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
Add library_name and pipeline_tag to metadata
#1
by nielsr HF Staff - opened
Hi! 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.
yuhangzang changed pull request status to merged
Hi Niels. Thank you for the PR and for helping improve the model card! We’ve updated the corresponding metadata and documentation accordingly.