Instructions to use mit-han-lab/svdq-flux.1-schnell-pix2pix-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mit-han-lab/svdq-flux.1-schnell-pix2pix-turbo 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("mit-han-lab/svdq-flux.1-schnell-pix2pix-turbo", 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:
- c9c4d40f05bdaa917bb853169bdbd691075c456efd9c1b3b0cfb522659301e9e
- Size of remote file:
- 184 MB
- SHA256:
- 5b29ce83bc08a0da56dbd58c3e104f67742dac1c659f9ba588e8039b58e90f3b
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