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BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models
| Date | Stars |
|---|---|
| 2026-07-31 | 459 |
| 2026-08-03 | 460 |
| 2026-08-06 | 460 |
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# Brownian Bridge Diffusion Models *** #### [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://arxiv.org/abs/2205.07680) https://arxiv.org/abs/2205.07680 **Bo Li, Kai-Tao Xue, Bin Liu, Yu-Kun Lai**  ## Requirements ```commandline cond env create -f environment.yml conda activate BBDM ``` ## Data preparation ### Paired translation task For datasets that have paired image data, the path should be formatted as: ```yaml your_dataset_path/train/A # training reference your_dataset_path/train/B # training ground truth your_dataset_path/val/A # validating reference your_dataset_path/val/B # validating ground truth your_dataset_path/test/A # testing reference your_dataset_path/test/B # testing ground truth ``` After that, the dataset configuration should be specified in config file as: ```yaml dataset_name: 'your_dataset_name' dataset_type: 'custom_aligned' dataset_config: dataset_path: 'your_dataset_path' ``` ### Colorization and Inpainting For colorization and inpainting tasks, the references may be generated from ground truth. The path should be formatted as: ```yaml your_dataset_path/train # training ground truth your_dataset_path/val # validating ground truth your_dataset_path/test # testing ground truth ``` #### Colorization For generalization, the gray image and ground truth are all in RGB format in colorization task. You can use our dataset type or implement your own. ```yaml dataset_name: 'your_dataset_name' dataset_type: 'custom_colorization or implement_your_dataset_type' dataset_config: dataset_path: 'your_dataset_path' ``` #### Inpainting We randomly mask 25%-50% of the ground truth. You can use our dataset type or implement your own. ```yaml dataset_name: 'your_dataset_name' dataset_type: 'custom_inpainting or implement_your_dataset_type' dataset_config: dataset_path: 'your_dataset_path' ``` ## Train and Test ### Specify your configuration file Modify the configuration file based on our templates in <font color=violet><b>configs/Template-*.yaml</b></font> The template of BBDM in pixel space are named <font color=violet><b>Template-BBDM.yaml</b></font> that can be found in **configs/** and <font color=violet><b>Template-LBBDM-f4.yaml Template-LBBDM-f8.yaml Template-LBBDM-f16.yaml</b></font> are templates for latent space BBDM with latent depth of 4/8/16. Don't forget to specify your VQGAN checkpoint path and dataset path. ### Specity your training and tesing shell Specity your shell file based on our templates in <font color=violet><b>configs/Template-shell.sh</b></font> If you wish to train from the beginning ```commandline python3 main.py --config configs/Template_LBBDM_f4.yaml --train --sample_at_start --save_top --gpu_ids 0 ``` If you wish to continue training, specify the model checkpoint path and optimizer checkpoint path in the train part. ```commandline python3 main.py --config configs/Template_LBBDM_f4.yaml --train --sample_at_start --save_top --gpu_ids 0 --resume_model path/to/model_ckpt --resume_optim path/to/optim_ckpt ``` If you wish to sample the whole test dataset to evaluate metrics ```commandline python3 main.py --config configs/Template_LBBDM_f4.yaml --sample_to_eval --gpu_ids 0 --resume_model path/to/model_ckpt ``` Note that optimizer checkpoint is not needed in test and specifying checkpoint path in commandline has higher priority than specifying in configuration file. For distributed training, just modify the configuration of **--gpu_ids** with your specified gpus. ```commandline python3 main.py --config configs/Template_LBBDM_f4.yaml --sample_to_eval --gpu_ids 0,1,2,3 --resume_model path/to/model_ckpt ``` ### Run ```commandline sh shell/your_shell.sh ``` ## Pretrained Models For simplicity, we re-trained all of the models based on the same VQGAN model from LDM. The pre-trained VQGAN models provided by LDM can be directly used for all tasks. https://github.com/CompVis/latent-diffusion#bibtex Th
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:315f313c647b847c, llm:Repository title and description: 'BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models' (image-to-image translation, diffusion models).
matched fp:315f313c647b847c, llm:Repository title and description: 'BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models' (image-to-image translation, diffusion models).
matched fp:315f313c647b847c, llm:Repository title and description: 'BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models' (image-to-image translation, diffusion models).