Instructions to use Jamichsu/Stream-DiffVSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jamichsu/Stream-DiffVSR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jamichsu/Stream-DiffVSR", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9f14755e9fea2d036b34fc333895ffc35be3dbc342e5c4de4b6844c147aab8a
- Size of remote file:
- 831 MB
- SHA256:
- f801cc49443bc9cfc685d57bfacd2b17d852f6c9095b6344c0e55ae41adb5b98
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