Update README

README.md

@@ -1,3 +1,36 @@
----
-license: mit
----
+---
+license: mit
+---
+
+# CleanDIFT Model Card
+
+
+Diffusion models learn powerful world representations that have proven valuable for tasks like semantic correspondence detection, 
+depth estimation, semantic segmentation, and classification. 
+However, diffusion models require noisy input images, which destroys information and introduces the noise level as a hyperparameter that needs to be tuned for each task. 
+
+
+
+
+We introduce CleanDIFT, a novel method to extract noise-free, timestep-independent features by enabling diffusion models to work directly with clean input images. 
+The approach is efficient, training on a single GPU in just 30 minutes. We publish these models alongside our paper ["CleanDIFT: Diffusion Features without Noise"](https://compvis.github.io/CleanDIFT/).
+
+We provide checkpoints for Stable Diffusion 1.5 and Stable Diffusion 2.1.
+
+
+## Usage
+
+For detailed examples on how to extract features with CleanDIFT and how to use them for downstream tasks, please refer to the notebooks provided [here](https://github.com/CompVis/CleanDIFT/tree/main/notebooks).
+
+Our checkpoints are fully compatible with the `diffusers` library. 
+If you already have a pipeline using SD 1.5 or SD 2.1 from `diffusers`, you can simply replace the U-Net state dict:
+```python
+from diffusers import UNet2DConditionModel
+from huggingface_hub import hf_hub_download
+
+unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-2-1", subfolder="unet")
+ckpt_pth = hf_hub_download(repo_id="CompVis/cleandift", filename="cleandift_sd21_unet.safetensors")
+state_dict = load_file(ckpt_pth)
+unet.load_state_dict(state_dict, strict=True)
+```
+