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
Commit History
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Add library_name and pipeline_tag to metadata
Verified signatureHi! I'm Niels from the Hugging Face community team. I've noticed this model is compatible with the `diffusers` library but is missing the corresponding metadata. I'm opening this PR to add the `library_name` and `pipeline_tag` to the model card. This will help users discover the model through task-based filtering and enable the "Use in Diffusers" button on the UI. I've also updated the citation section with the BibTeX information for the paper [EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models](https://huggingface.co/papers/2603.12252).008e2ba
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