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
Improve model card
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
image-to-imagepipeline tag, making the model discoverable at https://huggingface.co/models?pipeline_tag=image-to-image. - Specifying the
diffuserslibrary, enabling convenient usage examples in the UI. - Linking to the paper and the Spaces demo.
HReynaud changed pull request status to merged