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support num_workers,save_steps,find_unused_parameters
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@@ -249,6 +249,7 @@ The script includes the following parameters:
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* `--width`: Width of the image or video. Leave `height` and `width` empty to enable dynamic resolution.
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* `--data_file_keys`: Data file keys in the metadata. Separate with commas.
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* `--dataset_repeat`: Number of times the dataset repeats per epoch.
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* `--dataset_num_workers`: Number of workers for data loading.
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* Model
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* `--model_paths`: Paths to load models. In JSON format.
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* `--model_id_with_origin_paths`: Model ID with original paths, e.g., black-forest-labs/FLUX.1-dev:flux1-dev.safetensors. Separate with commas.
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@@ -257,6 +258,8 @@ The script includes the following parameters:
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* `--num_epochs`: Number of epochs.
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* `--output_path`: Save path.
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* `--remove_prefix_in_ckpt`: Remove prefix in checkpoint.
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* `--save_steps`: Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.
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* `--find_unused_parameters`: Whether to find unused parameters in DDP.
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* Trainable Modules
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* `--trainable_models`: Models that can be trained, e.g., dit, vae, text_encoder.
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* `--lora_base_model`: Which model to add LoRA to.
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@@ -249,6 +249,7 @@ FLUX 系列模型训练通过统一的 [`./model_training/train.py`](./model_tra
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* `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。
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* `--data_file_keys`: 元数据中的数据文件键。用逗号分隔。
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* `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
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* `--dataset_num_workers`: 每个 Dataloder 的进程数量。
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* 模型
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* `--model_paths`: 要加载的模型路径。JSON 格式。
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* `--model_id_with_origin_paths`: 带原始路径的模型 ID,例如 black-forest-labs/FLUX.1-dev:flux1-dev.safetensors。用逗号分隔。
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@@ -257,6 +258,8 @@ FLUX 系列模型训练通过统一的 [`./model_training/train.py`](./model_tra
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* `--num_epochs`: 轮数(Epoch)。
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* `--output_path`: 保存路径。
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* `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。
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* `--save_steps`: 保存模型的间隔 step 数量,如果设置为 None ,则每个 epoch 保存一次
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* `--find_unused_parameters`: DDP 训练中是否存在未使用的参数
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* 可训练模块
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* `--trainable_models`: 可训练的模型,例如 dit、vae、text_encoder。
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* `--lora_base_model`: LoRA 添加到哪个模型上。
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@@ -121,4 +121,7 @@ if __name__ == "__main__":
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dataset, model, model_logger, optimizer, scheduler,
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num_epochs=args.num_epochs,
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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save_steps=args.save_steps,
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find_unused_parameters=args.find_unused_parameters,
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num_workers=args.dataset_num_workers,
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)
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