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support ltx2 train -2
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mi804
@@ -67,7 +67,7 @@ write_video_audio_ltx2(
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## Model Overview
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|Model ID|Additional Parameters|Inference|Low VRAM Inference|Full Training|Validation After Full Training|LoRA Training|Validation After LoRA Training|
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|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
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|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py)|
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|[Lightricks/LTX-2: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-TwoStage.py)|-|-|-|-|
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|[Lightricks/LTX-2: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-DistilledPipeline.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-DistilledPipeline.py)|-|-|-|-|
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|[Lightricks/LTX-2: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-I2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-OneStage.py)|-|-|-|-|
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@@ -113,4 +113,55 @@ If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_
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## Model Training
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The LTX-2 series models currently do not support training functionality. We will add related support as soon as possible.
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LTX-2 series models are uniformly trained through [`examples/ltx2/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/train.py), and the script parameters include:
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* General Training Parameters
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* Dataset Basic Configuration
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* `--dataset_base_path`: Root directory of the dataset.
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* `--dataset_metadata_path`: Metadata file path of the dataset.
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* `--dataset_repeat`: Number of times the dataset is repeated in each epoch.
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* `--dataset_num_workers`: Number of processes for each DataLoader.
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* `--data_file_keys`: Field names to be loaded from metadata, usually image or video file paths, separated by `,`.
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* Model Loading Configuration
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* `--model_paths`: Paths of models to be loaded. JSON format.
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* `--model_id_with_origin_paths`: Model IDs with original paths, e.g., `"Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors"`. Separated by commas.
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* `--extra_inputs`: Extra input parameters required by the model Pipeline, e.g., extra parameters when training image editing models, separated by `,`.
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* `--fp8_models`: Models loaded in FP8 format, consistent with `--model_paths` or `--model_id_with_origin_paths` format. Currently only supports models whose parameters are not updated by gradients (no gradient backpropagation, or gradients only update their LoRA).
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* Training Basic Configuration
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* `--learning_rate`: Learning rate.
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* `--num_epochs`: Number of epochs.
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* `--trainable_models`: Trainable models, e.g., `dit`, `vae`, `text_encoder`.
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* `--find_unused_parameters`: Whether there are unused parameters in DDP training. Some models contain redundant parameters that do not participate in gradient calculation, and this setting needs to be enabled to avoid errors in multi-GPU training.
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* `--weight_decay`: Weight decay size, see [torch.optim.AdamW](https://docs.pytorch.org/docs/stable/generated/torch.optim.AdamW.html).
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* `--task`: Training task, default is `sft`. Some models support more training modes, please refer to the documentation of each specific model.
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* Output Configuration
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* `--output_path`: Model saving path.
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* `--remove_prefix_in_ckpt`: Remove prefix in the state dict of the model file.
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* `--save_steps`: Interval of training steps to save the model. If this parameter is left blank, the model is saved once per epoch.
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* LoRA Configuration
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* `--lora_base_model`: Which model to add LoRA to.
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* `--lora_target_modules`: Which layers to add LoRA to.
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* `--lora_rank`: Rank of LoRA.
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* `--lora_checkpoint`: Path of the LoRA checkpoint. If this path is provided, LoRA will be loaded from this checkpoint.
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* `--preset_lora_path`: Preset LoRA checkpoint path. If this path is provided, this LoRA will be loaded in the form of being merged into the base model. This parameter is used for LoRA differential training.
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* `--preset_lora_model`: Model that the preset LoRA is merged into, e.g., `dit`.
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* Gradient Configuration
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* `--use_gradient_checkpointing`: Whether to enable gradient checkpointing.
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* `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to memory.
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* `--gradient_accumulation_steps`: Number of gradient accumulation steps.
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* Video Width/Height Configuration
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* `--height`: Height of the video. Leave `height` and `width` blank to enable dynamic resolution.
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* `--width`: Width of the video. Leave `height` and `width` blank to enable dynamic resolution.
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* `--max_pixels`: Maximum pixel area of video frames. When dynamic resolution is enabled, video frames with resolution larger than this value will be downscaled, and video frames with resolution smaller than this value will remain unchanged.
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* `--num_frames`: Number of frames in the video.
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* LTX-2 Series Specific Parameters
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* `--tokenizer_path`: Path of the tokenizer, applicable to text-to-video models, leave blank to automatically download from remote.
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* `--frame_rate`: frame rate of the training videos.
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We have built a sample video dataset for your testing. You can download this dataset with the following command:
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```shell
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modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
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```
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We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
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@@ -67,7 +67,7 @@ write_video_audio_ltx2(
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## 模型总览
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|模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
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|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
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|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py)|
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|[Lightricks/LTX-2: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-TwoStage.py)|-|-|-|-|
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|[Lightricks/LTX-2: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-DistilledPipeline.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-DistilledPipeline.py)|-|-|-|-|
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|[Lightricks/LTX-2: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-I2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-OneStage.py)|-|-|-|-|
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@@ -113,4 +113,55 @@ write_video_audio_ltx2(
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## 模型训练
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LTX-2 系列模型目前暂不支持训练功能。我们将尽快添加相关支持。
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LTX-2 系列模型统一通过 [`examples/ltx2/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/train.py) 进行训练，脚本的参数包括：
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* 通用训练参数
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* 数据集基础配置
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* `--dataset_base_path`: 数据集的根目录。
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* `--dataset_metadata_path`: 数据集的元数据文件路径。
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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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* `--data_file_keys`: 元数据中需要加载的字段名称，通常是图像或视频文件的路径，以 `,` 分隔。
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* 模型加载配置
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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 `"Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors"`。用逗号分隔。
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* `--extra_inputs`: 模型 Pipeline 所需的额外输入参数，例如训练图像编辑模型时需要额外参数，以 `,` 分隔。
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* `--fp8_models`：以 FP8 格式加载的模型，格式与 `--model_paths` 或 `--model_id_with_origin_paths` 一致，目前仅支持参数不被梯度更新的模型（不需要梯度回传，或梯度仅更新其 LoRA）。
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* 训练基础配置
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* `--learning_rate`: 学习率。
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* `--num_epochs`: 轮数（Epoch）。
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* `--trainable_models`: 可训练的模型，例如 `dit`、`vae`、`text_encoder`。
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* `--find_unused_parameters`: DDP 训练中是否存在未使用的参数，少数模型包含不参与梯度计算的冗余参数，需开启这一设置避免在多 GPU 训练中报错。
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* `--weight_decay`：权重衰减大小，详见 [torch.optim.AdamW](https://docs.pytorch.org/docs/stable/generated/torch.optim.AdamW.html)。
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* `--task`: 训练任务，默认为 `sft`，部分模型支持更多训练模式，请参考每个特定模型的文档。
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* 输出配置
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* `--output_path`: 模型保存路径。
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* `--remove_prefix_in_ckpt`: 在模型文件的 state dict 中移除前缀。
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* `--save_steps`: 保存模型的训练步数间隔，若此参数留空，则每个 epoch 保存一次。
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* LoRA 配置
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* `--lora_base_model`: LoRA 添加到哪个模型上。
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* `--lora_target_modules`: LoRA 添加到哪些层上。
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* `--lora_rank`: LoRA 的秩（Rank）。
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* `--lora_checkpoint`: LoRA 检查点的路径。如果提供此路径，LoRA 将从此检查点加载。
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* `--preset_lora_path`: 预置 LoRA 检查点路径，如果提供此路径，这一 LoRA 将会以融入基础模型的形式加载。此参数用于 LoRA 差分训练。
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* `--preset_lora_model`: 预置 LoRA 融入的模型，例如 `dit`。
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* 梯度配置
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* `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。
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* `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。
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* `--gradient_accumulation_steps`: 梯度累积步数。
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* 视频宽高配置
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* `--height`: 视频的高度。将 `height` 和 `width` 留空以启用动态分辨率。
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* `--width`: 视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。
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* `--max_pixels`: 视频帧的最大像素面积，当启用动态分辨率时，分辨率大于这个数值的视频帧都会被缩小，分辨率小于这个数值的视频帧保持不变。
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* `--num_frames`: 视频的帧数。
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* LTX-2 系列特定参数
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* `--tokenizer_path`: 分词器路径，适用于文生视频模型，留空则从远程自动下载。
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* `--frame_rate`: 训练视频的帧率。
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我们构建了一个样例视频数据集，以方便您进行测试，通过以下命令可以下载这个数据集：
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```shell
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modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
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```
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我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
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