Instructions to use HReynaud/EchoFlow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HReynaud/EchoFlow 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("HReynaud/EchoFlow", 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
Commit History
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Improve model card
Verified signatureThis PR improves the model card by: - Adding the `image-to-image` pipeline tag, making the model discoverable at https://huggingface.co/models?pipeline_tag=image-to-image. - Specifying the `diffusers` library, enabling convenient usage examples in the UI. - Linking to the paper and the Spaces demo.f98f1db
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Merge branch 'main' of hf.co:HReynaud/EchoFlow
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