From 9ac876d2b064acbf8c7afe6b88e2f3c4e6f621e8 Mon Sep 17 00:00:00 2001 From: Zheng Ding Date: Mon, 16 Oct 2023 18:51:36 -0700 Subject: [PATCH 1/3] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6fcaa88..323c752 100755 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ 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} \ @@ -24,7 +24,7 @@ python img2lmdb.py --input ${input_img_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 \ @@ -77,6 +77,6 @@ python test.py --batch_size 1 \ ## 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). From 9f40bbf962adff66116277b31e2f446197c9fe0b Mon Sep 17 00:00:00 2001 From: dingzheng2016 Date: Tue, 16 Jan 2024 16:49:47 -0800 Subject: [PATCH 2/3] update README.md --- .gitignore | 2 ++ README.md | 12 ++++++------ __pycache__/choices.cpython-39.pyc | Bin 5126 -> 0 bytes __pycache__/config.cpython-39.pyc | Bin 12213 -> 0 bytes __pycache__/config_base.cpython-39.pyc | Bin 2436 -> 0 bytes __pycache__/dataset.cpython-39.pyc | Bin 22599 -> 0 bytes __pycache__/dataset_util.cpython-39.pyc | Bin 509 -> 0 bytes __pycache__/dist_utils.cpython-39.pyc | Bin 1142 -> 0 bytes __pycache__/experiment.cpython-36.pyc | Bin 23254 -> 0 bytes __pycache__/experiment.cpython-38.pyc | Bin 23604 -> 0 bytes __pycache__/experiment.cpython-39.pyc | Bin 20384 -> 0 bytes __pycache__/lmdb_writer.cpython-39.pyc | Bin 4256 -> 0 bytes __pycache__/metrics.cpython-39.pyc | Bin 6396 -> 0 bytes __pycache__/renderer.cpython-39.pyc | Bin 1606 -> 0 bytes __pycache__/ssim.cpython-39.pyc | Bin 2689 -> 0 bytes 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delete mode 100644 utils/__pycache__/choices.cpython-39.pyc delete mode 100644 utils/__pycache__/dataset.cpython-39.pyc delete mode 100644 utils/__pycache__/dataset_util.cpython-39.pyc delete mode 100644 utils/__pycache__/dist_utils.cpython-39.pyc delete mode 100644 utils/__pycache__/lmdb_writer.cpython-39.pyc delete mode 100644 utils/__pycache__/metrics.cpython-39.pyc delete mode 100644 utils/__pycache__/renderer.cpython-39.pyc diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..372c13e --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ + diff --git a/README.md b/README.md index 323c752..bb478e8 100755 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -# [Patched Denoising Diffusion Models For High-Resolution Image Synthesis](https://arxiv.org/abs/2308.01316) +# [Patched Denoising Diffusion Models For High-Resolution Image Synthesis](https://arxiv.org/abs/2308.01316) (ICLR 2024) -Zheng Ding\*, Mengqi Zhang\*, Jiajun Wu, and Zhuowen Tu +Zheng Ding\*, Mengqi Zhang\*, Jiajun Wu, Zhuowen Tu ![teaser](figs/teaser.png) @@ -67,11 +67,11 @@ 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} } ``` diff --git a/__pycache__/choices.cpython-39.pyc b/__pycache__/choices.cpython-39.pyc deleted file mode 100644 index 0792d8718c569aa2961ffc3c5db62a9358d8dff4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5126 zcmbVQTa)8P6_#XqM!t@B_8!sM}-h%96Jw z&#2TbGp_2oqNlo)v6n~ zqTgOuf8Q{Se-N>F*&ue%k}uFnLzOM?y2ErG^f|c+dXwn} 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zcmY*Z&yO256t+Dx&Lo*+lk7?@D})f@KoHufaN&Sp+d?Uo$Yoa`RY;>Db~ckvW+K~x z-Ia5pJ+l8n4_Qw1|Ka2-r(6;0p(;P#n@M)Jw&u~aAkv?_wLl9mvn-ABAV0ppTgc71S=nwjV^G8KKcEPL=GIKQzewkFRoh3%* zCf<&_qb!}r8)IIiT8^3!VB2;{0a zHA^zQAu|PB4-2Hi)H{ujRUL=OI5GwbY^{?;q0a`KJu67!0lPFVeS!SXVALC?a1L+l@ zXw{fC5(11Yqz%deI{q*B)u&+X^Wku)e9#o4^Pta$!+zay2ry1hpoWmz2cv~Z6qciF zmW6b`n%%o66Z7@mp}t$)0qYAM&!f%b>Hk<1S(%!0s~cxU{;2Z6;b(x;B9ouvW1#Vt bA&WwInYaBVWSTJwiGLa00qZN!(enC#4VSG> From f1ae7c5f5b27ceaa94bb5366b9df7912b2bb4643 Mon Sep 17 00:00:00 2001 From: Zheng Ding Date: Tue, 16 Jan 2024 16:50:28 -0800 Subject: [PATCH 3/3] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bb478e8..5aa2b9a 100755 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# [Patched Denoising Diffusion Models For High-Resolution Image Synthesis](https://arxiv.org/abs/2308.01316) (ICLR 2024) +# [Patched Denoising Diffusion Models For High-Resolution Image Synthesis (ICLR 2024)](https://arxiv.org/abs/2308.01316) Zheng Ding\*, Mengqi Zhang\*, Jiajun Wu, Zhuowen Tu