Merge pull request #749 from mi804/training_args

support num_workers,save_steps,find_unused_parameters

diffsynth/trainers/utils.py

@@ -4,6 +4,7 @@ from PIL import Image
 import pandas as pd
 from tqdm import tqdm
 from accelerate import Accelerator
+from accelerate.utils import DistributedDataParallelKwargs
 
 
 
@@ -364,10 +365,13 @@ class ModelLogger:
         self.output_path = output_path
         self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
         self.state_dict_converter = state_dict_converter
+        self.num_steps = 0
 
 
-    def on_step_end(self, loss):
+    def on_step_end(self, accelerator, model, save_steps=None):
-        pass
+        self.num_steps += 1
+        if save_steps is not None and self.num_steps % save_steps == 0:
+            self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors")
 
 
     def on_epoch_end(self, accelerator, model, epoch_id):
@@ -381,6 +385,21 @@ class ModelLogger:
             accelerator.save(state_dict, path, safe_serialization=True)
 
 
+    def on_training_end(self, accelerator, model, save_steps=None):
+        if save_steps is not None and self.num_steps % save_steps != 0:
+            self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors")
+
+
+    def save_model(self, accelerator, model, file_name):
+        accelerator.wait_for_everyone()
+        if accelerator.is_main_process:
+            state_dict = accelerator.get_state_dict(model)
+            state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
+            state_dict = self.state_dict_converter(state_dict)
+            os.makedirs(self.output_path, exist_ok=True)
+            path = os.path.join(self.output_path, file_name)
+            accelerator.save(state_dict, path, safe_serialization=True)
+
 
 def launch_training_task(
     dataset: torch.utils.data.Dataset,
@@ -388,11 +407,17 @@ def launch_training_task(
     model_logger: ModelLogger,
     optimizer: torch.optim.Optimizer,
     scheduler: torch.optim.lr_scheduler.LRScheduler,
+    num_workers: int = 8,
+    save_steps: int = None,
     num_epochs: int = 1,
     gradient_accumulation_steps: int = 1,
+    find_unused_parameters: bool = False,
 ):
-    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0])
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
-    accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps)
+    accelerator = Accelerator(
+        gradient_accumulation_steps=gradient_accumulation_steps,
+        kwargs_handlers=[DistributedDataParallelKwargs(find_unused_parameters=find_unused_parameters)],
+    )
     model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
     
     for epoch_id in range(num_epochs):
@@ -402,10 +427,11 @@ def launch_training_task(
                 loss = model(data)
                 accelerator.backward(loss)
                 optimizer.step()
-                model_logger.on_step_end(loss)
+                model_logger.on_step_end(accelerator, model, save_steps)
                 scheduler.step()
+        if save_steps is None:
             model_logger.on_epoch_end(accelerator, model, epoch_id)
-
+    model_logger.on_training_end(accelerator, model, save_steps)
 
 
 def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"):
@@ -446,6 +472,9 @@ def wan_parser():
     parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
     parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).")
     parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).")
+    parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
+    parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
+    parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
     return parser
 
 
@@ -474,6 +503,9 @@ def flux_parser():
     parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
     parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
     parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
+    parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
+    parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
+    parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
     return parser
 
 
@@ -503,4 +535,7 @@ def qwen_image_parser():
     parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
     parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
     parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
+    parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
+    parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
+    parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
     return parser

examples/flux/README.md

@@ -249,6 +249,7 @@ The script includes the following parameters:
   * `--width`: Width of the image or video. Leave `height` and `width` empty to enable dynamic resolution.
   * `--data_file_keys`: Data file keys in the metadata. Separate with commas.
   * `--dataset_repeat`: Number of times the dataset repeats per epoch.
+  * `--dataset_num_workers`: Number of workers for data loading.
 * Model
   * `--model_paths`: Paths to load models. In JSON format.
   * `--model_id_with_origin_paths`: Model ID with original paths, e.g., black-forest-labs/FLUX.1-dev:flux1-dev.safetensors. Separate with commas.
@@ -257,6 +258,8 @@ The script includes the following parameters:
   * `--num_epochs`: Number of epochs.
   * `--output_path`: Save path.
   * `--remove_prefix_in_ckpt`: Remove prefix in checkpoint.
+  * `--save_steps`: Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.
+  * `--find_unused_parameters`: Whether to find unused parameters in DDP.
 * Trainable Modules
   * `--trainable_models`: Models that can be trained, e.g., dit, vae, text_encoder.
   * `--lora_base_model`: Which model to add LoRA to.

examples/flux/README_zh.md

@@ -249,6 +249,7 @@ FLUX 系列模型训练通过统一的 [`./model_training/train.py`](./model_tra
   * `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。
   * `--data_file_keys`: 元数据中的数据文件键。用逗号分隔。
   * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
+  * `--dataset_num_workers`: 每个 Dataloder 的进程数量。
 * 模型
   * `--model_paths`: 要加载的模型路径。JSON 格式。
   * `--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。
@@ -257,6 +258,8 @@ FLUX 系列模型训练通过统一的 [`./model_training/train.py`](./model_tra
   * `--num_epochs`: 轮数（Epoch）。
   * `--output_path`: 保存路径。
   * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。
+  * `--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
+  * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数
 * 可训练模块
   * `--trainable_models`: 可训练的模型，例如 dit、vae、text_encoder。
   * `--lora_base_model`: LoRA 添加到哪个模型上。

examples/flux/model_training/train.py

@@ -121,4 +121,7 @@ if __name__ == "__main__":
         dataset, model, model_logger, optimizer, scheduler,
         num_epochs=args.num_epochs,
         gradient_accumulation_steps=args.gradient_accumulation_steps,
+        save_steps=args.save_steps,
+        find_unused_parameters=args.find_unused_parameters,
+        num_workers=args.dataset_num_workers,
     )

examples/qwen_image/README.md

@@ -219,6 +219,7 @@ The script includes the following parameters:
   * `--width`: Width of image or video. Leave `height` and `width` empty to enable dynamic resolution.
   * `--data_file_keys`: Data file keys in metadata. Separate with commas.
   * `--dataset_repeat`: Number of times the dataset repeats per epoch.
+  * `--dataset_num_workers`: Number of workers for data loading.
 * Model
   * `--model_paths`: Model paths to load. In JSON format.
   * `--model_id_with_origin_paths`: Model ID with original paths, e.g., Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors. Separate with commas.
@@ -228,6 +229,8 @@ The script includes the following parameters:
   * `--num_epochs`: Number of epochs.
   * `--output_path`: Save path.
   * `--remove_prefix_in_ckpt`: Remove prefix in checkpoint.
+  * `--save_steps`: Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.
+  * `--find_unused_parameters`: Whether to find unused parameters in DDP.
 * Trainable Modules
   * `--trainable_models`: Models to train, e.g., dit, vae, text_encoder.
   * `--lora_base_model`: Which model to add LoRA to.

examples/qwen_image/README_zh.md

@@ -219,6 +219,7 @@ Qwen-Image 系列模型训练通过统一的 [`./model_training/train.py`](./mod
   * `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。
   * `--data_file_keys`: 元数据中的数据文件键。用逗号分隔。
   * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
+  * `--dataset_num_workers`: 每个 Dataloder 的进程数量。
 * 模型
   * `--model_paths`: 要加载的模型路径。JSON 格式。
   * `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors。用逗号分隔。
@@ -228,6 +229,8 @@ Qwen-Image 系列模型训练通过统一的 [`./model_training/train.py`](./mod
   * `--num_epochs`: 轮数（Epoch）。
   * `--output_path`: 保存路径。
   * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。
+  * `--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
+  * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数
 * 可训练模块
   * `--trainable_models`: 可训练的模型，例如 dit、vae、text_encoder。
   * `--lora_base_model`: LoRA 添加到哪个模型上。

examples/qwen_image/model_training/lora/Qwen-Image.sh

@@ -12,4 +12,6 @@ accelerate launch examples/qwen_image/model_training/train.py \
   --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
   --lora_rank 32 \
   --align_to_opensource_format \
-  --use_gradient_checkpointing
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters

examples/qwen_image/model_training/train.py

@@ -117,4 +117,7 @@ if __name__ == "__main__":
         dataset, model, model_logger, optimizer, scheduler,
         num_epochs=args.num_epochs,
         gradient_accumulation_steps=args.gradient_accumulation_steps,
+        save_steps=args.save_steps,
+        find_unused_parameters=args.find_unused_parameters,
+        num_workers=args.dataset_num_workers,
     )

examples/wanvideo/README.md

@@ -280,6 +280,7 @@ The script includes the following parameters:
   * `--num_frames`: Number of frames per video. Frames are sampled from the video prefix.
   * `--data_file_keys`: Data file keys in the metadata. Comma-separated.
   * `--dataset_repeat`: Number of times to repeat the dataset per epoch.
+  * `--dataset_num_workers`: Number of workers for data loading.
 * Models
   * `--model_paths`: Paths to load models. In JSON format.
   * `--model_id_with_origin_paths`: Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.
@@ -290,6 +291,8 @@ The script includes the following parameters:
   * `--num_epochs`: Number of epochs.
   * `--output_path`: Output save path.
   * `--remove_prefix_in_ckpt`: Remove prefix in ckpt.
+  * `--save_steps`: Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.
+  * `--find_unused_parameters`: Whether to find unused parameters in DDP.
 * Trainable Modules
   * `--trainable_models`: Models to train, e.g., dit, vae, text_encoder.
   * `--lora_base_model`: Which model LoRA is added to.

examples/wanvideo/README_zh.md

@@ -282,6 +282,7 @@ Wan 系列模型训练通过统一的 [`./model_training/train.py`](./model_trai
   * `--num_frames`: 每个视频中的帧数。帧从视频前缀中采样。
   * `--data_file_keys`: 元数据中的数据文件键。用逗号分隔。
   * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
+  * `--dataset_num_workers`: 每个 Dataloder 的进程数量。
 * 模型
   * `--model_paths`: 要加载的模型路径。JSON 格式。
   * `--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。
@@ -292,6 +293,8 @@ Wan 系列模型训练通过统一的 [`./model_training/train.py`](./model_trai
   * `--num_epochs`: 轮数（Epoch）。
   * `--output_path`: 保存路径。
   * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。
+  * `--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
+  * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数
 * 可训练模块
   * `--trainable_models`: 可训练的模型，例如 dit、vae、text_encoder。
   * `--lora_base_model`: LoRA 添加到哪个模型上。

examples/wanvideo/model_training/train.py

@@ -127,4 +127,7 @@ if __name__ == "__main__":
         dataset, model, model_logger, optimizer, scheduler,
         num_epochs=args.num_epochs,
         gradient_accumulation_steps=args.gradient_accumulation_steps,
+        save_steps=args.save_steps,
+        find_unused_parameters=args.find_unused_parameters,
+        num_workers=args.dataset_num_workers,
     )