license: apache-2.0
library_name: diffusers
pipeline_tag: image-to-image
This repository contains the model for the paper V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration.
Overview
Large-scale video generative models are trained on vast and diverse visual data, enabling them to internalize rich structural, semantic, and dynamic priors of the visual world. V-Bridge is a framework that bridges this latent capacity to versatile few-shot image restoration tasks. By reinterpreting image restoration as a progressive generative process, V-Bridge leverages video models to simulate the gradual refinement from degraded inputs to high-fidelity outputs.
Surprisingly, with only 1,000 multi-task training samples (less than 2% of existing restoration methods), pretrained video models can be induced to perform competitive image restoration, achieving multiple tasks with a single model and rivaling specialized architectures designed explicitly for this purpose.
Details
Our model uses a full fine-tuning approach, with the base model being Wan2.2-TI2V-5B.
The following are some of the detailed parameters for inference:
cfg_skip_ratio = 0.15
sampler_name = "Flow_Unipc"
shift = 5
video_length = 5
fps = 24
weight_dtype = torch.bfloat16
prompt = (
"A restoration-focused video strictly based on the input image. "
"The camera is completely static with no movement, no zoom, and no rotation. "
"The original composition, objects, layout, and perspective are preserved exactly. "
"Focus on visual restoration and enhancement: remove noise, reduce blur, eliminate rain artifacts, "
"remove compression artifacts, and improve clarity, sharpness, and fine details while maintaining "
"natural textures, accurate colors, and balanced lighting. "
"Only extremely subtle and natural temporal consistency is allowed. "
"The video should appear stable, clean, and realistic, as if the input image has been gently restored over time."
)
negative_prompt = (
"camera movement, panning, tilting, zooming, rotation, "
"scene change, object movement, new objects, object deformation, "
"style change, artistic style, illustration, painting, cartoon, "
"over-saturated colors, overexposure, underexposure, "
"motion blur, jitter, flickering, shaking, "
"low quality, worst quality, noise, blur, rain, fog, "
"compression artifacts, jpeg artifacts, aliasing, "
"text, subtitles, watermark, logo, "
"distorted anatomy, extra limbs, duplicated objects, "
"exaggerated motion, creative animation"
)
guidance_scale = 6.0
num_inference_steps = 50
More details and usage instructions can be found on GitHub.
Acknowledgements
We would like to thank the contributors to Wan-AI, VideoX-Fun and HuggingFace repositories, for their open research.
Citation
@article{zheng2026V-Bridge,
title={V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration},
author={Zheng, Shenghe and Jiang, Junpeng and Li, Wenbo},
journal={arXiv preprint arXiv:2603.13089},
year={2026}
}