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| ## Tainging Example | |
| ### 1. training example for sft or lora | |
| - Data format | |
| You need to create a jsonl file with key-values in the table below: | |
| | key_word | Required | Description | Example | | |
| |:---------------:| :------: |:----------------:|:-----------:| | |
| | `img_path` | Required | image path | `./data_example/images/0.png` | | |
| | `prompt` | Required | text | `A lovely little girl.` | | |
| | `width` | Required | image width | ` 1024 ` | | |
| | `height` | Required | image height | ` 1024 ` | | |
| - Tainging Scripts | |
| ```bash | |
| bash ./examples/sft/train.sh | |
| # All training setting in train_config.yaml | |
| # --data_csv_root: data csv_filepath | |
| # --aspect_ratio_type: data bucketing strategy, mar_256、mar_512、mar_1024 | |
| # --pretrained_model_name_or_path: root directory of the model | |
| # --diffusion_pretrain_weight: if a specified diffusion weight path is provided, load the model parameters from the current directory. | |
| # --work_dir: the save root directory for ckpt and logs | |
| # --resume_from_checkpoint: If 'resume_from_checkpoint' is set to 'latest', load the most recent step checkpoint. If a specific directory is provided, resume training from that directory. | |
| ``` | |
| ### 2. training example for dpo | |
| - Data format | |
| You need to create a txt file with key-values in the table below: | |
| | key_word | Required | Description | Example | | |
| |:---------------:| :------: |:----------------:|:-----------:| | |
| | `img_path_win` | Required | win image path | `./data_example/images/0.png` | | |
| | `img_path_lose` | Required | lose image path | `./data_example/images/1.png` | | |
| | `prompt` | Required | text | `A lovely little girl.` | | |
| | `width` | Required | image width | ` 1024 ` | | |
| | `height` | Required | image height | ` 1024 ` | | |
| - Tainging Scripts | |
| ```bash | |
| bash ./examples/dpo/train.sh | |
| # All training setting in train_config.yaml | |
| # --data_txt_root: data txt_filepath | |
| # --aspect_ratio_type: data bucketing strategy, mar_256、mar_512、mar_1024 | |
| # --pretrained_model_name_or_path: root directory of the model | |
| # --diffusion_pretrain_weight: if a specified diffusion weight path is provided, load the model parameters from the current directory. | |
| # --work_dir: the save root directory for ckpt and logs | |
| # --resume_from_checkpoint: If 'resume_from_checkpoint' is set to 'latest', load the most recent step checkpoint. If a specific directory is provided, resume training from that directory. | |
| ``` | |
| ### 3. training example for image-edit | |
| - Data format | |
| You need to create a txt file with key-values in the table below: | |
| | key_word | Required | Description | Example | | |
| |:---------------:| :------: |:----------------:|:-----------:| | |
| | `img_path` | Required | edited image path | `./data_example/images/0_edited.png` | | |
| | `ref_img_path` | Required | raw image path | `./data_example/images/0.png` | | |
| | `prompt` | Required | edit instruction | `change the dog to cat.` | | |
| | `width` | Required | image width | ` 1024 ` | | |
| | `height` | Required | image height | ` 1024 ` | | |
| - Tainging Scripts | |
| ```bash | |
| bash ./examples/edit/train.sh | |
| # All training setting in train_config.yaml | |
| # --data_txt_root: data txt_filepath | |
| # --aspect_ratio_type: data bucketing strategy, mar_256、mar_512、mar_1024 | |
| # --pretrained_model_name_or_path: root directory of the model | |
| # --diffusion_pretrain_weight: if a specified diffusion weight path is provided, load the model parameters from the current directory. | |
| # --work_dir: the save root directory for ckpt and logs | |
| # --resume_from_checkpoint: If 'resume_from_checkpoint' is set to 'latest', load the most recent step checkpoint. If a specific directory is provided, resume training from that directory. | |
| ``` | |