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Merged
merged 2 commits into from
Apr 2, 2025

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@elismasilva elismasilva commented Apr 1, 2025

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

import random
import numpy as np
import torch
from diffusers import AutoencoderKL
from pipeline_faithdiff_stable_diffusion_xl import FaithDiffStableDiffusionXLPipeline
from huggingface_hub import hf_hub_download
from diffusers.utils import load_image
from PIL import Image

device = "cuda"
dtype = torch.float16
MAX_SEED = np.iinfo(np.int32).max

# Download weights for additional unet layers
model_file = hf_hub_download(
    "jychen9811/FaithDiff",
    filename="FaithDiff.bin", local_dir="./proc_data/faithdiff", local_dir_use_symlinks=False
)

# Initialize the models and pipeline
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)

model_id = "SG161222/RealVisXL_V4.0"
pipe = FaithDiffStableDiffusionXLPipeline.from_pretrained(
    model_id,
    torch_dtype=dtype,
    vae=vae,
    unet=None, #<- Do not load with original model.    
    use_safetensors=True,
    variant="fp16",
).to(device)

# Here we need use pipeline internal unet model
pipe.unet = pipe.unet_model.from_pretrained(model_id, subfolder="unet", variant="fp16", use_safetensors=True)

# Load aditional layers to the model
pipe.unet.load_additional_layers(weight_path="proc_data/faithdiff/FaithDiff.bin", dtype=dtype)

# Enable vae tiling
pipe.set_encoder_tile_settings()
pipe.enable_vae_tiling()

# Optimization
pipe.enable_model_cpu_offload()

#input params
prompt = "The image features a woman in her 55s with blonde hair and a white shirt, smiling at the camera. She appears to be in a good mood and is wearing a white scarf around her neck. "
upscale = 2 # scale here
start_point = "lr" # or "noise"
latent_tiled_overlap = 0.5
latent_tiled_size = 1024

# Load image
lq_image = load_image("https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/woman.png")
original_height = lq_image.height
original_width = lq_image.width
print(f"Current resolution: H:{original_height} x W:{original_width}")

width = original_width * int(upscale)
height = original_height * int(upscale)
print(f"Final resolution: H:{height} x W:{width}")

# Restoration
image = lq_image.resize((width, height), Image.LANCZOS)
input_image, width_init, height_init, width_now, height_now = pipe.check_image_size(image)

generator = torch.Generator(device=device).manual_seed(random.randint(0, MAX_SEED))
gen_image = pipe(lr_img=input_image, 
                 prompt = prompt,                  
                 num_inference_steps=20, 
                 guidance_scale=5, 
                 generator=generator, 
                 start_point=start_point, 
                 height = height_now, 
                 width=width_now, 
                 overlap=latent_tiled_overlap, 
                 target_size=(latent_tiled_size, latent_tiled_size)
                ).images[0]

cropped_image = gen_image.crop((0, 0, width_init, height_init))
cropped_image.save("data/result.png")

Local testing after published

import random
import numpy as np
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
from huggingface_hub import hf_hub_download
from diffusers.utils import load_image
from PIL import Image

device = "cuda"
dtype = torch.float16
MAX_SEED = np.iinfo(np.int32).max

# Download weights for additional unet layers
model_file = hf_hub_download(
    "jychen9811/FaithDiff",
    filename="FaithDiff.bin", local_dir="./proc_data/faithdiff", local_dir_use_symlinks=False
)

# Initialize the models and pipeline
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)

model_id = "SG161222/RealVisXL_V4.0"
pipe = DiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=dtype,
    vae=vae,
    unet=None, #<- Do not load with original model.
    custom_pipeline="pipeline_faithdiff_stable_diffusion_xl",    
    use_safetensors=True,
    variant="fp16",
).to(device)

# Here we need use pipeline internal unet model
pipe.unet = pipe.unet_model.from_pretrained(model_id, subfolder="unet", variant="fp16", use_safetensors=True)

# Load aditional layers to the model
pipe.unet.load_additional_layers(weight_path="proc_data/faithdiff/FaithDiff.bin", dtype=dtype)

# Enable vae tiling
pipe.set_encoder_tile_settings()
pipe.enable_vae_tiling()

# Optimization
pipe.enable_model_cpu_offload()

#input params
prompt = "The image features a woman in her 55s with blonde hair and a white shirt, smiling at the camera. She appears to be in a good mood and is wearing a white scarf around her neck. "
upscale = 2 # scale here
start_point = "lr" # or "noise"
latent_tiled_overlap = 0.5
latent_tiled_size = 1024

# Load image
lq_image = load_image("https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/woman.png")
original_height = lq_image.height
original_width = lq_image.width
print(f"Current resolution: H:{original_height} x W:{original_width}")

width = original_width * int(upscale)
height = original_height * int(upscale)
print(f"Final resolution: H:{height} x W:{width}")

# Restoration
image = lq_image.resize((width, height), Image.LANCZOS)
input_image, width_init, height_init, width_now, height_now = pipe.check_image_size(image)

generator = torch.Generator(device=device).manual_seed(random.randint(0, MAX_SEED))
gen_image = pipe(lr_img=input_image, 
                 prompt = prompt,                  
                 num_inference_steps=20, 
                 guidance_scale=5, 
                 generator=generator, 
                 start_point=start_point, 
                 height = height_now, 
                 width=width_now, 
                 overlap=latent_tiled_overlap, 
                 target_size=(latent_tiled_size, latent_tiled_size)
                ).images[0]

cropped_image = gen_image.crop((0, 0, width_init, height_init))
cropped_image.save("data/result.png")

Before submitting

Result

Who can review?

@asomoza @sayakpaul @yiyixuxu

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@yiyixuxu yiyixuxu left a comment

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cool, thank you!

@HuggingFaceDocBuilderDev

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.

@yiyixuxu yiyixuxu merged commit c4646a3 into huggingface:main Apr 2, 2025
7 of 9 checks passed
jonluca added a commit to weights-ai/diffusers that referenced this pull request Apr 3, 2025
* 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 <dhruv.nair@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* [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 <matrix.yao@intel.com>

* fix autocast (huggingface#11190)

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* 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 <spsayakpaul@gmail.com>

* [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 <aryan@huggingface.co>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* 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 <matrix.yao@intel.com>
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
Co-authored-by: Aryan <aryan@huggingface.co>
Co-authored-by: Mark <remarkablemark@users.noreply.github.com>
Co-authored-by: hlky <hlky@hlky.ac>
Co-authored-by: kakukakujirori <63725741+kakukakujirori@users.noreply.github.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Fanli Lin <fanli.lin@intel.com>
Co-authored-by: Yao Matrix <matrix.yao@intel.com>
Co-authored-by: jiqing-feng <jiqing.feng@intel.com>
Co-authored-by: Eliseu Silva <elismasilva@gmail.com>
Co-authored-by: Bruno Magalhaes <bruno.magalhaes@synthesia.io>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: lakshay sharma <31830611+Lakshaysharma048@users.noreply.github.com>
Co-authored-by: Abhipsha Das <ad6489@nyu.edu>
Co-authored-by: Basile Lewandowski <basile.lewan@gmail.com>
Co-authored-by: célina <hanouticelina@gmail.com>
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3 participants