Instructions to use desimfj/V-Bridge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use desimfj/V-Bridge with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("desimfj/V-Bridge", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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📄 <a href="https://arxiv.org" target="_blank">Paper</a> |
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🖥️ <a href="https://github.com/Zhengsh123/V-Bridge" target="_blank">
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This repo contains the model for the paper V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration.
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license: apache-2.0
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📄 <a href="https://arxiv.org/pdf/2603.13089" target="_blank">Paper</a> |
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🖥️ <a href="https://github.com/Zhengsh123/V-Bridge" target="_blank">Code</a>
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🌐 <a href="https://zhengsh123.github.io/V-Bridge/" target="_blank">Website</a>
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</p>
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This repo contains the model for the paper V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration.
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