From c0e11c3451f546bd0ed660fbf9ee7490a5aa8e33 Mon Sep 17 00:00:00 2001 From: Alistair Keiller <43255248+AlistairKeiller@users.noreply.github.com> Date: Sat, 30 Dec 2023 07:12:32 -0600 Subject: [PATCH 01/12] Update readme.md (#1655) --- readme.md | 56 +++++++++++++++++++++++++++---------------------------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/readme.md b/readme.md index 6b458e74..5dfd96cb 100644 --- a/readme.md +++ b/readme.md @@ -38,7 +38,7 @@ Using Fooocus is as easy as (probably easier than) Midjourney – but this does | Midjourney | Fooocus | | - | - | -| High-quality text-to-image without needing much prompt engineering or parameter tuning.
(Unknown method) | High-quality text-to-image without needing much prompt engineering or parameter tuning.
(Fooocus has offline GPT-2 based prompt processing engine and lots of sampling improvements so that results are always beautiful, no matter your prompt is as short as “house in garden” or as long as 1000 words) | +| High-quality text-to-image without needing much prompt engineering or parameter tuning.
(Unknown method) | High-quality text-to-image without needing much prompt engineering or parameter tuning.
(Fooocus has an offline GPT-2 based prompt processing engine and lots of sampling improvements so that results are always beautiful, no matter if your prompt is as short as “house in garden” or as long as 1000 words) | | V1 V2 V3 V4 | Input Image -> Upscale or Variation -> Vary (Subtle) / Vary (Strong)| | U1 U2 U3 U4 | Input Image -> Upscale or Variation -> Upscale (1.5x) / Upscale (2x) | | Inpaint / Up / Down / Left / Right (Pan) | Input Image -> Inpaint or Outpaint -> Inpaint / Up / Down / Left / Right
(Fooocus uses its own inpaint algorithm and inpaint models so that results are more satisfying than all other software that uses standard SDXL inpaint method/model) | @@ -73,16 +73,16 @@ You can directly download Fooocus with: **[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/release/Fooocus_win64_2-1-831.7z)** -After you download the file, please uncompress it, and then run the "run.bat". +After you download the file, please uncompress it and then run the "run.bat". ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/c49269c4-c274-4893-b368-047c401cc58c) -In the first time you launch the software, it will automatically download models: +The first time you launch the software, it will automatically download models: 1. It will download [default models](#models) to the folder "Fooocus\models\checkpoints" given different presets. You can download them in advance if you do not want automatic download. 2. Note that if you use inpaint, at the first time you inpaint an image, it will download [Fooocus's own inpaint control model from here](https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch) as the file "Fooocus\models\inpaint\inpaint_v26.fooocus.patch" (the size of this file is 1.28GB). -After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and requires different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679). +After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and require different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679). ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/d386f817-4bd7-490c-ad89-c1e228c23447) @@ -99,7 +99,7 @@ Besides, recently many other software report that Nvidia driver above 532 is som Note that the minimal requirement is **4GB Nvidia GPU memory (4GB VRAM)** and **8GB system memory (8GB RAM)**. This requires using Microsoft’s Virtual Swap technique, which is automatically enabled by your Windows installation in most cases, so you often do not need to do anything about it. However, if you are not sure, or if you manually turned it off (would anyone really do that?), or **if you see any "RuntimeError: CPUAllocator"**, you can enable it here:
-Click here to the see the image instruction. +Click here to see the image instructions. ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9) @@ -123,7 +123,7 @@ See also the common problems and troubleshoots [here](troubleshoot.md). In Colab, you can modify the last line to `!python entry_with_update.py --share` or `!python entry_with_update.py --preset anime --share` or `!python entry_with_update.py --preset realistic --share` for Fooocus Default/Anime/Realistic Edition. -Note that this Colab will disable refiner by default because Colab free's resource is relatively limited (and some "big" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab. +Note that this Colab will disable refiner by default because Colab free's resources are relatively limited (and some "big" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab. Thanks to [camenduru](https://github.com/camenduru)! @@ -142,7 +142,7 @@ Then download the models: download [default models](#models) to the folder "Fooo conda activate fooocus python entry_with_update.py -Or if you want to open a remote port, use +Or, if you want to open a remote port, use conda activate fooocus python entry_with_update.py --listen @@ -151,7 +151,7 @@ Use `python entry_with_update.py --preset anime` or `python entry_with_update.py ### Linux (Using Python Venv) -Your Linux needs to have **Python 3.10** installed, and lets say your Python can be called with command **python3** with your venv system working, you can +Your Linux needs to have **Python 3.10** installed, and let's say your Python can be called with the command **python3** with your venv system working; you can git clone https://github.com/lllyasviel/Fooocus.git cd Fooocus @@ -164,7 +164,7 @@ See the above sections for model downloads. You can launch the software with: source fooocus_env/bin/activate python entry_with_update.py -Or if you want to open a remote port, use +Or, if you want to open a remote port, use source fooocus_env/bin/activate python entry_with_update.py --listen @@ -173,7 +173,7 @@ Use `python entry_with_update.py --preset anime` or `python entry_with_update.py ### Linux (Using native system Python) -If you know what you are doing, and your Linux already has **Python 3.10** installed, and your Python can be called with command **python3** (and Pip with **pip3**), you can +If you know what you are doing, and your Linux already has **Python 3.10** installed, and your Python can be called with the command **python3** (and Pip with **pip3**), you can git clone https://github.com/lllyasviel/Fooocus.git cd Fooocus @@ -183,7 +183,7 @@ See the above sections for model downloads. You can launch the software with: python3 entry_with_update.py -Or if you want to open a remote port, use +Or, if you want to open a remote port, use python3 entry_with_update.py --listen @@ -193,7 +193,7 @@ Use `python entry_with_update.py --preset anime` or `python entry_with_update.py Note that the [minimal requirement](#minimal-requirement) for different platforms is different. -Same with the above instructions. You need to change torch to AMD version +Same with the above instructions. You need to change torch to the AMD version pip uninstall torch torchvision torchaudio torchtext functorch xformers pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6 @@ -206,7 +206,7 @@ Use `python entry_with_update.py --preset anime` or `python entry_with_update.py Note that the [minimal requirement](#minimal-requirement) for different platforms is different. -Same with Windows. Download the software, edit the content of `run.bat` as: +Same with Windows. Download the software and edit the content of `run.bat` as: .\python_embeded\python.exe -m pip uninstall torch torchvision torchaudio torchtext functorch xformers -y .\python_embeded\python.exe -m pip install torch-directml @@ -233,7 +233,7 @@ You can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or 1. Create a new conda environment, `conda env create -f environment.yaml`. 1. Activate your new conda environment, `conda activate fooocus`. 1. Install the packages required by Fooocus, `pip install -r requirements_versions.txt`. -1. Launch Fooocus by running `python entry_with_update.py`. (Some Mac M2 users may need `python entry_with_update.py --disable-offload-from-vram` to speed up model loading/unloading.) The first time you run Fooocus, it will automatically download the Stable Diffusion SDXL models and will take a significant time, depending on your internet connection. +1. Launch Fooocus by running `python entry_with_update.py`. (Some Mac M2 users may need `python entry_with_update.py --disable-offload-from-vram` to speed up model loading/unloading.) The first time you run Fooocus, it will automatically download the Stable Diffusion SDXL models and will take a significant amount of time, depending on your internet connection. Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. @@ -261,7 +261,7 @@ Below is the minimal requirement for running Fooocus locally. If your device cap * AMD GPU ROCm (on hold): The AMD is still working on supporting ROCm on Windows. -* Nvidia GTX 1XXX 6GB uncertain: Some people reports 6GB success on GTX 10XX but some other people reports failure cases. +* Nvidia GTX 1XXX 6GB uncertain: Some people report 6GB success on GTX 10XX, but some other people report failure cases. *Note that Fooocus is only for extremely high quality image generating. We will not support smaller models to reduce the requirement and sacrifice result quality.* @@ -272,7 +272,7 @@ See the common problems [here](troubleshoot.md). ## Default Models -Given different goals, the default models and configs of Fooocus is different: +Given different goals, the default models and configs of Fooocus are different: | Task | Windows | Linux args | Main Model | Refiner | Config | | --- | --- | --- | --- | --- | --- | @@ -285,26 +285,26 @@ Note that the download is **automatic** - you do not need to do anything if the ## List of "Hidden" Tricks -Below things are already inside the software, and **users do not need to do anything about these**. +The below things are already inside the software, and **users do not need to do anything about these**. 1. GPT2-based [prompt expansion as a dynamic style "Fooocus V2".](https://github.com/lllyasviel/Fooocus/discussions/117#raw) (similar to Midjourney's hidden pre-processsing and "raw" mode, or the LeonardoAI's Prompt Magic). -2. Native refiner swap inside one single k-sampler. The advantage is that now the refiner model can reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually I discussed this with Automatic1111 several days ago and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!) -3. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Drawing Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!) -4. We implemented a carefully tuned variation of the Section 5.1 of ["Improving Sample Quality of Diffusion Models Using Self-Attention Guidance"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure that the XL will never yield overly smooth or plastic appearance (examples [here](https://github.com/lllyasviel/Fooocus/discussions/117#sharpness)). This can almostly eliminate all cases that XL still occasionally produce overly smooth results even with negative ADM guidance. (Update 2023 Aug 18, the Gaussian kernel of SAG is changed to an anisotropic kernel for better structure preservation and fewer artifacts.) +2. Native refiner swap inside one single k-sampler. The advantage is that the refiner model can now reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually, I discussed this with Automatic1111 several days ago, and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!) +3. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results to look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Drawing Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!) +4. We implemented a carefully tuned variation of Section 5.1 of ["Improving Sample Quality of Diffusion Models Using Self-Attention Guidance"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure that the XL will never yield an overly smooth or plastic appearance (examples [here](https://github.com/lllyasviel/Fooocus/discussions/117#sharpness)). This can almost eliminate all cases for which XL still occasionally produces overly smooth results, even with negative ADM guidance. (Update 2023 Aug 18, the Gaussian kernel of SAG is changed to an anisotropic kernel for better structure preservation and fewer artifacts.) 5. We modified the style templates a bit and added the "cinematic-default". 6. We tested the "sd_xl_offset_example-lora_1.0.safetensors" and it seems that when the lora weight is below 0.5, the results are always better than XL without lora. 7. The parameters of samplers are carefully tuned. -8. Because XL uses positional encoding for generation resolution, images generated by several fixed resolutions look a bit better than that from arbitrary resolutions (because the positional encoding is not very good at handling int numbers that are unseen during training). This suggests that the resolutions in UI may be hard coded for best results. -9. Separated prompts for two different text encoders seem unnecessary. Separated prompts for base model and refiner may work but the effects are random, and we refrain from implement this. -10. DPM family seems well-suited for XL, since XL sometimes generates overly smooth texture but DPM family sometimes generate overly dense detail in texture. Their joint effect looks neutral and appealing to human perception. +8. Because XL uses positional encoding for generation resolution, images generated by several fixed resolutions look a bit better than those from arbitrary resolutions (because the positional encoding is not very good at handling int numbers that are unseen during training). This suggests that the resolutions in UI may be hard coded for best results. +9. Separated prompts for two different text encoders seem unnecessary. Separated prompts for the base model and refiner may work, but the effects are random, and we refrain from implementing this. +10. The DPM family seems well-suited for XL since XL sometimes generates overly smooth texture, but the DPM family sometimes generates overly dense detail in texture. Their joint effect looks neutral and appealing to human perception. 11. A carefully designed system for balancing multiple styles as well as prompt expansion. -12. Using automatic1111's method to normalize prompt emphasizing. This significantly improve results when users directly copy prompts from civitai. -13. The joint swap system of refiner now also support img2img and upscale in a seamless way. +12. Using automatic1111's method to normalize prompt emphasizing. This significantly improves results when users directly copy prompts from civitai. +13. The joint swap system of the refiner now also supports img2img and upscale in a seamless way. 14. CFG Scale and TSNR correction (tuned for SDXL) when CFG is bigger than 10. ## Customization -After the first time you run Fooocus, a config file will be generated at `Fooocus\config.txt`. This file can be edited for changing the model path or default parameters. +After the first time you run Fooocus, a config file will be generated at `Fooocus\config.txt`. This file can be edited to change the model path or default parameters. For example, an edited `Fooocus\config.txt` (this file will be generated after the first launch) may look like this: @@ -340,7 +340,7 @@ Many other keys, formats, and examples are in `Fooocus\config_modification_tutor Consider twice before you really change the config. If you find yourself breaking things, just delete `Fooocus\config.txt`. Fooocus will go back to default. -A safter way is just to try "run_anime.bat" or "run_realistic.bat" - they should be already good enough for different tasks. +A safer way is just to try "run_anime.bat" or "run_realistic.bat" - they should already be good enough for different tasks. ~Note that `user_path_config.txt` is deprecated and will be removed soon.~ (Edit: it is already removed.) @@ -394,7 +394,7 @@ The log is [here](update_log.md). ## Localization/Translation/I18N -**We need your help!** Please help with translating Fooocus to international languages. +**We need your help!** Please help translate Fooocus into international languages. You can put json files in the `language` folder to translate the user interface. From 8e62a72a63b30a3067d1a1bc3f8d226824bd9283 Mon Sep 17 00:00:00 2001 From: lllyasviel Date: Sat, 30 Dec 2023 06:30:59 -0800 Subject: [PATCH 02/12] (requested) support AMD 8GB GPUs via Windows DirectML this update is requested by users --- fooocus_version.py | 2 +- ldm_patched/contrib/external.py | 41 ++++++--- .../contrib/external_custom_sampler.py | 6 +- ldm_patched/contrib/external_mask.py | 3 +- ldm_patched/contrib/external_rebatch.py | 32 ++++++- ldm_patched/contrib/external_sag.py | 2 +- ldm_patched/contrib/external_stable3d.py | 60 ++++++++++++ ldm_patched/ldm/models/autoencoder.py | 5 +- .../ldm/modules/diffusionmodules/model.py | 2 +- .../ldm/modules/diffusionmodules/upscaling.py | 4 +- .../ldm/modules/diffusionmodules/util.py | 6 +- .../ldm/modules/encoders/noise_aug_modules.py | 4 +- ldm_patched/ldm/modules/temporal_ae.py | 8 +- ldm_patched/modules/args_parser.py | 2 + ldm_patched/modules/clip_model.py | 2 +- ldm_patched/modules/controlnet.py | 2 +- ldm_patched/modules/model_base.py | 46 +++++++++- ldm_patched/modules/model_management.py | 69 +++++++++----- ldm_patched/modules/model_patcher.py | 16 ++-- ldm_patched/modules/ops.py | 92 ++++++++++++++----- ldm_patched/modules/sample.py | 3 +- ldm_patched/modules/samplers.py | 16 ++-- ldm_patched/modules/supported_models.py | 29 +++++- ldm_patched/taesd/taesd.py | 5 +- ldm_patched/utils/path_utils.py | 3 +- modules/ops.py | 19 ++++ modules/patch.py | 6 +- modules/patch_clip.py | 20 +--- modules/sample_hijack.py | 16 ++-- readme.md | 25 +++-- update_log.md | 4 + 31 files changed, 402 insertions(+), 148 deletions(-) create mode 100644 ldm_patched/contrib/external_stable3d.py create mode 100644 modules/ops.py diff --git a/fooocus_version.py b/fooocus_version.py index b8f79381..06533e42 100644 --- a/fooocus_version.py +++ b/fooocus_version.py @@ -1 +1 @@ -version = '2.1.856' +version = '2.1.857' diff --git a/ldm_patched/contrib/external.py b/ldm_patched/contrib/external.py index 7f95f084..9d2238df 100644 --- a/ldm_patched/contrib/external.py +++ b/ldm_patched/contrib/external.py @@ -11,7 +11,7 @@ import math import time import random -from PIL import Image, ImageOps +from PIL import Image, ImageOps, ImageSequence from PIL.PngImagePlugin import PngInfo import numpy as np import safetensors.torch @@ -1412,17 +1412,30 @@ class LoadImage: FUNCTION = "load_image" def load_image(self, 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") - image = np.array(image).astype(np.float32) / 255.0 - image = torch.from_numpy(image)[None,] - if 'A' in i.getbands(): - mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 - mask = 1. - torch.from_numpy(mask) + img = Image.open(image_path) + output_images = [] + output_masks = [] + for i in ImageSequence.Iterator(img): + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + output_images.append(image) + output_masks.append(mask.unsqueeze(0)) + + if len(output_images) > 1: + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) else: - mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") - return (image, mask.unsqueeze(0)) + output_image = output_images[0] + output_mask = output_masks[0] + + return (output_image, output_mask) @classmethod def IS_CHANGED(s, image): @@ -1480,13 +1493,10 @@ class LoadImageMask: return m.digest().hex() @classmethod - def VALIDATE_INPUTS(s, image, channel): + def VALIDATE_INPUTS(s, 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: - return "Invalid color channel: {}".format(channel) - return True class ImageScale: @@ -1871,6 +1881,7 @@ def init_custom_nodes(): "nodes_video_model.py", "nodes_sag.py", "nodes_perpneg.py", + "nodes_stable3d.py", ] for node_file in extras_files: diff --git a/ldm_patched/contrib/external_custom_sampler.py b/ldm_patched/contrib/external_custom_sampler.py index 9413a58f..6e5a769b 100644 --- a/ldm_patched/contrib/external_custom_sampler.py +++ b/ldm_patched/contrib/external_custom_sampler.py @@ -89,6 +89,7 @@ class SDTurboScheduler: return {"required": {"model": ("MODEL",), "steps": ("INT", {"default": 1, "min": 1, "max": 10}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) @@ -96,8 +97,9 @@ class SDTurboScheduler: FUNCTION = "get_sigmas" - def get_sigmas(self, model, steps): - timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[:steps] + def get_sigmas(self, model, steps, denoise): + start_step = 10 - int(10 * denoise) + timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] sigmas = model.model.model_sampling.sigma(timesteps) sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) return (sigmas, ) diff --git a/ldm_patched/contrib/external_mask.py b/ldm_patched/contrib/external_mask.py index ab1da4c6..a86a7fe6 100644 --- a/ldm_patched/contrib/external_mask.py +++ b/ldm_patched/contrib/external_mask.py @@ -8,6 +8,7 @@ import ldm_patched.modules.utils from ldm_patched.contrib.external import MAX_RESOLUTION def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): + source = source.to(destination.device) if resize_source: source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") @@ -22,7 +23,7 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou if mask is None: mask = torch.ones_like(source) else: - mask = mask.clone() + mask = mask.to(destination.device, copy=True) 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 = ldm_patched.modules.utils.repeat_to_batch_size(mask, source.shape[0]) diff --git a/ldm_patched/contrib/external_rebatch.py b/ldm_patched/contrib/external_rebatch.py index 607c7feb..c24cc8c3 100644 --- a/ldm_patched/contrib/external_rebatch.py +++ b/ldm_patched/contrib/external_rebatch.py @@ -101,10 +101,40 @@ class LatentRebatch: return (output_list,) +class ImageRebatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "images": ("IMAGE",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("IMAGE",) + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + FUNCTION = "rebatch" + + CATEGORY = "image/batch" + + def rebatch(self, images, batch_size): + batch_size = batch_size[0] + + output_list = [] + all_images = [] + for img in images: + for i in range(img.shape[0]): + all_images.append(img[i:i+1]) + + for i in range(0, len(all_images), batch_size): + output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) + + return (output_list,) + NODE_CLASS_MAPPINGS = { "RebatchLatents": LatentRebatch, + "RebatchImages": ImageRebatch, } NODE_DISPLAY_NAME_MAPPINGS = { "RebatchLatents": "Rebatch Latents", -} \ No newline at end of file + "RebatchImages": "Rebatch Images", +} diff --git a/ldm_patched/contrib/external_sag.py b/ldm_patched/contrib/external_sag.py index 06ca67fa..9cffe879 100644 --- a/ldm_patched/contrib/external_sag.py +++ b/ldm_patched/contrib/external_sag.py @@ -153,7 +153,7 @@ class SelfAttentionGuidance: (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) + m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) # from diffusers: # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch diff --git a/ldm_patched/contrib/external_stable3d.py b/ldm_patched/contrib/external_stable3d.py new file mode 100644 index 00000000..2913a3d0 --- /dev/null +++ b/ldm_patched/contrib/external_stable3d.py @@ -0,0 +1,60 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +import ldm_patched.contrib.external +import ldm_patched.modules.utils + +def camera_embeddings(elevation, azimuth): + elevation = torch.as_tensor([elevation]) + azimuth = torch.as_tensor([azimuth]) + embeddings = torch.stack( + [ + torch.deg2rad( + (90 - elevation) - (90) + ), # Zero123 polar is 90-elevation + torch.sin(torch.deg2rad(azimuth)), + torch.cos(torch.deg2rad(azimuth)), + torch.deg2rad( + 90 - torch.full_like(elevation, 0) + ), + ], dim=-1).unsqueeze(1) + + return embeddings + + +class StableZero123_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): + 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] + t = vae.encode(encode_pixels) + cam_embeds = camera_embeddings(elevation, azimuth) + cond = torch.cat([pooled, cam_embeds.repeat((pooled.shape[0], 1, 1))], dim=-1) + + positive = [[cond, {"concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + +NODE_CLASS_MAPPINGS = { + "StableZero123_Conditioning": StableZero123_Conditioning, +} diff --git a/ldm_patched/ldm/models/autoencoder.py b/ldm_patched/ldm/models/autoencoder.py index 14224ad3..c809a0c3 100644 --- a/ldm_patched/ldm/models/autoencoder.py +++ b/ldm_patched/ldm/models/autoencoder.py @@ -8,6 +8,7 @@ from ldm_patched.ldm.modules.distributions.distributions import DiagonalGaussian from ldm_patched.ldm.util import instantiate_from_config from ldm_patched.ldm.modules.ema import LitEma +import ldm_patched.modules.ops class DiagonalGaussianRegularizer(torch.nn.Module): def __init__(self, sample: bool = True): @@ -161,12 +162,12 @@ class AutoencodingEngineLegacy(AutoencodingEngine): }, **kwargs, ) - self.quant_conv = torch.nn.Conv2d( + self.quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d( (1 + ddconfig["double_z"]) * ddconfig["z_channels"], (1 + ddconfig["double_z"]) * embed_dim, 1, ) - self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.post_quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim def get_autoencoder_params(self) -> list: diff --git a/ldm_patched/ldm/modules/diffusionmodules/model.py b/ldm_patched/ldm/modules/diffusionmodules/model.py index 9c898639..1901145c 100644 --- a/ldm_patched/ldm/modules/diffusionmodules/model.py +++ b/ldm_patched/ldm/modules/diffusionmodules/model.py @@ -41,7 +41,7 @@ def nonlinearity(x): def Normalize(in_channels, num_groups=32): - return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) class Upsample(nn.Module): diff --git a/ldm_patched/ldm/modules/diffusionmodules/upscaling.py b/ldm_patched/ldm/modules/diffusionmodules/upscaling.py index 59d4d3cc..2cde80c5 100644 --- a/ldm_patched/ldm/modules/diffusionmodules/upscaling.py +++ b/ldm_patched/ldm/modules/diffusionmodules/upscaling.py @@ -43,8 +43,8 @@ class AbstractLowScaleModel(nn.Module): def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) - return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise) def forward(self, x): return x, None diff --git a/ldm_patched/ldm/modules/diffusionmodules/util.py b/ldm_patched/ldm/modules/diffusionmodules/util.py index ca0f4b99..e261e06a 100644 --- a/ldm_patched/ldm/modules/diffusionmodules/util.py +++ b/ldm_patched/ldm/modules/diffusionmodules/util.py @@ -51,9 +51,9 @@ class AlphaBlender(nn.Module): if self.merge_strategy == "fixed": # make shape compatible # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) - alpha = self.mix_factor + alpha = self.mix_factor.to(image_only_indicator.device) elif self.merge_strategy == "learned": - alpha = torch.sigmoid(self.mix_factor) + alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device)) # make shape compatible # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) elif self.merge_strategy == "learned_with_images": @@ -61,7 +61,7 @@ class AlphaBlender(nn.Module): alpha = torch.where( image_only_indicator.bool(), torch.ones(1, 1, device=image_only_indicator.device), - rearrange(torch.sigmoid(self.mix_factor), "... -> ... 1"), + rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), ) alpha = rearrange(alpha, self.rearrange_pattern) # make shape compatible diff --git a/ldm_patched/ldm/modules/encoders/noise_aug_modules.py b/ldm_patched/ldm/modules/encoders/noise_aug_modules.py index b59bf204..66767b58 100644 --- a/ldm_patched/ldm/modules/encoders/noise_aug_modules.py +++ b/ldm_patched/ldm/modules/encoders/noise_aug_modules.py @@ -15,12 +15,12 @@ class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): def scale(self, x): # re-normalize to centered mean and unit variance - x = (x - self.data_mean) * 1. / self.data_std + x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device) return x def unscale(self, x): # back to original data stats - x = (x * self.data_std) + self.data_mean + x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) return x def forward(self, x, noise_level=None): diff --git a/ldm_patched/ldm/modules/temporal_ae.py b/ldm_patched/ldm/modules/temporal_ae.py index 248d850b..ee851921 100644 --- a/ldm_patched/ldm/modules/temporal_ae.py +++ b/ldm_patched/ldm/modules/temporal_ae.py @@ -82,14 +82,14 @@ class VideoResBlock(ResnetBlock): x = self.time_stack(x, temb) - alpha = self.get_alpha(bs=b // timesteps) + alpha = self.get_alpha(bs=b // timesteps).to(x.device) 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): +class AE3DConv(ops.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): @@ -97,7 +97,7 @@ class AE3DConv(torch.nn.Conv2d): else: padding = int(video_kernel_size // 2) - self.time_mix_conv = torch.nn.Conv3d( + self.time_mix_conv = ops.Conv3d( in_channels=out_channels, out_channels=out_channels, kernel_size=video_kernel_size, @@ -167,7 +167,7 @@ class AttnVideoBlock(AttnBlock): emb = emb[:, None, :] x_mix = x_mix + emb - alpha = self.get_alpha() + alpha = self.get_alpha().to(x.device) x_mix = self.time_mix_block(x_mix, timesteps=timesteps) x = alpha * x + (1.0 - alpha) * x_mix # alpha merge diff --git a/ldm_patched/modules/args_parser.py b/ldm_patched/modules/args_parser.py index 7957783e..7ffc4a81 100644 --- a/ldm_patched/modules/args_parser.py +++ b/ldm_patched/modules/args_parser.py @@ -66,6 +66,8 @@ 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") +parser.add_argument("--vae-in-cpu", 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") diff --git a/ldm_patched/modules/clip_model.py b/ldm_patched/modules/clip_model.py index e7f3fb2d..4c4588c3 100644 --- a/ldm_patched/modules/clip_model.py +++ b/ldm_patched/modules/clip_model.py @@ -151,7 +151,7 @@ class CLIPVisionEmbeddings(torch.nn.Module): def forward(self, pixel_values): embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) - return torch.cat([self.class_embedding.expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight + return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device) class CLIPVision(torch.nn.Module): diff --git a/ldm_patched/modules/controlnet.py b/ldm_patched/modules/controlnet.py index e478e221..a7224660 100644 --- a/ldm_patched/modules/controlnet.py +++ b/ldm_patched/modules/controlnet.py @@ -283,7 +283,7 @@ class ControlLora(ControlNet): cm = self.control_model.state_dict() for k in sd: - weight = ldm_patched.modules.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k) + weight = sd[k] try: ldm_patched.modules.utils.set_attr(self.control_model, k, weight) except: diff --git a/ldm_patched/modules/model_base.py b/ldm_patched/modules/model_base.py index 1374a669..c04ccb3e 100644 --- a/ldm_patched/modules/model_base.py +++ b/ldm_patched/modules/model_base.py @@ -126,9 +126,15 @@ class BaseModel(torch.nn.Module): cond_concat.append(blank_inpaint_image_like(noise)) data = torch.cat(cond_concat, dim=1) out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(data) + adm = self.encode_adm(**kwargs) if adm is not None: out['y'] = ldm_patched.modules.conds.CONDRegular(adm) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) + return out def load_model_weights(self, sd, unet_prefix=""): @@ -156,11 +162,7 @@ class BaseModel(torch.nn.Module): def state_dict_for_saving(self, clip_state_dict, vae_state_dict): clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict) - unet_sd = self.diffusion_model.state_dict() - unet_state_dict = {} - for k in unet_sd: - unet_state_dict[k] = ldm_patched.modules.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k) - + unet_state_dict = self.diffusion_model.state_dict() unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict) if self.get_dtype() == torch.float16: @@ -322,9 +324,43 @@ class SVD_img2vid(BaseModel): out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) + if "time_conditioning" in kwargs: out["time_context"] = ldm_patched.modules.conds.CONDCrossAttn(kwargs["time_conditioning"]) out['image_only_indicator'] = ldm_patched.modules.conds.CONDConstant(torch.zeros((1,), device=device)) out['num_video_frames'] = ldm_patched.modules.conds.CONDConstant(noise.shape[0]) return out + +class Stable_Zero123(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): + super().__init__(model_config, model_type, device=device) + self.cc_projection = ldm_patched.modules.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) + self.cc_projection.weight.copy_(cc_projection_weight) + self.cc_projection.bias.copy_(cc_projection_bias) + + def extra_conds(self, **kwargs): + out = {} + + latent_image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + + if latent_image is None: + latent_image = torch.zeros_like(noise) + + if latent_image.shape[1:] != noise.shape[1:]: + latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) + + out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + if cross_attn.shape[-1] != 768: + cross_attn = self.cc_projection(cross_attn) + out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) + return out diff --git a/ldm_patched/modules/model_management.py b/ldm_patched/modules/model_management.py index 31cf95da..59f0f3d0 100644 --- a/ldm_patched/modules/model_management.py +++ b/ldm_patched/modules/model_management.py @@ -186,6 +186,9 @@ except: if is_intel_xpu(): VAE_DTYPE = torch.bfloat16 +if args.vae_in_cpu: + VAE_DTYPE = torch.float32 + if args.vae_in_fp16: VAE_DTYPE = torch.float16 elif args.vae_in_bf16: @@ -218,15 +221,8 @@ if args.all_in_fp16: FORCE_FP16 = True if lowvram_available: - try: - import accelerate - if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): - vram_state = set_vram_to - except Exception as e: - import traceback - print(traceback.format_exc()) - print("ERROR: LOW VRAM MODE NEEDS accelerate.") - lowvram_available = False + if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): + vram_state = set_vram_to if cpu_state != CPUState.GPU: @@ -266,6 +262,14 @@ print("VAE dtype:", VAE_DTYPE) current_loaded_models = [] +def module_size(module): + module_mem = 0 + sd = module.state_dict() + for k in sd: + t = sd[k] + module_mem += t.nelement() * t.element_size() + return module_mem + class LoadedModel: def __init__(self, model): self.model = model @@ -298,8 +302,20 @@ class LoadedModel: if lowvram_model_memory > 0: print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024)) - device_map = accelerate.infer_auto_device_map(self.real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"}) - accelerate.dispatch_model(self.real_model, device_map=device_map, main_device=self.device) + mem_counter = 0 + for m in self.real_model.modules(): + if hasattr(m, "ldm_patched_cast_weights"): + m.prev_ldm_patched_cast_weights = m.ldm_patched_cast_weights + m.ldm_patched_cast_weights = True + module_mem = module_size(m) + if mem_counter + module_mem < lowvram_model_memory: + m.to(self.device) + mem_counter += module_mem + elif hasattr(m, "weight"): #only modules with ldm_patched_cast_weights can be set to lowvram mode + m.to(self.device) + mem_counter += module_size(m) + print("lowvram: loaded module regularly", m) + self.model_accelerated = True if is_intel_xpu() and not args.disable_ipex_hijack: @@ -309,7 +325,11 @@ class LoadedModel: def model_unload(self): if self.model_accelerated: - accelerate.hooks.remove_hook_from_submodules(self.real_model) + for m in self.real_model.modules(): + if hasattr(m, "prev_ldm_patched_cast_weights"): + m.ldm_patched_cast_weights = m.prev_ldm_patched_cast_weights + del m.prev_ldm_patched_cast_weights + self.model_accelerated = False self.model.unpatch_model(self.model.offload_device) @@ -402,14 +422,14 @@ def load_models_gpu(models, memory_required=0): if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM): model_size = loaded_model.model_memory_required(torch_dev) current_free_mem = get_free_memory(torch_dev) - lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) + lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary vram_set_state = VRAMState.LOW_VRAM else: lowvram_model_memory = 0 if vram_set_state == VRAMState.NO_VRAM: - lowvram_model_memory = 256 * 1024 * 1024 + lowvram_model_memory = 64 * 1024 * 1024 cur_loaded_model = loaded_model.model_load(lowvram_model_memory) current_loaded_models.insert(0, loaded_model) @@ -538,6 +558,8 @@ def intermediate_device(): return torch.device("cpu") def vae_device(): + if args.vae_in_cpu: + return torch.device("cpu") return get_torch_device() def vae_offload_device(): @@ -566,6 +588,11 @@ def supports_dtype(device, dtype): #TODO return True return False +def device_supports_non_blocking(device): + if is_device_mps(device): + return False #pytorch bug? mps doesn't support non blocking + return True + def cast_to_device(tensor, device, dtype, copy=False): device_supports_cast = False if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: @@ -576,9 +603,7 @@ def cast_to_device(tensor, device, dtype, copy=False): elif is_intel_xpu(): device_supports_cast = True - non_blocking = True - if is_device_mps(device): - non_blocking = False #pytorch bug? mps doesn't support non blocking + non_blocking = device_supports_non_blocking(device) if device_supports_cast: if copy: @@ -742,11 +767,11 @@ def soft_empty_cache(force=False): torch.cuda.empty_cache() torch.cuda.ipc_collect() -def resolve_lowvram_weight(weight, model, key): - if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break. - key_split = key.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device. - op = ldm_patched.modules.utils.get_attr(model, '.'.join(key_split[:-1])) - weight = op._hf_hook.weights_map[key_split[-1]] +def unload_all_models(): + free_memory(1e30, get_torch_device()) + + +def resolve_lowvram_weight(weight, model, key): #TODO: remove return weight #TODO: might be cleaner to put this somewhere else diff --git a/ldm_patched/modules/model_patcher.py b/ldm_patched/modules/model_patcher.py index ae795ca9..0945a13c 100644 --- a/ldm_patched/modules/model_patcher.py +++ b/ldm_patched/modules/model_patcher.py @@ -28,13 +28,9 @@ class ModelPatcher: if self.size > 0: return self.size model_sd = self.model.state_dict() - size = 0 - for k in model_sd: - t = model_sd[k] - size += t.nelement() * t.element_size() - self.size = size + self.size = ldm_patched.modules.model_management.module_size(self.model) self.model_keys = set(model_sd.keys()) - return size + return self.size def clone(self): n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update) @@ -55,14 +51,18 @@ class ModelPatcher: def memory_required(self, input_shape): return self.model.memory_required(input_shape=input_shape) - def set_model_sampler_cfg_function(self, sampler_cfg_function): + def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False): if len(inspect.signature(sampler_cfg_function).parameters) == 3: self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way else: self.model_options["sampler_cfg_function"] = sampler_cfg_function + if disable_cfg1_optimization: + self.model_options["disable_cfg1_optimization"] = True - def set_model_sampler_post_cfg_function(self, post_cfg_function): + def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] + if disable_cfg1_optimization: + self.model_options["disable_cfg1_optimization"] = True def set_model_unet_function_wrapper(self, unet_wrapper_function): self.model_options["model_function_wrapper"] = unet_wrapper_function diff --git a/ldm_patched/modules/ops.py b/ldm_patched/modules/ops.py index 08c63384..435aba57 100644 --- a/ldm_patched/modules/ops.py +++ b/ldm_patched/modules/ops.py @@ -1,27 +1,93 @@ import torch from contextlib import contextmanager +import ldm_patched.modules.model_management + +def cast_bias_weight(s, input): + bias = None + non_blocking = ldm_patched.modules.model_management.device_supports_non_blocking(input.device) + if s.bias is not None: + bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) + weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) + return weight, bias + class disable_weight_init: class Linear(torch.nn.Linear): + ldm_patched_cast_weights = False def reset_parameters(self): return None + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + class Conv2d(torch.nn.Conv2d): + ldm_patched_cast_weights = False def reset_parameters(self): return None + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + class Conv3d(torch.nn.Conv3d): + ldm_patched_cast_weights = False def reset_parameters(self): return None + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + class GroupNorm(torch.nn.GroupNorm): + ldm_patched_cast_weights = False def reset_parameters(self): return None + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class LayerNorm(torch.nn.LayerNorm): + ldm_patched_cast_weights = False def reset_parameters(self): return None + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + @classmethod def conv_nd(s, dims, *args, **kwargs): if dims == 2: @@ -31,35 +97,19 @@ class disable_weight_init: else: raise ValueError(f"unsupported dimensions: {dims}") -def cast_bias_weight(s, input): - bias = None - if s.bias is not None: - bias = s.bias.to(device=input.device, dtype=input.dtype) - weight = s.weight.to(device=input.device, dtype=input.dtype) - return weight, bias class manual_cast(disable_weight_init): class Linear(disable_weight_init.Linear): - def forward(self, input): - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.linear(input, weight, bias) + ldm_patched_cast_weights = True class Conv2d(disable_weight_init.Conv2d): - def forward(self, input): - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) + ldm_patched_cast_weights = True class Conv3d(disable_weight_init.Conv3d): - def forward(self, input): - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) + ldm_patched_cast_weights = True class GroupNorm(disable_weight_init.GroupNorm): - def forward(self, input): - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) + ldm_patched_cast_weights = True class LayerNorm(disable_weight_init.LayerNorm): - def forward(self, input): - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) + ldm_patched_cast_weights = True diff --git a/ldm_patched/modules/sample.py b/ldm_patched/modules/sample.py index 7a7e3092..b5576cee 100644 --- a/ldm_patched/modules/sample.py +++ b/ldm_patched/modules/sample.py @@ -47,7 +47,8 @@ def convert_cond(cond): temp = c[1].copy() model_conds = temp.get("model_conds", {}) if c[0] is not None: - model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) + model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) #TODO: remove + temp["cross_attn"] = c[0] temp["model_conds"] = model_conds out.append(temp) return out diff --git a/ldm_patched/modules/samplers.py b/ldm_patched/modules/samplers.py index bfcb3f56..fc17ef4d 100644 --- a/ldm_patched/modules/samplers.py +++ b/ldm_patched/modules/samplers.py @@ -244,7 +244,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #The main sampling function shared by all the samplers #Returns denoised def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): - if math.isclose(cond_scale, 1.0): + if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: uncond_ = None else: uncond_ = uncond @@ -599,6 +599,13 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model calculate_start_end_timesteps(model, negative) calculate_start_end_timesteps(model, positive) + if latent_image is not None: + latent_image = model.process_latent_in(latent_image) + + if hasattr(model, 'extra_conds'): + positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) + negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) + #make sure each cond area has an opposite one with the same area for c in positive: create_cond_with_same_area_if_none(negative, c) @@ -610,13 +617,6 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) - if latent_image is not None: - latent_image = model.process_latent_in(latent_image) - - if hasattr(model, 'extra_conds'): - positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) - negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) - extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) diff --git a/ldm_patched/modules/supported_models.py b/ldm_patched/modules/supported_models.py index 2f2dee87..251bf6ac 100644 --- a/ldm_patched/modules/supported_models.py +++ b/ldm_patched/modules/supported_models.py @@ -252,5 +252,32 @@ class SVD_img2vid(supported_models_base.BASE): def clip_target(self): return None -models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega] +class Stable_Zero123(supported_models_base.BASE): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + "in_channels": 8, + } + + unet_extra_config = { + "num_heads": 8, + "num_head_channels": -1, + } + + clip_vision_prefix = "cond_stage_model.model.visual." + + latent_format = latent_formats.SD15 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) + return out + + def clip_target(self): + return None + + +models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega] models += [SVD_img2vid] diff --git a/ldm_patched/taesd/taesd.py b/ldm_patched/taesd/taesd.py index ac88e594..0b4b885f 100644 --- a/ldm_patched/taesd/taesd.py +++ b/ldm_patched/taesd/taesd.py @@ -7,9 +7,10 @@ import torch import torch.nn as nn import ldm_patched.modules.utils +import ldm_patched.modules.ops def conv(n_in, n_out, **kwargs): - return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + return ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) class Clamp(nn.Module): def forward(self, x): @@ -19,7 +20,7 @@ class Block(nn.Module): def __init__(self, n_in, n_out): super().__init__() self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) - self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() + self.skip = ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() self.fuse = nn.ReLU() def forward(self, x): return self.fuse(self.conv(x) + self.skip(x)) diff --git a/ldm_patched/utils/path_utils.py b/ldm_patched/utils/path_utils.py index 34cd52c9..d21b6485 100644 --- a/ldm_patched/utils/path_utils.py +++ b/ldm_patched/utils/path_utils.py @@ -184,8 +184,7 @@ def cached_filename_list_(folder_name): if folder_name not in filename_list_cache: return None out = filename_list_cache[folder_name] - if time.perf_counter() < (out[2] + 0.5): - return out + for x in out[1]: time_modified = out[1][x] folder = x diff --git a/modules/ops.py b/modules/ops.py new file mode 100644 index 00000000..ee0e7756 --- /dev/null +++ b/modules/ops.py @@ -0,0 +1,19 @@ +import torch +import contextlib + + +@contextlib.contextmanager +def use_patched_ops(operations): + op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm'] + backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names} + + try: + for op_name in op_names: + setattr(torch.nn, op_name, getattr(operations, op_name)) + + yield + + finally: + for op_name in op_names: + setattr(torch.nn, op_name, backups[op_name]) + return diff --git a/modules/patch.py b/modules/patch.py index 66b243cb..2e2409c5 100644 --- a/modules/patch.py +++ b/modules/patch.py @@ -218,7 +218,7 @@ def compute_cfg(uncond, cond, cfg_scale, t): def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None): global eps_record - if math.isclose(cond_scale, 1.0): + if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False): final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0] if eps_record is not None: @@ -480,6 +480,10 @@ def build_loaded(module, loader_name): def patch_all(): + if ldm_patched.modules.model_management.directml_enabled: + ldm_patched.modules.model_management.lowvram_available = True + ldm_patched.modules.model_management.OOM_EXCEPTION = Exception + patch_all_precision() patch_all_clip() diff --git a/modules/patch_clip.py b/modules/patch_clip.py index 74ee436a..06b7f01b 100644 --- a/modules/patch_clip.py +++ b/modules/patch_clip.py @@ -16,30 +16,12 @@ import ldm_patched.modules.samplers import ldm_patched.modules.sd import ldm_patched.modules.sd1_clip import ldm_patched.modules.clip_vision -import ldm_patched.modules.model_management as model_management import ldm_patched.modules.ops as ops -import contextlib +from modules.ops import use_patched_ops from transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection -@contextlib.contextmanager -def use_patched_ops(operations): - op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm'] - backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names} - - try: - for op_name in op_names: - setattr(torch.nn, op_name, getattr(operations, op_name)) - - yield - - finally: - for op_name in op_names: - setattr(torch.nn, op_name, backups[op_name]) - return - - def patched_encode_token_weights(self, token_weight_pairs): to_encode = list() max_token_len = 0 diff --git a/modules/sample_hijack.py b/modules/sample_hijack.py index 7d8f757b..5936a096 100644 --- a/modules/sample_hijack.py +++ b/modules/sample_hijack.py @@ -99,6 +99,13 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas calculate_start_end_timesteps(model, negative) calculate_start_end_timesteps(model, positive) + if latent_image is not None: + latent_image = model.process_latent_in(latent_image) + + if hasattr(model, 'extra_conds'): + positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) + negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) + #make sure each cond area has an opposite one with the same area for c in positive: create_cond_with_same_area_if_none(negative, c) @@ -111,13 +118,6 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) - if latent_image is not None: - latent_image = model.process_latent_in(latent_image) - - if hasattr(model, 'extra_conds'): - positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) - negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) - extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} if current_refiner is not None and hasattr(current_refiner.model, 'extra_conds'): @@ -174,7 +174,7 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps): elif scheduler_name == "sgm_uniform": sigmas = normal_scheduler(model, steps, sgm=True) elif scheduler_name == "turbo": - sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps)[0] + sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0] else: raise TypeError("error invalid scheduler") return sigmas diff --git a/readme.md b/readme.md index 5dfd96cb..5fd1baa7 100644 --- a/readme.md +++ b/readme.md @@ -245,19 +245,18 @@ See the guidelines [here](https://github.com/lllyasviel/Fooocus/discussions/1405 Below is the minimal requirement for running Fooocus locally. If your device capability is lower than this spec, you may not be able to use Fooocus locally. (Please let us know, in any case, if your device capability is lower but Fooocus still works.) -| Operating System | GPU | Minimal GPU Memory | Minimal System Memory | [System Swap](troubleshoot.md) | Note | -|-------------------|------------------------------|---------------------------|---------------------------|--------------------------------|---------------------------------------| -| Windows/Linux | Nvidia RTX 4XXX | 4GB | 8GB | Required | fastest | -| Windows/Linux | Nvidia RTX 3XXX | 4GB | 8GB | Required | usually faster than RTX 2XXX | -| Windows/Linux | Nvidia RTX 2XXX | 4GB | 8GB | Required | usually faster than GTX 1XXX | -| Windows/Linux | Nvidia GTX 1XXX | 8GB (* 6GB uncertain) | 8GB | Required | only marginally faster than CPU | -| Windows/Linux | Nvidia GTX 9XX | 8GB | 8GB | Required | faster or slower than CPU | -| Windows/Linux | Nvidia GTX < 9XX | Not supported | / | / | / | -| Windows | AMD GPU | 16GB | 8GB | Required | via DirectML | -| Linux | AMD GPU | 8GB | 8GB | Required | via ROCm | -| Windows | * AMD GPU ROCm (on hold) | 8GB (on hold) | 8GB (on hold) | Required (on hold) | via ROCm (on hold) | -| Mac | M1/M2 MPS | Shared | Shared | Shared | about 9x slower than Nvidia RTX 3XXX | -| Windows/Linux/Mac | only use CPU | 0GB | 32GB | Required | about 17x slower than Nvidia RTX 3XXX | +| Operating System | GPU | Minimal GPU Memory | Minimal System Memory | [System Swap](troubleshoot.md) | Note | +|-------------------|------------------------------|------------------------------|---------------------------|--------------------------------|----------------------------------------------------------------------------| +| Windows/Linux | Nvidia RTX 4XXX | 4GB | 8GB | Required | fastest | +| Windows/Linux | Nvidia RTX 3XXX | 4GB | 8GB | Required | usually faster than RTX 2XXX | +| Windows/Linux | Nvidia RTX 2XXX | 4GB | 8GB | Required | usually faster than GTX 1XXX | +| Windows/Linux | Nvidia GTX 1XXX | 8GB (* 6GB uncertain) | 8GB | Required | only marginally faster than CPU | +| Windows/Linux | Nvidia GTX 9XX | 8GB | 8GB | Required | faster or slower than CPU | +| Windows/Linux | Nvidia GTX < 9XX | Not supported | / | / | / | +| Windows | AMD GPU | 8GB (updated 2023 Dec 30) | 8GB | Required | via DirectML (* ROCm is on hold), about 3x slower than Nvidia RTX 3XXX | +| Linux | AMD GPU | 8GB | 8GB | Required | via ROCm, about 1.5x slower than Nvidia RTX 3XXX | +| Mac | M1/M2 MPS | Shared | Shared | Shared | about 9x slower than Nvidia RTX 3XXX | +| Windows/Linux/Mac | only use CPU | 0GB | 32GB | Required | about 17x slower than Nvidia RTX 3XXX | * AMD GPU ROCm (on hold): The AMD is still working on supporting ROCm on Windows. diff --git a/update_log.md b/update_log.md index 216a2bc4..66176145 100644 --- a/update_log.md +++ b/update_log.md @@ -1,5 +1,9 @@ **(2023 Dec 21) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid or late Jan. However, you may still see updates if other collaborators are fixing bugs or solving problems.** +# 2.1.857 (requested update) + +* Begin to support 8GB AMD GPU on Windows. + # 2.1.854 * Add a button to copy parameters to clipboard in log. From 2521af308b888d065dd4e5c3e58f788661550692 Mon Sep 17 00:00:00 2001 From: lllyasviel Date: Sun, 31 Dec 2023 03:38:16 -0800 Subject: [PATCH 03/12] try fix #1671 (#1678) --- extras/ip_adapter.py | 21 ++++++++++++--------- fooocus_version.py | 2 +- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/extras/ip_adapter.py b/extras/ip_adapter.py index cb1d366f..22527d24 100644 --- a/extras/ip_adapter.py +++ b/extras/ip_adapter.py @@ -2,12 +2,13 @@ import torch import ldm_patched.modules.clip_vision import safetensors.torch as sf import ldm_patched.modules.model_management as model_management -import contextlib import ldm_patched.ldm.modules.attention as attention from extras.resampler import Resampler from ldm_patched.modules.model_patcher import ModelPatcher from modules.core import numpy_to_pytorch +from modules.ops import use_patched_ops +from ldm_patched.modules.ops import manual_cast SD_V12_CHANNELS = [320] * 4 + [640] * 4 + [1280] * 4 + [1280] * 6 + [640] * 6 + [320] * 6 + [1280] * 2 @@ -116,14 +117,16 @@ def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path): clip_extra_context_tokens = ip_state_dict["image_proj"]["proj.weight"].shape[0] // cross_attention_dim clip_embeddings_dim = None - ip_adapter = IPAdapterModel( - ip_state_dict, - plus=plus, - cross_attention_dim=cross_attention_dim, - clip_embeddings_dim=clip_embeddings_dim, - clip_extra_context_tokens=clip_extra_context_tokens, - sdxl_plus=sdxl_plus - ) + with use_patched_ops(manual_cast): + ip_adapter = IPAdapterModel( + ip_state_dict, + plus=plus, + cross_attention_dim=cross_attention_dim, + clip_embeddings_dim=clip_embeddings_dim, + clip_extra_context_tokens=clip_extra_context_tokens, + sdxl_plus=sdxl_plus + ) + ip_adapter.sdxl = sdxl ip_adapter.load_device = load_device ip_adapter.offload_device = offload_device diff --git a/fooocus_version.py b/fooocus_version.py index 06533e42..66847e88 100644 --- a/fooocus_version.py +++ b/fooocus_version.py @@ -1 +1 @@ -version = '2.1.857' +version = '2.1.858' From 0c4f20a0d261d4fe045aae6e5a0fcad185ba6804 Mon Sep 17 00:00:00 2001 From: lllyasviel Date: Sun, 31 Dec 2023 04:38:31 -0800 Subject: [PATCH 04/12] try fix #1761 again (#1680) --- extras/resampler.py | 5 ++--- fooocus_version.py | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/extras/resampler.py b/extras/resampler.py index 4521c8c3..539f309d 100644 --- a/extras/resampler.py +++ b/extras/resampler.py @@ -108,8 +108,7 @@ class Resampler(nn.Module): ) def forward(self, x): - - latents = self.latents.repeat(x.size(0), 1, 1) + latents = self.latents.repeat(x.size(0), 1, 1).to(x) x = self.proj_in(x) @@ -118,4 +117,4 @@ class Resampler(nn.Module): latents = ff(latents) + latents latents = self.proj_out(latents) - return self.norm_out(latents) \ No newline at end of file + return self.norm_out(latents) diff --git a/fooocus_version.py b/fooocus_version.py index 66847e88..8cde204d 100644 --- a/fooocus_version.py +++ b/fooocus_version.py @@ -1 +1 @@ -version = '2.1.858' +version = '2.1.859' From b5163e057f7cad3dac2f885be1ad9140e693ed4b Mon Sep 17 00:00:00 2001 From: xhoxye <129571231+xhoxye@users.noreply.github.com> Date: Tue, 2 Jan 2024 23:17:02 +0800 Subject: [PATCH 05/12] =?UTF-8?q?Fooocus=20mask=20enhance,=20mask=20upload?= =?UTF-8?q?=20and=20invert.=E8=92=99=E7=89=88=E5=A2=9E=E5=BC=BA=E5=8A=9F?= =?UTF-8?q?=E8=83=BD=EF=BC=8C=E4=B8=8A=E4=BC=A0=E8=92=99=E7=89=88=E3=80=81?= =?UTF-8?q?=E5=8F=8D=E8=BD=AC=E6=89=8B=E6=B6=82=E8=92=99=E7=89=88=20(#1645?= =?UTF-8?q?)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update webui.py Added a mask upload interface.添加蒙版上传界面。 * Add mask upload feature Add mask upload feature.添加蒙版上传功能。 * Add mask upload feature Add mask upload feature.添加蒙版上传功能。 * Try to fix the problem when drawing mates with external enrichment. Swap the judgment criteria and try to fix the problem when drawing mates with external enrichment. 调换判断条件，尝试修复和外部扩充绘制配合时出现的问题 * 修改注释 * 修改注释 * Update readme.md 更新日志 * Add mask upload check box, Add check box, default does not show mask upload box, check it and then show it. 添加复选框，默认不显示蒙版上传框，勾选后再显示。 * back to normal 还原 * Try adding a mask inversion feature Try adding a mask inversion feature. 尝试添加蒙版反转功能。 增加一个复选框来决定是否将蒙版反转。 * Try adding a mask inversion feature Try adding a mask inversion feature. 尝试添加蒙版反转功能。 增加一个复选框来决定是否将蒙版反转。 * Fixed word errors * Fix the words of the description --- modules/async_worker.py | 32 +++++++++++++++++++++++++++++++- webui.py | 23 +++++++++++++++++++++-- 2 files changed, 52 insertions(+), 3 deletions(-) diff --git a/modules/async_worker.py b/modules/async_worker.py index fab2508e..48626b4c 100644 --- a/modules/async_worker.py +++ b/modules/async_worker.py @@ -22,6 +22,12 @@ def worker(): import shared import random import copy + + # xhoxye4 + # 导入 OpenCV 库 + import cv2 + # xhoxye4 + import modules.default_pipeline as pipeline import modules.core as core import modules.flags as flags @@ -138,6 +144,14 @@ def worker(): uov_input_image = args.pop() outpaint_selections = args.pop() inpaint_input_image = args.pop() + + # xhoxye5 + # 接收参数 + inpaint_mask_image = args.pop() + inpaint_mask_image_checkbox = args.pop() + invert_mask_checkbox = args.pop() + # xhoxye5 + inpaint_additional_prompt = args.pop() cn_tasks = {x: [] for x in flags.ip_list} @@ -273,7 +287,23 @@ def worker(): current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \ and isinstance(inpaint_input_image, dict): inpaint_image = inpaint_input_image['image'] - inpaint_mask = inpaint_input_image['mask'][:, :, 0] + + # xhoxye6 + #inpaint_mask = inpaint_input_image['mask'][:, :, 0] + # use uploaded inpaint mask image, if not brush for inpaint. + # 如果没有手涂蒙版，则使用上传蒙版，并缩放。调换判断条件，尝试修复和外部扩充绘制配合时出现的问题. + # 添加反转手涂蒙版的判断 + if inpaint_mask_image_checkbox and not np.any(inpaint_input_image['mask'] == [255, 255, 255]) and inpaint_mask_image is not None: + inpaint_height, inpaint_width = inpaint_image.shape[:2] + resized_mask_image = cv2.resize(inpaint_mask_image, (inpaint_width, inpaint_height)) + + inpaint_mask = resized_mask_image[:, :, 0] + else: + inpaint_mask = inpaint_input_image['mask'][:, :, 0] + if invert_mask_checkbox: + inpaint_mask = np.invert(inpaint_mask) + # xhoxye6 + inpaint_image = HWC3(inpaint_image) if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \ and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0): diff --git a/webui.py b/webui.py index 8653a554..f7b09f27 100644 --- a/webui.py +++ b/webui.py @@ -186,12 +186,28 @@ with shared.gradio_root: outputs=ip_ad_cols + ip_types + ip_stops + ip_weights, queue=False, show_progress=False) with gr.TabItem(label='Inpaint or Outpaint') as inpaint_tab: - inpaint_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas') + # xhoxye1 + #inpaint_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas') + + with gr.Row(): + inpaint_input_image = grh.Image(label='Drag inpaint or outpaint image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas') + + inpaint_mask_image = grh.Image(label='Drag inpaint mask image to here', source='upload', type='numpy', height=500,visible=False) + # xhoxye1 + with gr.Row(): inpaint_additional_prompt = gr.Textbox(placeholder="Describe what you want to inpaint.", elem_id='inpaint_additional_prompt', label='Inpaint Additional Prompt', visible=False) outpaint_selections = gr.CheckboxGroup(choices=['Left', 'Right', 'Top', 'Bottom'], value=[], label='Outpaint Direction') inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.flags.inpaint_option_default, label='Method') example_inpaint_prompts = gr.Dataset(samples=modules.config.example_inpaint_prompts, label='Additional Prompt Quick List', components=[inpaint_additional_prompt], visible=False) + + # xhoxye2 + with gr.TabItem(label='Inpaint advanced') as inpaint_advanced: + inpaint_mask_image_checkbox = gr.Checkbox(label='Enable upload mask', value=False, container=False) + inpaint_mask_image_checkbox.change(lambda x: gr.update(visible=x), inputs=inpaint_mask_image_checkbox, outputs=inpaint_mask_image, queue=False) + invert_mask_checkbox = gr.Checkbox(label='Invert hand-drawn mask', value=False, container=False) + # xhoxye2 + gr.HTML('* Powered by Fooocus Inpaint Engine \U0001F4D4 Document') example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False) with gr.TabItem(label='Describe') as desc_tab: @@ -510,7 +526,10 @@ with shared.gradio_root: ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls ctrls += [input_image_checkbox, current_tab] ctrls += [uov_method, uov_input_image] - ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt] + # xhoxye3 + #ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt] + ctrls += [outpaint_selections, inpaint_input_image, inpaint_mask_image, inpaint_mask_image_checkbox, invert_mask_checkbox, inpaint_additional_prompt] + # xhoxye3 ctrls += ip_ctrls state_is_generating = gr.State(False) From e5af760db847a2cf374e7aaa18e62998f8445d1c Mon Sep 17 00:00:00 2001 From: lllyasviel Date: Tue, 2 Jan 2024 07:37:53 -0800 Subject: [PATCH 06/12] mask upload --- fooocus_version.py | 2 +- modules/advanced_parameters.py | 9 ++++--- modules/async_worker.py | 47 ++++++++++++---------------------- modules/util.py | 10 ++++++++ update_log.md | 4 +++ webui.py | 38 +++++++++++++-------------- 6 files changed, 56 insertions(+), 54 deletions(-) diff --git a/fooocus_version.py b/fooocus_version.py index 8cde204d..482cc12c 100644 --- a/fooocus_version.py +++ b/fooocus_version.py @@ -1 +1 @@ -version = '2.1.859' +version = '2.1.860' diff --git a/modules/advanced_parameters.py b/modules/advanced_parameters.py index ea04db6c..0caa3eec 100644 --- a/modules/advanced_parameters.py +++ b/modules/advanced_parameters.py @@ -5,7 +5,8 @@ disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adapt debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \ refiner_swap_method, \ freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \ - debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field = [None] * 32 + debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \ + inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = [None] * 35 def set_all_advanced_parameters(*args): @@ -16,7 +17,8 @@ def set_all_advanced_parameters(*args): debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \ refiner_swap_method, \ freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \ - debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field + debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \ + inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, \ scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, \ @@ -25,6 +27,7 @@ def set_all_advanced_parameters(*args): debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, \ refiner_swap_method, \ freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \ - debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field = args + debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \ + inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = args return diff --git a/modules/async_worker.py b/modules/async_worker.py index 48626b4c..b2af6712 100644 --- a/modules/async_worker.py +++ b/modules/async_worker.py @@ -22,12 +22,6 @@ def worker(): import shared import random import copy - - # xhoxye4 - # 导入 OpenCV 库 - import cv2 - # xhoxye4 - import modules.default_pipeline as pipeline import modules.core as core import modules.flags as flags @@ -46,7 +40,7 @@ def worker(): from modules.private_logger import log from extras.expansion import safe_str from modules.util import remove_empty_str, HWC3, resize_image, \ - get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image + get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate from modules.upscaler import perform_upscale try: @@ -144,15 +138,8 @@ def worker(): uov_input_image = args.pop() outpaint_selections = args.pop() inpaint_input_image = args.pop() - - # xhoxye5 - # 接收参数 - inpaint_mask_image = args.pop() - inpaint_mask_image_checkbox = args.pop() - invert_mask_checkbox = args.pop() - # xhoxye5 - inpaint_additional_prompt = args.pop() + inpaint_mask_image_upload = args.pop() cn_tasks = {x: [] for x in flags.ip_list} for _ in range(4): @@ -287,22 +274,22 @@ def worker(): current_tab == 'ip' and advanced_parameters.mixing_image_prompt_and_inpaint)) \ and isinstance(inpaint_input_image, dict): inpaint_image = inpaint_input_image['image'] - - # xhoxye6 - #inpaint_mask = inpaint_input_image['mask'][:, :, 0] - # use uploaded inpaint mask image, if not brush for inpaint. - # 如果没有手涂蒙版，则使用上传蒙版，并缩放。调换判断条件，尝试修复和外部扩充绘制配合时出现的问题. - # 添加反转手涂蒙版的判断 - if inpaint_mask_image_checkbox and not np.any(inpaint_input_image['mask'] == [255, 255, 255]) and inpaint_mask_image is not None: - inpaint_height, inpaint_width = inpaint_image.shape[:2] - resized_mask_image = cv2.resize(inpaint_mask_image, (inpaint_width, inpaint_height)) + inpaint_mask = inpaint_input_image['mask'][:, :, 0] + + if advanced_parameters.inpaint_mask_upload_checkbox: + if isinstance(inpaint_mask_image_upload, np.ndarray): + if inpaint_mask_image_upload.ndim == 3: + H, W, C = inpaint_image.shape + inpaint_mask_image_upload = resample_image(inpaint_mask_image_upload, width=W, height=H) + inpaint_mask_image_upload = np.mean(inpaint_mask_image_upload, axis=2) + inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255 + inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload) - inpaint_mask = resized_mask_image[:, :, 0] - else: - inpaint_mask = inpaint_input_image['mask'][:, :, 0] - if invert_mask_checkbox: - inpaint_mask = np.invert(inpaint_mask) - # xhoxye6 + if int(advanced_parameters.inpaint_erode_or_dilate) != 0: + inpaint_mask = erode_or_dilate(inpaint_mask, advanced_parameters.inpaint_erode_or_dilate) + + if advanced_parameters.invert_mask_checkbox: + inpaint_mask = 255 - inpaint_mask inpaint_image = HWC3(inpaint_image) if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \ diff --git a/modules/util.py b/modules/util.py index fce7efd7..052b746b 100644 --- a/modules/util.py +++ b/modules/util.py @@ -3,6 +3,7 @@ import datetime import random import math import os +import cv2 from PIL import Image @@ -10,6 +11,15 @@ from PIL import Image LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) +def erode_or_dilate(x, k): + k = int(k) + if k > 0: + return cv2.dilate(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=k) + if k < 0: + return cv2.erode(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=-k) + return x + + def resample_image(im, width, height): im = Image.fromarray(im) im = im.resize((int(width), int(height)), resample=LANCZOS) diff --git a/update_log.md b/update_log.md index 66176145..e9a829be 100644 --- a/update_log.md +++ b/update_log.md @@ -1,5 +1,9 @@ **(2023 Dec 21) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid or late Jan. However, you may still see updates if other collaborators are fixing bugs or solving problems.** +# 2.1.860 (requested update) + +* Allow upload inpaint mask in developer mode. + # 2.1.857 (requested update) * Begin to support 8GB AMD GPU on Windows. diff --git a/webui.py b/webui.py index f7b09f27..581e3101 100644 --- a/webui.py +++ b/webui.py @@ -186,28 +186,15 @@ with shared.gradio_root: outputs=ip_ad_cols + ip_types + ip_stops + ip_weights, queue=False, show_progress=False) with gr.TabItem(label='Inpaint or Outpaint') as inpaint_tab: - # xhoxye1 - #inpaint_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas') - with gr.Row(): inpaint_input_image = grh.Image(label='Drag inpaint or outpaint image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas') - - inpaint_mask_image = grh.Image(label='Drag inpaint mask image to here', source='upload', type='numpy', height=500,visible=False) - # xhoxye1 - + inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', height=500, visible=False) + with gr.Row(): inpaint_additional_prompt = gr.Textbox(placeholder="Describe what you want to inpaint.", elem_id='inpaint_additional_prompt', label='Inpaint Additional Prompt', visible=False) outpaint_selections = gr.CheckboxGroup(choices=['Left', 'Right', 'Top', 'Bottom'], value=[], label='Outpaint Direction') inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.flags.inpaint_option_default, label='Method') example_inpaint_prompts = gr.Dataset(samples=modules.config.example_inpaint_prompts, label='Additional Prompt Quick List', components=[inpaint_additional_prompt], visible=False) - - # xhoxye2 - with gr.TabItem(label='Inpaint advanced') as inpaint_advanced: - inpaint_mask_image_checkbox = gr.Checkbox(label='Enable upload mask', value=False, container=False) - inpaint_mask_image_checkbox.change(lambda x: gr.update(visible=x), inputs=inpaint_mask_image_checkbox, outputs=inpaint_mask_image, queue=False) - invert_mask_checkbox = gr.Checkbox(label='Invert hand-drawn mask', value=False, container=False) - # xhoxye2 - gr.HTML('* Powered by Fooocus Inpaint Engine \U0001F4D4 Document') example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False) with gr.TabItem(label='Describe') as desc_tab: @@ -435,7 +422,21 @@ with shared.gradio_root: 'Value 1 is same as "Whole Image" in A1111. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') - inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field] + inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate', + minimum=-64, maximum=64, step=1, value=0, + info='Positive value will make white area in the mask larger, ' + 'negative value will make white area smaller.' + '(default is 0, always process before any mask invert)') + inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False) + invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False) + + inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, + inpaint_strength, inpaint_respective_field, + inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate] + + inpaint_mask_upload_checkbox.change(lambda x: gr.update(visible=x), + inputs=inpaint_mask_upload_checkbox, + outputs=inpaint_mask_image, queue=False, show_progress=False) with gr.Tab(label='FreeU'): freeu_enabled = gr.Checkbox(label='Enabled', value=False) @@ -526,10 +527,7 @@ with shared.gradio_root: ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls ctrls += [input_image_checkbox, current_tab] ctrls += [uov_method, uov_input_image] - # xhoxye3 - #ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt] - ctrls += [outpaint_selections, inpaint_input_image, inpaint_mask_image, inpaint_mask_image_checkbox, invert_mask_checkbox, inpaint_additional_prompt] - # xhoxye3 + ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image] ctrls += ip_ctrls state_is_generating = gr.State(False) From 176faf6f347b90866afe676fc9fb2c2d74587d7b Mon Sep 17 00:00:00 2001 From: Manuel Schmid <9307310+mashb1t@users.noreply.github.com> Date: Thu, 4 Jan 2024 14:46:17 +0100 Subject: [PATCH 07/12] fixes https://github.com/lllyasviel/Fooocus/issues/1747 (#1750) --- readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/readme.md b/readme.md index 5fd1baa7..87c44b83 100644 --- a/readme.md +++ b/readme.md @@ -217,7 +217,7 @@ Then run the `run.bat`. AMD is not intensively tested, however. The AMD support is in beta. -For AMD, use `python entry_with_update.py --directml --preset anime` or `python entry_with_update.py --directml --preset realistic` for Fooocus Anime/Realistic Edition. +For AMD, use `.\python_embeded\python.exe entry_with_update.py --directml --preset anime` or `.\python_embeded\python.exe entry_with_update.py --directml --preset realistic` for Fooocus Anime/Realistic Edition. ### Mac From f298ebca76054ccaeb3030e5ff0544956ed5ad9f Mon Sep 17 00:00:00 2001 From: Chris Rohrer Date: Mon, 8 Jan 2024 00:10:42 +0100 Subject: [PATCH 08/12] feat: adds preview overlay for styles (#1762) * feat: adds preview overlay for styles * fix: implement correct path resolution for all OS solves issues for Windows * fix: makes preview overlay also work with other languages * feat: scale down images to 258x200, fix aspect ratio in css reduces file size from 19,6MB to 3,4MB * fix: adjust z-index to correctly overlay gradio border pulse when generating images * update files * 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sdxl_styles/samples/sai_origami.jpg create mode 100644 sdxl_styles/samples/sai_photographic.jpg create mode 100644 sdxl_styles/samples/sai_pixel_art.jpg create mode 100644 sdxl_styles/samples/sai_texture.jpg create mode 100644 sdxl_styles/samples/silhouette_art.jpg create mode 100644 sdxl_styles/samples/simple_vector_art.jpg create mode 100644 sdxl_styles/samples/steampunk_2.jpg create mode 100644 sdxl_styles/samples/sticker_designs.jpg create mode 100644 sdxl_styles/samples/tranquil_relaxing_atmosphere.jpg create mode 100644 sdxl_styles/samples/vibrant_rim_light.jpg create mode 100644 sdxl_styles/samples/volumetric_lighting.jpg create mode 100644 sdxl_styles/samples/watercolor_2.jpg create mode 100644 sdxl_styles/samples/whimsical_and_playful.jpg diff --git a/css/style.css b/css/style.css index b87b20a7..6ebfd432 100644 --- a/css/style.css +++ b/css/style.css @@ -196,3 +196,18 @@ pointer-events: none; display: none; } + +#stylePreviewOverlay { + display: none; + width: 128px; + height: 128px; + position: fixed; + top: 0px; + left: 0px; + border: solid 1px lightgrey; + transform: translate(-140px, 20px); + background-size: cover; + background-position: center; + border-radius: 5px; + z-index: 100; +} diff --git a/fooocus_version.py b/fooocus_version.py index 482cc12c..f66fe765 100644 --- a/fooocus_version.py +++ b/fooocus_version.py @@ -1 +1 @@ -version = '2.1.860' +version = '2.1.861' diff --git a/javascript/localization.js b/javascript/localization.js index 8fda68e2..0a8394ca 100644 --- a/javascript/localization.js +++ b/javascript/localization.js @@ -45,6 +45,9 @@ function processTextNode(node) { var tl = getTranslation(text); if (tl !== undefined) { node.textContent = tl; + if (text && node.parentElement) { + node.parentElement.setAttribute("data-original-text", text); + } } } diff --git a/javascript/script.js b/javascript/script.js index 593d8c36..7c540b38 100644 --- a/javascript/script.js +++ b/javascript/script.js @@ -119,6 +119,7 @@ document.addEventListener("DOMContentLoaded", function() { } }); mutationObserver.observe(gradioApp(), {childList: true, subtree: true}); + initStylePreviewOverlay(); }); /** @@ -145,6 +146,39 @@ document.addEventListener('keydown', function(e) { } }); +function initStylePreviewOverlay() { + let overlayVisible = false; + const samplesPath = document.querySelector("meta[name='samples-path']").getAttribute("content") + const overlay = document.createElement('div'); + overlay.id = 'stylePreviewOverlay'; + document.body.appendChild(overlay); + document.addEventListener('mouseover', function(e) { + const label = e.target.closest('.style_selections label'); + if (!label) return; + label.removeEventListener("mouseout", onMouseLeave); + label.addEventListener("mouseout", onMouseLeave); + overlayVisible = true; + overlay.style.display = "block"; + const originalText = label.querySelector("span").getAttribute("data-original-text"); + const name = originalText || label.querySelector("span").textContent; + overlay.style.backgroundImage = `url("${samplesPath.replace( + "fooocus_v2", + name.toLowerCase().replaceAll(" ", "_") + )}")`; + function onMouseLeave() { + overlayVisible = false; + overlay.style.display = "none"; + overlay.style.backgroundImage = ""; + label.removeEventListener("mouseout", onMouseLeave); + } + }); + document.addEventListener('mousemove', function(e) { + if(!overlayVisible) return; + overlay.style.left = `${e.clientX}px`; + overlay.style.top = `${e.clientY}px`; + }); +} + /** * checks that a UI element is not in another hidden element or tab content */ diff --git a/modules/ui_gradio_extensions.py b/modules/ui_gradio_extensions.py index e59b151b..bebf9f8c 100644 --- a/modules/ui_gradio_extensions.py +++ b/modules/ui_gradio_extensions.py @@ -30,6 +30,7 @@ def javascript_html(): edit_attention_js_path = webpath('javascript/edit-attention.js') viewer_js_path = webpath('javascript/viewer.js') image_viewer_js_path = webpath('javascript/imageviewer.js') + samples_path = webpath(os.path.abspath('./sdxl_styles/samples/fooocus_v2.jpg')) head = f'\n' head += f'\n' head += f'\n' @@ -38,6 +39,7 @@ def javascript_html(): head += f'\n' head += f'\n' head += f'\n' + head += f'\n' if args_manager.args.theme: head += f'\n' diff --git a/sdxl_styles/samples/Academia.jpg b/sdxl_styles/samples/Academia.jpg new file mode 100644 index 0000000000000000000000000000000000000000..26a700d000bb2a2de08c2ec637fa763f9dd52673 GIT binary patch literal 29098 zcmbrlXH-*L)HaGoQ2`Z^CJ1tr-W3SF98tgo0SOQwv;#;9G4xIV3q`t=z%eu_Ap{5r zB!m`_E+8OXLJvjhgc_=s_kMSLcZ~b%j(gW0`^O%8?KSqZ)?9Nx^Ob>+(CE7$%9*QLJ#F3!trR~R2WzIx}0@ilud_J?wBuXAW8 z=T){a3Ce@FIUT$QnYn}%=7nkh1MUAr_WuspyZ=wf{x`7y8yAJ)#^p;Fi+7ogL5txq zce0hb8P0&b9kpOF;+L5@aR2mKLsY2qmf46H6kdEU;u1qpS*tW5V&A+Yo(Y<% zoOTq$dNF%e($a%9Gn>D*Rz}_S0ZmnhuhuI{hI*f&2Q@vao$@wjw~e0F2yisJ^%*SH z5gWeH1r~HwBTJ^BGgeHEKJ)*5MpKr1p1W9*tfb~9pUd3QX(c>+?NL1kx<9EFFCh$0 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