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feat: [Community Pipeline] - FaithDiff Stable Diffusion XL Pipeline #11188
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cool, thank you!
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
* Raise warning and round down if Wan num_frames is not 4k + 1 (huggingface#11167) * update * raise warning and round to nearest multiple of scale factor * [Docs] Fix environment variables in `installation.md` (huggingface#11179) * Add `latents_mean` and `latents_std` to `SDXLLongPromptWeightingPipeline` (huggingface#11034) * Bug fix in LTXImageToVideoPipeline.prepare_latents() when latents is already set (huggingface#10918) * Bug fix in ltx * Assume packed latents. --------- Co-authored-by: Dhruv Nair <[email protected]> Co-authored-by: YiYi Xu <[email protected]> * [tests] no hard-coded cuda (huggingface#11186) no cuda only * [WIP] Add Wan Video2Video (huggingface#11053) * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * map BACKEND_RESET_MAX_MEMORY_ALLOCATED to reset_peak_memory_stats on XPU (huggingface#11191) Signed-off-by: YAO Matrix <[email protected]> * fix autocast (huggingface#11190) Signed-off-by: jiqing-feng <[email protected]> * fix: for checking mandatory and optional pipeline components (huggingface#11189) fix: optional componentes verification on load * remove unnecessary call to `F.pad` (huggingface#10620) * rewrite memory count without implicitly using dimensions by @ic-synth * replace F.pad by built-in padding in Conv3D * in-place sums to reduce memory allocations * fixed trailing whitespace * file reformatted * in-place sums * simpler in-place expressions * removed in-place sum, may affect backward propagation logic * removed in-place sum, may affect backward propagation logic * removed in-place sum, may affect backward propagation logic * reverted change * allow models to run with a user-provided dtype map instead of a single dtype (huggingface#10301) * allow models to run with a user-provided dtype map instead of a single dtype * make style * Add warning, change `_` to `default` * make style * add test * handle shared tensors * remove warning --------- Co-authored-by: Sayak Paul <[email protected]> * [tests] HunyuanDiTControlNetPipeline inference precision issue on XPU (huggingface#11197) * add xpu part * fix more cases * remove some cases * no canny * format fix * Revert `save_model` in ModelMixin save_pretrained and use safe_serialization=False in test (huggingface#11196) * [docs] `torch_dtype` map (huggingface#11194) * Fix enable_sequential_cpu_offload in CogView4Pipeline (huggingface#11195) * Fix enable_sequential_cpu_offload in CogView4Pipeline * make fix-copies * SchedulerMixin from_pretrained and ConfigMixin Self type annotation (huggingface#11192) * Update import_utils.py (huggingface#10329) added onnxruntime-vitisai for custom build onnxruntime pkg * Add CacheMixin to Wan and LTX Transformers (huggingface#11187) * update * update * update * feat: [Community Pipeline] - FaithDiff Stable Diffusion XL Pipeline (huggingface#11188) * feat: [Community Pipeline] - FaithDiff Stable Diffusion XL Pipeline for Image SR. * added pipeline * [Model Card] standardize advanced diffusion training sdxl lora (huggingface#7615) * model card gen code * push modelcard creation * remove optional from params * add import * add use_dora check * correct lora var use in tags * make style && make quality --------- Co-authored-by: Aryan <[email protected]> Co-authored-by: Sayak Paul <[email protected]> * Change KolorsPipeline LoRA Loader to StableDiffusion (huggingface#11198) Change LoRA Loader to StableDiffusion Replace the SDXL LoRA Loader Mixin inheritance with the StableDiffusion one * Update Style Bot workflow (huggingface#11202) update style bot workflow --------- Signed-off-by: YAO Matrix <[email protected]> Signed-off-by: jiqing-feng <[email protected]> Co-authored-by: Aryan <[email protected]> Co-authored-by: Mark <[email protected]> Co-authored-by: hlky <[email protected]> Co-authored-by: kakukakujirori <[email protected]> Co-authored-by: Dhruv Nair <[email protected]> Co-authored-by: YiYi Xu <[email protected]> Co-authored-by: Fanli Lin <[email protected]> Co-authored-by: Yao Matrix <[email protected]> Co-authored-by: jiqing-feng <[email protected]> Co-authored-by: Eliseu Silva <[email protected]> Co-authored-by: Bruno Magalhaes <[email protected]> Co-authored-by: Sayak Paul <[email protected]> Co-authored-by: lakshay sharma <[email protected]> Co-authored-by: Abhipsha Das <[email protected]> Co-authored-by: Basile Lewandowski <[email protected]> Co-authored-by: célina <[email protected]>
What does this PR do?
This PR implements a pipeline for (CVPR 2025) FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution
[Project Page] [Paper]
I think this can complement #9740, with a step 1 where it is possible to restore low quality images, in addition to it also allowing a great upscale. For more details see the project links above.
Thanks to @JyChen9811 for his amazing work! Questions about the paper can be directed to him directly.
Example Usage
This example upscale and restores a low-quality image. The input image has a resolution of 512x512 and will be upscaled at a scale of 2x, to a final resolution of 1024x1024. It is possible to upscale to a larger scale, but it is recommended that the input image be at least 1024x1024 in these cases. To upscale this image by 4x, for example, it would be recommended to re-input the result into a new 2x processing, thus performing progressive scaling.
Local Test
Local testing after published
Before submitting
documentation guidelines, and
here are tips on formatting docstrings.
Result
Who can review?
@asomoza @sayakpaul @yiyixuxu