Merge branch 'main' of https://github.com/mlpc-ucsd/Patch-DM

mlpc-ucsd · May 17, 2024 · f4a9e0a · f4a9e0a
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diff --git a/.gitignore b/.gitignore
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+__pycache__/
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diff --git a/README.md b/README.md
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-# [Patched Denoising Diffusion Models For High-Resolution Image Synthesis](https://arxiv.org/abs/2308.01316)
+# [Patched Denoising Diffusion Models For High-Resolution Image Synthesis (ICLR 2024)](https://arxiv.org/abs/2308.01316) 
 
-Zheng Ding\*, Mengqi Zhang\*, Jiajun Wu, and Zhuowen Tu 
+Zheng Ding\*, Mengqi Zhang\*, Jiajun Wu, Zhuowen Tu 
 
 ![teaser](figs/teaser.png)
 
@@ -16,15 +16,15 @@ pip install -r requirements.txt
 
 ## Data Preprocessing
 
-We provide the scipts converting images to lmdb for training. For generating dataset:
+We provide the scripts converting images to lmdb for training.
 
 ```
 python img2lmdb.py --input ${input_img_path} \
   --output ${lmdb_path}
 ```
 
 ## Train Patch-DM
-To initialize semantic code for global style consistency, we need to extract image embedding from CLIP pretrained model first and optimized it during training. The following code will generate initial semantic codes for training.
+To initialize semantic code for global style consistency, we need to extract image embedding from CLIP pretrained model first and optimize it during training. The following code will generate initial semantic codes for training.
 
 ```
 python initialize.py --data_path ${data_path} \
@@ -43,7 +43,7 @@ python train.py --batch_size 64 \
 
 ## Train Latent Model
 
-In our unconditional image generation, a latent diffusion model is trained on optimized semantic code embedding space for providing infinite new global semantic conditions. To train latent model, we directly derive semantic embedding layers from model without additional inference. Code is provided in `train_latent.py`, run following command for training:
+In our unconditional image generation, a latent diffusion model is trained on optimized semantic code embedding space for providing infinite new global semantic conditions. To train latent model, we directly derive semantic embedding layers from model without additional inference. Code is provided in `train_latent.py`, run the following command for training:
 
 ```
 python train_latent.py --name exp_latent \
@@ -67,16 +67,16 @@ python test.py --batch_size 1 \
 ## Citation
 
 ```
-@article{ding2023patched,
+@inproceedings{ding2024patched,
   title={Patched Denoising Diffusion Models For High-Resolution Image Synthesis},
-  author={Ding, Zheng and Zhang, Mengqi and Wu, Jiajun and Tu, Zhuowen},
-  journal={arXiv preprint arXiv:2308.01316},
-  year={2023}
+  author={Zheng Ding and Mengqi Zhang and Jiajun Wu and Zhuowen Tu},
+  booktitle={The Twelfth International Conference on Learning Representations},
+  year={2024}
 }
 ```
 
 ## Acknowledgement
 
-This project leverages knowledge from following amazing works:
+This project leverages knowledge from the following amazing works:
 [DiffAE](https://github.com/phizaz/diffae),
 [Guided Diffusion](https://github.com/openai/guided-diffusion).
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