Instructions to use internlm/EndoCoT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use internlm/EndoCoT 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("internlm/EndoCoT", 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:
- 0167df6edff4fd7cb79b18a2d57f838738e159730e8b1bbec95783b762191cb8
- Size of remote file:
- 107 kB
- SHA256:
- 87c92750c6f75f30eca17297055e26a980f843f184ea6c0dccf900616c552550
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