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
Update README.md
Browse files
README.md
CHANGED
|
@@ -40,7 +40,7 @@ base_model:
|
|
| 40 |
|
| 41 |
## 📰News
|
| 42 |
|
| 43 |
-
- 🚀 [2026/3/12] We have released the EndoCoT [repository](
|
| 44 |
|
| 45 |
## 🌟Highlight
|
| 46 |
|
|
|
|
| 40 |
|
| 41 |
## 📰News
|
| 42 |
|
| 43 |
+
- 🚀 [2026/3/12] We have released the EndoCoT [repository](https://github.com/InternLM/EndoCoT) and [ckpts](https://huggingface.co/internlm/EndoCoT).
|
| 44 |
|
| 45 |
## 🌟Highlight
|
| 46 |
|