kernel_size, list): + padding = [k // 2 for k in kernel_size] + else: + padding = kernel_size // 2 self.in_layers = nn.Sequential( - nn.GroupNorm(32, channels, dtype=dtype, device=device), + operations.GroupNorm(32, channels, dtype=dtype, device=device), nn.SiLU(), - operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device), + operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device), ) self.updown = up or down @@ -175,27 +193,31 @@ class ResBlock(TimestepBlock): else: self.h_upd = self.x_upd = nn.Identity() - self.emb_layers = nn.Sequential( - nn.SiLU(), - operations.Linear( - emb_channels, - 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device - ), - ) + self.skip_t_emb = skip_t_emb + if self.skip_t_emb: + self.emb_layers = None + self.exchange_temb_dims = False + else: + self.emb_layers = nn.Sequential( + nn.SiLU(), + operations.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device + ), + ) self.out_layers = nn.Sequential( - nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device), + operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), nn.SiLU(), nn.Dropout(p=dropout), - zero_module( - operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device) - ), + operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device) + , ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = operations.conv_nd( - dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device + dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device ) else: self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) @@ -221,19 +243,110 @@ class ResBlock(TimestepBlock): h = in_conv(h) else: h = self.in_layers(x) - emb_out = self.emb_layers(emb).type(h.dtype) - while len(emb_out.shape) < len(h.shape): - emb_out = emb_out[..., None] + + emb_out = None + if not self.skip_t_emb: + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] - scale, shift = th.chunk(emb_out, 2, dim=1) - h = out_norm(h) * (1 + scale) + shift + h = out_norm(h) + if emb_out is not None: + scale, shift = th.chunk(emb_out, 2, dim=1) + h *= (1 + scale) + h += shift h = out_rest(h) else: - h = h + emb_out + if emb_out is not None: + if self.exchange_temb_dims: + emb_out = rearrange(emb_out, "b t c ... -> b c t ...") + h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h + +class VideoResBlock(ResBlock): + def __init__( + self, + channels: int, + emb_channels: int, + dropout: float, + video_kernel_size=3, + merge_strategy: str = "fixed", + merge_factor: float = 0.5, + out_channels=None, + use_conv: bool = False, + use_scale_shift_norm: bool = False, + dims: int = 2, + use_checkpoint: bool = False, + up: bool = False, + down: bool = False, + dtype=None, + device=None, + operations=ops + ): + super().__init__( + channels, + emb_channels, + dropout, + out_channels=out_channels, + use_conv=use_conv, + use_scale_shift_norm=use_scale_shift_norm, + dims=dims, + use_checkpoint=use_checkpoint, + up=up, + down=down, + dtype=dtype, + device=device, + operations=operations + ) + + self.time_stack = ResBlock( + default(out_channels, channels), + emb_channels, + dropout=dropout, + dims=3, + out_channels=default(out_channels, channels), + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=use_checkpoint, + exchange_temb_dims=True, + dtype=dtype, + device=device, + operations=operations + ) + self.time_mixer = AlphaBlender( + alpha=merge_factor, + merge_strategy=merge_strategy, + rearrange_pattern="b t -> b 1 t 1 1", + ) + + def forward( + self, + x: th.Tensor, + emb: th.Tensor, + num_video_frames: int, + image_only_indicator = None, + ) -> th.Tensor: + x = super().forward(x, emb) + + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) + x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) + + x = self.time_stack( + x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) + ) + x = self.time_mixer( + x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator + ) + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + class Timestep(nn.Module): def __init__(self, dim): super().__init__() @@ -310,8 +423,18 @@ class UNetModel(nn.Module): adm_in_channels=None, transformer_depth_middle=None, transformer_depth_output=None, + use_temporal_resblock=False, + use_temporal_attention=False, + time_context_dim=None, + extra_ff_mix_layer=False, + use_spatial_context=False, + merge_strategy=None, + merge_factor=0.0, + video_kernel_size=None, + disable_temporal_crossattention=False, + max_ddpm_temb_period=10000, device=None, - operations=fcbh.ops, + operations=ops, ): super().__init__() assert use_spatial_transformer == True, "use_spatial_transformer has to be true" @@ -364,8 +487,12 @@ class UNetModel(nn.Module): self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample + self.use_temporal_resblocks = use_temporal_resblock self.predict_codebook_ids = n_embed is not None + self.default_num_video_frames = None + self.default_image_only_indicator = None + time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), @@ -402,13 +529,104 @@ class UNetModel(nn.Module): input_block_chans = [model_channels] ch = model_channels ds = 1 + + def get_attention_layer( + ch, + num_heads, + dim_head, + depth=1, + context_dim=None, + use_checkpoint=False, + disable_self_attn=False, + ): + if use_temporal_attention: + return SpatialVideoTransformer( + ch, + num_heads, + dim_head, + depth=depth, + context_dim=context_dim, + time_context_dim=time_context_dim, + dropout=dropout, + ff_in=extra_ff_mix_layer, + use_spatial_context=use_spatial_context, + merge_strategy=merge_strategy, + merge_factor=merge_factor, + checkpoint=use_checkpoint, + use_linear=use_linear_in_transformer, + disable_self_attn=disable_self_attn, + disable_temporal_crossattention=disable_temporal_crossattention, + max_time_embed_period=max_ddpm_temb_period, + dtype=self.dtype, device=device, operations=operations + ) + else: + return SpatialTransformer( + ch, num_heads, dim_head, depth=depth, context_dim=context_dim, + disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + ) + + def get_resblock( + merge_factor, + merge_strategy, + video_kernel_size, + ch, + time_embed_dim, + dropout, + out_channels, + dims, + use_checkpoint, + use_scale_shift_norm, + down=False, + up=False, + dtype=None, + device=None, + operations=ops + ): + if self.use_temporal_resblocks: + return VideoResBlock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations + ) + else: + return ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + use_checkpoint=use_checkpoint, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations + ) + for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ - ResBlock( - ch, - time_embed_dim, - dropout, + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, @@ -435,11 +653,9 @@ class UNetModel(nn.Module): disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: - layers.append(SpatialTransformer( + layers.append(get_attention_layer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, - disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations - ) + disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch @@ -448,10 +664,13 @@ class UNetModel(nn.Module): out_ch = ch self.input_blocks.append( TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, @@ -481,10 +700,14 @@ class UNetModel(nn.Module): #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels mid_block = [ - ResBlock( - ch, - time_embed_dim, - dropout, + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, @@ -493,15 +716,18 @@ class UNetModel(nn.Module): operations=operations )] if transformer_depth_middle >= 0: - mid_block += [SpatialTransformer( # always uses a self-attn + mid_block += [get_attention_layer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, - disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint ), - ResBlock( - ch, - time_embed_dim, - dropout, + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, @@ -517,10 +743,13 @@ class UNetModel(nn.Module): for i in range(self.num_res_blocks[level] + 1): ich = input_block_chans.pop() layers = [ - ResBlock( - ch + ich, - time_embed_dim, - dropout, + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch + ich, + time_embed_dim=time_embed_dim, + dropout=dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, @@ -548,19 +777,21 @@ class UNetModel(nn.Module): if not exists(num_attention_blocks) or i < num_attention_blocks[level]: layers.append( - SpatialTransformer( + get_attention_layer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, - disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint ) ) if level and i == self.num_res_blocks[level]: out_ch = ch layers.append( - ResBlock( - ch, - time_embed_dim, - dropout, + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, @@ -578,13 +809,13 @@ class UNetModel(nn.Module): self._feature_size += ch self.out = nn.Sequential( - nn.GroupNorm(32, ch, dtype=self.dtype, device=device), + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), nn.SiLU(), zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( - nn.GroupNorm(32, ch, dtype=self.dtype, device=device), + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) @@ -599,24 +830,28 @@ class UNetModel(nn.Module): :return: an [N x C x ...] Tensor of outputs. """ transformer_options["original_shape"] = list(x.shape) - transformer_options["current_index"] = 0 + transformer_options["transformer_index"] = 0 transformer_patches = transformer_options.get("patches", {}) + num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator) + time_context = kwargs.get("time_context", None) + assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype) + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) - h = x.type(self.dtype) + h = x for id, module in enumerate(self.input_blocks): transformer_options["block"] = ("input", id) - h = forward_timestep_embed(module, h, emb, context, transformer_options) + h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'input') if "input_block_patch" in transformer_patches: patch = transformer_patches["input_block_patch"] @@ -630,9 +865,10 @@ class UNetModel(nn.Module): h = p(h, transformer_options) transformer_options["block"] = ("middle", 0) - h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options) + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'middle') + for id, module in enumerate(self.output_blocks): transformer_options["block"] = ("output", id) hsp = hs.pop() @@ -649,7 +885,7 @@ class UNetModel(nn.Module): output_shape = hs[-1].shape else: output_shape = None - h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape) + h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) diff --git a/backend/headless/fcbh/ldm/modules/diffusionmodules/upscaling.py b/ldm_patched/ldm/modules/diffusionmodules/upscaling.py similarity index 98% rename from backend/headless/fcbh/ldm/modules/diffusionmodules/upscaling.py rename to ldm_patched/ldm/modules/diffusionmodules/upscaling.py index a7684f45..59d4d3cc 100644 --- a/backend/headless/fcbh/ldm/modules/diffusionmodules/upscaling.py +++ b/ldm_patched/ldm/modules/diffusionmodules/upscaling.py @@ -4,7 +4,7 @@ import numpy as np from functools import partial from .util import extract_into_tensor, make_beta_schedule -from fcbh.ldm.util import default +from ldm_patched.ldm.util import default class AbstractLowScaleModel(nn.Module): diff --git a/backend/headless/fcbh/ldm/modules/diffusionmodules/util.py b/ldm_patched/ldm/modules/diffusionmodules/util.py similarity index 77% rename from backend/headless/fcbh/ldm/modules/diffusionmodules/util.py rename to ldm_patched/ldm/modules/diffusionmodules/util.py index 13b53929..ca0f4b99 100644 --- a/backend/headless/fcbh/ldm/modules/diffusionmodules/util.py +++ b/ldm_patched/ldm/modules/diffusionmodules/util.py @@ -13,10 +13,76 @@ import math import torch import torch.nn as nn import numpy as np -from einops import repeat +from einops import repeat, rearrange + +from ldm_patched.ldm.util import instantiate_from_config + +class AlphaBlender(nn.Module): + strategies = ["learned", "fixed", "learned_with_images"] + + def __init__( + self, + alpha: float, + merge_strategy: str = "learned_with_images", + rearrange_pattern: str = "b t -> (b t) 1 1", + ): + super().__init__() + self.merge_strategy = merge_strategy + self.rearrange_pattern = rearrange_pattern + + assert ( + merge_strategy in self.strategies + ), f"merge_strategy needs to be in {self.strategies}" + + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif ( + self.merge_strategy == "learned" + or self.merge_strategy == "learned_with_images" + ): + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor: + # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t) + if self.merge_strategy == "fixed": + # make shape compatible + # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) + alpha = self.mix_factor + elif self.merge_strategy == "learned": + alpha = torch.sigmoid(self.mix_factor) + # make shape compatible + # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) + elif self.merge_strategy == "learned_with_images": + assert image_only_indicator is not None, "need image_only_indicator ..." + alpha = torch.where( + image_only_indicator.bool(), + torch.ones(1, 1, device=image_only_indicator.device), + rearrange(torch.sigmoid(self.mix_factor), "... -> ... 1"), + ) + alpha = rearrange(alpha, self.rearrange_pattern) + # make shape compatible + # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) + else: + raise NotImplementedError() + return alpha + + def forward( + self, + x_spatial, + x_temporal, + image_only_indicator=None, + ) -> torch.Tensor: + alpha = self.get_alpha(image_only_indicator) + x = ( + alpha.to(x_spatial.dtype) * x_spatial + + (1.0 - alpha).to(x_spatial.dtype) * x_temporal + ) + return x -from fcbh.ldm.util import instantiate_from_config -import fcbh.ops def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if schedule == "linear": @@ -206,46 +272,6 @@ def mean_flat(tensor): return tensor.mean(dim=list(range(1, len(tensor.shape)))) -def normalization(channels, dtype=None): - """ - Make a standard normalization layer. - :param channels: number of input channels. - :return: an nn.Module for normalization. - """ - return GroupNorm32(32, channels, dtype=dtype) - - -# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. -class SiLU(nn.Module): - def forward(self, x): - return x * torch.sigmoid(x) - - -class GroupNorm32(nn.GroupNorm): - def forward(self, x): - return super().forward(x.float()).type(x.dtype) - - -def conv_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D convolution module. - """ - if dims == 1: - return nn.Conv1d(*args, **kwargs) - elif dims == 2: - return fcbh.ops.Conv2d(*args, **kwargs) - elif dims == 3: - return nn.Conv3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def linear(*args, **kwargs): - """ - Create a linear module. - """ - return fcbh.ops.Linear(*args, **kwargs) - - def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. diff --git a/backend/headless/fcbh/ldm/modules/distributions/__init__.py b/ldm_patched/ldm/modules/distributions/__init__.py similarity index 100% rename from backend/headless/fcbh/ldm/modules/distributions/__init__.py rename to ldm_patched/ldm/modules/distributions/__init__.py diff --git a/backend/headless/fcbh/ldm/modules/distributions/distributions.py b/ldm_patched/ldm/modules/distributions/distributions.py similarity index 100% rename from backend/headless/fcbh/ldm/modules/distributions/distributions.py rename to ldm_patched/ldm/modules/distributions/distributions.py diff --git a/backend/headless/fcbh/ldm/modules/ema.py b/ldm_patched/ldm/modules/ema.py similarity index 100% rename from backend/headless/fcbh/ldm/modules/ema.py rename to ldm_patched/ldm/modules/ema.py diff --git a/backend/headless/fcbh/ldm/modules/encoders/__init__.py b/ldm_patched/ldm/modules/encoders/__init__.py similarity index 100% rename from backend/headless/fcbh/ldm/modules/encoders/__init__.py rename to ldm_patched/ldm/modules/encoders/__init__.py diff --git a/backend/headless/fcbh/ldm/modules/encoders/noise_aug_modules.py b/ldm_patched/ldm/modules/encoders/noise_aug_modules.py similarity index 100% rename from backend/headless/fcbh/ldm/modules/encoders/noise_aug_modules.py rename to ldm_patched/ldm/modules/encoders/noise_aug_modules.py diff --git a/backend/headless/fcbh/ldm/modules/sub_quadratic_attention.py b/ldm_patched/ldm/modules/sub_quadratic_attention.py similarity index 99% rename from backend/headless/fcbh/ldm/modules/sub_quadratic_attention.py rename to ldm_patched/ldm/modules/sub_quadratic_attention.py index 11d1dd45..cabf1f67 100644 --- a/backend/headless/fcbh/ldm/modules/sub_quadratic_attention.py +++ b/ldm_patched/ldm/modules/sub_quadratic_attention.py @@ -24,7 +24,7 @@ except ImportError: from torch import Tensor from typing import List -from fcbh import model_management +from ldm_patched.modules import model_management def dynamic_slice( x: Tensor, diff --git a/ldm_patched/ldm/modules/temporal_ae.py b/ldm_patched/ldm/modules/temporal_ae.py new file mode 100644 index 00000000..248d850b --- /dev/null +++ b/ldm_patched/ldm/modules/temporal_ae.py @@ -0,0 +1,245 @@ +import functools +from typing import Callable, Iterable, Union + +import torch +from einops import rearrange, repeat + +import ldm_patched.modules.ops +ops = ldm_patched.modules.ops.disable_weight_init + +from .diffusionmodules.model import ( + AttnBlock, + Decoder, + ResnetBlock, +) +from .diffusionmodules.openaimodel import ResBlock, timestep_embedding +from .attention import BasicTransformerBlock + +def partialclass(cls, *args, **kwargs): + class NewCls(cls): + __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) + + return NewCls + + +class VideoResBlock(ResnetBlock): + def __init__( + self, + out_channels, + *args, + dropout=0.0, + video_kernel_size=3, + alpha=0.0, + merge_strategy="learned", + **kwargs, + ): + super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) + if video_kernel_size is None: + video_kernel_size = [3, 1, 1] + self.time_stack = ResBlock( + channels=out_channels, + emb_channels=0, + dropout=dropout, + dims=3, + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=False, + skip_t_emb=True, + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, bs): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError() + + def forward(self, x, temb, skip_video=False, timesteps=None): + b, c, h, w = x.shape + if timesteps is None: + timesteps = b + + x = super().forward(x, temb) + + if not skip_video: + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = self.time_stack(x, temb) + + alpha = self.get_alpha(bs=b // timesteps) + x = alpha * x + (1.0 - alpha) * x_mix + + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class AE3DConv(torch.nn.Conv2d): + def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): + super().__init__(in_channels, out_channels, *args, **kwargs) + if isinstance(video_kernel_size, Iterable): + padding = [int(k // 2) for k in video_kernel_size] + else: + padding = int(video_kernel_size // 2) + + self.time_mix_conv = torch.nn.Conv3d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=video_kernel_size, + padding=padding, + ) + + def forward(self, input, timesteps=None, skip_video=False): + if timesteps is None: + timesteps = input.shape[0] + x = super().forward(input) + if skip_video: + return x + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + x = self.time_mix_conv(x) + return rearrange(x, "b c t h w -> (b t) c h w") + + +class AttnVideoBlock(AttnBlock): + def __init__( + self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" + ): + super().__init__(in_channels) + # no context, single headed, as in base class + self.time_mix_block = BasicTransformerBlock( + dim=in_channels, + n_heads=1, + d_head=in_channels, + checkpoint=False, + ff_in=True, + ) + + time_embed_dim = self.in_channels * 4 + self.video_time_embed = torch.nn.Sequential( + ops.Linear(self.in_channels, time_embed_dim), + torch.nn.SiLU(), + ops.Linear(time_embed_dim, self.in_channels), + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def forward(self, x, timesteps=None, skip_time_block=False): + if skip_time_block: + return super().forward(x) + + if timesteps is None: + timesteps = x.shape[0] + + x_in = x + x = self.attention(x) + h, w = x.shape[2:] + x = rearrange(x, "b c h w -> b (h w) c") + + x_mix = x + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) + emb = self.video_time_embed(t_emb) # b, n_channels + emb = emb[:, None, :] + x_mix = x_mix + emb + + alpha = self.get_alpha() + x_mix = self.time_mix_block(x_mix, timesteps=timesteps) + x = alpha * x + (1.0 - alpha) * x_mix # alpha merge + + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + x = self.proj_out(x) + + return x_in + x + + def get_alpha( + self, + ): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") + + + +def make_time_attn( + in_channels, + attn_type="vanilla", + attn_kwargs=None, + alpha: float = 0, + merge_strategy: str = "learned", +): + return partialclass( + AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy + ) + + +class Conv2DWrapper(torch.nn.Conv2d): + def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: + return super().forward(input) + + +class VideoDecoder(Decoder): + available_time_modes = ["all", "conv-only", "attn-only"] + + def __init__( + self, + *args, + video_kernel_size: Union[int, list] = 3, + alpha: float = 0.0, + merge_strategy: str = "learned", + time_mode: str = "conv-only", + **kwargs, + ): + self.video_kernel_size = video_kernel_size + self.alpha = alpha + self.merge_strategy = merge_strategy + self.time_mode = time_mode + assert ( + self.time_mode in self.available_time_modes + ), f"time_mode parameter has to be in {self.available_time_modes}" + + if self.time_mode != "attn-only": + kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) + if self.time_mode not in ["conv-only", "only-last-conv"]: + kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) + if self.time_mode not in ["attn-only", "only-last-conv"]: + kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) + + super().__init__(*args, **kwargs) + + def get_last_layer(self, skip_time_mix=False, **kwargs): + if self.time_mode == "attn-only": + raise NotImplementedError("TODO") + else: + return ( + self.conv_out.time_mix_conv.weight + if not skip_time_mix + else self.conv_out.weight + ) diff --git a/backend/headless/fcbh/ldm/util.py b/ldm_patched/ldm/util.py similarity index 100% rename from backend/headless/fcbh/ldm/util.py rename to ldm_patched/ldm/util.py diff --git a/ldm_patched/modules/args_parser.py b/ldm_patched/modules/args_parser.py new file mode 100644 index 00000000..3931997d --- /dev/null +++ b/ldm_patched/modules/args_parser.py @@ -0,0 +1,122 @@ +import argparse +import enum +import ldm_patched.modules.options + +class EnumAction(argparse.Action): + """ + Argparse action for handling Enums + """ + def __init__(self, **kwargs): + # Pop off the type value + enum_type = kwargs.pop("type", None) + + # Ensure an Enum subclass is provided + if enum_type is None: + raise ValueError("type must be assigned an Enum when using EnumAction") + if not issubclass(enum_type, enum.Enum): + raise TypeError("type must be an Enum when using EnumAction") + + # Generate choices from the Enum + choices = tuple(e.value for e in enum_type) + kwargs.setdefault("choices", choices) + kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") + + super(EnumAction, self).__init__(**kwargs) + + self._enum = enum_type + + def __call__(self, parser, namespace, values, option_string=None): + # Convert value back into an Enum + value = self._enum(values) + setattr(namespace, self.dest, value) + + +parser = argparse.ArgumentParser() + +parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0") +parser.add_argument("--port", type=int, default=8188) +parser.add_argument("--disable-header-check", type=str, default=None, metavar="ORIGIN", nargs="?", const="*") +parser.add_argument("--web-upload-size", type=float, default=100) + +parser.add_argument("--external-working-path", type=str, default=None, metavar="PATH", nargs='+', action='append') +parser.add_argument("--output-path", type=str, default=None) +parser.add_argument("--temp-path", type=str, default=None) +parser.add_argument("--cache-path", type=str, default=None) +parser.add_argument("--in-browser", action="store_true") +parser.add_argument("--disable-in-browser", action="store_true") +parser.add_argument("--gpu-device-id", type=int, default=None, metavar="DEVICE_ID") +cm_group = parser.add_mutually_exclusive_group() +cm_group.add_argument("--async-cuda-allocation", action="store_true") +cm_group.add_argument("--disable-async-cuda-allocation", action="store_true") + +parser.add_argument("--disable-attention-upcast", action="store_true") + +fp_group = parser.add_mutually_exclusive_group() +fp_group.add_argument("--all-in-fp32", action="store_true") +fp_group.add_argument("--all-in-fp16", action="store_true") + +fpunet_group = parser.add_mutually_exclusive_group() +fpunet_group.add_argument("--unet-in-bf16", action="store_true") +fpunet_group.add_argument("--unet-in-fp16", action="store_true") +fpunet_group.add_argument("--unet-in-fp8-e4m3fn", action="store_true") +fpunet_group.add_argument("--unet-in-fp8-e5m2", action="store_true") + +fpvae_group = parser.add_mutually_exclusive_group() +fpvae_group.add_argument("--vae-in-fp16", action="store_true") +fpvae_group.add_argument("--vae-in-fp32", action="store_true") +fpvae_group.add_argument("--vae-in-bf16", action="store_true") + +fpte_group = parser.add_mutually_exclusive_group() +fpte_group.add_argument("--clip-in-fp8-e4m3fn", action="store_true") +fpte_group.add_argument("--clip-in-fp8-e5m2", action="store_true") +fpte_group.add_argument("--clip-in-fp16", action="store_true") +fpte_group.add_argument("--clip-in-fp32", action="store_true") + + +parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1) + +parser.add_argument("--disable-ipex-hijack", action="store_true") + +class LatentPreviewMethod(enum.Enum): + NoPreviews = "none" + Auto = "auto" + Latent2RGB = "fast" + TAESD = "taesd" + +parser.add_argument("--preview-option", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, action=EnumAction) + +attn_group = parser.add_mutually_exclusive_group() +attn_group.add_argument("--attention-split", action="store_true") +attn_group.add_argument("--attention-quad", action="store_true") +attn_group.add_argument("--attention-pytorch", action="store_true") + +parser.add_argument("--disable-xformers", action="store_true") + +vram_group = parser.add_mutually_exclusive_group() +vram_group.add_argument("--always-gpu", action="store_true") +vram_group.add_argument("--always-high-vram", action="store_true") +vram_group.add_argument("--always-normal-vram", action="store_true") +vram_group.add_argument("--always-low-vram", action="store_true") +vram_group.add_argument("--always-no-vram", action="store_true") +vram_group.add_argument("--always-cpu", action="store_true") + + +parser.add_argument("--always-offload-from-vram", action="store_true") + + +parser.add_argument("--disable-server-log", action="store_true") +parser.add_argument("--debug-mode", action="store_true") +parser.add_argument("--is-windows-embedded-python", action="store_true") + +parser.add_argument("--disable-server-info", action="store_true") + +if ldm_patched.modules.options.args_parsing: + args = parser.parse_args([]) +else: + args = parser.parse_args([]) + +if args.is_windows_embedded_python: + args.in_browser = True + +if args.disable_in_browser: + args.in_browser = False diff --git a/backend/headless/fcbh/checkpoint_pickle.py b/ldm_patched/modules/checkpoint_pickle.py similarity index 100% rename from backend/headless/fcbh/checkpoint_pickle.py rename to ldm_patched/modules/checkpoint_pickle.py diff --git a/backend/headless/fcbh/clip_config_bigg.json b/ldm_patched/modules/clip_config_bigg.json similarity index 100% rename from backend/headless/fcbh/clip_config_bigg.json rename to ldm_patched/modules/clip_config_bigg.json diff --git a/ldm_patched/modules/clip_model.py b/ldm_patched/modules/clip_model.py new file mode 100644 index 00000000..e7f3fb2d --- /dev/null +++ b/ldm_patched/modules/clip_model.py @@ -0,0 +1,188 @@ +import torch +from ldm_patched.ldm.modules.attention import optimized_attention_for_device + +class CLIPAttention(torch.nn.Module): + def __init__(self, embed_dim, heads, dtype, device, operations): + super().__init__() + + self.heads = heads + self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x, mask=None, optimized_attention=None): + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + out = optimized_attention(q, k, v, self.heads, mask) + return self.out_proj(out) + +ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), + "gelu": torch.nn.functional.gelu, +} + +class CLIPMLP(torch.nn.Module): + def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): + super().__init__() + self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) + self.activation = ACTIVATIONS[activation] + self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x): + x = self.fc1(x) + x = self.activation(x) + x = self.fc2(x) + return x + +class CLIPLayer(torch.nn.Module): + def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): + super().__init__() + self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) + self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) + + def forward(self, x, mask=None, optimized_attention=None): + x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) + x += self.mlp(self.layer_norm2(x)) + return x + + +class CLIPEncoder(torch.nn.Module): + def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): + super().__init__() + self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) + + def forward(self, x, mask=None, intermediate_output=None): + optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layers) + intermediate_output + + intermediate = None + for i, l in enumerate(self.layers): + x = l(x, mask, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + +class CLIPEmbeddings(torch.nn.Module): + def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None): + super().__init__() + self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) + self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) + + def forward(self, input_tokens): + return self.token_embedding(input_tokens) + self.position_embedding.weight + + +class CLIPTextModel_(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + + super().__init__() + self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device) + self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) + self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + + def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True): + x = self.embeddings(input_tokens) + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).unsqueeze(1).unsqueeze(1).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) + if mask is not None: + mask += causal_mask + else: + mask = causal_mask + + x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) + x = self.final_layer_norm(x) + if i is not None and final_layer_norm_intermediate: + i = self.final_layer_norm(i) + + pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),] + return x, i, pooled_output + +class CLIPTextModel(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.num_layers = config_dict["num_hidden_layers"] + self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) + self.dtype = dtype + + def get_input_embeddings(self): + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, embeddings): + self.text_model.embeddings.token_embedding = embeddings + + def forward(self, *args, **kwargs): + return self.text_model(*args, **kwargs) + +class CLIPVisionEmbeddings(torch.nn.Module): + def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None): + super().__init__() + self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) + + self.patch_embedding = operations.Conv2d( + in_channels=num_channels, + out_channels=embed_dim, + kernel_size=patch_size, + stride=patch_size, + bias=False, + dtype=dtype, + device=device + ) + + num_patches = (image_size // patch_size) ** 2 + num_positions = num_patches + 1 + self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) + + def forward(self, pixel_values): + embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) + return torch.cat([self.class_embedding.expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight + + +class CLIPVision(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + + self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations) + self.pre_layrnorm = operations.LayerNorm(embed_dim) + self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) + self.post_layernorm = operations.LayerNorm(embed_dim) + + def forward(self, pixel_values, attention_mask=None, intermediate_output=None): + x = self.embeddings(pixel_values) + x = self.pre_layrnorm(x) + #TODO: attention_mask? + x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) + pooled_output = self.post_layernorm(x[:, 0, :]) + return x, i, pooled_output + +class CLIPVisionModelProjection(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.vision_model = CLIPVision(config_dict, dtype, device, operations) + self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) + + def forward(self, *args, **kwargs): + x = self.vision_model(*args, **kwargs) + out = self.visual_projection(x[2]) + return (x[0], x[1], out) diff --git a/backend/headless/fcbh/clip_vision.py b/ldm_patched/modules/clip_vision.py similarity index 68% rename from backend/headless/fcbh/clip_vision.py rename to ldm_patched/modules/clip_vision.py index 5c01fb0b..eda441af 100644 --- a/backend/headless/fcbh/clip_vision.py +++ b/ldm_patched/modules/clip_vision.py @@ -1,13 +1,20 @@ -from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, modeling_utils from .utils import load_torch_file, transformers_convert, common_upscale import os import torch import contextlib +import json -import fcbh.ops -import fcbh.model_patcher -import fcbh.model_management -import fcbh.utils +import ldm_patched.modules.ops +import ldm_patched.modules.model_patcher +import ldm_patched.modules.model_management +import ldm_patched.modules.utils +import ldm_patched.modules.clip_model + +class Output: + def __getitem__(self, key): + return getattr(self, key) + def __setitem__(self, key, item): + setattr(self, key, item) def clip_preprocess(image, size=224): mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype) @@ -22,24 +29,23 @@ def clip_preprocess(image, size=224): class ClipVisionModel(): def __init__(self, json_config): - config = CLIPVisionConfig.from_json_file(json_config) - self.load_device = fcbh.model_management.text_encoder_device() - offload_device = fcbh.model_management.text_encoder_offload_device() + with open(json_config) as f: + config = json.load(f) + + self.load_device = ldm_patched.modules.model_management.text_encoder_device() + offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() self.dtype = torch.float32 - if fcbh.model_management.should_use_fp16(self.load_device, prioritize_performance=False): + if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False): self.dtype = torch.float16 - with fcbh.ops.use_fcbh_ops(offload_device, self.dtype): - with modeling_utils.no_init_weights(): - self.model = CLIPVisionModelWithProjection(config) - self.model.to(self.dtype) + self.model = ldm_patched.modules.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, ldm_patched.modules.ops.disable_weight_init) - self.patcher = fcbh.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) + self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) def load_sd(self, sd): return self.model.load_state_dict(sd, strict=False) def encode_image(self, image): - fcbh.model_management.load_model_gpu(self.patcher) + ldm_patched.modules.model_management.load_model_gpu(self.patcher) pixel_values = clip_preprocess(image.to(self.load_device)) if self.dtype != torch.float32: @@ -47,18 +53,13 @@ class ClipVisionModel(): else: precision_scope = lambda a, b: contextlib.nullcontext(a) - with precision_scope(fcbh.model_management.get_autocast_device(self.load_device), torch.float32): - outputs = self.model(pixel_values=pixel_values, output_hidden_states=True) - - for k in outputs: - t = outputs[k] - if t is not None: - if k == 'hidden_states': - outputs["penultimate_hidden_states"] = t[-2].cpu() - outputs["hidden_states"] = None - else: - outputs[k] = t.cpu() + with precision_scope(ldm_patched.modules.model_management.get_autocast_device(self.load_device), torch.float32): + out = self.model(pixel_values=pixel_values, intermediate_output=-2) + outputs = Output() + outputs["last_hidden_state"] = out[0].to(ldm_patched.modules.model_management.intermediate_device()) + outputs["image_embeds"] = out[2].to(ldm_patched.modules.model_management.intermediate_device()) + outputs["penultimate_hidden_states"] = out[1].to(ldm_patched.modules.model_management.intermediate_device()) return outputs def convert_to_transformers(sd, prefix): @@ -99,7 +100,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False): clip = ClipVisionModel(json_config) m, u = clip.load_sd(sd) if len(m) > 0: - print("extra keys clip vision:", m) + print("extra clip vision:", m) u = set(u) keys = list(sd.keys()) for k in keys: diff --git a/backend/headless/fcbh/clip_vision_config_g.json b/ldm_patched/modules/clip_vision_config_g.json similarity index 100% rename from backend/headless/fcbh/clip_vision_config_g.json rename to ldm_patched/modules/clip_vision_config_g.json diff --git a/backend/headless/fcbh/clip_vision_config_h.json b/ldm_patched/modules/clip_vision_config_h.json similarity index 100% rename from backend/headless/fcbh/clip_vision_config_h.json rename to ldm_patched/modules/clip_vision_config_h.json diff --git a/backend/headless/fcbh/clip_vision_config_vitl.json b/ldm_patched/modules/clip_vision_config_vitl.json similarity index 100% rename from backend/headless/fcbh/clip_vision_config_vitl.json rename to ldm_patched/modules/clip_vision_config_vitl.json diff --git a/backend/headless/fcbh/conds.py b/ldm_patched/modules/conds.py similarity index 89% rename from backend/headless/fcbh/conds.py rename to ldm_patched/modules/conds.py index 8fddab35..a7325680 100644 --- a/backend/headless/fcbh/conds.py +++ b/ldm_patched/modules/conds.py @@ -1,7 +1,7 @@ import enum import torch import math -import fcbh.utils +import ldm_patched.modules.utils def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9) @@ -15,7 +15,7 @@ class CONDRegular: return self.__class__(cond) def process_cond(self, batch_size, device, **kwargs): - return self._copy_with(fcbh.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) + return self._copy_with(ldm_patched.modules.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) def can_concat(self, other): if self.cond.shape != other.cond.shape: @@ -31,7 +31,7 @@ class CONDRegular: class CONDNoiseShape(CONDRegular): def process_cond(self, batch_size, device, area, **kwargs): data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] - return self._copy_with(fcbh.utils.repeat_to_batch_size(data, batch_size).to(device)) + return self._copy_with(ldm_patched.modules.utils.repeat_to_batch_size(data, batch_size).to(device)) class CONDCrossAttn(CONDRegular): diff --git a/backend/headless/fcbh/controlnet.py b/ldm_patched/modules/controlnet.py similarity index 77% rename from backend/headless/fcbh/controlnet.py rename to ldm_patched/modules/controlnet.py index 20943762..e478e221 100644 --- a/backend/headless/fcbh/controlnet.py +++ b/ldm_patched/modules/controlnet.py @@ -1,13 +1,15 @@ import torch import math import os -import fcbh.utils -import fcbh.model_management -import fcbh.model_detection -import fcbh.model_patcher +import contextlib +import ldm_patched.modules.utils +import ldm_patched.modules.model_management +import ldm_patched.modules.model_detection +import ldm_patched.modules.model_patcher +import ldm_patched.modules.ops -import fcbh.cldm.cldm -import fcbh.t2i_adapter.adapter +import ldm_patched.controlnet.cldm +import ldm_patched.t2ia.adapter def broadcast_image_to(tensor, target_batch_size, batched_number): @@ -34,13 +36,13 @@ class ControlBase: self.cond_hint = None self.strength = 1.0 self.timestep_percent_range = (0.0, 1.0) + self.global_average_pooling = False self.timestep_range = None if device is None: - device = fcbh.model_management.get_torch_device() + device = ldm_patched.modules.model_management.get_torch_device() self.device = device self.previous_controlnet = None - self.global_average_pooling = False def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)): self.cond_hint_original = cond_hint @@ -75,6 +77,7 @@ class ControlBase: c.cond_hint_original = self.cond_hint_original c.strength = self.strength c.timestep_percent_range = self.timestep_percent_range + c.global_average_pooling = self.global_average_pooling def inference_memory_requirements(self, dtype): if self.previous_controlnet is not None: @@ -127,12 +130,14 @@ class ControlBase: return out class ControlNet(ControlBase): - def __init__(self, control_model, global_average_pooling=False, device=None): + def __init__(self, control_model, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None): super().__init__(device) self.control_model = control_model - self.control_model_wrapped = fcbh.model_patcher.ModelPatcher(self.control_model, load_device=fcbh.model_management.get_torch_device(), offload_device=fcbh.model_management.unet_offload_device()) + self.load_device = load_device + self.control_model_wrapped = ldm_patched.modules.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=ldm_patched.modules.model_management.unet_offload_device()) self.global_average_pooling = global_average_pooling self.model_sampling_current = None + self.manual_cast_dtype = manual_cast_dtype def get_control(self, x_noisy, t, cond, batched_number): control_prev = None @@ -146,28 +151,31 @@ class ControlNet(ControlBase): else: return None + dtype = self.control_model.dtype + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + output_dtype = x_noisy.dtype if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.cond_hint = None - self.cond_hint = fcbh.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device) + self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) - context = cond['c_crossattn'] y = cond.get('y', None) if y is not None: - y = y.to(self.control_model.dtype) + y = y.to(dtype) timestep = self.model_sampling_current.timestep(t) x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) - control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(self.control_model.dtype), y=y) + control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y) return self.control_merge(None, control, control_prev, output_dtype) def copy(self): - c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling) + c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) self.copy_to(c) return c @@ -198,10 +206,11 @@ class ControlLoraOps: self.bias = None def forward(self, input): + weight, bias = ldm_patched.modules.ops.cast_bias_weight(self, input) if self.up is not None: - return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias) + return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) else: - return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias) + return torch.nn.functional.linear(input, weight, bias) class Conv2d(torch.nn.Module): def __init__( @@ -237,16 +246,11 @@ class ControlLoraOps: def forward(self, input): + weight, bias = ldm_patched.modules.ops.cast_bias_weight(self, input) if self.up is not None: - return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups) + return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) else: - return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups) - - def conv_nd(self, dims, *args, **kwargs): - if dims == 2: - return self.Conv2d(*args, **kwargs) - else: - raise ValueError(f"unsupported dimensions: {dims}") + return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) class ControlLora(ControlNet): @@ -260,25 +264,34 @@ class ControlLora(ControlNet): controlnet_config = model.model_config.unet_config.copy() controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] - controlnet_config["operations"] = ControlLoraOps() - self.control_model = fcbh.cldm.cldm.ControlNet(**controlnet_config) + self.manual_cast_dtype = model.manual_cast_dtype dtype = model.get_dtype() - self.control_model.to(dtype) - self.control_model.to(fcbh.model_management.get_torch_device()) + if self.manual_cast_dtype is None: + class control_lora_ops(ControlLoraOps, ldm_patched.modules.ops.disable_weight_init): + pass + else: + class control_lora_ops(ControlLoraOps, ldm_patched.modules.ops.manual_cast): + pass + dtype = self.manual_cast_dtype + + controlnet_config["operations"] = control_lora_ops + controlnet_config["dtype"] = dtype + self.control_model = ldm_patched.controlnet.cldm.ControlNet(**controlnet_config) + self.control_model.to(ldm_patched.modules.model_management.get_torch_device()) diffusion_model = model.diffusion_model sd = diffusion_model.state_dict() cm = self.control_model.state_dict() for k in sd: - weight = fcbh.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k) + weight = ldm_patched.modules.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k) try: - fcbh.utils.set_attr(self.control_model, k, weight) + ldm_patched.modules.utils.set_attr(self.control_model, k, weight) except: pass for k in self.control_weights: if k not in {"lora_controlnet"}: - fcbh.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(fcbh.model_management.get_torch_device())) + ldm_patched.modules.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(ldm_patched.modules.model_management.get_torch_device())) def copy(self): c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) @@ -295,18 +308,18 @@ class ControlLora(ControlNet): return out def inference_memory_requirements(self, dtype): - return fcbh.utils.calculate_parameters(self.control_weights) * fcbh.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) + return ldm_patched.modules.utils.calculate_parameters(self.control_weights) * ldm_patched.modules.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) def load_controlnet(ckpt_path, model=None): - controlnet_data = fcbh.utils.load_torch_file(ckpt_path, safe_load=True) + controlnet_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) if "lora_controlnet" in controlnet_data: return ControlLora(controlnet_data) controlnet_config = None if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format - unet_dtype = fcbh.model_management.unet_dtype() - controlnet_config = fcbh.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype) - diffusers_keys = fcbh.utils.unet_to_diffusers(controlnet_config) + unet_dtype = ldm_patched.modules.model_management.unet_dtype() + controlnet_config = ldm_patched.modules.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype) + diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(controlnet_config) diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" @@ -365,16 +378,20 @@ def load_controlnet(ckpt_path, model=None): return net if controlnet_config is None: - unet_dtype = fcbh.model_management.unet_dtype() - controlnet_config = fcbh.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config + unet_dtype = ldm_patched.modules.model_management.unet_dtype() + controlnet_config = ldm_patched.modules.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config + load_device = ldm_patched.modules.model_management.get_torch_device() + manual_cast_dtype = ldm_patched.modules.model_management.unet_manual_cast(unet_dtype, load_device) + if manual_cast_dtype is not None: + controlnet_config["operations"] = ldm_patched.modules.ops.manual_cast controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] - control_model = fcbh.cldm.cldm.ControlNet(**controlnet_config) + control_model = ldm_patched.controlnet.cldm.ControlNet(**controlnet_config) if pth: if 'difference' in controlnet_data: if model is not None: - fcbh.model_management.load_models_gpu([model]) + ldm_patched.modules.model_management.load_models_gpu([model]) model_sd = model.model_state_dict() for x in controlnet_data: c_m = "control_model." @@ -395,14 +412,12 @@ def load_controlnet(ckpt_path, model=None): missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) print(missing, unexpected) - control_model = control_model.to(unet_dtype) - global_average_pooling = False filename = os.path.splitext(ckpt_path)[0] if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling global_average_pooling = True - control = ControlNet(control_model, global_average_pooling=global_average_pooling) + control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) return control class T2IAdapter(ControlBase): @@ -436,7 +451,7 @@ class T2IAdapter(ControlBase): self.control_input = None self.cond_hint = None width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8) - self.cond_hint = fcbh.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) + self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) if self.channels_in == 1 and self.cond_hint.shape[1] > 1: self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) if x_noisy.shape[0] != self.cond_hint.shape[0]: @@ -469,12 +484,12 @@ def load_t2i_adapter(t2i_data): prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2) prefix_replace["adapter."] = "" - t2i_data = fcbh.utils.state_dict_prefix_replace(t2i_data, prefix_replace) + t2i_data = ldm_patched.modules.utils.state_dict_prefix_replace(t2i_data, prefix_replace) keys = t2i_data.keys() if "body.0.in_conv.weight" in keys: cin = t2i_data['body.0.in_conv.weight'].shape[1] - model_ad = fcbh.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) + model_ad = ldm_patched.t2ia.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) elif 'conv_in.weight' in keys: cin = t2i_data['conv_in.weight'].shape[1] channel = t2i_data['conv_in.weight'].shape[0] @@ -486,7 +501,7 @@ def load_t2i_adapter(t2i_data): xl = False if cin == 256 or cin == 768: xl = True - model_ad = fcbh.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) + model_ad = ldm_patched.t2ia.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) else: return None missing, unexpected = model_ad.load_state_dict(t2i_data) diff --git a/backend/headless/fcbh/diffusers_convert.py b/ldm_patched/modules/diffusers_convert.py similarity index 100% rename from backend/headless/fcbh/diffusers_convert.py rename to ldm_patched/modules/diffusers_convert.py diff --git a/backend/headless/fcbh/diffusers_load.py b/ldm_patched/modules/diffusers_load.py similarity index 79% rename from backend/headless/fcbh/diffusers_load.py rename to ldm_patched/modules/diffusers_load.py index 6e09723d..79fbbd55 100644 --- a/backend/headless/fcbh/diffusers_load.py +++ b/ldm_patched/modules/diffusers_load.py @@ -1,7 +1,7 @@ import json import os -import fcbh.sd +import ldm_patched.modules.sd def first_file(path, filenames): for f in filenames: @@ -23,15 +23,15 @@ def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_dire if text_encoder2_path is not None: text_encoder_paths.append(text_encoder2_path) - unet = fcbh.sd.load_unet(unet_path) + unet = ldm_patched.modules.sd.load_unet(unet_path) clip = None if output_clip: - clip = fcbh.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) + clip = ldm_patched.modules.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) vae = None if output_vae: - sd = fcbh.utils.load_torch_file(vae_path) - vae = fcbh.sd.VAE(sd=sd) + sd = ldm_patched.modules.utils.load_torch_file(vae_path) + vae = ldm_patched.modules.sd.VAE(sd=sd) return (unet, clip, vae) diff --git a/backend/headless/fcbh/gligen.py b/ldm_patched/modules/gligen.py similarity index 99% rename from backend/headless/fcbh/gligen.py rename to ldm_patched/modules/gligen.py index 8d182839..8dbd5fa4 100644 --- a/backend/headless/fcbh/gligen.py +++ b/ldm_patched/modules/gligen.py @@ -1,6 +1,6 @@ import torch from torch import nn, einsum -from .ldm.modules.attention import CrossAttention +from ldm_patched.ldm.modules.attention import CrossAttention from inspect import isfunction diff --git a/backend/headless/fcbh/latent_formats.py b/ldm_patched/modules/latent_formats.py similarity index 100% rename from backend/headless/fcbh/latent_formats.py rename to ldm_patched/modules/latent_formats.py diff --git a/backend/headless/fcbh/lora.py b/ldm_patched/modules/lora.py similarity index 87% rename from backend/headless/fcbh/lora.py rename to ldm_patched/modules/lora.py index 61c94041..cc5a29da 100644 --- a/backend/headless/fcbh/lora.py +++ b/ldm_patched/modules/lora.py @@ -1,4 +1,4 @@ -import fcbh.utils +import ldm_patched.modules.utils LORA_CLIP_MAP = { "mlp.fc1": "mlp_fc1", @@ -43,7 +43,7 @@ def load_lora(lora, to_load): if mid_name is not None and mid_name in lora.keys(): mid = lora[mid_name] loaded_keys.add(mid_name) - patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid) + patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid)) loaded_keys.add(A_name) loaded_keys.add(B_name) @@ -64,7 +64,7 @@ def load_lora(lora, to_load): loaded_keys.add(hada_t1_name) loaded_keys.add(hada_t2_name) - patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2) + patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)) loaded_keys.add(hada_w1_a_name) loaded_keys.add(hada_w1_b_name) loaded_keys.add(hada_w2_a_name) @@ -116,8 +116,19 @@ def load_lora(lora, to_load): loaded_keys.add(lokr_t2_name) if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): - patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2) + patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)) + #glora + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha)) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) w_norm_name = "{}.w_norm".format(x) b_norm_name = "{}.b_norm".format(x) @@ -126,21 +137,21 @@ def load_lora(lora, to_load): if w_norm is not None: loaded_keys.add(w_norm_name) - patch_dict[to_load[x]] = (w_norm,) + patch_dict[to_load[x]] = ("diff", (w_norm,)) if b_norm is not None: loaded_keys.add(b_norm_name) - patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = (b_norm,) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) diff_name = "{}.diff".format(x) diff_weight = lora.get(diff_name, None) if diff_weight is not None: - patch_dict[to_load[x]] = (diff_weight,) + patch_dict[to_load[x]] = ("diff", (diff_weight,)) loaded_keys.add(diff_name) diff_bias_name = "{}.diff_b".format(x) diff_bias = lora.get(diff_bias_name, None) if diff_bias is not None: - patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = (diff_bias,) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) loaded_keys.add(diff_bias_name) for x in lora.keys(): @@ -197,7 +208,7 @@ def model_lora_keys_unet(model, key_map={}): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = k - diffusers_keys = fcbh.utils.unet_to_diffusers(model.model_config.unet_config) + diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model.model_config.unet_config) for k in diffusers_keys: if k.endswith(".weight"): unet_key = "diffusion_model.{}".format(diffusers_keys[k]) diff --git a/backend/headless/fcbh/model_base.py b/ldm_patched/modules/model_base.py similarity index 73% rename from backend/headless/fcbh/model_base.py rename to ldm_patched/modules/model_base.py index 9ac1e8e7..1374a669 100644 --- a/backend/headless/fcbh/model_base.py +++ b/ldm_patched/modules/model_base.py @@ -1,26 +1,33 @@ import torch -from fcbh.ldm.modules.diffusionmodules.openaimodel import UNetModel -from fcbh.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation -from fcbh.ldm.modules.diffusionmodules.openaimodel import Timestep -import fcbh.model_management -import fcbh.conds +from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel +from ldm_patched.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation +from ldm_patched.ldm.modules.diffusionmodules.openaimodel import Timestep +import ldm_patched.modules.model_management +import ldm_patched.modules.conds +import ldm_patched.modules.ops from enum import Enum +import contextlib from . import utils class ModelType(Enum): EPS = 1 V_PREDICTION = 2 + V_PREDICTION_EDM = 3 -from fcbh.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete +from ldm_patched.modules.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete, ModelSamplingContinuousEDM + def model_sampling(model_config, model_type): + s = ModelSamplingDiscrete + if model_type == ModelType.EPS: c = EPS elif model_type == ModelType.V_PREDICTION: c = V_PREDICTION - - s = ModelSamplingDiscrete + elif model_type == ModelType.V_PREDICTION_EDM: + c = V_PREDICTION + s = ModelSamplingContinuousEDM class ModelSampling(s, c): pass @@ -35,9 +42,14 @@ class BaseModel(torch.nn.Module): unet_config = model_config.unet_config self.latent_format = model_config.latent_format self.model_config = model_config + self.manual_cast_dtype = model_config.manual_cast_dtype if not unet_config.get("disable_unet_model_creation", False): - self.diffusion_model = UNetModel(**unet_config, device=device) + if self.manual_cast_dtype is not None: + operations = ldm_patched.modules.ops.manual_cast + else: + operations = ldm_patched.modules.ops.disable_weight_init + self.diffusion_model = UNetModel(**unet_config, device=device, operations=operations) self.model_type = model_type self.model_sampling = model_sampling(model_config, model_type) @@ -46,7 +58,7 @@ class BaseModel(torch.nn.Module): self.adm_channels = 0 self.inpaint_model = False print("model_type", model_type.name) - print("adm", self.adm_channels) + print("UNet ADM Dimension", self.adm_channels) def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): sigma = t @@ -56,6 +68,10 @@ class BaseModel(torch.nn.Module): context = c_crossattn dtype = self.get_dtype() + + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + xc = xc.to(dtype) t = self.model_sampling.timestep(t).float() context = context.to(dtype) @@ -65,6 +81,7 @@ class BaseModel(torch.nn.Module): if hasattr(extra, "to"): extra = extra.to(dtype) extra_conds[o] = extra + model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() return self.model_sampling.calculate_denoised(sigma, model_output, x) @@ -108,10 +125,10 @@ class BaseModel(torch.nn.Module): elif ck == "masked_image": cond_concat.append(blank_inpaint_image_like(noise)) data = torch.cat(cond_concat, dim=1) - out['c_concat'] = fcbh.conds.CONDNoiseShape(data) + out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(data) adm = self.encode_adm(**kwargs) if adm is not None: - out['y'] = fcbh.conds.CONDRegular(adm) + out['y'] = ldm_patched.modules.conds.CONDRegular(adm) return out def load_model_weights(self, sd, unet_prefix=""): @@ -142,7 +159,7 @@ class BaseModel(torch.nn.Module): unet_sd = self.diffusion_model.state_dict() unet_state_dict = {} for k in unet_sd: - unet_state_dict[k] = fcbh.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k) + unet_state_dict[k] = ldm_patched.modules.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k) unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict) @@ -159,12 +176,16 @@ class BaseModel(torch.nn.Module): self.inpaint_model = True def memory_required(self, input_shape): - area = input_shape[0] * input_shape[2] * input_shape[3] - if fcbh.model_management.xformers_enabled() or fcbh.model_management.pytorch_attention_flash_attention(): + if ldm_patched.modules.model_management.xformers_enabled() or ldm_patched.modules.model_management.pytorch_attention_flash_attention(): + dtype = self.get_dtype() + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype #TODO: this needs to be tweaked - return (area / (fcbh.model_management.dtype_size(self.get_dtype()) * 10)) * (1024 * 1024) + area = input_shape[0] * input_shape[2] * input_shape[3] + return (area * ldm_patched.modules.model_management.dtype_size(dtype) / 50) * (1024 * 1024) else: #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. + area = input_shape[0] * input_shape[2] * input_shape[3] return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024) @@ -262,3 +283,48 @@ class SDXL(BaseModel): out.append(self.embedder(torch.Tensor([target_width]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + +class SVD_img2vid(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + + def encode_adm(self, **kwargs): + fps_id = kwargs.get("fps", 6) - 1 + motion_bucket_id = kwargs.get("motion_bucket_id", 127) + augmentation = kwargs.get("augmentation_level", 0) + + out = [] + out.append(self.embedder(torch.Tensor([fps_id]))) + out.append(self.embedder(torch.Tensor([motion_bucket_id]))) + out.append(self.embedder(torch.Tensor([augmentation]))) + + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) + return flat + + def extra_conds(self, **kwargs): + out = {} + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = ldm_patched.modules.conds.CONDRegular(adm) + + latent_image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if latent_image is None: + latent_image = torch.zeros_like(noise) + + if latent_image.shape[1:] != noise.shape[1:]: + latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) + + out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image) + + if "time_conditioning" in kwargs: + out["time_context"] = ldm_patched.modules.conds.CONDCrossAttn(kwargs["time_conditioning"]) + + out['image_only_indicator'] = ldm_patched.modules.conds.CONDConstant(torch.zeros((1,), device=device)) + out['num_video_frames'] = ldm_patched.modules.conds.CONDConstant(noise.shape[0]) + return out diff --git a/backend/headless/fcbh/model_detection.py b/ldm_patched/modules/model_detection.py similarity index 83% rename from backend/headless/fcbh/model_detection.py rename to ldm_patched/modules/model_detection.py index 4509c9a6..e8fc87ac 100644 --- a/backend/headless/fcbh/model_detection.py +++ b/ldm_patched/modules/model_detection.py @@ -1,5 +1,5 @@ -import fcbh.supported_models -import fcbh.supported_models_base +import ldm_patched.modules.supported_models +import ldm_patched.modules.supported_models_base def count_blocks(state_dict_keys, prefix_string): count = 0 @@ -24,7 +24,8 @@ def calculate_transformer_depth(prefix, state_dict_keys, state_dict): last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 - return last_transformer_depth, context_dim, use_linear_in_transformer + time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict + return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack return None def detect_unet_config(state_dict, key_prefix, dtype): @@ -57,6 +58,7 @@ def detect_unet_config(state_dict, key_prefix, dtype): context_dim = None use_linear_in_transformer = False + video_model = False current_res = 1 count = 0 @@ -99,6 +101,7 @@ def detect_unet_config(state_dict, key_prefix, dtype): if context_dim is None: context_dim = out[1] use_linear_in_transformer = out[2] + video_model = out[3] else: transformer_depth.append(0) @@ -127,10 +130,23 @@ def detect_unet_config(state_dict, key_prefix, dtype): unet_config["transformer_depth_middle"] = transformer_depth_middle unet_config['use_linear_in_transformer'] = use_linear_in_transformer unet_config["context_dim"] = context_dim + + if video_model: + unet_config["extra_ff_mix_layer"] = True + unet_config["use_spatial_context"] = True + unet_config["merge_strategy"] = "learned_with_images" + unet_config["merge_factor"] = 0.0 + unet_config["video_kernel_size"] = [3, 1, 1] + unet_config["use_temporal_resblock"] = True + unet_config["use_temporal_attention"] = True + else: + unet_config["use_temporal_resblock"] = False + unet_config["use_temporal_attention"] = False + return unet_config def model_config_from_unet_config(unet_config): - for model_config in fcbh.supported_models.models: + for model_config in ldm_patched.modules.supported_models.models: if model_config.matches(unet_config): return model_config(unet_config) @@ -141,7 +157,7 @@ def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_ma unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype) model_config = model_config_from_unet_config(unet_config) if model_config is None and use_base_if_no_match: - return fcbh.supported_models_base.BASE(unet_config) + return ldm_patched.modules.supported_models_base.BASE(unet_config) else: return model_config @@ -216,54 +232,70 @@ def unet_config_from_diffusers_unet(state_dict, dtype): SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, - 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10]} + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4, - 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0]} + 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, - 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]} + 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, - 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]} + 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, - 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]} + 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, - 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]} + 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1, - 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1]} + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, - 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0]} + 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, - 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10]} + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], - 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64} + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} - supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B] + Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega] for unet_config in supported_models: matches = True diff --git a/backend/headless/fcbh/model_management.py b/ldm_patched/modules/model_management.py similarity index 87% rename from backend/headless/fcbh/model_management.py rename to ldm_patched/modules/model_management.py index e9086d1b..0e783b36 100644 --- a/backend/headless/fcbh/model_management.py +++ b/ldm_patched/modules/model_management.py @@ -1,7 +1,7 @@ import psutil from enum import Enum -from fcbh.cli_args import args -import fcbh.utils +from ldm_patched.modules.args_parser import args +import ldm_patched.modules.utils import torch import sys @@ -55,7 +55,7 @@ try: except: pass -if args.cpu: +if args.always_cpu: cpu_state = CPUState.CPU def is_intel_xpu(): @@ -114,9 +114,9 @@ def get_total_memory(dev=None, torch_total_too=False): total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) -if not args.normalvram and not args.cpu: +if not args.always_normal_vram and not args.always_cpu: if lowvram_available and total_vram <= 4096: - print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram") + print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --always-normal-vram") set_vram_to = VRAMState.LOW_VRAM try: @@ -159,7 +159,7 @@ def is_nvidia(): return False ENABLE_PYTORCH_ATTENTION = False -if args.use_pytorch_cross_attention: +if args.attention_pytorch: ENABLE_PYTORCH_ATTENTION = True XFORMERS_IS_AVAILABLE = False @@ -169,12 +169,12 @@ try: if is_nvidia(): torch_version = torch.version.__version__ if int(torch_version[0]) >= 2: - if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + if ENABLE_PYTORCH_ATTENTION == False and args.attention_split == False and args.attention_quad == False: ENABLE_PYTORCH_ATTENTION = True if torch.cuda.is_bf16_supported(): VAE_DTYPE = torch.bfloat16 if is_intel_xpu(): - if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + if args.attention_split == False and args.attention_quad == False: ENABLE_PYTORCH_ATTENTION = True except: pass @@ -182,11 +182,11 @@ except: if is_intel_xpu(): VAE_DTYPE = torch.bfloat16 -if args.fp16_vae: +if args.vae_in_fp16: VAE_DTYPE = torch.float16 -elif args.bf16_vae: +elif args.vae_in_bf16: VAE_DTYPE = torch.bfloat16 -elif args.fp32_vae: +elif args.vae_in_fp32: VAE_DTYPE = torch.float32 @@ -195,21 +195,21 @@ if ENABLE_PYTORCH_ATTENTION: torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) -if args.lowvram: +if args.always_low_vram: set_vram_to = VRAMState.LOW_VRAM lowvram_available = True -elif args.novram: +elif args.always_no_vram: set_vram_to = VRAMState.NO_VRAM -elif args.highvram or args.gpu_only: +elif args.always_high_vram or args.always_gpu: vram_state = VRAMState.HIGH_VRAM FORCE_FP32 = False FORCE_FP16 = False -if args.force_fp32: +if args.all_in_fp32: print("Forcing FP32, if this improves things please report it.") FORCE_FP32 = True -if args.force_fp16: +if args.all_in_fp16: print("Forcing FP16.") FORCE_FP16 = True @@ -233,10 +233,10 @@ if cpu_state == CPUState.MPS: print(f"Set vram state to: {vram_state.name}") -DISABLE_SMART_MEMORY = args.disable_smart_memory +ALWAYS_VRAM_OFFLOAD = args.always_offload_from_vram -if DISABLE_SMART_MEMORY: - print("Disabling smart memory management") +if ALWAYS_VRAM_OFFLOAD: + print("Always offload VRAM") def get_torch_device_name(device): if hasattr(device, 'type'): @@ -298,7 +298,7 @@ class LoadedModel: accelerate.dispatch_model(self.real_model, device_map=device_map, main_device=self.device) self.model_accelerated = True - if is_intel_xpu() and not args.disable_ipex_optimize: + if is_intel_xpu() and not args.disable_ipex_hijack: self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True) return self.real_model @@ -330,7 +330,7 @@ def unload_model_clones(model): def free_memory(memory_required, device, keep_loaded=[]): unloaded_model = False for i in range(len(current_loaded_models) -1, -1, -1): - if not DISABLE_SMART_MEMORY: + if not ALWAYS_VRAM_OFFLOAD: if get_free_memory(device) > memory_required: break shift_model = current_loaded_models[i] @@ -430,6 +430,13 @@ def dtype_size(dtype): dtype_size = 4 if dtype == torch.float16 or dtype == torch.bfloat16: dtype_size = 2 + elif dtype == torch.float32: + dtype_size = 4 + else: + try: + dtype_size = dtype.itemsize + except: #Old pytorch doesn't have .itemsize + pass return dtype_size def unet_offload_device(): @@ -444,7 +451,7 @@ def unet_inital_load_device(parameters, dtype): return torch_dev cpu_dev = torch.device("cpu") - if DISABLE_SMART_MEMORY: + if ALWAYS_VRAM_OFFLOAD: return cpu_dev model_size = dtype_size(dtype) * parameters @@ -457,20 +464,40 @@ def unet_inital_load_device(parameters, dtype): return cpu_dev def unet_dtype(device=None, model_params=0): - if args.bf16_unet: + if args.unet_in_bf16: return torch.bfloat16 + if args.unet_in_fp16: + return torch.float16 + if args.unet_in_fp8_e4m3fn: + return torch.float8_e4m3fn + if args.unet_in_fp8_e5m2: + return torch.float8_e5m2 if should_use_fp16(device=device, model_params=model_params): return torch.float16 return torch.float32 +# None means no manual cast +def unet_manual_cast(weight_dtype, inference_device): + if weight_dtype == torch.float32: + return None + + fp16_supported = ldm_patched.modules.model_management.should_use_fp16(inference_device, prioritize_performance=False) + if fp16_supported and weight_dtype == torch.float16: + return None + + if fp16_supported: + return torch.float16 + else: + return torch.float32 + def text_encoder_offload_device(): - if args.gpu_only: + if args.always_gpu: return get_torch_device() else: return torch.device("cpu") def text_encoder_device(): - if args.gpu_only: + if args.always_gpu: return get_torch_device() elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: if is_intel_xpu(): @@ -483,25 +510,34 @@ def text_encoder_device(): return torch.device("cpu") def text_encoder_dtype(device=None): - if args.fp8_e4m3fn_text_enc: + if args.clip_in_fp8_e4m3fn: return torch.float8_e4m3fn - elif args.fp8_e5m2_text_enc: + elif args.clip_in_fp8_e5m2: return torch.float8_e5m2 - elif args.fp16_text_enc: + elif args.clip_in_fp16: return torch.float16 - elif args.fp32_text_enc: + elif args.clip_in_fp32: return torch.float32 + if is_device_cpu(device): + return torch.float16 + if should_use_fp16(device, prioritize_performance=False): return torch.float16 else: return torch.float32 +def intermediate_device(): + if args.always_gpu: + return get_torch_device() + else: + return torch.device("cpu") + def vae_device(): return get_torch_device() def vae_offload_device(): - if args.gpu_only: + if args.always_gpu: return get_torch_device() else: return torch.device("cpu") @@ -515,6 +551,17 @@ def get_autocast_device(dev): return dev.type return "cuda" +def supports_dtype(device, dtype): #TODO + if dtype == torch.float32: + return True + if is_device_cpu(device): + return False + if dtype == torch.float16: + return True + if dtype == torch.bfloat16: + return True + return False + def cast_to_device(tensor, device, dtype, copy=False): device_supports_cast = False if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: @@ -525,15 +572,19 @@ def cast_to_device(tensor, device, dtype, copy=False): elif is_intel_xpu(): device_supports_cast = True + non_blocking = True + if is_device_mps(device): + non_blocking = False #pytorch bug? mps doesn't support non blocking + if device_supports_cast: if copy: if tensor.device == device: - return tensor.to(dtype, copy=copy) - return tensor.to(device, copy=copy).to(dtype) + return tensor.to(dtype, copy=copy, non_blocking=non_blocking) + return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) else: - return tensor.to(device).to(dtype) + return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) else: - return tensor.to(dtype).to(device, copy=copy) + return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking) def xformers_enabled(): global directml_enabled @@ -690,7 +741,7 @@ def soft_empty_cache(force=False): def resolve_lowvram_weight(weight, model, key): if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break. key_split = key.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device. - op = fcbh.utils.get_attr(model, '.'.join(key_split[:-1])) + op = ldm_patched.modules.utils.get_attr(model, '.'.join(key_split[:-1])) weight = op._hf_hook.weights_map[key_split[-1]] return weight diff --git a/backend/headless/fcbh/model_patcher.py b/ldm_patched/modules/model_patcher.py similarity index 66% rename from backend/headless/fcbh/model_patcher.py rename to ldm_patched/modules/model_patcher.py index 124deb5f..ae795ca9 100644 --- a/backend/headless/fcbh/model_patcher.py +++ b/ldm_patched/modules/model_patcher.py @@ -2,8 +2,8 @@ import torch import copy import inspect -import fcbh.utils -import fcbh.model_management +import ldm_patched.modules.utils +import ldm_patched.modules.model_management class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False): @@ -61,6 +61,9 @@ class ModelPatcher: else: self.model_options["sampler_cfg_function"] = sampler_cfg_function + def set_model_sampler_post_cfg_function(self, post_cfg_function): + self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] + def set_model_unet_function_wrapper(self, unet_wrapper_function): self.model_options["model_function_wrapper"] = unet_wrapper_function @@ -70,13 +73,17 @@ class ModelPatcher: to["patches"] = {} to["patches"][name] = to["patches"].get(name, []) + [patch] - def set_model_patch_replace(self, patch, name, block_name, number): + def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): to = self.model_options["transformer_options"] if "patches_replace" not in to: to["patches_replace"] = {} if name not in to["patches_replace"]: to["patches_replace"][name] = {} - to["patches_replace"][name][(block_name, number)] = patch + if transformer_index is not None: + block = (block_name, number, transformer_index) + else: + block = (block_name, number) + to["patches_replace"][name][block] = patch def set_model_attn1_patch(self, patch): self.set_model_patch(patch, "attn1_patch") @@ -84,11 +91,11 @@ class ModelPatcher: def set_model_attn2_patch(self, patch): self.set_model_patch(patch, "attn2_patch") - def set_model_attn1_replace(self, patch, block_name, number): - self.set_model_patch_replace(patch, "attn1", block_name, number) + def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None): + self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index) - def set_model_attn2_replace(self, patch, block_name, number): - self.set_model_patch_replace(patch, "attn2", block_name, number) + def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None): + self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index) def set_model_attn1_output_patch(self, patch): self.set_model_patch(patch, "attn1_output_patch") @@ -145,7 +152,7 @@ class ModelPatcher: return list(p) def get_key_patches(self, filter_prefix=None): - fcbh.model_management.unload_model_clones(self) + ldm_patched.modules.model_management.unload_model_clones(self) model_sd = self.model_state_dict() p = {} for k in model_sd: @@ -188,14 +195,14 @@ class ModelPatcher: self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update) if device_to is not None: - temp_weight = fcbh.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) + temp_weight = ldm_patched.modules.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) else: temp_weight = weight.to(torch.float32, copy=True) out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) if inplace_update: - fcbh.utils.copy_to_param(self.model, key, out_weight) + ldm_patched.modules.utils.copy_to_param(self.model, key, out_weight) else: - fcbh.utils.set_attr(self.model, key, out_weight) + ldm_patched.modules.utils.set_attr(self.model, key, out_weight) del temp_weight if device_to is not None: @@ -217,27 +224,33 @@ class ModelPatcher: v = (self.calculate_weight(v[1:], v[0].clone(), key), ) if len(v) == 1: + patch_type = "diff" + elif len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "diff": w1 = v[0] if alpha != 0.0: if w1.shape != weight.shape: print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: - weight += alpha * fcbh.model_management.cast_to_device(w1, weight.device, weight.dtype) - elif len(v) == 4: #lora/locon - mat1 = fcbh.model_management.cast_to_device(v[0], weight.device, torch.float32) - mat2 = fcbh.model_management.cast_to_device(v[1], weight.device, torch.float32) + weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif patch_type == "lora": #lora/locon + mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) + mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) if v[2] is not None: alpha *= v[2] / mat2.shape[0] if v[3] is not None: #locon mid weights, hopefully the math is fine because I didn't properly test it - mat3 = fcbh.model_management.cast_to_device(v[3], weight.device, torch.float32) + mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32) final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) try: weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) - elif len(v) == 8: #lokr + elif patch_type == "lokr": w1 = v[0] w2 = v[1] w1_a = v[3] @@ -249,23 +262,23 @@ class ModelPatcher: if w1 is None: dim = w1_b.shape[0] - w1 = torch.mm(fcbh.model_management.cast_to_device(w1_a, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w1_b, weight.device, torch.float32)) + w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32)) else: - w1 = fcbh.model_management.cast_to_device(w1, weight.device, torch.float32) + w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32) if w2 is None: dim = w2_b.shape[0] if t2 is None: - w2 = torch.mm(fcbh.model_management.cast_to_device(w2_a, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2_b, weight.device, torch.float32)) + w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32)) else: w2 = torch.einsum('i j k l, j r, i p -> p r k l', - fcbh.model_management.cast_to_device(t2, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2_b, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2_a, weight.device, torch.float32)) + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32)) else: - w2 = fcbh.model_management.cast_to_device(w2, weight.device, torch.float32) + w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32) if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) @@ -276,7 +289,7 @@ class ModelPatcher: weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) - else: #loha + elif patch_type == "loha": w1a = v[0] w1b = v[1] if v[2] is not None: @@ -287,24 +300,36 @@ class ModelPatcher: t1 = v[5] t2 = v[6] m1 = torch.einsum('i j k l, j r, i p -> p r k l', - fcbh.model_management.cast_to_device(t1, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w1b, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w1a, weight.device, torch.float32)) + ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32)) m2 = torch.einsum('i j k l, j r, i p -> p r k l', - fcbh.model_management.cast_to_device(t2, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2b, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2a, weight.device, torch.float32)) + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32)) else: - m1 = torch.mm(fcbh.model_management.cast_to_device(w1a, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w1b, weight.device, torch.float32)) - m2 = torch.mm(fcbh.model_management.cast_to_device(w2a, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2b, weight.device, torch.float32)) + m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32)) + m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32)) try: weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) + elif patch_type == "glora": + if v[4] is not None: + alpha *= v[4] / v[0].shape[0] + + a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) + a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) + b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) + b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) + + weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + else: + print("patch type not recognized", patch_type, key) return weight @@ -313,10 +338,10 @@ class ModelPatcher: if self.weight_inplace_update: for k in keys: - fcbh.utils.copy_to_param(self.model, k, self.backup[k]) + ldm_patched.modules.utils.copy_to_param(self.model, k, self.backup[k]) else: for k in keys: - fcbh.utils.set_attr(self.model, k, self.backup[k]) + ldm_patched.modules.utils.set_attr(self.model, k, self.backup[k]) self.backup = {} diff --git a/backend/headless/fcbh/model_sampling.py b/ldm_patched/modules/model_sampling.py similarity index 58% rename from backend/headless/fcbh/model_sampling.py rename to ldm_patched/modules/model_sampling.py index 01a54a46..f39e275d 100644 --- a/backend/headless/fcbh/model_sampling.py +++ b/ldm_patched/modules/model_sampling.py @@ -1,7 +1,7 @@ import torch import numpy as np -from fcbh.ldm.modules.diffusionmodules.util import make_beta_schedule - +from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule +import math class EPS: def calculate_input(self, sigma, noise): @@ -22,10 +22,17 @@ class V_PREDICTION(EPS): class ModelSamplingDiscrete(torch.nn.Module): def __init__(self, model_config=None): super().__init__() - beta_schedule = "linear" + if model_config is not None: - beta_schedule = model_config.sampling_settings.get("beta_schedule", beta_schedule) - self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + beta_schedule = sampling_settings.get("beta_schedule", "linear") + linear_start = sampling_settings.get("linear_start", 0.00085) + linear_end = sampling_settings.get("linear_end", 0.012) + + self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3) self.sigma_data = 1.0 def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, @@ -65,15 +72,15 @@ class ModelSamplingDiscrete(torch.nn.Module): def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] - return dists.abs().argmin(dim=0).view(sigma.shape) + return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) def sigma(self, timestep): - t = torch.clamp(timestep.float(), min=0, max=(len(self.sigmas) - 1)) + t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] - return log_sigma.exp() + return log_sigma.exp().to(timestep.device) def percent_to_sigma(self, percent): if percent <= 0.0: @@ -83,3 +90,47 @@ class ModelSamplingDiscrete(torch.nn.Module): percent = 1.0 - percent return self.sigma(torch.tensor(percent * 999.0)).item() + +class ModelSamplingContinuousEDM(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + self.sigma_data = 1.0 + + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + sigma_min = sampling_settings.get("sigma_min", 0.002) + sigma_max = sampling_settings.get("sigma_max", 120.0) + self.set_sigma_range(sigma_min, sigma_max) + + def set_sigma_range(self, sigma_min, sigma_max): + sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() + + self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers + self.register_buffer('log_sigmas', sigmas.log()) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return 0.25 * sigma.log() + + def sigma(self, timestep): + return (timestep / 0.25).exp() + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + percent = 1.0 - percent + + log_sigma_min = math.log(self.sigma_min) + return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) diff --git a/ldm_patched/modules/ops.py b/ldm_patched/modules/ops.py new file mode 100644 index 00000000..08c63384 --- /dev/null +++ b/ldm_patched/modules/ops.py @@ -0,0 +1,65 @@ +import torch +from contextlib import contextmanager + +class disable_weight_init: + class Linear(torch.nn.Linear): + def reset_parameters(self): + return None + + class Conv2d(torch.nn.Conv2d): + def reset_parameters(self): + return None + + class Conv3d(torch.nn.Conv3d): + def reset_parameters(self): + return None + + class GroupNorm(torch.nn.GroupNorm): + def reset_parameters(self): + return None + + class LayerNorm(torch.nn.LayerNorm): + def reset_parameters(self): + return None + + @classmethod + def conv_nd(s, dims, *args, **kwargs): + if dims == 2: + return s.Conv2d(*args, **kwargs) + elif dims == 3: + return s.Conv3d(*args, **kwargs) + else: + raise ValueError(f"unsupported dimensions: {dims}") + +def cast_bias_weight(s, input): + bias = None + if s.bias is not None: + bias = s.bias.to(device=input.device, dtype=input.dtype) + weight = s.weight.to(device=input.device, dtype=input.dtype) + return weight, bias + +class manual_cast(disable_weight_init): + class Linear(disable_weight_init.Linear): + def forward(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + + class Conv2d(disable_weight_init.Conv2d): + def forward(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + class Conv3d(disable_weight_init.Conv3d): + def forward(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + class GroupNorm(disable_weight_init.GroupNorm): + def forward(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) + + class LayerNorm(disable_weight_init.LayerNorm): + def forward(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) diff --git a/backend/headless/fcbh/options.py b/ldm_patched/modules/options.py similarity index 100% rename from backend/headless/fcbh/options.py rename to ldm_patched/modules/options.py diff --git a/backend/headless/fcbh/sample.py b/ldm_patched/modules/sample.py similarity index 75% rename from backend/headless/fcbh/sample.py rename to ldm_patched/modules/sample.py index 5e5cc9ab..7a7e3092 100644 --- a/backend/headless/fcbh/sample.py +++ b/ldm_patched/modules/sample.py @@ -1,8 +1,8 @@ import torch -import fcbh.model_management -import fcbh.samplers -import fcbh.conds -import fcbh.utils +import ldm_patched.modules.model_management +import ldm_patched.modules.samplers +import ldm_patched.modules.conds +import ldm_patched.modules.utils import math import numpy as np @@ -30,7 +30,7 @@ def prepare_mask(noise_mask, shape, device): noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") noise_mask = noise_mask.round() noise_mask = torch.cat([noise_mask] * shape[1], dim=1) - noise_mask = fcbh.utils.repeat_to_batch_size(noise_mask, shape[0]) + noise_mask = ldm_patched.modules.utils.repeat_to_batch_size(noise_mask, shape[0]) noise_mask = noise_mask.to(device) return noise_mask @@ -47,7 +47,7 @@ def convert_cond(cond): temp = c[1].copy() model_conds = temp.get("model_conds", {}) if c[0] is not None: - model_conds["c_crossattn"] = fcbh.conds.CONDCrossAttn(c[0]) + model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) temp["model_conds"] = model_conds out.append(temp) return out @@ -83,7 +83,7 @@ def prepare_sampling(model, noise_shape, positive, negative, noise_mask): real_model = None models, inference_memory = get_additional_models(positive, negative, model.model_dtype()) - fcbh.model_management.load_models_gpu([model] + models, model.memory_required(noise_shape) + inference_memory) + ldm_patched.modules.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) real_model = model.model return real_model, positive, negative, noise_mask, models @@ -95,13 +95,13 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative noise = noise.to(model.load_device) latent_image = latent_image.to(model.load_device) - sampler = fcbh.samplers.KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) + sampler = ldm_patched.modules.samplers.KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) - samples = samples.cpu() + samples = samples.to(ldm_patched.modules.model_management.intermediate_device()) cleanup_additional_models(models) - cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))) + cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) return samples def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): @@ -110,9 +110,9 @@ def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent latent_image = latent_image.to(model.load_device) sigmas = sigmas.to(model.load_device) - samples = fcbh.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) - samples = samples.cpu() + samples = ldm_patched.modules.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.to(ldm_patched.modules.model_management.intermediate_device()) cleanup_additional_models(models) - cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))) + cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) return samples diff --git a/backend/headless/fcbh/samplers.py b/ldm_patched/modules/samplers.py similarity index 65% rename from backend/headless/fcbh/samplers.py rename to ldm_patched/modules/samplers.py index 34dec1f6..9996e74c 100644 --- a/backend/headless/fcbh/samplers.py +++ b/ldm_patched/modules/samplers.py @@ -1,260 +1,267 @@ -from .k_diffusion import sampling as k_diffusion_sampling -from .extra_samplers import uni_pc +from ldm_patched.k_diffusion import sampling as k_diffusion_sampling +from ldm_patched.unipc import uni_pc import torch import enum -from fcbh import model_management +from ldm_patched.modules import model_management import math -from fcbh import model_base -import fcbh.utils -import fcbh.conds +from ldm_patched.modules import model_base +import ldm_patched.modules.utils +import ldm_patched.modules.conds +def get_area_and_mult(conds, x_in, timestep_in): + area = (x_in.shape[2], x_in.shape[3], 0, 0) + strength = 1.0 + + if 'timestep_start' in conds: + timestep_start = conds['timestep_start'] + if timestep_in[0] > timestep_start: + return None + if 'timestep_end' in conds: + timestep_end = conds['timestep_end'] + if timestep_in[0] < timestep_end: + return None + if 'area' in conds: + area = conds['area'] + if 'strength' in conds: + strength = conds['strength'] + + input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] + if 'mask' in conds: + # Scale the mask to the size of the input + # The mask should have been resized as we began the sampling process + mask_strength = 1.0 + if "mask_strength" in conds: + mask_strength = conds["mask_strength"] + mask = conds['mask'] + assert(mask.shape[1] == x_in.shape[2]) + assert(mask.shape[2] == x_in.shape[3]) + mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength + mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) + else: + mask = torch.ones_like(input_x) + mult = mask * strength + + if 'mask' not in conds: + rr = 8 + if area[2] != 0: + for t in range(rr): + mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1)) + if (area[0] + area[2]) < x_in.shape[2]: + for t in range(rr): + mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1)) + if area[3] != 0: + for t in range(rr): + mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1)) + if (area[1] + area[3]) < x_in.shape[3]: + for t in range(rr): + mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1)) + + conditioning = {} + model_conds = conds["model_conds"] + for c in model_conds: + conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) + + control = None + if 'control' in conds: + control = conds['control'] + + patches = None + if 'gligen' in conds: + gligen = conds['gligen'] + patches = {} + gligen_type = gligen[0] + gligen_model = gligen[1] + if gligen_type == "position": + gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) + else: + gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) + + patches['middle_patch'] = [gligen_patch] + + return (input_x, mult, conditioning, area, control, patches) + +def cond_equal_size(c1, c2): + if c1 is c2: + return True + if c1.keys() != c2.keys(): + return False + for k in c1: + if not c1[k].can_concat(c2[k]): + return False + return True + +def can_concat_cond(c1, c2): + if c1[0].shape != c2[0].shape: + return False + + #control + if (c1[4] is None) != (c2[4] is None): + return False + if c1[4] is not None: + if c1[4] is not c2[4]: + return False + + #patches + if (c1[5] is None) != (c2[5] is None): + return False + if (c1[5] is not None): + if c1[5] is not c2[5]: + return False + + return cond_equal_size(c1[2], c2[2]) + +def cond_cat(c_list): + c_crossattn = [] + c_concat = [] + c_adm = [] + crossattn_max_len = 0 + + temp = {} + for x in c_list: + for k in x: + cur = temp.get(k, []) + cur.append(x[k]) + temp[k] = cur + + out = {} + for k in temp: + conds = temp[k] + out[k] = conds[0].concat(conds[1:]) + + return out + +def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): + out_cond = torch.zeros_like(x_in) + out_count = torch.ones_like(x_in) * 1e-37 + + out_uncond = torch.zeros_like(x_in) + out_uncond_count = torch.ones_like(x_in) * 1e-37 + + COND = 0 + UNCOND = 1 + + to_run = [] + for x in cond: + p = get_area_and_mult(x, x_in, timestep) + if p is None: + continue + + to_run += [(p, COND)] + if uncond is not None: + for x in uncond: + p = get_area_and_mult(x, x_in, timestep) + if p is None: + continue + + to_run += [(p, UNCOND)] + + while len(to_run) > 0: + first = to_run[0] + first_shape = first[0][0].shape + to_batch_temp = [] + for x in range(len(to_run)): + if can_concat_cond(to_run[x][0], first[0]): + to_batch_temp += [x] + + to_batch_temp.reverse() + to_batch = to_batch_temp[:1] + + free_memory = model_management.get_free_memory(x_in.device) + for i in range(1, len(to_batch_temp) + 1): + batch_amount = to_batch_temp[:len(to_batch_temp)//i] + input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] + if model.memory_required(input_shape) < free_memory: + to_batch = batch_amount + break + + input_x = [] + mult = [] + c = [] + cond_or_uncond = [] + area = [] + control = None + patches = None + for x in to_batch: + o = to_run.pop(x) + p = o[0] + input_x += [p[0]] + mult += [p[1]] + c += [p[2]] + area += [p[3]] + cond_or_uncond += [o[1]] + control = p[4] + patches = p[5] + + batch_chunks = len(cond_or_uncond) + input_x = torch.cat(input_x) + c = cond_cat(c) + timestep_ = torch.cat([timestep] * batch_chunks) + + if control is not None: + c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) + + transformer_options = {} + if 'transformer_options' in model_options: + transformer_options = model_options['transformer_options'].copy() + + if patches is not None: + if "patches" in transformer_options: + cur_patches = transformer_options["patches"].copy() + for p in patches: + if p in cur_patches: + cur_patches[p] = cur_patches[p] + patches[p] + else: + cur_patches[p] = patches[p] + else: + transformer_options["patches"] = patches + + transformer_options["cond_or_uncond"] = cond_or_uncond[:] + transformer_options["sigmas"] = timestep + + c['transformer_options'] = transformer_options + + if 'model_function_wrapper' in model_options: + output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) + else: + output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) + del input_x + + for o in range(batch_chunks): + if cond_or_uncond[o] == COND: + out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] + out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] + else: + out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] + out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] + del mult + + out_cond /= out_count + del out_count + out_uncond /= out_uncond_count + del out_uncond_count + return out_cond, out_uncond #The main sampling function shared by all the samplers #Returns denoised def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): - def get_area_and_mult(conds, x_in, timestep_in): - area = (x_in.shape[2], x_in.shape[3], 0, 0) - strength = 1.0 - - if 'timestep_start' in conds: - timestep_start = conds['timestep_start'] - if timestep_in[0] > timestep_start: - return None - if 'timestep_end' in conds: - timestep_end = conds['timestep_end'] - if timestep_in[0] < timestep_end: - return None - if 'area' in conds: - area = conds['area'] - if 'strength' in conds: - strength = conds['strength'] - - input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] - if 'mask' in conds: - # Scale the mask to the size of the input - # The mask should have been resized as we began the sampling process - mask_strength = 1.0 - if "mask_strength" in conds: - mask_strength = conds["mask_strength"] - mask = conds['mask'] - assert(mask.shape[1] == x_in.shape[2]) - assert(mask.shape[2] == x_in.shape[3]) - mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength - mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) - else: - mask = torch.ones_like(input_x) - mult = mask * strength - - if 'mask' not in conds: - rr = 8 - if area[2] != 0: - for t in range(rr): - mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1)) - if (area[0] + area[2]) < x_in.shape[2]: - for t in range(rr): - mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1)) - if area[3] != 0: - for t in range(rr): - mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1)) - if (area[1] + area[3]) < x_in.shape[3]: - for t in range(rr): - mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1)) - - conditionning = {} - model_conds = conds["model_conds"] - for c in model_conds: - conditionning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) - - control = None - if 'control' in conds: - control = conds['control'] - - patches = None - if 'gligen' in conds: - gligen = conds['gligen'] - patches = {} - gligen_type = gligen[0] - gligen_model = gligen[1] - if gligen_type == "position": - gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) - else: - gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) - - patches['middle_patch'] = [gligen_patch] - - return (input_x, mult, conditionning, area, control, patches) - - def cond_equal_size(c1, c2): - if c1 is c2: - return True - if c1.keys() != c2.keys(): - return False - for k in c1: - if not c1[k].can_concat(c2[k]): - return False - return True - - def can_concat_cond(c1, c2): - if c1[0].shape != c2[0].shape: - return False - - #control - if (c1[4] is None) != (c2[4] is None): - return False - if c1[4] is not None: - if c1[4] is not c2[4]: - return False - - #patches - if (c1[5] is None) != (c2[5] is None): - return False - if (c1[5] is not None): - if c1[5] is not c2[5]: - return False - - return cond_equal_size(c1[2], c2[2]) - - def cond_cat(c_list): - c_crossattn = [] - c_concat = [] - c_adm = [] - crossattn_max_len = 0 - - temp = {} - for x in c_list: - for k in x: - cur = temp.get(k, []) - cur.append(x[k]) - temp[k] = cur - - out = {} - for k in temp: - conds = temp[k] - out[k] = conds[0].concat(conds[1:]) - - return out - - def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): - out_cond = torch.zeros_like(x_in) - out_count = torch.ones_like(x_in) * 1e-37 - - out_uncond = torch.zeros_like(x_in) - out_uncond_count = torch.ones_like(x_in) * 1e-37 - - COND = 0 - UNCOND = 1 - - to_run = [] - for x in cond: - p = get_area_and_mult(x, x_in, timestep) - if p is None: - continue - - to_run += [(p, COND)] - if uncond is not None: - for x in uncond: - p = get_area_and_mult(x, x_in, timestep) - if p is None: - continue - - to_run += [(p, UNCOND)] - - while len(to_run) > 0: - first = to_run[0] - first_shape = first[0][0].shape - to_batch_temp = [] - for x in range(len(to_run)): - if can_concat_cond(to_run[x][0], first[0]): - to_batch_temp += [x] - - to_batch_temp.reverse() - to_batch = to_batch_temp[:1] - - free_memory = model_management.get_free_memory(x_in.device) - for i in range(1, len(to_batch_temp) + 1): - batch_amount = to_batch_temp[:len(to_batch_temp)//i] - input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] - if model.memory_required(input_shape) < free_memory: - to_batch = batch_amount - break - - input_x = [] - mult = [] - c = [] - cond_or_uncond = [] - area = [] - control = None - patches = None - for x in to_batch: - o = to_run.pop(x) - p = o[0] - input_x += [p[0]] - mult += [p[1]] - c += [p[2]] - area += [p[3]] - cond_or_uncond += [o[1]] - control = p[4] - patches = p[5] - - batch_chunks = len(cond_or_uncond) - input_x = torch.cat(input_x) - c = cond_cat(c) - timestep_ = torch.cat([timestep] * batch_chunks) - - if control is not None: - c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) - - transformer_options = {} - if 'transformer_options' in model_options: - transformer_options = model_options['transformer_options'].copy() - - if patches is not None: - if "patches" in transformer_options: - cur_patches = transformer_options["patches"].copy() - for p in patches: - if p in cur_patches: - cur_patches[p] = cur_patches[p] + patches[p] - else: - cur_patches[p] = patches[p] - else: - transformer_options["patches"] = patches - - transformer_options["cond_or_uncond"] = cond_or_uncond[:] - transformer_options["sigmas"] = timestep - - c['transformer_options'] = transformer_options - - if 'model_function_wrapper' in model_options: - output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) - else: - output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) - del input_x - - for o in range(batch_chunks): - if cond_or_uncond[o] == COND: - out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] - out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] - else: - out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] - out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] - del mult - - out_cond /= out_count - del out_count - out_uncond /= out_uncond_count - del out_uncond_count - return out_cond, out_uncond - - if math.isclose(cond_scale, 1.0): - uncond = None - - cond, uncond = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options) - if "sampler_cfg_function" in model_options: - args = {"cond": x - cond, "uncond": x - uncond, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep} - return x - model_options["sampler_cfg_function"](args) + uncond_ = None else: - return uncond + (cond - uncond) * cond_scale + uncond_ = uncond + + cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options) + if "sampler_cfg_function" in model_options: + args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep} + cfg_result = x - model_options["sampler_cfg_function"](args) + else: + cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale + + for fn in model_options.get("sampler_post_cfg_function", []): + args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, + "sigma": timestep, "model_options": model_options, "input": x} + cfg_result = fn(args) + + return cfg_result class CFGNoisePredictor(torch.nn.Module): def __init__(self, model): @@ -276,10 +283,7 @@ class KSamplerX0Inpaint(torch.nn.Module): x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) if denoise_mask is not None: - out *= denoise_mask - - if denoise_mask is not None: - out += self.latent_image * latent_mask + out = out * denoise_mask + self.latent_image * latent_mask return out def simple_scheduler(model, steps): diff --git a/backend/headless/fcbh/sd.py b/ldm_patched/modules/sd.py similarity index 67% rename from backend/headless/fcbh/sd.py rename to ldm_patched/modules/sd.py index 5df89bb2..3caa92da 100644 --- a/backend/headless/fcbh/sd.py +++ b/ldm_patched/modules/sd.py @@ -2,12 +2,12 @@ import torch import contextlib import math -from fcbh import model_management -from .ldm.util import instantiate_from_config -from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine +from ldm_patched.modules import model_management +from ldm_patched.ldm.util import instantiate_from_config +from ldm_patched.ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine import yaml -import fcbh.utils +import ldm_patched.modules.utils from . import clip_vision from . import gligen @@ -19,11 +19,11 @@ from . import sd1_clip from . import sd2_clip from . import sdxl_clip -import fcbh.model_patcher -import fcbh.lora -import fcbh.t2i_adapter.adapter -import fcbh.supported_models_base -import fcbh.taesd.taesd +import ldm_patched.modules.model_patcher +import ldm_patched.modules.lora +import ldm_patched.t2ia.adapter +import ldm_patched.modules.supported_models_base +import ldm_patched.taesd.taesd def load_model_weights(model, sd): m, u = model.load_state_dict(sd, strict=False) @@ -36,7 +36,7 @@ def load_model_weights(model, sd): w = sd.pop(x) del w if len(m) > 0: - print("extra keys", m) + print("extra", m) return model def load_clip_weights(model, sd): @@ -51,18 +51,18 @@ def load_clip_weights(model, sd): if ids.dtype == torch.float32: sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() - sd = fcbh.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) + sd = ldm_patched.modules.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) return load_model_weights(model, sd) def load_lora_for_models(model, clip, lora, strength_model, strength_clip): key_map = {} if model is not None: - key_map = fcbh.lora.model_lora_keys_unet(model.model, key_map) + key_map = ldm_patched.modules.lora.model_lora_keys_unet(model.model, key_map) if clip is not None: - key_map = fcbh.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + key_map = ldm_patched.modules.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) - loaded = fcbh.lora.load_lora(lora, key_map) + loaded = ldm_patched.modules.lora.load_lora(lora, key_map) if model is not None: new_modelpatcher = model.clone() k = new_modelpatcher.add_patches(loaded, strength_model) @@ -101,7 +101,7 @@ class CLIP: self.cond_stage_model = clip(**(params)) self.tokenizer = tokenizer(embedding_directory=embedding_directory) - self.patcher = fcbh.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) + self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) self.layer_idx = None def clone(self): @@ -151,7 +151,7 @@ class CLIP: return self.patcher.get_key_patches() class VAE: - def __init__(self, sd=None, device=None, config=None): + def __init__(self, sd=None, device=None, config=None, dtype=None): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) @@ -159,8 +159,16 @@ class VAE: self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) if config is None: - if "taesd_decoder.1.weight" in sd: - self.first_stage_model = fcbh.taesd.taesd.TAESD() + if "decoder.mid.block_1.mix_factor" in sd: + encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + decoder_config = encoder_config.copy() + decoder_config["video_kernel_size"] = [3, 1, 1] + decoder_config["alpha"] = 0.0 + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, + decoder_config={'target': "ldm_patched.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) + elif "taesd_decoder.1.weight" in sd: + self.first_stage_model = ldm_patched.taesd.taesd.TAESD() else: #default SD1.x/SD2.x VAE parameters ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} @@ -179,90 +187,89 @@ class VAE: if device is None: device = model_management.vae_device() self.device = device - self.offload_device = model_management.vae_offload_device() - self.vae_dtype = model_management.vae_dtype() + offload_device = model_management.vae_offload_device() + if dtype is None: + dtype = model_management.vae_dtype() + self.vae_dtype = dtype self.first_stage_model.to(self.vae_dtype) + self.output_device = model_management.intermediate_device() + + self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): - steps = samples.shape[0] * fcbh.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) - steps += samples.shape[0] * fcbh.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) - steps += samples.shape[0] * fcbh.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) - pbar = fcbh.utils.ProgressBar(steps) + steps = samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) + steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = ldm_patched.modules.utils.ProgressBar(steps) decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float() output = torch.clamp(( - (fcbh.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) + - fcbh.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) + - fcbh.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar)) + (ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar) + + ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar) + + ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar)) / 3.0) / 2.0, min=0.0, max=1.0) return output def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): - steps = pixel_samples.shape[0] * fcbh.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) - steps += pixel_samples.shape[0] * fcbh.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) - steps += pixel_samples.shape[0] * fcbh.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) - pbar = fcbh.utils.ProgressBar(steps) + steps = pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) + steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = ldm_patched.modules.utils.ProgressBar(steps) encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float() - samples = fcbh.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) - samples += fcbh.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) - samples += fcbh.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) + samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar) + samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar) + samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar) samples /= 3.0 return samples def decode(self, samples_in): - self.first_stage_model = self.first_stage_model.to(self.device) try: memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) - model_management.free_memory(memory_used, self.device) + model_management.load_models_gpu([self.patcher], memory_required=memory_used) free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) - pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu") + pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device=self.output_device) for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) - pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).cpu().float() + 1.0) / 2.0, min=0.0, max=1.0) + pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0) except model_management.OOM_EXCEPTION as e: print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") pixel_samples = self.decode_tiled_(samples_in) - self.first_stage_model = self.first_stage_model.to(self.offload_device) - pixel_samples = pixel_samples.cpu().movedim(1,-1) + pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) return pixel_samples def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16): - self.first_stage_model = self.first_stage_model.to(self.device) + model_management.load_model_gpu(self.patcher) output = self.decode_tiled_(samples, tile_x, tile_y, overlap) - self.first_stage_model = self.first_stage_model.to(self.offload_device) return output.movedim(1,-1) def encode(self, pixel_samples): - self.first_stage_model = self.first_stage_model.to(self.device) pixel_samples = pixel_samples.movedim(-1,1) try: memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) - model_management.free_memory(memory_used, self.device) + model_management.load_models_gpu([self.patcher], memory_required=memory_used) free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) - samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu") + samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device=self.output_device) for x in range(0, pixel_samples.shape[0], batch_number): pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device) - samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).cpu().float() + samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float() except model_management.OOM_EXCEPTION as e: print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") samples = self.encode_tiled_(pixel_samples) - self.first_stage_model = self.first_stage_model.to(self.offload_device) return samples def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): - self.first_stage_model = self.first_stage_model.to(self.device) + model_management.load_model_gpu(self.patcher) pixel_samples = pixel_samples.movedim(-1,1) samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) - self.first_stage_model = self.first_stage_model.to(self.offload_device) return samples def get_sd(self): @@ -277,10 +284,10 @@ class StyleModel: def load_style_model(ckpt_path): - model_data = fcbh.utils.load_torch_file(ckpt_path, safe_load=True) + model_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) keys = model_data.keys() if "style_embedding" in keys: - model = fcbh.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) + model = ldm_patched.t2ia.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) else: raise Exception("invalid style model {}".format(ckpt_path)) model.load_state_dict(model_data) @@ -290,14 +297,14 @@ def load_style_model(ckpt_path): def load_clip(ckpt_paths, embedding_directory=None): clip_data = [] for p in ckpt_paths: - clip_data.append(fcbh.utils.load_torch_file(p, safe_load=True)) + clip_data.append(ldm_patched.modules.utils.load_torch_file(p, safe_load=True)) class EmptyClass: pass for i in range(len(clip_data)): if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: - clip_data[i] = fcbh.utils.transformers_convert(clip_data[i], "", "text_model.", 32) + clip_data[i] = ldm_patched.modules.utils.transformers_convert(clip_data[i], "", "text_model.", 32) clip_target = EmptyClass() clip_target.params = {} @@ -326,11 +333,11 @@ def load_clip(ckpt_paths, embedding_directory=None): return clip def load_gligen(ckpt_path): - data = fcbh.utils.load_torch_file(ckpt_path, safe_load=True) + data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() - return fcbh.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) + return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): #TODO: this function is a mess and should be removed eventually @@ -368,12 +375,12 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl pass if state_dict is None: - state_dict = fcbh.utils.load_torch_file(ckpt_path) + state_dict = ldm_patched.modules.utils.load_torch_file(ckpt_path) class EmptyClass: pass - model_config = fcbh.supported_models_base.BASE({}) + model_config = ldm_patched.modules.supported_models_base.BASE({}) from . import latent_formats model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) @@ -395,7 +402,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl model.load_model_weights(state_dict, "model.diffusion_model.") if output_vae: - vae_sd = fcbh.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True) + vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True) vae = VAE(sd=vae_sd, config=vae_config) if output_clip: @@ -414,10 +421,10 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl w.cond_stage_model = clip.cond_stage_model.clip_l load_clip_weights(w, state_dict) - return (fcbh.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) + return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True): - sd = fcbh.utils.load_torch_file(ckpt_path) + sd = ldm_patched.modules.utils.load_torch_file(ckpt_path) sd_keys = sd.keys() clip = None clipvision = None @@ -426,13 +433,17 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o model_patcher = None clip_target = None - parameters = fcbh.utils.calculate_parameters(sd, "model.diffusion_model.") + parameters = ldm_patched.modules.utils.calculate_parameters(sd, "model.diffusion_model.") unet_dtype = model_management.unet_dtype(model_params=parameters) + load_device = model_management.get_torch_device() + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) class WeightsLoader(torch.nn.Module): pass model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype) + model_config.set_manual_cast(manual_cast_dtype) + if model_config is None: raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) @@ -447,7 +458,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o model.load_model_weights(sd, "model.diffusion_model.") if output_vae: - vae_sd = fcbh.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True) + vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True) vae_sd = model_config.process_vae_state_dict(vae_sd) vae = VAE(sd=vae_sd) @@ -465,7 +476,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o print("left over keys:", left_over) if output_model: - model_patcher = fcbh.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device(), current_device=inital_load_device) + model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device) if inital_load_device != torch.device("cpu"): print("loaded straight to GPU") model_management.load_model_gpu(model_patcher) @@ -473,23 +484,24 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o return (model_patcher, clip, vae, clipvision) -def load_unet(unet_path): #load unet in diffusers format - sd = fcbh.utils.load_torch_file(unet_path) - parameters = fcbh.utils.calculate_parameters(sd) +def load_unet_state_dict(sd): #load unet in diffusers format + parameters = ldm_patched.modules.utils.calculate_parameters(sd) unet_dtype = model_management.unet_dtype(model_params=parameters) + load_device = model_management.get_torch_device() + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) + if "input_blocks.0.0.weight" in sd: #ldm model_config = model_detection.model_config_from_unet(sd, "", unet_dtype) if model_config is None: - raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) + return None new_sd = sd else: #diffusers model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype) if model_config is None: - print("ERROR UNSUPPORTED UNET", unet_path) return None - diffusers_keys = fcbh.utils.unet_to_diffusers(model_config.unet_config) + diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model_config.unet_config) new_sd = {} for k in diffusers_keys: @@ -498,15 +510,24 @@ def load_unet(unet_path): #load unet in diffusers format else: print(diffusers_keys[k], k) offload_device = model_management.unet_offload_device() + model_config.set_manual_cast(manual_cast_dtype) model = model_config.get_model(new_sd, "") model = model.to(offload_device) model.load_model_weights(new_sd, "") left_over = sd.keys() if len(left_over) > 0: print("left over keys in unet:", left_over) - return fcbh.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device) + return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) + +def load_unet(unet_path): + sd = ldm_patched.modules.utils.load_torch_file(unet_path) + model = load_unet_state_dict(sd) + if model is None: + print("ERROR UNSUPPORTED UNET", unet_path) + raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) + return model def save_checkpoint(output_path, model, clip, vae, metadata=None): model_management.load_models_gpu([model, clip.load_model()]) sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd()) - fcbh.utils.save_torch_file(sd, output_path, metadata=metadata) + ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/backend/headless/fcbh/sd1_clip.py b/ldm_patched/modules/sd1_clip.py similarity index 84% rename from backend/headless/fcbh/sd1_clip.py rename to ldm_patched/modules/sd1_clip.py index 83e03557..736d6167 100644 --- a/backend/headless/fcbh/sd1_clip.py +++ b/ldm_patched/modules/sd1_clip.py @@ -1,12 +1,14 @@ import os -from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig, modeling_utils -import fcbh.ops +from transformers import CLIPTokenizer +import ldm_patched.modules.ops import torch import traceback import zipfile from . import model_management import contextlib +import ldm_patched.modules.clip_model +import json def gen_empty_tokens(special_tokens, length): start_token = special_tokens.get("start", None) @@ -37,7 +39,7 @@ class ClipTokenWeightEncoder: out, pooled = self.encode(to_encode) if pooled is not None: - first_pooled = pooled[0:1].cpu() + first_pooled = pooled[0:1].to(model_management.intermediate_device()) else: first_pooled = pooled @@ -54,8 +56,8 @@ class ClipTokenWeightEncoder: output.append(z) if (len(output) == 0): - return out[-1:].cpu(), first_pooled - return torch.cat(output, dim=-2).cpu(), first_pooled + return out[-1:].to(model_management.intermediate_device()), first_pooled + return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): """Uses the CLIP transformer encoder for text (from huggingface)""" @@ -65,31 +67,19 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): "hidden" ] def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, - freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, textmodel_path=None, dtype=None, - special_tokens={"start": 49406, "end": 49407, "pad": 49407},layer_norm_hidden_state=True, config_class=CLIPTextConfig, - model_class=CLIPTextModel, inner_name="text_model"): # clip-vit-base-patch32 + freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=ldm_patched.modules.clip_model.CLIPTextModel, + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True): # clip-vit-base-patch32 super().__init__() assert layer in self.LAYERS - self.num_layers = 12 - if textmodel_path is not None: - self.transformer = model_class.from_pretrained(textmodel_path) - else: - if textmodel_json_config is None: - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") - config = config_class.from_json_file(textmodel_json_config) - self.num_layers = config.num_hidden_layers - with fcbh.ops.use_fcbh_ops(device, dtype): - with modeling_utils.no_init_weights(): - self.transformer = model_class(config) - self.inner_name = inner_name - if dtype is not None: - self.transformer.to(dtype) - inner_model = getattr(self.transformer, self.inner_name) - if hasattr(inner_model, "embeddings"): - inner_model.embeddings.to(torch.float32) - else: - self.transformer.set_input_embeddings(self.transformer.get_input_embeddings().to(torch.float32)) + if textmodel_json_config is None: + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") + + with open(textmodel_json_config) as f: + config = json.load(f) + + self.transformer = model_class(config, dtype, device, ldm_patched.modules.ops.manual_cast) + self.num_layers = self.transformer.num_layers self.max_length = max_length if freeze: @@ -104,7 +94,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.layer_norm_hidden_state = layer_norm_hidden_state if layer == "hidden": assert layer_idx is not None - assert abs(layer_idx) <= self.num_layers + assert abs(layer_idx) < self.num_layers self.clip_layer(layer_idx) self.layer_default = (self.layer, self.layer_idx) @@ -115,7 +105,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): param.requires_grad = False def clip_layer(self, layer_idx): - if abs(layer_idx) >= self.num_layers: + if abs(layer_idx) > self.num_layers: self.layer = "last" else: self.layer = "hidden" @@ -170,41 +160,31 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): tokens = self.set_up_textual_embeddings(tokens, backup_embeds) tokens = torch.LongTensor(tokens).to(device) - if getattr(self.transformer, self.inner_name).final_layer_norm.weight.dtype != torch.float32: - precision_scope = torch.autocast + attention_mask = None + if self.enable_attention_masks: + attention_mask = torch.zeros_like(tokens) + max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 + for x in range(attention_mask.shape[0]): + for y in range(attention_mask.shape[1]): + attention_mask[x, y] = 1 + if tokens[x, y] == max_token: + break + + outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state) + self.transformer.set_input_embeddings(backup_embeds) + + if self.layer == "last": + z = outputs[0] else: - precision_scope = lambda a, dtype: contextlib.nullcontext(a) + z = outputs[1] - with precision_scope(model_management.get_autocast_device(device), dtype=torch.float32): - attention_mask = None - if self.enable_attention_masks: - attention_mask = torch.zeros_like(tokens) - max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 - for x in range(attention_mask.shape[0]): - for y in range(attention_mask.shape[1]): - attention_mask[x, y] = 1 - if tokens[x, y] == max_token: - break + if outputs[2] is not None: + pooled_output = outputs[2].float() + else: + pooled_output = None - outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask, output_hidden_states=self.layer=="hidden") - self.transformer.set_input_embeddings(backup_embeds) - - if self.layer == "last": - z = outputs.last_hidden_state - elif self.layer == "pooled": - z = outputs.pooler_output[:, None, :] - else: - z = outputs.hidden_states[self.layer_idx] - if self.layer_norm_hidden_state: - z = getattr(self.transformer, self.inner_name).final_layer_norm(z) - - if hasattr(outputs, "pooler_output"): - pooled_output = outputs.pooler_output.float() - else: - pooled_output = None - - if self.text_projection is not None and pooled_output is not None: - pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() + if self.text_projection is not None and pooled_output is not None: + pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() return z.float(), pooled_output def encode(self, tokens): diff --git a/backend/headless/fcbh/sd1_clip_config.json b/ldm_patched/modules/sd1_clip_config.json similarity index 100% rename from backend/headless/fcbh/sd1_clip_config.json rename to ldm_patched/modules/sd1_clip_config.json diff --git a/backend/headless/fcbh/sd1_tokenizer/merges.txt b/ldm_patched/modules/sd1_tokenizer/merges.txt similarity index 100% rename from backend/headless/fcbh/sd1_tokenizer/merges.txt rename to ldm_patched/modules/sd1_tokenizer/merges.txt diff --git a/backend/headless/fcbh/sd1_tokenizer/special_tokens_map.json b/ldm_patched/modules/sd1_tokenizer/special_tokens_map.json similarity index 100% rename from backend/headless/fcbh/sd1_tokenizer/special_tokens_map.json rename to ldm_patched/modules/sd1_tokenizer/special_tokens_map.json diff --git a/backend/headless/fcbh/sd1_tokenizer/tokenizer_config.json b/ldm_patched/modules/sd1_tokenizer/tokenizer_config.json similarity index 100% rename from backend/headless/fcbh/sd1_tokenizer/tokenizer_config.json rename to ldm_patched/modules/sd1_tokenizer/tokenizer_config.json diff --git a/backend/headless/fcbh/sd1_tokenizer/vocab.json b/ldm_patched/modules/sd1_tokenizer/vocab.json similarity index 100% rename from backend/headless/fcbh/sd1_tokenizer/vocab.json rename to ldm_patched/modules/sd1_tokenizer/vocab.json diff --git a/backend/headless/fcbh/sd2_clip.py b/ldm_patched/modules/sd2_clip.py similarity index 81% rename from backend/headless/fcbh/sd2_clip.py rename to ldm_patched/modules/sd2_clip.py index a07d5dc9..41f9e388 100644 --- a/backend/headless/fcbh/sd2_clip.py +++ b/ldm_patched/modules/sd2_clip.py @@ -1,15 +1,15 @@ -from fcbh import sd1_clip +from ldm_patched.modules import sd1_clip import torch import os class SD2ClipHModel(sd1_clip.SDClipModel): - def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=None, dtype=None): + def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): if layer == "penultimate": layer="hidden" - layer_idx=23 + layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") - super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}) + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}) class SD2ClipHTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None): diff --git a/backend/headless/fcbh/sd2_clip_config.json b/ldm_patched/modules/sd2_clip_config.json similarity index 100% rename from backend/headless/fcbh/sd2_clip_config.json rename to ldm_patched/modules/sd2_clip_config.json diff --git a/backend/headless/fcbh/sdxl_clip.py b/ldm_patched/modules/sdxl_clip.py similarity index 92% rename from backend/headless/fcbh/sdxl_clip.py rename to ldm_patched/modules/sdxl_clip.py index dc2fb344..9d3d83d8 100644 --- a/backend/headless/fcbh/sdxl_clip.py +++ b/ldm_patched/modules/sdxl_clip.py @@ -1,15 +1,15 @@ -from fcbh import sd1_clip +from ldm_patched.modules import sd1_clip import torch import os class SDXLClipG(sd1_clip.SDClipModel): - def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=None, dtype=None): + def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): if layer == "penultimate": layer="hidden" layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") - super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype, + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False) def load_sd(self, sd): @@ -37,7 +37,7 @@ class SDXLTokenizer: class SDXLClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None): super().__init__() - self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype, layer_norm_hidden_state=False) + self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False) self.clip_g = SDXLClipG(device=device, dtype=dtype) def clip_layer(self, layer_idx): diff --git a/backend/headless/fcbh/supported_models.py b/ldm_patched/modules/supported_models.py similarity index 83% rename from backend/headless/fcbh/supported_models.py rename to ldm_patched/modules/supported_models.py index fdd4ea4f..2f2dee87 100644 --- a/backend/headless/fcbh/supported_models.py +++ b/ldm_patched/modules/supported_models.py @@ -17,6 +17,7 @@ class SD15(supported_models_base.BASE): "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, + "use_temporal_attention": False, } unet_extra_config = { @@ -56,6 +57,7 @@ class SD20(supported_models_base.BASE): "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": None, + "use_temporal_attention": False, } latent_format = latent_formats.SD15 @@ -69,6 +71,10 @@ class SD20(supported_models_base.BASE): return model_base.ModelType.EPS def process_clip_state_dict(self, state_dict): + replace_prefix = {} + replace_prefix["conditioner.embedders.0.model."] = "cond_stage_model.model." #SD2 in sgm format + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.clip_h.transformer.text_model.", 24) return state_dict @@ -88,6 +94,7 @@ class SD21UnclipL(SD20): "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": 1536, + "use_temporal_attention": False, } clip_vision_prefix = "embedder.model.visual." @@ -100,6 +107,7 @@ class SD21UnclipH(SD20): "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": 2048, + "use_temporal_attention": False, } clip_vision_prefix = "embedder.model.visual." @@ -112,6 +120,7 @@ class SDXLRefiner(supported_models_base.BASE): "context_dim": 1280, "adm_in_channels": 2560, "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], + "use_temporal_attention": False, } latent_format = latent_formats.SDXL @@ -148,7 +157,8 @@ class SDXL(supported_models_base.BASE): "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 10, 10], "context_dim": 2048, - "adm_in_channels": 2816 + "adm_in_channels": 2816, + "use_temporal_attention": False, } latent_format = latent_formats.SDXL @@ -203,8 +213,44 @@ class SSD1B(SDXL): "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 4, 4], "context_dim": 2048, - "adm_in_channels": 2816 + "adm_in_channels": 2816, + "use_temporal_attention": False, } +class Segmind_Vega(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 1, 1, 2, 2], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } -models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B] +class SVD_img2vid(supported_models_base.BASE): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 768, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." + + latent_format = latent_formats.SD15 + + sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SVD_img2vid(self, device=device) + return out + + def clip_target(self): + return None + +models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega] +models += [SVD_img2vid] diff --git a/backend/headless/fcbh/supported_models_base.py b/ldm_patched/modules/supported_models_base.py similarity index 94% rename from backend/headless/fcbh/supported_models_base.py rename to ldm_patched/modules/supported_models_base.py index 3412cfea..49087d23 100644 --- a/backend/headless/fcbh/supported_models_base.py +++ b/ldm_patched/modules/supported_models_base.py @@ -22,6 +22,8 @@ class BASE: sampling_settings = {} latent_format = latent_formats.LatentFormat + manual_cast_dtype = None + @classmethod def matches(s, unet_config): for k in s.unet_config: @@ -71,3 +73,5 @@ class BASE: replace_prefix = {"": "first_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) + def set_manual_cast(self, manual_cast_dtype): + self.manual_cast_dtype = manual_cast_dtype diff --git a/backend/headless/fcbh/utils.py b/ldm_patched/modules/utils.py similarity index 94% rename from backend/headless/fcbh/utils.py rename to ldm_patched/modules/utils.py index 87e12674..f8283a86 100644 --- a/backend/headless/fcbh/utils.py +++ b/ldm_patched/modules/utils.py @@ -1,7 +1,7 @@ import torch import math import struct -import fcbh.checkpoint_pickle +import ldm_patched.modules.checkpoint_pickle import safetensors.torch import numpy as np from PIL import Image @@ -19,7 +19,7 @@ def load_torch_file(ckpt, safe_load=False, device=None): if safe_load: pl_sd = torch.load(ckpt, map_location=device, weights_only=True) else: - pl_sd = torch.load(ckpt, map_location=device, pickle_module=fcbh.checkpoint_pickle) + pl_sd = torch.load(ckpt, map_location=device, pickle_module=ldm_patched.modules.checkpoint_pickle) if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: @@ -239,6 +239,26 @@ def repeat_to_batch_size(tensor, batch_size): return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size] return tensor +def resize_to_batch_size(tensor, batch_size): + in_batch_size = tensor.shape[0] + if in_batch_size == batch_size: + return tensor + + if batch_size <= 1: + return tensor[:batch_size] + + output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device) + if batch_size < in_batch_size: + scale = (in_batch_size - 1) / (batch_size - 1) + for i in range(batch_size): + output[i] = tensor[min(round(i * scale), in_batch_size - 1)] + else: + scale = in_batch_size / batch_size + for i in range(batch_size): + output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)] + + return output + def convert_sd_to(state_dict, dtype): keys = list(state_dict.keys()) for k in keys: @@ -356,7 +376,7 @@ def lanczos(samples, width, height): images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] result = torch.stack(images) - return result + return result.to(samples.device, samples.dtype) def common_upscale(samples, width, height, upscale_method, crop): if crop == "center": @@ -385,17 +405,17 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) @torch.inference_mode() -def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None): - output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu") +def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): + output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device) for b in range(samples.shape[0]): s = samples[b:b+1] - out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu") - out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu") + out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) + out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) for y in range(0, s.shape[2], tile_y - overlap): for x in range(0, s.shape[3], tile_x - overlap): s_in = s[:,:,y:y+tile_y,x:x+tile_x] - ps = function(s_in).cpu() + ps = function(s_in).to(output_device) mask = torch.ones_like(ps) feather = round(overlap * upscale_amount) for t in range(feather): diff --git a/backend/headless/fcbh_extras/chainner_models/__init__.py b/ldm_patched/pfn/__init__.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/__init__.py rename to ldm_patched/pfn/__init__.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/DAT.py b/ldm_patched/pfn/architecture/DAT.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/DAT.py rename to ldm_patched/pfn/architecture/DAT.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/HAT.py b/ldm_patched/pfn/architecture/HAT.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/HAT.py rename to ldm_patched/pfn/architecture/HAT.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-DAT b/ldm_patched/pfn/architecture/LICENSE-DAT similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-DAT rename to ldm_patched/pfn/architecture/LICENSE-DAT diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-ESRGAN b/ldm_patched/pfn/architecture/LICENSE-ESRGAN similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-ESRGAN rename to ldm_patched/pfn/architecture/LICENSE-ESRGAN diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-HAT b/ldm_patched/pfn/architecture/LICENSE-HAT similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-HAT rename to ldm_patched/pfn/architecture/LICENSE-HAT diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-RealESRGAN b/ldm_patched/pfn/architecture/LICENSE-RealESRGAN similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-RealESRGAN rename to ldm_patched/pfn/architecture/LICENSE-RealESRGAN diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/LICENSE-SCUNet b/ldm_patched/pfn/architecture/LICENSE-SCUNet similarity index 100% rename from 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b/ldm_patched/pfn/architecture/face/restoreformer_arch.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/face/restoreformer_arch.py rename to ldm_patched/pfn/architecture/face/restoreformer_arch.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/face/stylegan2_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_arch.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/face/stylegan2_arch.py rename to ldm_patched/pfn/architecture/face/stylegan2_arch.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py rename to ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/face/stylegan2_clean_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/face/stylegan2_clean_arch.py rename to ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/face/upfirdn2d.py b/ldm_patched/pfn/architecture/face/upfirdn2d.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/face/upfirdn2d.py rename to ldm_patched/pfn/architecture/face/upfirdn2d.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/timm/LICENSE b/ldm_patched/pfn/architecture/timm/LICENSE similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/timm/LICENSE rename to ldm_patched/pfn/architecture/timm/LICENSE diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/timm/drop.py b/ldm_patched/pfn/architecture/timm/drop.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/timm/drop.py rename to ldm_patched/pfn/architecture/timm/drop.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/timm/helpers.py b/ldm_patched/pfn/architecture/timm/helpers.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/timm/helpers.py rename to ldm_patched/pfn/architecture/timm/helpers.py diff --git a/backend/headless/fcbh_extras/chainner_models/architecture/timm/weight_init.py b/ldm_patched/pfn/architecture/timm/weight_init.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/architecture/timm/weight_init.py rename to ldm_patched/pfn/architecture/timm/weight_init.py diff --git a/backend/headless/fcbh_extras/chainner_models/model_loading.py b/ldm_patched/pfn/model_loading.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/model_loading.py rename to ldm_patched/pfn/model_loading.py diff --git a/backend/headless/fcbh_extras/chainner_models/types.py b/ldm_patched/pfn/types.py similarity index 100% rename from backend/headless/fcbh_extras/chainner_models/types.py rename to ldm_patched/pfn/types.py diff --git a/backend/headless/fcbh/t2i_adapter/adapter.py b/ldm_patched/t2ia/adapter.py similarity index 100% rename from backend/headless/fcbh/t2i_adapter/adapter.py rename to ldm_patched/t2ia/adapter.py diff --git a/backend/headless/fcbh/taesd/taesd.py b/ldm_patched/taesd/taesd.py similarity index 90% rename from backend/headless/fcbh/taesd/taesd.py rename to ldm_patched/taesd/taesd.py index cb61fcc4..ac88e594 100644 --- a/backend/headless/fcbh/taesd/taesd.py +++ b/ldm_patched/taesd/taesd.py @@ -6,7 +6,7 @@ Tiny AutoEncoder for Stable Diffusion import torch import torch.nn as nn -import fcbh.utils +import ldm_patched.modules.utils def conv(n_in, n_out, **kwargs): return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs) @@ -53,9 +53,9 @@ class TAESD(nn.Module): self.taesd_decoder = Decoder() self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) if encoder_path is not None: - self.taesd_encoder.load_state_dict(fcbh.utils.load_torch_file(encoder_path, safe_load=True)) + self.taesd_encoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(encoder_path, safe_load=True)) if decoder_path is not None: - self.taesd_decoder.load_state_dict(fcbh.utils.load_torch_file(decoder_path, safe_load=True)) + self.taesd_decoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(decoder_path, safe_load=True)) @staticmethod def scale_latents(x): diff --git a/backend/headless/fcbh/extra_samplers/uni_pc.py b/ldm_patched/unipc/uni_pc.py similarity index 100% rename from backend/headless/fcbh/extra_samplers/uni_pc.py rename to ldm_patched/unipc/uni_pc.py diff --git a/backend/headless/latent_preview.py b/ldm_patched/utils/latent_visualization.py similarity index 86% rename from backend/headless/latent_preview.py rename to ldm_patched/utils/latent_visualization.py index 13f18937..a1ad403a 100644 --- a/backend/headless/latent_preview.py +++ b/ldm_patched/utils/latent_visualization.py @@ -2,10 +2,10 @@ import torch from PIL import Image import struct import numpy as np -from fcbh.cli_args import args, LatentPreviewMethod -from fcbh.taesd.taesd import TAESD -import folder_paths -import fcbh.utils +from ldm_patched.modules.args_parser import args, LatentPreviewMethod +from ldm_patched.taesd.taesd import TAESD +import ldm_patched.utils.path_utils +import ldm_patched.modules.utils MAX_PREVIEW_RESOLUTION = 512 @@ -48,17 +48,17 @@ class Latent2RGBPreviewer(LatentPreviewer): def get_previewer(device, latent_format): previewer = None - method = args.preview_method + method = args.preview_option if method != LatentPreviewMethod.NoPreviews: # TODO previewer methods taesd_decoder_path = None if latent_format.taesd_decoder_name is not None: taesd_decoder_path = next( - (fn for fn in folder_paths.get_filename_list("vae_approx") + (fn for fn in ldm_patched.utils.path_utils.get_filename_list("vae_approx") if fn.startswith(latent_format.taesd_decoder_name)), "" ) - taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path) + taesd_decoder_path = ldm_patched.utils.path_utils.get_full_path("vae_approx", taesd_decoder_path) if method == LatentPreviewMethod.Auto: method = LatentPreviewMethod.Latent2RGB @@ -84,7 +84,7 @@ def prepare_callback(model, steps, x0_output_dict=None): previewer = get_previewer(model.load_device, model.model.latent_format) - pbar = fcbh.utils.ProgressBar(steps) + pbar = ldm_patched.modules.utils.ProgressBar(steps) def callback(step, x0, x, total_steps): if x0_output_dict is not None: x0_output_dict["x0"] = x0 diff --git a/backend/headless/folder_paths.py b/ldm_patched/utils/path_utils.py similarity index 92% rename from backend/headless/folder_paths.py rename to ldm_patched/utils/path_utils.py index 2293045b..34cd52c9 100644 --- a/backend/headless/folder_paths.py +++ b/ldm_patched/utils/path_utils.py @@ -5,7 +5,7 @@ supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors']) folder_names_and_paths = {} -base_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) +base_path = os.getcwd() models_dir = os.path.join(base_path, "models") folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) @@ -31,14 +31,17 @@ folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetwo folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) -output_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))), "output") -temp_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))), "temp") -input_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))), "input") +output_directory = os.path.join(os.getcwd(), "output") +temp_directory = os.path.join(os.getcwd(), "temp") +input_directory = os.path.join(os.getcwd(), "input") filename_list_cache = {} if not os.path.exists(input_directory): - os.makedirs(input_directory) + try: + pass # os.makedirs(input_directory) + except: + print("Failed to create input directory") def set_output_directory(output_dir): global output_directory @@ -228,8 +231,12 @@ def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height full_output_folder = os.path.join(output_dir, subfolder) if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: - print("Saving image outside the output folder is not allowed.") - return {} + err = "**** ERROR: Saving image outside the output folder is not allowed." + \ + "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ + "\n output_dir: " + output_dir + \ + "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) + print(err) + raise Exception(err) try: counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 diff --git a/models/clip_vision/wd-v1-4-moat-tagger-v2.csv b/models/clip_vision/wd-v1-4-moat-tagger-v2.csv new file mode 100644 index 00000000..71796801 --- /dev/null +++ b/models/clip_vision/wd-v1-4-moat-tagger-v2.csv @@ -0,0 +1,9084 @@ +tag_id,name,category,count +9999999,general,9,807858 +9999998,sensitive,9,3771700 +9999997,questionable,9,769899 +9999996,explicit,9,560281 +470575,1girl,0,4225150 +212816,solo,0,3515897 +13197,long_hair,0,2982517 +8601,breasts,0,2323580 +469576,looking_at_viewer,0,2089971 +3389,blush,0,2040471 +1815,smile,0,1903619 +15080,short_hair,0,1568265 +11906,open_mouth,0,1565950 +16751,bangs,0,1516840 +10959,blue_eyes,0,1225129 +566835,multiple_girls,0,1120328 +429,skirt,0,1100620 +87788,blonde_hair,0,1098200 +403247,large_breasts,0,1083979 +412368,simple_background,0,1074818 +16867,brown_hair,0,1072209 +12590,shirt,0,1001030 +13200,black_hair,0,981413 +380350,hair_ornament,0,939495 +8526,red_eyes,0,897316 +1882,thighhighs,0,890813 +5735,gloves,0,886283 +383159,long_sleeves,0,883900 +540830,1boy,0,881194 +2373,hat,0,879697 +515193,white_background,0,874291 +2241,dress,0,838290 +4563,bow,0,795194 +464575,ribbon,0,793922 +9294,navel,0,786293 +375387,holding,0,732899 +1821,2girls,0,729721 +6126,animal_ears,0,722334 +4607,cleavage,0,693321 +658573,hair_between_eyes,0,692014 +376054,bare_shoulders,0,656975 +1709,twintails,0,648384 +16578,brown_eyes,0,645874 +16613,jewelry,0,644654 +667868,medium_breasts,0,642525 +12289,sitting,0,630993 +417660,very_long_hair,0,622920 +572080,closed_mouth,0,618062 +464906,underwear,0,610036 +8889,nipples,0,591774 +16509,school_uniform,0,585679 +10960,green_eyes,0,584783 +10953,blue_hair,0,564360 +15675,standing,0,551783 +15654,purple_eyes,0,536623 +466499,collarbone,0,520875 +391,panties,0,506334 +3843,jacket,0,493731 +15674,tail,0,487490 +1681,monochrome,0,478584 +444,swimsuit,0,467619 +608813,full_body,0,463008 +465619,closed_eyes,0,455512 +464561,hair_ribbon,0,449452 +89189,yellow_eyes,0,447582 +376766,white_shirt,0,435867 +547463,upper_body,0,434670 +2355,ponytail,0,431021 +11449,weapon,0,430315 +11429,pink_hair,0,427100 +16442,purple_hair,0,426385 +8101,ass,0,423113 +4334,braid,0,417832 +464559,flower,0,411874 +63,comic,0,411524 +3522,ahoge,0,408654 +16581,white_hair,0,407226 +472154,short_sleeves,0,389311 +384553,:d,0,387533 +622137,hetero,0,384576 +374844,hair_bow,0,381335 +513837,greyscale,0,377967 +16580,grey_hair,0,375103 +1300281,male_focus,0,371503 +2750,heart,0,361897 +2363,pantyhose,0,356177 +484168,sidelocks,0,354421 +6539,bikini,0,349809 +3870,thighs,0,348316 +2365,nude,0,341086 +5403,red_hair,0,338682 +390728,multicolored_hair,0,336863 +660909,cowboy_shot,0,336374 +4569,sweat,0,334084 +383282,pleated_skirt,0,332323 +2376,hairband,0,329485 +13804,earrings,0,328755 +465265,small_breasts,0,325542 +5827,boots,0,320845 +13879,outdoors,0,320641 +301022,lying,0,312125 +4352,censored,0,310189 +194013,frills,0,305699 +664375,parted_lips,0,304757 +387884,detached_sleeves,0,297463 +461042,one_eye_closed,0,294831 +1575,food,0,294463 +1707,japanese_clothes,0,289163 +8388,green_hair,0,286703 +568656,multiple_boys,0,286561 +375669,open_clothes,0,286373 +2866,wings,0,284183 +384774,necktie,0,281254 +2785,horns,0,279479 +406,sky,0,279456 +4190,penis,0,276603 +8672,shoes,0,273266 +6532,glasses,0,264431 +3985,shorts,0,263445 +11826,barefoot,0,260159 +6054,teeth,0,259880 +4378,pussy,0,254400 +268819,serafuku,0,250901 +400314,sleeveless,0,247486 +931006,solo_focus,0,246572 +431446,alternate_costume,0,245996 +4025,choker,0,245304 +435324,day,0,243849 +10863,tongue,0,243841 +15522,pointy_ears,0,237263 +474820,black_gloves,0,236325 +1731,socks,0,235479 +2646,hairclip,0,224901 +10228,elbow_gloves,0,224390 +1793,fang,0,220644 +9843,midriff,0,217384 +2177,striped,0,215308 +581144,puffy_sleeves,0,214965 +2772,shiny,0,213040 +478849,collared_shirt,0,210998 +9864,belt,0,210075 +410129,looking_back,0,208023 +10926,pants,0,206605 +401228,sword,0,203029 +657955,artist_name,0,200583 +455880,black_thighhighs,0,199445 +464549,cloud,0,197453 +375171,indoors,0,197331 +1407561,virtual_youtuber,0,190790 +72,cat_ears,0,189097 +6010,tears,0,187990 +474821,white_gloves,0,187659 +539367,hand_up,0,184840 +411263,signature,0,183546 +400123,hair_flower,0,183132 +71730,dark_skin,0,182935 +399836,3girls,0,181979 +16252,spread_legs,0,179394 +1842,cum,0,177804 +5948,hood,0,176872 +449194,2boys,0,176291 +2357,sex,0,176106 +502710,tongue_out,0,174092 +9168,miniskirt,0,173815 +394970,wide_sleeves,0,172751 +537684,blunt_bangs,0,172693 +401481,on_back,0,172347 +375020,fingerless_gloves,0,171771 +11628,bowtie,0,171509 +374628,black_skirt,0,171449 +426781,medium_hair,0,170863 +16750,pink_eyes,0,168674 +5576,armpits,0,168191 +389378,sailor_collar,0,166004 +445,kimono,0,163145 +632214,grey_background,0,159811 +1573,water,0,158702 +4068,necklace,0,156154 +375229,off_shoulder,0,153621 +467104,stomach,0,153138 +4333,bag,0,151509 +454933,hair_bun,0,151363 +1747,chibi,0,151305 +473983,clothes_lift,0,150801 +1254363,twitter_username,0,150476 +234192,from_behind,0,150284 +656624,star_(symbol),0,150164 +3198,scarf,0,149539 +6346,cape,0,149374 +102962,nail_polish,0,148880 +659098,black_footwear,0,148127 +670071,holding_weapon,0,147624 +3796,bra,0,147612 +14620,white_dress,0,147542 +87676,orange_hair,0,147142 +261,yuri,0,146254 +125238,sweatdrop,0,145506 +4659,armor,0,143723 +465583,rabbit_ears,0,143630 +4019,mole,0,143178 +374791,white_panties,0,142500 +399541,hair_over_one_eye,0,141370 +384884,grin,0,140525 +6107,blurry,0,140047 +393959,huge_breasts,0,138103 +641577,looking_at_another,0,137202 +14599,:o,0,137129 +4152,uniform,0,136675 +13199,black_eyes,0,136390 +273,apron,0,136126 +510962,character_name,0,135131 +6176,vest,0,133100 +39127,black_dress,0,132376 +682673,mosaic_censoring,0,131028 +475187,arm_up,0,130488 +10484,vaginal,0,130137 +470807,red_bow,0,129853 +9882,high_heels,0,129652 +524070,shiny_hair,0,128887 +52138,twin_braids,0,127920 +399827,arms_up,0,126645 +378032,flat_chest,0,126144 +381629,side_ponytail,0,126049 +4052,collar,0,125695 +11904,bracelet,0,125557 +2060,feet,0,125245 +515329,covered_nipples,0,124425 +511141,from_side,0,122971 +560958,dated,0,122085 +413564,two-tone_hair,0,121190 +89368,aqua_eyes,0,120894 +3477,sweater,0,120879 +440465,speech_bubble,0,120449 +375002,white_thighhighs,0,119709 +590165,english_text,0,118831 +6295,leotard,0,118770 +379615,open_jacket,0,116898 +1515536,official_alternate_costume,0,116756 +390401,red_ribbon,0,116423 +633894,dark-skinned_female,0,115383 +652604,two_side_up,0,114391 +464586,tree,0,113682 +464553,cup,0,113550 +490655,blue_sky,0,113376 +1931,sketch,0,112175 +684639,puffy_short_sleeves,0,111946 +5501,lips,0,111075 +428173,blue_skirt,0,110413 +10644,zettai_ryouiki,0,109096 +719985,streaked_hair,0,108228 +5569,coat,0,108099 +547860,black_jacket,0,107631 +395321,crop_top,0,107314 +754325,groin,0,107206 +427008,fingernails,0,106798 +4867,wet,0,105408 +1445905,v-shaped_eyebrows,0,104747 +389404,cat_tail,0,104720 +438623,neckerchief,0,103697 +95405,orange_eyes,0,103149 +1486996,animal_ear_fluff,0,102828 +431755,head_tilt,0,102674 +451371,see-through,0,102344 +391631,gradient,0,101686 +451155,hand_on_hip,0,100931 +589,gun,0,100672 +4311,legs,0,100492 +494251,one-piece_swimsuit,0,100304 +494744,shiny_skin,0,100275 +479939,sleeves_past_wrists,0,100013 +551772,parted_bangs,0,99655 +579466,looking_to_the_side,0,99629 +464572,pillow,0,99176 +412078,wrist_cuffs,0,98658 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+1797173,hyakumantenbara_salome,4,683 +1619673,puru-see_(hoshizuki_(seigetsu)),4,682 +1152270,ibuki_tsubasa,4,681 +1227600,mochizuki_anna,4,681 +1275005,ousaka_shizuku,4,681 +1782428,bb_(swimsuit_mooncancer)_(third_ascension)_(fate),4,679 +1693037,le_malin_(listless_lapin)_(azur_lane),4,679 +1768854,mysterious_heroine_x_alter_(first_ascension)_(fate),4,678 +1631348,shinshuu_maru_(kancolle),4,678 +480982,asbel_lhant,4,677 +1316228,nakagawa_natsuki,4,677 +1667164,g41_(girls'_frontline),4,677 +404920,sage_(dq3),4,676 +1331206,kawakami_mai,4,676 +1423942,eris_greyrat,4,676 +1560517,utage_(arknights),4,676 +1405059,sand_cat_(kemono_friends),4,675 +1680295,mika_(blue_archive),4,675 +664407,himekawa_yuki,4,674 +1631118,kishinami_(kancolle),4,674 +1285963,alena_(dq4),4,672 +1645008,smart_falcon_(umamusume),4,672 +497689,cheria_barnes,4,671 +1631097,kasuga_maru_(kancolle),4,671 +1432300,yatadera_narumi,4,669 +1387122,mordred_(swimsuit_rider)_(fate),4,667 +1529378,sakata_kintoki_(fate),4,666 +1408392,golden_snub-nosed_monkey_(kemono_friends),4,666 +1780213,hina_(swimsuit)_(blue_archive),4,666 +1875916,tiki_(adult)_(fire_emblem),4,665 +1386436,amami_rantarou,4,664 +548087,hasegawa_kobato,4,663 +1257848,hoshimiya_kate,4,663 +1335159,sugimoto_saichi,4,663 +596743,fukawa_touko,4,662 +1476820,yozora_mel,4,662 +1789595,saijo_juri,4,661 +1327917,yuel_(granblue_fantasy),4,660 +1460549,nekomiya_hinata,4,660 +688953,igarashi_kyoko,4,658 +1639090,maya_kai_ni_(kancolle),4,658 +1588338,tomoe_gozen_(swimsuit_saber)_(fate),4,658 +1657407,blue_poison_(shoal_beat)_(arknights),4,658 +1856336,hasumi_(gym_uniform)_(blue_archive),4,656 +1598223,kayoko_(blue_archive),4,655 +1434774,uehara_himari,4,654 +1344209,roxy_migurdia,4,653 +1837736,saren_(summer)_(princess_connect!),4,653 +1485459,jean_bart_(azur_lane),4,653 +713347,katagiri_sanae,4,652 +1440285,queen_elizabeth_(azur_lane),4,652 +1667205,9a-91_(girls'_frontline),4,651 +1479271,rengoku_kyoujurou,4,650 +1386290,keebo,4,649 +1599618,whislash_(arknights),4,646 +971934,vampy,4,645 +1639099,naganami_kai_ni_(kancolle),4,644 +1326283,jeanne_d'arc_(granblue_fantasy),4,643 +1644991,matikanefukukitaru_(umamusume),4,643 +1645010,sweep_tosho_(umamusume),4,642 +879608,toyokawa_fuka,4,641 +1387119,kiyohime_(swimsuit_lancer)_(fate),4,641 +1836895,karyl_(summer)_(princess_connect!),4,641 +1596254,razor_(genshin_impact),4,640 +1480768,siro_(dennou_shoujo_youtuber_siro),4,639 +1491126,tsuyuri_kanao,4,637 +1667169,negev_(girls'_frontline),4,636 +1440310,eldridge_(azur_lane),4,636 +1284942,midway_princess,4,635 +1420829,matsubara_kanon,4,633 +1541801,magallan_(arknights),4,633 +1782915,ijichi_nijika,4,632 +1411715,okusawa_misaki,4,628 +1875918,tiki_(young)_(fire_emblem),4,627 +1790615,spicy_nun_(diva),4,625 +1067206,hayasaka_mirei,4,623 +1781831,jeanne_d'arc_(swimsuit_archer)_(second_ascension)_(fate),4,623 +1420746,udagawa_tomoe,4,621 +1445024,shirasagi_chisato,4,621 +1678878,la_pluma_(arknights),4,619 +1265247,sato_shin,4,617 +1491428,takamiya_rion,4,612 +1623487,crypto_(apex_legends),4,612 +1533772,uzuki_sayaka,4,607 +1533771,kumada_masaru,4,600 diff --git a/modules/async_worker.py b/modules/async_worker.py index f78d04e1..a3cbe2cd 100644 --- a/modules/async_worker.py +++ b/modules/async_worker.py @@ -27,17 +27,17 @@ def worker(): import modules.flags as flags import modules.config import modules.patch - import fcbh.model_management - import fooocus_extras.preprocessors as preprocessors + import ldm_patched.modules.model_management + import extras.preprocessors as preprocessors import modules.inpaint_worker as inpaint_worker import modules.constants as constants import modules.advanced_parameters as advanced_parameters - import fooocus_extras.ip_adapter as ip_adapter - import fooocus_extras.face_crop + import extras.ip_adapter as ip_adapter + import extras.face_crop from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion from modules.private_logger import log - from modules.expansion import safe_str + from extras.expansion import safe_str from modules.util import remove_empty_str, HWC3, resize_image, \ get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image from modules.upscaler import perform_upscale @@ -657,7 +657,7 @@ def worker(): cn_img = HWC3(cn_img) if not advanced_parameters.skipping_cn_preprocessor: - cn_img = fooocus_extras.face_crop.crop_image(cn_img) + cn_img = extras.face_crop.crop_image(cn_img) # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) @@ -788,7 +788,7 @@ def worker(): log(x, d, single_line_number=3) yield_result(async_task, imgs, do_not_show_finished_images=len(tasks) == 1) - except fcbh.model_management.InterruptProcessingException as e: + except ldm_patched.modules.model_management.InterruptProcessingException as e: if shared.last_stop == 'skip': print('User skipped') continue diff --git a/modules/core.py b/modules/core.py index e0fcd13f..86c56b5c 100644 --- a/modules/core.py +++ b/modules/core.py @@ -8,26 +8,25 @@ import einops import torch import numpy as np -import fcbh.model_management -import fcbh.model_detection -import fcbh.model_patcher -import fcbh.utils -import fcbh.controlnet +import ldm_patched.modules.model_management +import ldm_patched.modules.model_detection +import ldm_patched.modules.model_patcher +import ldm_patched.modules.utils +import ldm_patched.modules.controlnet import modules.sample_hijack -import fcbh.samplers -import fcbh.latent_formats +import ldm_patched.modules.samplers +import ldm_patched.modules.latent_formats import modules.advanced_parameters -from fcbh.sd import load_checkpoint_guess_config -from nodes import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \ +from ldm_patched.modules.sd import load_checkpoint_guess_config +from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \ ControlNetApplyAdvanced -from fcbh_extras.nodes_freelunch import FreeU_V2 -from fcbh.sample import prepare_mask -from modules.patch import patched_sampler_cfg_function +from ldm_patched.contrib.external_freelunch import FreeU_V2 +from ldm_patched.modules.sample import prepare_mask from modules.lora import match_lora -from fcbh.lora import model_lora_keys_unet, model_lora_keys_clip +from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip from modules.config import path_embeddings -from fcbh_extras.nodes_model_advanced import ModelSamplingDiscrete +from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete opEmptyLatentImage = EmptyLatentImage() @@ -98,7 +97,7 @@ class StableDiffusionModel: self.clip_with_lora = self.clip.clone() if self.clip is not None else None for lora_filename, weight in loras_to_load: - lora_unmatch = fcbh.utils.load_torch_file(lora_filename, safe_load=False) + lora_unmatch = ldm_patched.modules.utils.load_torch_file(lora_filename, safe_load=False) lora_unet, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_unet) lora_clip, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_clip) @@ -136,7 +135,7 @@ def apply_freeu(model, b1, b2, s1, s2): @torch.no_grad() @torch.inference_mode() def load_controlnet(ckpt_filename): - return fcbh.controlnet.load_controlnet(ckpt_filename) + return ldm_patched.modules.controlnet.load_controlnet(ckpt_filename) @torch.no_grad() @@ -150,7 +149,6 @@ def apply_controlnet(positive, negative, control_net, image, strength, start_per @torch.inference_mode() def load_model(ckpt_filename): unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings) - unet.model_options['sampler_cfg_function'] = patched_sampler_cfg_function return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename) @@ -230,7 +228,7 @@ def get_previewer(model): global VAE_approx_models from modules.config import path_vae_approx - is_sdxl = isinstance(model.model.latent_format, fcbh.latent_formats.SDXL) + is_sdxl = isinstance(model.model.latent_format, ldm_patched.modules.latent_formats.SDXL) vae_approx_filename = os.path.join(path_vae_approx, 'xlvaeapp.pth' if is_sdxl else 'vaeapp_sd15.pth') if vae_approx_filename in VAE_approx_models: @@ -242,14 +240,14 @@ def get_previewer(model): del sd VAE_approx_model.eval() - if fcbh.model_management.should_use_fp16(): + if ldm_patched.modules.model_management.should_use_fp16(): VAE_approx_model.half() VAE_approx_model.current_type = torch.float16 else: VAE_approx_model.float() VAE_approx_model.current_type = torch.float32 - VAE_approx_model.to(fcbh.model_management.get_torch_device()) + VAE_approx_model.to(ldm_patched.modules.model_management.get_torch_device()) VAE_approx_models[vae_approx_filename] = VAE_approx_model @torch.no_grad() @@ -273,7 +271,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None): if sigmas is not None: - sigmas = sigmas.clone().to(fcbh.model_management.get_torch_device()) + sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device()) latent_image = latent["samples"] @@ -281,7 +279,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None - noise = fcbh.sample.prepare_noise(latent_image, seed, batch_inds) + noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds) if isinstance(noise_mean, torch.Tensor): noise = noise + noise_mean - torch.mean(noise, dim=1, keepdim=True) @@ -299,7 +297,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa previewer_end = steps def callback(step, x0, x, total_steps): - fcbh.model_management.throw_exception_if_processing_interrupted() + ldm_patched.modules.model_management.throw_exception_if_processing_interrupted() y = None if previewer is not None and not modules.advanced_parameters.disable_preview: y = previewer(x0, previewer_start + step, previewer_end) @@ -309,14 +307,18 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa disable_pbar = False modules.sample_hijack.current_refiner = refiner modules.sample_hijack.refiner_switch_step = refiner_switch - fcbh.samplers.sample = modules.sample_hijack.sample_hacked + ldm_patched.modules.samplers.sample = modules.sample_hijack.sample_hacked try: - samples = fcbh.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, - denoise=denoise, disable_noise=disable_noise, start_step=start_step, - last_step=last_step, - force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, - disable_pbar=disable_pbar, seed=seed, sigmas=sigmas) + samples = ldm_patched.modules.sample.sample(model, + noise, steps, cfg, sampler_name, scheduler, + positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, + start_step=start_step, + last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, + callback=callback, + disable_pbar=disable_pbar, seed=seed, sigmas=sigmas) out = latent.copy() out["samples"] = samples diff --git a/modules/default_pipeline.py b/modules/default_pipeline.py index 8d5ef2fb..6001d97f 100644 --- a/modules/default_pipeline.py +++ b/modules/default_pipeline.py @@ -3,13 +3,13 @@ import os import torch import modules.patch import modules.config -import fcbh.model_management -import fcbh.latent_formats +import ldm_patched.modules.model_management +import ldm_patched.modules.latent_formats import modules.inpaint_worker -import fooocus_extras.vae_interpose as vae_interpose +import extras.vae_interpose as vae_interpose +from extras.expansion import FooocusExpansion -from fcbh.model_base import SDXL, SDXLRefiner -from modules.expansion import FooocusExpansion +from ldm_patched.modules.model_base import SDXL, SDXLRefiner from modules.sample_hijack import clip_separate @@ -208,7 +208,7 @@ def prepare_text_encoder(async_call=True): # TODO: make sure that this is always called in an async way so that users cannot feel it. pass assert_model_integrity() - fcbh.model_management.load_models_gpu([final_clip.patcher, final_expansion.patcher]) + ldm_patched.modules.model_management.load_models_gpu([final_clip.patcher, final_expansion.patcher]) return @@ -270,7 +270,7 @@ def vae_parse(latent): @torch.no_grad() @torch.inference_mode() def calculate_sigmas_all(sampler, model, scheduler, steps): - from fcbh.samplers import calculate_sigmas_scheduler + from ldm_patched.modules.samplers import calculate_sigmas_scheduler discard_penultimate_sigma = False if sampler in ['dpm_2', 'dpm_2_ancestral']: @@ -352,7 +352,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height print(f'[Sampler] sigma_min = {sigma_min}, sigma_max = {sigma_max}') modules.patch.BrownianTreeNoiseSamplerPatched.global_init( - initial_latent['samples'].to(fcbh.model_management.get_torch_device()), + initial_latent['samples'].to(ldm_patched.modules.model_management.get_torch_device()), sigma_min, sigma_max, seed=image_seed, cpu=False) decoded_latent = None diff --git a/modules/flags.py b/modules/flags.py index 2e1c0992..27f2d716 100644 --- a/modules/flags.py +++ b/modules/flags.py @@ -14,7 +14,7 @@ KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"] -SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm"] +SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"] SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] sampler_list = SAMPLER_NAMES @@ -39,3 +39,6 @@ inpaint_option_default = 'Inpaint or Outpaint (default)' inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)' inpaint_option_modify = 'Modify Content (add objects, change background, etc.)' inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option_modify] + +desc_type_photo = 'Photograph' +desc_type_anime = 'Art/Anime' diff --git a/modules/lora.py b/modules/lora.py index 9eca0ed2..088545c7 100644 --- a/modules/lora.py +++ b/modules/lora.py @@ -4,7 +4,7 @@ def match_lora(lora, to_load): for x in to_load: real_load_key = to_load[x] if real_load_key in lora: - patch_dict[real_load_key] = lora[real_load_key] + patch_dict[real_load_key] = ('fooocus', lora[real_load_key]) loaded_keys.add(real_load_key) continue @@ -37,7 +37,7 @@ def match_lora(lora, to_load): if mid_name is not None and mid_name in lora.keys(): mid = lora[mid_name] loaded_keys.add(mid_name) - patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid) + patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid)) loaded_keys.add(A_name) loaded_keys.add(B_name) @@ -58,7 +58,7 @@ def match_lora(lora, to_load): loaded_keys.add(hada_t1_name) loaded_keys.add(hada_t2_name) - patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2) + patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)) loaded_keys.add(hada_w1_a_name) loaded_keys.add(hada_w1_b_name) loaded_keys.add(hada_w2_a_name) @@ -110,7 +110,19 @@ def match_lora(lora, to_load): loaded_keys.add(lokr_t2_name) if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): - patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2) + patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)) + + #glora + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha)) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) w_norm_name = "{}.w_norm".format(x) b_norm_name = "{}.b_norm".format(x) @@ -119,21 +131,21 @@ def match_lora(lora, to_load): if w_norm is not None: loaded_keys.add(w_norm_name) - patch_dict[to_load[x]] = (w_norm,) + patch_dict[to_load[x]] = ("diff", (w_norm,)) if b_norm is not None: loaded_keys.add(b_norm_name) - patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = (b_norm,) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) diff_name = "{}.diff".format(x) diff_weight = lora.get(diff_name, None) if diff_weight is not None: - patch_dict[to_load[x]] = (diff_weight,) + patch_dict[to_load[x]] = ("diff", (diff_weight,)) loaded_keys.add(diff_name) diff_bias_name = "{}.diff_b".format(x) diff_bias = lora.get(diff_bias_name, None) if diff_bias is not None: - patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = (diff_bias,) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) loaded_keys.add(diff_bias_name) remaining_dict = {x: y for x, y in lora.items() if x not in loaded_keys} diff --git a/modules/patch.py b/modules/patch.py index a65e171f..c6012dfd 100644 --- a/modules/patch.py +++ b/modules/patch.py @@ -1,33 +1,30 @@ import os import torch -import math import time -import numpy as np -import fcbh.model_base -import fcbh.ldm.modules.diffusionmodules.openaimodel -import fcbh.samplers -import fcbh.model_management +import math +import ldm_patched.modules.model_base +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.modules.model_management import modules.anisotropic as anisotropic -import fcbh.ldm.modules.attention -import fcbh.k_diffusion.sampling -import fcbh.sd1_clip +import ldm_patched.ldm.modules.attention +import ldm_patched.k_diffusion.sampling +import ldm_patched.modules.sd1_clip import modules.inpaint_worker as inpaint_worker -import fcbh.ldm.modules.diffusionmodules.openaimodel -import fcbh.ldm.modules.diffusionmodules.model -import fcbh.sd -import fcbh.cldm.cldm -import fcbh.model_patcher -import fcbh.samplers -import fcbh.cli_args +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.ldm.modules.diffusionmodules.model +import ldm_patched.modules.sd +import ldm_patched.controlnet.cldm +import ldm_patched.modules.model_patcher +import ldm_patched.modules.samplers +import ldm_patched.modules.args_parser import modules.advanced_parameters as advanced_parameters import warnings import safetensors.torch import modules.constants as constants -from einops import repeat -from fcbh.k_diffusion.sampling import BatchedBrownianTree -from fcbh.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control -from fcbh.ldm.modules.diffusionmodules.util import make_beta_schedule +from ldm_patched.modules.samplers import calc_cond_uncond_batch +from ldm_patched.k_diffusion.sampling import BatchedBrownianTree +from ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control sharpness = 2.0 @@ -54,31 +51,25 @@ def calculate_weight_patched(self, patches, weight, key): v = (self.calculate_weight(v[1:], v[0].clone(), key),) if len(v) == 1: + patch_type = "diff" + elif len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "diff": w1 = v[0] if alpha != 0.0: if w1.shape != weight.shape: print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: - weight += alpha * fcbh.model_management.cast_to_device(w1, weight.device, weight.dtype) - elif len(v) == 3: - # fooocus - w1 = fcbh.model_management.cast_to_device(v[0], weight.device, torch.float32) - w_min = fcbh.model_management.cast_to_device(v[1], weight.device, torch.float32) - w_max = fcbh.model_management.cast_to_device(v[2], weight.device, torch.float32) - w1 = (w1 / 255.0) * (w_max - w_min) + w_min - if alpha != 0.0: - if w1.shape != weight.shape: - print("WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) - else: - weight += alpha * fcbh.model_management.cast_to_device(w1, weight.device, weight.dtype) - elif len(v) == 4: # lora/locon - mat1 = fcbh.model_management.cast_to_device(v[0], weight.device, torch.float32) - mat2 = fcbh.model_management.cast_to_device(v[1], weight.device, torch.float32) + weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif patch_type == "lora": + mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) + mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) if v[2] is not None: alpha *= v[2] / mat2.shape[0] if v[3] is not None: - # locon mid weights, hopefully the math is fine because I didn't properly test it - mat3 = fcbh.model_management.cast_to_device(v[3], weight.device, torch.float32) + mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32) final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) @@ -87,7 +78,17 @@ def calculate_weight_patched(self, patches, weight, key): weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) - elif len(v) == 8: # lokr + elif patch_type == "fooocus": + w1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) + w_min = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) + w_max = ldm_patched.modules.model_management.cast_to_device(v[2], weight.device, torch.float32) + w1 = (w1 / 255.0) * (w_max - w_min) + w_min + if alpha != 0.0: + if w1.shape != weight.shape: + print("WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + else: + weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif patch_type == "lokr": w1 = v[0] w2 = v[1] w1_a = v[3] @@ -99,23 +100,23 @@ def calculate_weight_patched(self, patches, weight, key): if w1 is None: dim = w1_b.shape[0] - w1 = torch.mm(fcbh.model_management.cast_to_device(w1_a, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w1_b, weight.device, torch.float32)) + w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32)) else: - w1 = fcbh.model_management.cast_to_device(w1, weight.device, torch.float32) + w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32) if w2 is None: dim = w2_b.shape[0] if t2 is None: - w2 = torch.mm(fcbh.model_management.cast_to_device(w2_a, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2_b, weight.device, torch.float32)) + w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32)) else: w2 = torch.einsum('i j k l, j r, i p -> p r k l', - fcbh.model_management.cast_to_device(t2, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2_b, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2_a, weight.device, torch.float32)) + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32)) else: - w2 = fcbh.model_management.cast_to_device(w2, weight.device, torch.float32) + w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32) if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) @@ -126,7 +127,7 @@ def calculate_weight_patched(self, patches, weight, key): weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) - else: # loha + elif patch_type == "loha": w1a = v[0] w1b = v[1] if v[2] is not None: @@ -137,24 +138,36 @@ def calculate_weight_patched(self, patches, weight, key): t1 = v[5] t2 = v[6] m1 = torch.einsum('i j k l, j r, i p -> p r k l', - fcbh.model_management.cast_to_device(t1, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w1b, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w1a, weight.device, torch.float32)) + ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32)) m2 = torch.einsum('i j k l, j r, i p -> p r k l', - fcbh.model_management.cast_to_device(t2, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2b, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2a, weight.device, torch.float32)) + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32)) else: - m1 = torch.mm(fcbh.model_management.cast_to_device(w1a, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w1b, weight.device, torch.float32)) - m2 = torch.mm(fcbh.model_management.cast_to_device(w2a, weight.device, torch.float32), - fcbh.model_management.cast_to_device(w2b, weight.device, torch.float32)) + m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32)) + m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32)) try: weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) + elif patch_type == "glora": + if v[4] is not None: + alpha *= v[4] / v[0].shape[0] + + a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) + a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) + b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) + b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) + + weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + else: + print("patch type not recognized", patch_type, key) return weight @@ -162,19 +175,17 @@ def calculate_weight_patched(self, patches, weight, key): class BrownianTreeNoiseSamplerPatched: transform = None tree = None - global_sigma_min = 1.0 - global_sigma_max = 1.0 @staticmethod def global_init(x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): + if ldm_patched.modules.model_management.directml_enabled: + cpu = True + t0, t1 = transform(torch.as_tensor(sigma_min)), transform(torch.as_tensor(sigma_max)) BrownianTreeNoiseSamplerPatched.transform = transform BrownianTreeNoiseSamplerPatched.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) - BrownianTreeNoiseSamplerPatched.global_sigma_min = sigma_min - BrownianTreeNoiseSamplerPatched.global_sigma_max = sigma_max - def __init__(self, *args, **kwargs): pass @@ -202,34 +213,47 @@ def compute_cfg(uncond, cond, cfg_scale, t): return real_eps -def patched_sampler_cfg_function(args): +def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None): + if math.isclose(cond_scale, 1.0): + return calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0] + global eps_record - positive_eps = args['cond'] - negative_eps = args['uncond'] - cfg_scale = args['cond_scale'] - positive_x0 = args['input'] - positive_eps - sigma = args['sigma'] + positive_x0, negative_x0 = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options) + + positive_eps = x - positive_x0 + negative_eps = x - negative_x0 + sigma = timestep alpha = 0.001 * sharpness * global_diffusion_progress + positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0) positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha) final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted, - cfg_scale=cfg_scale, t=global_diffusion_progress) + cfg_scale=cond_scale, t=global_diffusion_progress) if eps_record is not None: eps_record = (final_eps / sigma).cpu() - return final_eps + return x - final_eps + + +def round_to_64(x): + h = float(x) + h = h / 64.0 + h = round(h) + h = int(h) + h = h * 64 + return h def sdxl_encode_adm_patched(self, **kwargs): global positive_adm_scale, negative_adm_scale - clip_pooled = fcbh.model_base.sdxl_pooled(kwargs, self.noise_augmentor) - width = kwargs.get("width", 768) - height = kwargs.get("height", 768) + clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 1024) + height = kwargs.get("height", 1024) target_width = width target_height = height @@ -240,25 +264,21 @@ def sdxl_encode_adm_patched(self, **kwargs): width = float(width) * positive_adm_scale height = float(height) * positive_adm_scale - # Avoid artifacts - width = int(width) - height = int(height) - crop_w = 0 - crop_h = 0 - target_width = int(target_width) - target_height = int(target_height) + def embedder(number_list): + h = [self.embedder(torch.Tensor([number])) for number in number_list] + y = torch.flatten(torch.cat(h)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return y - out_a = [self.embedder(torch.Tensor([height])), self.embedder(torch.Tensor([width])), - self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])), - self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width]))] - flat_a = torch.flatten(torch.cat(out_a)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + width, height = round_to_64(width), round_to_64(height) + target_width, target_height = round_to_64(target_width), round_to_64(target_height) - out_b = [self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width])), - self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])), - self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width]))] - flat_b = torch.flatten(torch.cat(out_b)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + adm_emphasized = embedder([height, width, 0, 0, target_height, target_width]) + adm_consistent = embedder([target_height, target_width, 0, 0, target_height, target_width]) - return torch.cat((clip_pooled.to(flat_a.device), flat_a, clip_pooled.to(flat_b.device), flat_b), dim=1) + clip_pooled = clip_pooled.to(adm_emphasized) + final_adm = torch.cat((clip_pooled, adm_emphasized, clip_pooled, adm_consistent), dim=1) + + return final_adm def encode_token_weights_patched_with_a1111_method(self, token_weight_pairs): @@ -273,11 +293,11 @@ def encode_token_weights_patched_with_a1111_method(self, token_weight_pairs): sections = len(to_encode) if has_weights or sections == 0: - to_encode.append(fcbh.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len)) + to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len)) out, pooled = self.encode(to_encode) if pooled is not None: - first_pooled = pooled[0:1].cpu() + first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device()) else: first_pooled = pooled @@ -297,9 +317,8 @@ def encode_token_weights_patched_with_a1111_method(self, token_weight_pairs): output.append(z) if len(output) == 0: - return out[-1:].cpu(), first_pooled - - return torch.cat(output, dim=-2).cpu(), first_pooled + return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled + return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): @@ -344,27 +363,8 @@ def timed_adm(y, timesteps): return y -def patched_timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): - # Consistent with Kohya to reduce differences between model training and inference. - - if not repeat_only: - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half - ).to(device=timesteps.device) - args = timesteps[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - else: - embedding = repeat(timesteps, 'b -> b d', d=dim) - return embedding - - def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs): - t_emb = fcbh.ldm.modules.diffusionmodules.openaimodel.timestep_embedding( - timesteps, self.model_channels, repeat_only=False).to(self.dtype) - + t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) guided_hint = self.input_hint_block(hint, emb, context) @@ -378,7 +378,7 @@ def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs): assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) - h = x.type(self.dtype) + h = x for module, zero_conv in zip(self.input_blocks, self.zero_convs): if guided_hint is not None: h = module(h, emb, context) @@ -405,25 +405,31 @@ def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control= self.current_step = 1.0 - timesteps.to(x) / 999.0 global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0]) - transformer_options["original_shape"] = list(x.shape) - transformer_options["current_index"] = 0 - transformer_patches = transformer_options.get("patches", {}) - y = timed_adm(y, timesteps) + transformer_options["original_shape"] = list(x.shape) + transformer_options["transformer_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator) + time_context = kwargs.get("time_context", None) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" hs = [] - t_emb = fcbh.ldm.modules.diffusionmodules.openaimodel.timestep_embedding( - timesteps, self.model_channels, repeat_only=False).to(self.dtype) + t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) - h = x.type(self.dtype) + h = x for id, module in enumerate(self.input_blocks): transformer_options["block"] = ("input", id) - h = forward_timestep_embed(module, h, emb, context, transformer_options) + h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'input') if "input_block_patch" in transformer_patches: patch = transformer_patches["input_block_patch"] @@ -437,7 +443,7 @@ def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control= h = p(h, transformer_options) transformer_options["block"] = ("middle", 0) - h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options) + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'middle') for id, module in enumerate(self.output_blocks): @@ -456,7 +462,7 @@ def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control= output_shape = hs[-1].shape else: output_shape = None - h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape) + h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) @@ -464,34 +470,9 @@ def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control= return self.out(h) -def patched_register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - # Consistent with Kohya to reduce differences between model training and inference. - - if given_betas is not None: - betas = given_betas - else: - betas = make_beta_schedule( - beta_schedule, - timesteps, - linear_start=linear_start, - linear_end=linear_end, - cosine_s=cosine_s) - - alphas = 1. - betas - alphas_cumprod = np.cumprod(alphas, axis=0) - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.linear_start = linear_start - self.linear_end = linear_end - sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32) - self.set_sigmas(sigmas) - return - - def patched_load_models_gpu(*args, **kwargs): execution_start_time = time.perf_counter() - y = fcbh.model_management.load_models_gpu_origin(*args, **kwargs) + y = ldm_patched.modules.model_management.load_models_gpu_origin(*args, **kwargs) moving_time = time.perf_counter() - execution_start_time if moving_time > 0.1: print(f'[Fooocus Model Management] Moving model(s) has taken {moving_time:.2f} seconds') @@ -533,19 +514,18 @@ def build_loaded(module, loader_name): def patch_all(): - if not hasattr(fcbh.model_management, 'load_models_gpu_origin'): - fcbh.model_management.load_models_gpu_origin = fcbh.model_management.load_models_gpu + if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'): + ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu - fcbh.model_management.load_models_gpu = patched_load_models_gpu - fcbh.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched - fcbh.cldm.cldm.ControlNet.forward = patched_cldm_forward - fcbh.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward - fcbh.model_base.SDXL.encode_adm = sdxl_encode_adm_patched - fcbh.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_patched_with_a1111_method - fcbh.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward - fcbh.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched - fcbh.ldm.modules.diffusionmodules.openaimodel.timestep_embedding = patched_timestep_embedding - fcbh.model_base.ModelSamplingDiscrete._register_schedule = patched_register_schedule + ldm_patched.modules.model_management.load_models_gpu = patched_load_models_gpu + ldm_patched.modules.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched + ldm_patched.controlnet.cldm.ControlNet.forward = patched_cldm_forward + ldm_patched.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward + ldm_patched.modules.model_base.SDXL.encode_adm = sdxl_encode_adm_patched + ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_patched_with_a1111_method + ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward + ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched + ldm_patched.modules.samplers.sampling_function = patched_sampling_function warnings.filterwarnings(action='ignore', module='torchsde') diff --git a/modules/private_logger.py b/modules/private_logger.py index c2c04094..3a992cf6 100644 --- a/modules/private_logger.py +++ b/modules/private_logger.py @@ -35,16 +35,19 @@ def log(img, dic, single_line_number=3): div_name = only_name.replace('.', '_') item = f'
\n' - item += f"

{only_name}

\n" + item += "" + item += f"" + item += f"" + item += "

{only_name}

\n" for i, (k, v) in enumerate(dic): if i < single_line_number: - item += f"

{k}: {v}

\n" + item += f"

{k}: {v}

\n" else: if (i - single_line_number) % 2 == 0: item += f"

{k}: {v}, " else: item += f"{k}: {v}

\n" - item += f"

\n" + item += "
\n" existing_log = item + existing_log with open(html_name, 'w', encoding='utf-8') as f: diff --git a/modules/sample_hijack.py b/modules/sample_hijack.py index eafda2dd..7d8f757b 100644 --- a/modules/sample_hijack.py +++ b/modules/sample_hijack.py @@ -1,11 +1,15 @@ import torch -import fcbh.samplers -import fcbh.model_management +import ldm_patched.modules.samplers +import ldm_patched.modules.model_management -from fcbh.model_base import SDXLRefiner, SDXL -from fcbh.conds import CONDRegular -from fcbh.sample import get_additional_models, get_models_from_cond, cleanup_additional_models -from fcbh.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \ +from collections import namedtuple +from ldm_patched.contrib.external_custom_sampler import SDTurboScheduler +from ldm_patched.k_diffusion import sampling as k_diffusion_sampling +from ldm_patched.modules.samplers import normal_scheduler, simple_scheduler, ddim_scheduler +from ldm_patched.modules.model_base import SDXLRefiner, SDXL +from ldm_patched.modules.conds import CONDRegular +from ldm_patched.modules.sample import get_additional_models, get_models_from_cond, cleanup_additional_models +from ldm_patched.modules.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \ create_cond_with_same_area_if_none, pre_run_control, apply_empty_x_to_equal_area, encode_model_conds @@ -133,7 +137,9 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas extra_args['model_options'] = {k: {} if k == 'transformer_options' else v for k, v in extra_args['model_options'].items()} models, inference_memory = get_additional_models(positive_refiner, negative_refiner, current_refiner.model_dtype()) - fcbh.model_management.load_models_gpu([current_refiner] + models, current_refiner.memory_required(noise.shape) + inference_memory) + ldm_patched.modules.model_management.load_models_gpu( + [current_refiner] + models, + model.memory_required([noise.shape[0] * 2] + list(noise.shape[1:])) + inference_memory) model_wrap.inner_model = current_refiner.model print('Refiner Swapped') @@ -152,4 +158,27 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas return model.process_latent_out(samples.to(torch.float32)) -fcbh.samplers.sample = sample_hacked +@torch.no_grad() +@torch.inference_mode() +def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps): + if scheduler_name == "karras": + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + elif scheduler_name == "exponential": + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + elif scheduler_name == "normal": + sigmas = normal_scheduler(model, steps) + elif scheduler_name == "simple": + sigmas = simple_scheduler(model, steps) + elif scheduler_name == "ddim_uniform": + sigmas = ddim_scheduler(model, steps) + elif scheduler_name == "sgm_uniform": + sigmas = normal_scheduler(model, steps, sgm=True) + elif scheduler_name == "turbo": + sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps)[0] + else: + raise TypeError("error invalid scheduler") + return sigmas + + +ldm_patched.modules.samplers.calculate_sigmas_scheduler = calculate_sigmas_scheduler_hacked +ldm_patched.modules.samplers.sample = sample_hacked diff --git a/modules/upscaler.py b/modules/upscaler.py index c8dc29a0..974e4f37 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -2,8 +2,8 @@ import os import torch import modules.core as core -from fcbh_extras.chainner_models.architecture.RRDB import RRDBNet as ESRGAN -from fcbh_extras.nodes_upscale_model import ImageUpscaleWithModel +from ldm_patched.pfn.architecture.RRDB import RRDBNet as ESRGAN +from ldm_patched.contrib.external_upscale_model import ImageUpscaleWithModel from collections import OrderedDict from modules.config import path_upscale_models diff --git a/readme.md b/readme.md index ab59d872..8a91c320 100644 --- a/readme.md +++ b/readme.md @@ -51,6 +51,7 @@ Using Fooocus is as easy as (probably easier than) Midjourney – but this does | --no | Advanced -> Negative Prompt | | --ar | Advanced -> Aspect Ratios | | InsightFace | Input Image -> Image Prompt -> Advanced -> FaceSwap | +| Describe | Input Image -> Describe | We also have a few things borrowed from the best parts of LeonardoAI: @@ -68,7 +69,7 @@ Fooocus also developed many "fooocus-only" features for advanced users to get pe You can directly download Fooocus with: -**[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/release/Fooocus_win64_2-1-791.7z)** +**[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/release/Fooocus_win64_2-1-831.7z)** After you download the file, please uncompress it, and then run the "run.bat". @@ -79,7 +80,7 @@ In the first time you launch the software, it will automatically download models 1. It will download [default models](#models) to the folder "Fooocus\models\checkpoints" given different presets. You can download them in advance if you do not want automatic download. 2. Note that if you use inpaint, at the first time you inpaint an image, it will download [Fooocus's own inpaint control model from here](https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch) as the file "Fooocus\models\inpaint\inpaint_v26.fooocus.patch" (the size of this file is 1.28GB). -After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and requires different models, but thet will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679). +After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and requires different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679). ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/d386f817-4bd7-490c-ad89-c1e228c23447) @@ -106,9 +107,13 @@ Note that the minimal requirement is **4GB Nvidia GPU memory (4GB VRAM)** and ** Please open an issue if you use similar devices but still cannot achieve acceptable performances. +Note that the [minimal requirement](#minimal-requirement) for different platforms is different. + +See also the common problems and troubleshoots [here](troubleshoot.md). + ### Colab -(Last tested - 2023 Nov 15) +(Last tested - 2023 Dec 12) | Colab | Info | --- | --- | @@ -184,6 +189,8 @@ Use `python entry_with_update.py --preset anime` or `python entry_with_update.py ### Linux (AMD GPUs) +Note that the [minimal requirement](#minimal-requirement) for different platforms is different. + Same with the above instructions. You need to change torch to AMD version pip uninstall torch torchvision torchaudio torchtext functorch xformers @@ -195,6 +202,8 @@ Use `python entry_with_update.py --preset anime` or `python entry_with_update.py ### Windows(AMD GPUs) +Note that the [minimal requirement](#minimal-requirement) for different platforms is different. + Same with Windows. Download the software, edit the content of `run.bat` as: .\python_embeded\python.exe -m pip uninstall torch torchvision torchaudio torchtext functorch xformers -y @@ -206,10 +215,12 @@ Then run the `run.bat`. AMD is not intensively tested, however. The AMD support is in beta. -Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. +For AMD, use `python entry_with_update.py --directml --preset anime` or `python entry_with_update.py --directml --preset realistic` for Fooocus Anime/Realistic Edition. ### Mac +Note that the [minimal requirement](#minimal-requirement) for different platforms is different. + Mac is not intensively tested. Below is an unofficial guideline for using Mac. You can discuss problems [here](https://github.com/lllyasviel/Fooocus/pull/129). You can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or a newer version. Fooocus runs on Apple silicon computers via [PyTorch](https://pytorch.org/get-started/locally/) MPS device acceleration. Mac Silicon computers don't come with a dedicated graphics card, resulting in significantly longer image processing times compared to computers with dedicated graphics cards. @@ -220,17 +231,49 @@ You can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or 1. Create a new conda environment, `conda env create -f environment.yaml`. 1. Activate your new conda environment, `conda activate fooocus`. 1. Install the packages required by Fooocus, `pip install -r requirements_versions.txt`. -1. Launch Fooocus by running `python entry_with_update.py`. (Some Mac M2 users may need `python entry_with_update.py --enable-smart-memory` to speed up model loading/unloading.) The first time you run Fooocus, it will automatically download the Stable Diffusion SDXL models and will take a significant time, depending on your internet connection. +1. Launch Fooocus by running `python entry_with_update.py`. (Some Mac M2 users may need `python entry_with_update.py --disable-offload-from-vram` to speed up model loading/unloading.) The first time you run Fooocus, it will automatically download the Stable Diffusion SDXL models and will take a significant time, depending on your internet connection. Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. +### Download Previous Version + +See the guidelines [here](https://github.com/lllyasviel/Fooocus/discussions/1405). + +## Minimal Requirement + +Below is the minimal requirement for running Fooocus locally. If your device capability is lower than this spec, you may not be able to use Fooocus locally. (Please let us know, in any case, if your device capability is lower but Fooocus still works.) + +| Operating System | GPU | Minimal GPU Memory | Minimal System Memory | [System Swap](troubleshoot.md) | Note | +|-------------------|------------------------------|---------------------------|---------------------------|--------------------------------|---------------------------------------| +| Windows/Linux | Nvidia RTX 4XXX | 4GB | 8GB | Required | fastest | +| Windows/Linux | Nvidia RTX 3XXX | 4GB | 8GB | Required | usually faster than RTX 2XXX | +| Windows/Linux | Nvidia RTX 2XXX | 4GB | 8GB | Required | usually faster than GTX 1XXX | +| Windows/Linux | Nvidia GTX 1XXX | 8GB (* 6GB uncertain) | 8GB | Required | only marginally faster than CPU | +| Windows/Linux | Nvidia GTX 9XX | 8GB | 8GB | Required | faster or slower than CPU | +| Windows/Linux | Nvidia GTX < 9XX | Not supported | / | / | / | +| Windows | AMD GPU | 16GB | 8GB | Required | via DirectML | +| Linux | AMD GPU | 8GB | 8GB | Required | via ROCm | +| Windows | * AMD GPU ROCm (on hold) | 8GB (on hold) | 8GB (on hold) | Required (on hold) | via ROCm (on hold) | +| Mac | M1/M2 MPS | Shared | Shared | Shared | about 9x slower than Nvidia RTX 3XXX | +| Windows/Linux/Mac | only use CPU | 0GB | 32GB | Required | about 17x slower than Nvidia RTX 3XXX | + +* AMD GPU ROCm (on hold): The AMD is still working on supporting ROCm on Windows. + +* Nvidia GTX 1XXX 6GB uncertain: Some people reports 6GB success on GTX 10XX but some other people reports failure cases. + +*Note that Fooocus is only for extremely high quality image generating. We will not support smaller models to reduce the requirement and sacrifice result quality.* + +## Troubleshoot + +See the common problems [here](troubleshoot.md). + ## Default Models Given different goals, the default models and configs of Fooocus is different: | Task | Windows | Linux args | Main Model | Refiner | Config | -| - | - | - | - | - | - | +| --- | --- | --- | --- | --- | --- | | General | run.bat | | [juggernautXL v6_RunDiffusion](https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_version6Rundiffusion.safetensors) | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/modules/path.py) | | Realistic | run_realistic.bat | --preset realistic | [realistic_stock_photo](https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/realisticStockPhoto_v10.safetensors) | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/realistic.json) | | Anime | run_anime.bat | --preset anime | [bluepencil_v50](https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/bluePencilXL_v050.safetensors) | [dreamsharper_v8](https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/DreamShaper_8_pruned.safetensors) (SD1.5) | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/anime.json) | @@ -297,7 +340,35 @@ Consider twice before you really change the config. If you find yourself breakin A safter way is just to try "run_anime.bat" or "run_realistic.bat" - they should be already good enough for different tasks. -Note that `user_path_config.txt` is deprecated and will be removed soon. +~Note that `user_path_config.txt` is deprecated and will be removed soon.~ (Edit: it is already removed.) + +### All CMD Flags + +``` +entry_with_update.py [-h] [--listen [IP]] [--port PORT] + [--disable-header-check [ORIGIN]] + [--web-upload-size WEB_UPLOAD_SIZE] + [--external-working-path PATH [PATH ...]] + [--output-path OUTPUT_PATH] [--temp-path TEMP_PATH] + [--cache-path CACHE_PATH] [--in-browser] + [--disable-in-browser] [--gpu-device-id DEVICE_ID] + [--async-cuda-allocation | --disable-async-cuda-allocation] + [--disable-attention-upcast] [--all-in-fp32 | --all-in-fp16] + [--unet-in-bf16 | --unet-in-fp16 | --unet-in-fp8-e4m3fn | --unet-in-fp8-e5m2] + [--vae-in-fp16 | --vae-in-fp32 | --vae-in-bf16] + [--clip-in-fp8-e4m3fn | --clip-in-fp8-e5m2 | --clip-in-fp16 | --clip-in-fp32] + [--directml [DIRECTML_DEVICE]] [--disable-ipex-hijack] + [--preview-option [none,auto,fast,taesd]] + [--attention-split | --attention-quad | --attention-pytorch] + [--disable-xformers] + [--always-gpu | --always-high-vram | --always-normal-vram | + --always-low-vram | --always-no-vram | --always-cpu] + [--always-offload-from-vram] [--disable-server-log] + [--debug-mode] [--is-windows-embedded-python] + [--disable-server-info] [--share] [--preset PRESET] + [--language LANGUAGE] [--disable-offload-from-vram] + [--theme THEME] [--disable-image-log] +``` ## Advanced Features @@ -307,14 +378,12 @@ Fooocus also has many community forks, just like SD-WebUI's [vladmandic/automati | Fooocus' forks | | - | -| [fenneishi/Fooocus-Control](https://github.com/fenneishi/Fooocus-Control)
[runew0lf/RuinedFooocus](https://github.com/runew0lf/RuinedFooocus)
[MoonRide303/Fooocus-MRE](https://github.com/MoonRide303/Fooocus-MRE)
and so on ... | +| [fenneishi/Fooocus-Control](https://github.com/fenneishi/Fooocus-Control)
[runew0lf/RuinedFooocus](https://github.com/runew0lf/RuinedFooocus)
[MoonRide303/Fooocus-MRE](https://github.com/MoonRide303/Fooocus-MRE)
[metercai/SimpleSDXL](https://github.com/metercai/SimpleSDXL)
and so on ... | See also [About Forking and Promotion of Forks](https://github.com/lllyasviel/Fooocus/discussions/699). ## Thanks -Fooocus is powered by [FCBH backend](https://github.com/lllyasviel/Fooocus/tree/main/backend), which starts from an odd mixture of [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) and [ComfyUI](https://github.com/comfyanonymous/ComfyUI). - Special thanks to [twri](https://github.com/twri) and [3Diva](https://github.com/3Diva) for creating additional SDXL styles available in Fooocus. ## Update Log diff --git a/requirements_versions.txt b/requirements_versions.txt index 5769d1a9..f1794bb3 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -15,4 +15,6 @@ gradio==3.41.2 pygit2==1.12.2 opencv-contrib-python==4.8.0.74 httpx==0.24.1 +onnxruntime==1.16.3 +timm==0.9.2 translators==5.8.9 \ No newline at end of file diff --git a/troubleshoot.md b/troubleshoot.md new file mode 100644 index 00000000..0d4fbefa --- /dev/null +++ b/troubleshoot.md @@ -0,0 +1,178 @@ +Below are many common problems that people encountered: + +### RuntimeError: CPUAllocator + +See also the section: **System Swap** + +### Model loaded, then paused, then nothing happens + +See also the section: **System Swap** + +### Segmentation Fault + +See also the section: **System Swap** + +### Aborted + +See also the section: **System Swap** + +### core dumped + +See also the section: **System Swap** + +### Killed + +See also the section: **System Swap** + +### ^C, then quit + +See also the section: **System Swap** + +### adm 2816, then stuck + +See also the section: **System Swap** + +### Connection errored out + +See also the section: **System Swap** + +### Error 1006 + +See also the section: **System Swap** + +### WinError 10060 + +See also the section: **System Swap** + +### Read timed out + +See also the section: **System Swap** + +### No error, but the console close in a flash. Cannot find any error. + +See also the section: **System Swap** + +### Model loading is extremely slow (more than 1 minute) + +See also the section: **System Swap** + +### System Swap + +All above problems are caused by the fact that you do not have enough System Swap. + +Please make sure that you have at least 40GB System Swap. In fact, it does not need so much Swap, but 40Gb should be safe for you to run Fooocus in 100% success. + +(If you have more than 64GB RAM, then *perhaps* you do not need any System Swap, but we are not exactly sure about this.) + +Also, if your system swap is on HDD, the speed of model loading will be very slow. Please try best to put system swap on SSD. + +If you are using Linux/Mac, please follow your provider's instructions to set Swap Space. Herein, the "provider" refers to Ubuntu official, CentOS official, Mac official, etc. + +If you are using Windows, you can set Swap here: + +![swap](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9) + +If you use both HDD and SSD, you *may* test some settings on the above step 7 to try best to put swap area on SSD, so that the speed of model loading will be faster. + +**Important: Microsoft Windows 10/11 by default automate system swap for you so that you do not need to touch this dangerous setting. If you do not have enough system swap, just make sure that you have at least 40GB free space on each disk.** The Microsoft Windows 10/11 will automatically make swap areas for you. + +Also, if you obtain Microsoft Windows 10/11 from some unofficial Chinese or Russian provider, they may have modified the default setting of system swap to advertise some "Enhanced Windows 10/11" (but actually they are just making things worse rather than improve things). In those cases, you may need to manually check if your system swap setting is consistent to the above screenshot. + +Finally, note that you need to restart computer to activate any changes in system swap. + +### MetadataIncompleteBuffer + +See also the section: **Model corrupted** + +### PytorchStreamReader failed + +See also the section: **Model corrupted** + +### Model corrupted + +If you see Model Corrupted, then your model is corrupted. Fooocus will re-download corrupted models for you if your internet connection is good. Otherwise, you may also manually download models. You can find model url and their local location in the console each time a model download is requested. + +### UserWarning: The operator 'aten::std_mean.correction' is not currently supported on the DML + +This is a warning that you can ignore. + +### Torch not compiled with CUDA enabled + +You are not following the official installation guide. + +Please do not trust those wrong tutorials on the internet, and please only trust the official installation guide. + +### subprocess-exited-with-error + +Please use python 3.10 + +Also, you are not following the official installation guide. + +Please do not trust those wrong tutorials on the internet, and please only trust the official installation guide. + +### SSL: CERTIFICATE_VERIFY_FAILED + +Are you living in China? If yes, please consider turn off VPN, and/or try to download models manually. + +If you get this error elsewhere in the world, then you may need to look at [this search](https://www.google.com/search?q=SSL+Certificate+Error). We cannot give very specific guide to fix this since the cause can vary a lot. + +### CUDA kernel errors might be asynchronously reported at some other API call + +This problem is fixed two months ago. Please make sure that you are using the latest version of Fooocus (try fresh install). + +If it still does not work, try to upgrade your Nvidia driver. + +If it still does not work, open an issue with full log, and we will take a look. + + +### Found no NVIDIA driver on your system + +Please upgrade your Nvidia Driver. + +If you are using AMD, please follow official installation guide. + +### NVIDIA driver too old + +Please upgrade your Nvidia Driver. + +### I am using Mac, the speed is very slow. + +Some MAC users may need `--disable-offload-from-vram` to speed up model loading. + +Besides, the current support for MAC is very experimental, and we encourage users to also try Diffusionbee or Drawingthings: they are developed only for MAC. + +### I am using Nvidia with 8GB VRAM, I get CUDA Out Of Memory + +It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](readme.md#minimal-requirement). + +### I am using Nvidia with 6GB VRAM, I get CUDA Out Of Memory + +It is very likely a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](readme.md#minimal-requirement). + +### I am using Nvidia with 4GB VRAM with Float16 support, like RTX 3050, I get CUDA Out Of Memory + +It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](readme.md#minimal-requirement). + +### I am using Nvidia with 4GB VRAM without Float16 support, like GTX 960, I get CUDA Out Of Memory + +Supporting GPU with 4GB VRAM without fp16 is extremely difficult, and you may not be able to use SDXL. However, you may still make an issue and let us know. You may try SD1.5 in Automatic1111 or other software for your device. See also [minimal requirements](readme.md#minimal-requirement). + +### I am using AMD GPU on Windows, I get CUDA Out Of Memory + +Current AMD support is very experimental for Windows. If you see this, then perhaps you cannot use Fooocus on this device on Windows. + +However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. + +Besides, the AMD support on Linux is slightly better because it will use ROCM. You may also try it if you are willing to change OS to linux. See also [minimal requirements](readme.md#minimal-requirement). + +### I am using AMD GPU on Linux, I get CUDA Out Of Memory + +Current AMD support for Linux is better than that for Windows, but still, very experimental. However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. See also [minimal requirements](readme.md#minimal-requirement). + +### I tried flags like --lowvram or --gpu-only or --bf16 or so on, and things are not getting any better? + +Please remove these flags if you are mislead by some wrong tutorials. In most cases these flags are making things worse and introducing more problems. + +### Fooocus suddenly becomes very slow and I have not changed anything + +Are you accidentally running two Fooocus at the same time? diff --git a/update_log.md b/update_log.md index 78fb92c6..f889070b 100644 --- a/update_log.md +++ b/update_log.md @@ -1,5 +1,48 @@ +# 2.1.839 + +* Maintained some computation codes in backend for efficiency. +* Added a note about Seed Breaking Change. + +**Seed Breaking Change**: Note that 2.1.825-2.1.839 is seed breaking change. The computation float point is changed and some seeds may give slightly different results. The minor change in 2.1.825-2.1.839 do not influence image quality. See also [use previous version](https://github.com/lllyasviel/Fooocus/discussions/1405). + +# 2.1.837 + +* Fix some precision-related problems. + +# 2.1.836 + +* Avoid blip tokenizer download from torch hub + +# 2.1.831 + +* Input Image -> Describe (Midjourney Describe) + +# 2.1.830 + +* SegmindVega support. + +# 2.1.829 + +* Change SDE tree to CPU on AMD/DirectMl to avoid potential problems. + +# 2.1.828 + +* Allow to disable gradio analytics. +* Use html table in log. +* fix some SSL problems. + +# 2.1.826 + +* New backend. +* FP8 support (see also the new cmd flag list in Readme, eg, --unet-in-fp8-e4m3fn and --unet-in-fp8-e5m2). +* Fix some MPS problems. +* GLoRA support. +* Turbo scheduler. + # 2.1.823 +(2023 Nov 26) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid December. However, you may still see updates if other collaborators are fixing bugs or solving problems. + * Fix some potential problem when LoRAs has clip keys and user want to load those LoRAs to refiners. # 2.1.822 diff --git a/webui.py b/webui.py index 2f23dfb0..925af38f 100644 --- a/webui.py +++ b/webui.py @@ -21,7 +21,7 @@ from modules.ui_gradio_extensions import reload_javascript from modules.auth import auth_enabled, check_auth def generate_clicked(*args): - import fcbh.model_management as model_management + import ldm_patched.modules.model_management as model_management with model_management.interrupt_processing_mutex: model_management.interrupt_processing = False @@ -112,13 +112,13 @@ with shared.gradio_root: stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False) def stop_clicked(): - import fcbh.model_management as model_management + import ldm_patched.modules.model_management as model_management shared.last_stop = 'stop' model_management.interrupt_current_processing() return [gr.update(interactive=False)] * 2 def skip_clicked(): - import fcbh.model_management as model_management + import ldm_patched.modules.model_management as model_management shared.last_stop = 'skip' model_management.interrupt_current_processing() return @@ -181,7 +181,6 @@ with shared.gradio_root: ip_advanced.change(ip_advance_checked, inputs=ip_advanced, outputs=ip_ad_cols + ip_types + ip_stops + ip_weights, queue=False, show_progress=False) - with gr.TabItem(label='Inpaint or Outpaint') as inpaint_tab: inpaint_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas') with gr.Row(): @@ -191,7 +190,17 @@ with shared.gradio_root: example_inpaint_prompts = gr.Dataset(samples=modules.config.example_inpaint_prompts, label='Additional Prompt Quick List', components=[inpaint_additional_prompt], visible=False) gr.HTML('* Powered by Fooocus Inpaint Engine \U0001F4D4 Document') example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False) - + with gr.TabItem(label='Describe') as desc_tab: + with gr.Row(): + with gr.Column(): + desc_input_image = grh.Image(label='Drag any image to here', source='upload', type='numpy') + with gr.Column(): + desc_method = gr.Radio( + label='Content Type', + choices=[flags.desc_type_photo, flags.desc_type_anime], + value=flags.desc_type_photo) + desc_btn = gr.Button(value='Describe this Image into Prompt') + gr.HTML('\U0001F4D4 Document') switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}" down_js = "() => {viewer_to_bottom();}" @@ -203,6 +212,7 @@ with shared.gradio_root: uov_tab.select(lambda: 'uov', outputs=current_tab, queue=False, _js=down_js, show_progress=False) inpaint_tab.select(lambda: 'inpaint', outputs=current_tab, queue=False, _js=down_js, show_progress=False) ip_tab.select(lambda: 'ip', outputs=current_tab, queue=False, _js=down_js, show_progress=False) + desc_tab.select(lambda: 'desc', outputs=current_tab, queue=False, _js=down_js, show_progress=False) with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column: with gr.Tab(label='Setting'): @@ -445,12 +455,13 @@ with shared.gradio_root: model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls, queue=False, show_progress=False) - performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11, + performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11 + + [gr.update(visible=x != 'Extreme Speed')] * 1, inputs=performance_selection, outputs=[ guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive, adm_scaler_negative, refiner_switch, refiner_model, sampler_name, - scheduler_name, adaptive_cfg, refiner_swap_method + scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt ], queue=False, show_progress=False) advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, advanced_column, @@ -513,6 +524,18 @@ with shared.gradio_root: gr.Audio(interactive=False, value=notification_file, elem_id='audio_notification', visible=False) break + def trigger_describe(mode, img): + if mode == flags.desc_type_photo: + from extras.interrogate import default_interrogator as default_interrogator_photo + return default_interrogator_photo(img), ["Fooocus V2", "Fooocus Enhance", "Fooocus Sharp"] + if mode == flags.desc_type_anime: + from extras.wd14tagger import default_interrogator as default_interrogator_anime + return default_interrogator_anime(img), ["Fooocus V2", "Fooocus Masterpiece"] + return mode, ["Fooocus V2"] + + desc_btn.click(trigger_describe, inputs=[desc_method, desc_input_image], + outputs=[prompt, style_selections], show_progress=True, queue=False) + def dump_default_english_config(): from modules.localization import dump_english_config @@ -522,7 +545,7 @@ def dump_default_english_config(): # dump_default_english_config() shared.gradio_root.launch( - inbrowser=args_manager.args.auto_launch, + inbrowser=args_manager.args.in_browser, server_name=args_manager.args.listen, server_port=args_manager.args.port, share=args_manager.args.share,