fix readme

README.md

@@ -1,66 +1,11 @@
-# Linearly Constrained Diffusion Implicit Models
-![alt text](Teaser.jpg)
-
-### Authors
-[Vivek Jayaram](http://www.vivekjayaram.com/), [John Thickstun](https://johnthickstun.com/), [Ira Kemelmacher-Shlizerman](https://homes.cs.washington.edu/~kemelmi/), and [Steve Seitz](https://homes.cs.washington.edu/~seitz/)
-
-### Links
-[[Gradio Demo]](https://huggingface.co/spaces/vivjay30/cdim) [[Project Page]](https://grail.cs.washington.edu/projects/cdim/) [[Paper]](https://arxiv.org/abs/2411.00359)
-
-### Summary
-We solve noisy linear inverse problems with diffusion models. The method is fast and addresses many problems like inpainting, super-resolution, gaussian deblur, and poisson noise. 
-
-
-## Getting started 
-
-Recommended environment: Python 3.11, Cuda 12, Conda. For lower verions please adjust the dependencies below.
-
-### 1) Clone the repository
-
-```
-git clone https://github.com/vivjay30/cdim
-
-cd cdim
-```
-
-### 2) Install dependencies
-
-```
-conda create -n cdim python=3.11
-
-conda activate cdim
-
-pip install -r requirements.txt
-
-pip install torch==2.4.1+cu124 torchvision-0.19.1+cu124 --extra-index-url https://download.pytorch.org/whl/cu124
-```
-
-## Inference Examples
-
-(The underlying diffusion models will be automatically downloaded on the first run).
-
-#### CelebHQ Inpainting Example (T'=25 Denoising Steps)
-
-`python inference.py sample_images/celebhq/00001.jpg 25 operator_configs/box_inpainting_config.yaml noise_configs/gaussian_noise_config.yaml google/ddpm-celebahq-256`
- 
-#### LSUN Churches Gaussian Deblur Example (T'=25 Denoising Steps)
-`python inference.py sample_images/lsun_church.png 25 operator_configs/gaussian_blur_config.yaml noise_configs/gaussian_noise_config.yaml google/ddpm-church-256`
-
- 
-## FFHQ and Imagenet Models
-These models are generally not as strong as the google ddpm models, but are used for comparisons with baseline methods.
-
-From [this link](https://drive.google.com/drive/folders/1jElnRoFv7b31fG0v6pTSQkelbSX3xGZh?usp=sharing), download the checkpoints "ffhq_10m.pt" and "imagenet_256.pt" to models/
-
-#### Imagenet Super Resolution Example
-Here we set T'=50 to show the algorithm running slower
-`python inference.py sample_images/imagenet_val_00002.png 50 operator_configs/super_resolution_config.yaml noise_configs/gaussian_noise_config.yaml models/imagenet_model_config.yaml`
-
-#### FFHQ Random Inpainting (Faster)
-Here we set T'=10 to show the algorithm running faster
-`python inference.py sample_images/ffhq_00010.png 10 operator_configs/random_inpainting_config.yaml noise_configs/gaussian_noise_config.yaml models/ffhq_model_config.yaml`
-
-#### A Note on Exact Recovery
-If you set the measurement noise to 0 in gaussian_noise_config.yaml, then the recovered image should match the the observation y exactly (e.g. inpainting doesn't chance observed pixels). In practice, this doesn't happen because the diffusion schedule sets $\overline{\alpha}_0 = 0.999$ for numeric stability, meaning a tiny amount of noise is injected even at t=0.
-
-
+---
+title: CDIM
+emoji: 😃
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 5.1.0
+app_file: app.py
+pinned: true
+arxiv: https://arxiv.org/abs/2411.00359
+---