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