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Artiprocher
2025-07-01 11:33:04 +08:00
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examples/flux/README.md
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@@ -185,7 +185,7 @@ The script supports the following parameters:
* `--dataset_repeat`: Number of times the dataset repeats per epoch.
* Models
* `--model_paths`: Paths to load models. JSON format.
* `--model_id_with_origin_paths`: Model IDs with original paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.
* `--model_id_with_origin_paths`: Model IDs with original paths, e.g., black-forest-labs/FLUX.1-dev:flux1-dev.safetensors. Comma-separated.
* Training
* `--learning_rate`: Learning rate.
* `--num_epochs`: Number of training epochs.
@@ -214,16 +214,16 @@ The script supports the following parameters:
The dataset contains a series of files. We recommend organizing your dataset files as follows:
```
data/example_video_dataset/
data/example_image_dataset/
├── metadata.csv
├── image1.jpg
└── image2.jpg
```
Here, `image1.jpg`, `image2.jpg` are training video/image data, and `metadata.csv` is the metadata list, for example:
Here, `image1.jpg`, `image2.jpg` are training image data, and `metadata.csv` is the metadata list, for example:
```
video,prompt
image,prompt
image1.jpg,"a cat is sleeping"
image2.jpg,"a dog is running"
```
@@ -236,18 +236,18 @@ modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir
The dataset supports multiple image formats: `"jpg", "jpeg", "png", "webp"`.
The image resolution can be controlled via script parameters `--height` and `--width`. When both `--height` and `--width` are left empty, dynamic resolution will be enabled, allowing training with the actual width and height of each video or image in the dataset.
The image resolution can be controlled via script parameters `--height` and `--width`. When both `--height` and `--width` are left empty, dynamic resolution will be enabled, allowing training with the actual width and height of each image in the dataset.
**We strongly recommend using fixed-resolution training, as there may be load-balancing issues in multi-GPU training with dynamic resolution.**
When the model requires additional inputs—for instance, `kontext_images` required by the controllable model [`black-forest-labs/FLUX.1-Kontext-dev`](https://modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)—please add corresponding columns in the dataset, for example:
```
video,prompt,kontext_images
image,prompt,kontext_images
image1.jpg,"a cat is sleeping",image1_reference.jpg
```
If additional inputs include video or image files, you need to specify the column names to parse using the `--data_file_keys` parameter. You can add more column names accordingly, e.g., `--data_file_keys "image,kontext_images"`.
If additional inputs include image files, you need to specify the column names to parse using the `--data_file_keys` parameter. You can add more column names accordingly, e.g., `--data_file_keys "image,kontext_images"`.
</details>