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--- |
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language: |
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- en |
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pipeline_tag: image-to-image |
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tags: |
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- Diffusion Models |
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- Stable Diffusion |
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- ControlNet |
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- Perturbed-Attention Guidance |
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- PAG |
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--- |
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# Super-Resolution with Perturbed-Attention Guidance |
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[Project](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) / [arXiv](https://arxiv.org/abs/2403.17377) / [GitHub](https://github.com/KU-CVLAB/Perturbed-Attention-Guidance) |
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This repository is based on [Diffusers](https://huggingface.co/docs/diffusers/index). |
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[ControlNet](https://arxiv.org/abs/2302.05543) is a neural network structure to control diffusion models by adding extra conditions. The pipeline is a modification of StableDiffusionControlNetPipeline to support image generation with ControlNet and Perturbed-Attention Guidance (PAG). |
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## Quickstart |
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Loading ControlNet and Custom Piepline: |
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``` |
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from diffusers import ControlNetModel, StableDiffusionControlNetPipeline |
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controlnet = ControlNetModel.from_pretrained( |
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"lllyasviel/sd-controlnet-openpose", |
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torch_dtype=torch.float16 |
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) |
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pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", |
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custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance_controlnet", |
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controlnet=controlnet, |
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torch_dtype=torch.float16 |
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) |
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device="cuda" |
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pipe = pipe.to(device) |
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``` |
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Prepare Conditional Images: |
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``` |
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from controlnet_aux import OpenposeDetector |
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openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") |
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original_image = load_image( |
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png" |
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) |
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openpose_image = openpose(original_image) |
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prompts="" |
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``` |
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Conditional Generation with ControlNet and PAG: |
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``` |
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output = pipe( |
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prompts, |
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image=openpose_image, |
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num_inference_steps=50, |
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guidance_scale=0.0, |
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pag_scale=4.0, |
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pag_applied_layers_index=["m0"] |
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).images[0] |
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``` |
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## Parameters |
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guidance_scale : gudiance scale of CFG (ex: 7.5) |
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pag_scale : gudiance scale of PAG (ex: 4.0) |
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pag_applied_layers_index : index of the layer to apply perturbation (ex: ['m0']) |
