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Merge remote-tracking branch 'upstream/main' into feature/add-prompt-translate 

# Conflicts:
#	requirements_versions.txt
This commit is contained in:
Manuel Schmid 2023-12-14 22:54:02 +01:00
parent 8718e80dc2 bac5c882ba
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4
.github/ISSUE_TEMPLATE/bug_report.md vendored
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@ -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.

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.gitignore vendored
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@ -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

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args_manager.py
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@ -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

5
backend/doc
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@ -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.

674
backend/headless/LICENSE
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@ -1,674 +0,0 @@
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be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. 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.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
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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. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. 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
<https://www.gnu.org/licenses/why-not-lgpl.html>.

118
backend/headless/fcbh/cli_args.py
View File

@ -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

40
backend/headless/fcbh/ops.py
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@ -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

2
entry_with_update.py
View File

@ -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))

0
expansion_experiments.py → experiments_expansion.py
View File

2
face_experiments.py → experiments_face.py
View File

@ -1,5 +1,5 @@
import cv2
import fooocus_extras.face_crop as cropper
import extras.face_crop as cropper
img = cv2.imread('lena.png')

8
experiments_interrogate.py Normal file
View File

@ -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))

21
extras/BLIP/configs/bert_config.json Normal file
View File

@ -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
}

33
extras/BLIP/configs/caption_coco.yaml Normal file
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@ -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

21
extras/BLIP/configs/med_config.json Normal file
View File

@ -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
}

21
extras/BLIP/configs/nlvr.yaml Normal file
View File

@ -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

15
extras/BLIP/configs/nocaps.yaml Normal file
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@ -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 '

27
extras/BLIP/configs/pretrain.yaml Normal file
View File

@ -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

34
extras/BLIP/configs/retrieval_coco.yaml Normal file
View File

@ -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

34
extras/BLIP/configs/retrieval_flickr.yaml Normal file
View File

@ -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

12
extras/BLIP/configs/retrieval_msrvtt.yaml Normal file
View File

@ -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

25
extras/BLIP/configs/vqa.yaml Normal file
View File

@ -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

23
extras/BLIP/models/bert_tokenizer/config.json Normal file
View File

@ -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
}

1
extras/BLIP/models/bert_tokenizer/tokenizer.json Normal file
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File diff suppressed because one or more lines are too long

3
extras/BLIP/models/bert_tokenizer/tokenizer_config.json Normal file
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@ -0,0 +1,3 @@
{
"do_lower_case": true
}

30522
extras/BLIP/models/bert_tokenizer/vocab.txt Normal file
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File diff suppressed because it is too large Load Diff

239
extras/BLIP/models/blip.py Normal file
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@ -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

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extras/BLIP/models/blip_itm.py Normal file
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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

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extras/BLIP/models/blip_nlvr.py Normal file
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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

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extras/BLIP/models/blip_pretrain.py Normal file
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'''
* 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)

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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)

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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))

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'''
* 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 <https://arxiv.org/abs/1706.03762>`__ 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

843
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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 <https://arxiv.org/abs/1706.03762>`__ 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,
)

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'''
* 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

4
modules/expansion.py → extras/expansion.py
View File

@ -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()

4
fooocus_extras/face_crop.py → extras/face_crop.py
View File

@ -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()

2
fooocus_extras/facexlib/detection/__init__.py → extras/facexlib/detection/__init__.py
View File

@ -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

0
fooocus_extras/facexlib/detection/align_trans.py → extras/facexlib/detection/align_trans.py
View File

0
fooocus_extras/facexlib/detection/matlab_cp2tform.py → extras/facexlib/detection/matlab_cp2tform.py
View File

6
fooocus_extras/facexlib/detection/retinaface.py → extras/facexlib/detection/retinaface.py
View File

@ -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)

0
fooocus_extras/facexlib/detection/retinaface_net.py → extras/facexlib/detection/retinaface_net.py
View File

0
fooocus_extras/facexlib/detection/retinaface_utils.py → extras/facexlib/detection/retinaface_utils.py
View File

2
fooocus_extras/facexlib/parsing/__init__.py → extras/facexlib/parsing/__init__.py
View File

@ -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

0
fooocus_extras/facexlib/parsing/bisenet.py → extras/facexlib/parsing/bisenet.py
View File

0
fooocus_extras/facexlib/parsing/parsenet.py → extras/facexlib/parsing/parsenet.py
View File

0
fooocus_extras/facexlib/parsing/resnet.py → extras/facexlib/parsing/resnet.py
View File

0
fooocus_extras/facexlib/utils/__init__.py → extras/facexlib/utils/__init__.py
View File

6
fooocus_extras/facexlib/utils/face_restoration_helper.py → extras/facexlib/utils/face_restoration_helper.py
View File

@ -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):

6
fooocus_extras/facexlib/utils/face_utils.py → extras/facexlib/utils/face_utils.py
View File

@ -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]

0
fooocus_extras/facexlib/utils/misc.py → extras/facexlib/utils/misc.py
View File

63
extras/interrogate.py Normal file
View File

@ -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

31
fooocus_extras/ip_adapter.py → extras/ip_adapter.py
View File

@ -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)

0
fooocus_extras/preprocessors.py → extras/preprocessors.py
View File

0
fooocus_extras/resampler.py → extras/resampler.py
View File

10
fooocus_extras/vae_interpose.py → extras/vae_interpose.py
View File

@ -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)

98
extras/wd14tagger.py Normal file
View File

@ -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

2
fooocus_version.py
View File

@ -1 +1 @@
version = '2.1.824'
version = '2.1.839'

27
javascript/script.js
View File

@ -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();
}
});

20
launch.py
View File

@ -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()

240
backend/headless/nodes.py → ldm_patched/contrib/external.py
View File

@ -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:

8
backend/headless/fcbh_extras/nodes_canny.py → ldm_patched/contrib/external_canny.py
View File

@ -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 = {

4
backend/headless/fcbh_extras/nodes_clip_sdxl.py → ldm_patched/contrib/external_clip_sdxl.py
View File

@ -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

10
backend/headless/fcbh_extras/nodes_compositing.py → ldm_patched/contrib/external_compositing.py
View File

@ -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])

52
backend/headless/fcbh_extras/nodes_custom_sampler.py → ldm_patched/contrib/external_custom_sampler.py
View File

@ -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,

2
backend/headless/fcbh_extras/nodes_freelunch.py → ldm_patched/contrib/external_freelunch.py
View File

@ -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

12
backend/headless/fcbh_extras/nodes_hypernetwork.py → ldm_patched/contrib/external_hypernetwork.py
View File

@ -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:

20
backend/headless/fcbh_extras/nodes_hypertile.py → ldm_patched/contrib/external_hypertile.py
View File

@ -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)

79
backend/headless/fcbh_extras/nodes_images.py → ldm_patched/contrib/external_images.py
View File

@ -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,
}

8
backend/headless/fcbh_extras/nodes_latent.py → ldm_patched/contrib/external_latent.py
View File

@ -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:

10
backend/headless/fcbh_extras/nodes_mask.py → ldm_patched/contrib/external_mask.py
View File

@ -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

68
backend/headless/fcbh_extras/nodes_model_advanced.py → ldm_patched/contrib/external_model_advanced.py
View File

@ -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,
}

8
backend/headless/fcbh_extras/nodes_model_downscale.py → ldm_patched/contrib/external_model_downscale.py
View File

@ -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()

56
backend/headless/fcbh_extras/nodes_model_merging.py → ldm_patched/contrib/external_model_merging.py
View File

@ -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 = {

10
backend/headless/fcbh_extras/nodes_post_processing.py → ldm_patched/contrib/external_post_processing.py
View File

@ -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,)

2
backend/headless/fcbh_extras/nodes_rebatch.py → ldm_patched/contrib/external_rebatch.py
View File

@ -1,3 +1,5 @@
# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import torch
class LatentRebatch:

174
ldm_patched/contrib/external_sag.py Normal file
View File

@ -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",
}

2
backend/headless/fcbh_extras/nodes_tomesd.py → ldm_patched/contrib/external_tomesd.py
View File

@ -1,3 +1,5 @@
# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
#Taken from: https://github.com/dbolya/tomesd
import torch

24
backend/headless/fcbh_extras/nodes_upscale_model.py → ldm_patched/contrib/external_upscale_model.py
View File

@ -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

91
ldm_patched/contrib/external_video_model.py Normal file
View File

@ -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)",
}

45
backend/headless/fcbh/cldm/cldm.py → ldm_patched/controlnet/cldm.py
View File

@ -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)

0
backend/headless/fcbh/k_diffusion/sampling.py → ldm_patched/k_diffusion/sampling.py
View File

0
backend/headless/fcbh/k_diffusion/utils.py → ldm_patched/k_diffusion/utils.py
View File

12
backend/headless/fcbh/ldm/models/autoencoder.py → ldm_patched/ldm/models/autoencoder.py
View File

@ -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,

371
backend/headless/fcbh/ldm/modules/attention.py → ldm_patched/ldm/modules/attention.py
View File

@ -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

0
backend/headless/fcbh/ldm/modules/diffusionmodules/__init__.py → ldm_patched/ldm/modules/diffusionmodules/__init__.py
View File

43
backend/headless/fcbh/ldm/modules/diffusionmodules/model.py → ldm_patched/ldm/modules/diffusionmodules/model.py
View File

@ -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,

386
backend/headless/fcbh/ldm/modules/diffusionmodules/openaimodel.py → ldm_patched/ldm/modules/diffusionmodules/openaimodel.py
View File

@ -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)

2
backend/headless/fcbh/ldm/modules/diffusionmodules/upscaling.py → ldm_patched/ldm/modules/diffusionmodules/upscaling.py
View File

@ -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):

112
backend/headless/fcbh/ldm/modules/diffusionmodules/util.py → ldm_patched/ldm/modules/diffusionmodules/util.py
View File

@ -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.

0
backend/headless/fcbh/ldm/modules/distributions/__init__.py → ldm_patched/ldm/modules/distributions/__init__.py
View File

0
backend/headless/fcbh/ldm/modules/distributions/distributions.py → ldm_patched/ldm/modules/distributions/distributions.py
View File

0
backend/headless/fcbh/ldm/modules/ema.py → ldm_patched/ldm/modules/ema.py
View File

0
backend/headless/fcbh/ldm/modules/encoders/__init__.py → ldm_patched/ldm/modules/encoders/__init__.py
View File

0
backend/headless/fcbh/ldm/modules/encoders/noise_aug_modules.py → ldm_patched/ldm/modules/encoders/noise_aug_modules.py
View File

2
backend/headless/fcbh/ldm/modules/sub_quadratic_attention.py → ldm_patched/ldm/modules/sub_quadratic_attention.py
View File

@ -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,

245
ldm_patched/ldm/modules/temporal_ae.py Normal file
View File

@ -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
)

0
backend/headless/fcbh/ldm/util.py → ldm_patched/ldm/util.py
View File

122
ldm_patched/modules/args_parser.py Normal file
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@ -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

0
backend/headless/fcbh/checkpoint_pickle.py → ldm_patched/modules/checkpoint_pickle.py
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0
backend/headless/fcbh/clip_config_bigg.json → ldm_patched/modules/clip_config_bigg.json
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