Instructions to use jiagaoxiang/stable-video-diffusion-img2vid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jiagaoxiang/stable-video-diffusion-img2vid with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jiagaoxiang/stable-video-diffusion-img2vid", 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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Check out the documentation for more information.
All the model components are saved from fp16 format of stabilityai/stable-video-diffusion-img2vid except the vae folder is replaced with the fp32 format of stabilityai/stable-video-diffusion-img2vid. This may help solve the black image issue caused by the vae.
More context: For SDXL, converting vae to fp16 will cause NaNs which results in black images. This is because of overflow numbers inside vae weights. Link: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
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