Merge pull request #884 from modelscope/dev2-dzj

Unified Dataset & Splited Training

diffsynth/pipelines/qwen_image.py

@@ -174,9 +174,12 @@ class QwenImagePipeline(BasePipeline):
             computation_dtype=self.torch_dtype,
             computation_device="cuda",
         )
-        enable_vram_management(self.text_encoder, module_map=module_map, module_config=model_config)
+        if self.text_encoder is not None:
-        enable_vram_management(self.dit, module_map=module_map, module_config=model_config)
+            enable_vram_management(self.text_encoder, module_map=module_map, module_config=model_config)
-        enable_vram_management(self.vae, module_map=module_map, module_config=model_config)
+        if self.dit is not None:
+            enable_vram_management(self.dit, module_map=module_map, module_config=model_config)
+        if self.vae is not None:
+            enable_vram_management(self.vae, module_map=module_map, module_config=model_config)
     
     
     def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5, enable_dit_fp8_computation=False):

diffsynth/trainers/unified_dataset.py

@@ -0,0 +1,334 @@
+import torch, torchvision, imageio, os, json, pandas
+import imageio.v3 as iio
+from PIL import Image
+
+
+
+class DataProcessingPipeline:
+    def __init__(self, operators=None):
+        self.operators: list[DataProcessingOperator] = [] if operators is None else operators
+        
+    def __call__(self, data):
+        for operator in self.operators:
+            data = operator(data)
+        return data
+    
+    def __rshift__(self, pipe):
+        if isinstance(pipe, DataProcessingOperator):
+            pipe = DataProcessingPipeline([pipe])
+        return DataProcessingPipeline(self.operators + pipe.operators)
+
+
+
+class DataProcessingOperator:
+    def __call__(self, data):
+        raise NotImplementedError("DataProcessingOperator cannot be called directly.")
+    
+    def __rshift__(self, pipe):
+        if isinstance(pipe, DataProcessingOperator):
+            pipe = DataProcessingPipeline([pipe])
+        return DataProcessingPipeline([self]).__rshift__(pipe)
+
+
+
+class DataProcessingOperatorRaw(DataProcessingOperator):
+    def __call__(self, data):
+        return data
+
+
+
+class ToInt(DataProcessingOperator):
+    def __call__(self, data):
+        return int(data)
+
+
+
+class ToFloat(DataProcessingOperator):
+    def __call__(self, data):
+        return float(data)
+
+
+
+class ToStr(DataProcessingOperator):
+    def __init__(self, none_value=""):
+        self.none_value = none_value
+    
+    def __call__(self, data):
+        if data is None: data = self.none_value
+        return str(data)
+
+
+
+class LoadImage(DataProcessingOperator):
+    def __init__(self, convert_RGB=True):
+        self.convert_RGB = convert_RGB
+    
+    def __call__(self, data: str):
+        image = Image.open(data)
+        if self.convert_RGB: image = image.convert("RGB")
+        return image
+
+
+
+class ImageCropAndResize(DataProcessingOperator):
+    def __init__(self, height, width, max_pixels, height_division_factor, width_division_factor):
+        self.height = height
+        self.width = width
+        self.max_pixels = max_pixels
+        self.height_division_factor = height_division_factor
+        self.width_division_factor = width_division_factor
+
+    def crop_and_resize(self, image, target_height, target_width):
+        width, height = image.size
+        scale = max(target_width / width, target_height / height)
+        image = torchvision.transforms.functional.resize(
+            image,
+            (round(height*scale), round(width*scale)),
+            interpolation=torchvision.transforms.InterpolationMode.BILINEAR
+        )
+        image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
+        return image
+    
+    def get_height_width(self, image):
+        if self.height is None or self.width is None:
+            width, height = image.size
+            if width * height > self.max_pixels:
+                scale = (width * height / self.max_pixels) ** 0.5
+                height, width = int(height / scale), int(width / scale)
+            height = height // self.height_division_factor * self.height_division_factor
+            width = width // self.width_division_factor * self.width_division_factor
+        else:
+            height, width = self.height, self.width
+        return height, width
+    
+    
+    def __call__(self, data: Image.Image):
+        image = self.crop_and_resize(data, *self.get_height_width(data))
+        return image
+
+
+
+class ToList(DataProcessingOperator):
+    def __call__(self, data):
+        return [data]
+    
+
+
+class LoadVideo(DataProcessingOperator):
+    def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x):
+        self.num_frames = num_frames
+        self.time_division_factor = time_division_factor
+        self.time_division_remainder = time_division_remainder
+        # frame_processor is build in the video loader for high efficiency.
+        self.frame_processor = frame_processor
+        
+    def get_num_frames(self, reader):
+        num_frames = self.num_frames
+        if int(reader.count_frames()) < num_frames:
+            num_frames = int(reader.count_frames())
+            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames -= 1
+        return num_frames
+        
+    def __call__(self, data: str):
+        reader = imageio.get_reader(data)
+        num_frames = self.get_num_frames(reader)
+        frames = []
+        for frame_id in range(num_frames):
+            frame = reader.get_data(frame_id)
+            frame = Image.fromarray(frame)
+            frame = self.frame_processor(frame)
+            frames.append(frame)
+        reader.close()
+        return frames
+
+
+
+class SequencialProcess(DataProcessingOperator):
+    def __init__(self, operator=lambda x: x):
+        self.operator = operator
+        
+    def __call__(self, data):
+        return [self.operator(i) for i in data]
+
+
+
+class LoadGIF(DataProcessingOperator):
+    def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x):
+        self.num_frames = num_frames
+        self.time_division_factor = time_division_factor
+        self.time_division_remainder = time_division_remainder
+        # frame_processor is build in the video loader for high efficiency.
+        self.frame_processor = frame_processor
+        
+    def get_num_frames(self, path):
+        num_frames = self.num_frames
+        images = iio.imread(path, mode="RGB")
+        if len(images) < num_frames:
+            num_frames = len(images)
+            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames -= 1
+        return num_frames
+        
+    def __call__(self, data: str):
+        num_frames = self.get_num_frames(data)
+        frames = []
+        images = iio.imread(data, mode="RGB")
+        for img in images:
+            frame = Image.fromarray(img)
+            frame = self.frame_processor(frame)
+            frames.append(frame)
+            if len(frames) >= num_frames:
+                break
+        return frames
+    
+
+
+class RouteByExtensionName(DataProcessingOperator):
+    def __init__(self, operator_map):
+        self.operator_map = operator_map
+        
+    def __call__(self, data: str):
+        file_ext_name = data.split(".")[-1].lower()
+        for ext_names, operator in self.operator_map:
+            if ext_names is None or file_ext_name in ext_names:
+                return operator(data)
+        raise ValueError(f"Unsupported file: {data}")
+
+
+
+class RouteByType(DataProcessingOperator):
+    def __init__(self, operator_map):
+        self.operator_map = operator_map
+        
+    def __call__(self, data):
+        for dtype, operator in self.operator_map:
+            if dtype is None or isinstance(data, dtype):
+                return operator(data)
+        raise ValueError(f"Unsupported data: {data}")
+
+
+
+class LoadTorchPickle(DataProcessingOperator):
+    def __init__(self, map_location="cpu"):
+        self.map_location = map_location
+        
+    def __call__(self, data):
+        return torch.load(data, map_location=self.map_location, weights_only=False)
+
+
+
+class ToAbsolutePath(DataProcessingOperator):
+    def __init__(self, base_path=""):
+        self.base_path = base_path
+        
+    def __call__(self, data):
+        return os.path.join(self.base_path, data)
+
+
+
+class UnifiedDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        base_path=None, metadata_path=None,
+        repeat=1,
+        data_file_keys=tuple(),
+        main_data_operator=lambda x: x,
+        special_operator_map=None,
+    ):
+        self.base_path = base_path
+        self.metadata_path = metadata_path
+        self.repeat = repeat
+        self.data_file_keys = data_file_keys
+        self.main_data_operator = main_data_operator
+        self.cached_data_operator = LoadTorchPickle()
+        self.special_operator_map = {} if special_operator_map is None else special_operator_map
+        self.data = []
+        self.cached_data = []
+        self.load_from_cache = metadata_path is None
+        self.load_metadata(metadata_path)
+    
+    @staticmethod
+    def default_image_operator(
+        base_path="",
+        max_pixels=1920*1080, height=None, width=None,
+        height_division_factor=16, width_division_factor=16,
+    ):
+        return RouteByType(operator_map=[
+            (str, ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor)),
+            (list, SequencialProcess(ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor))),
+        ])
+    
+    @staticmethod
+    def default_video_operator(
+        base_path="",
+        max_pixels=1920*1080, height=None, width=None,
+        height_division_factor=16, width_division_factor=16,
+        num_frames=81, time_division_factor=4, time_division_remainder=1,
+    ):
+        return RouteByType(operator_map=[
+            (str, ToAbsolutePath(base_path) >> RouteByExtensionName(operator_map=[
+                (("jpg", "jpeg", "png", "webp"), LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor) >> ToList()),
+                (("gif",), LoadGIF(num_frames, time_division_factor, time_division_remainder) >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor)),
+                (("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), LoadVideo(
+                    num_frames, time_division_factor, time_division_remainder,
+                    frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor),
+                )),
+            ])),
+        ])
+        
+    def search_for_cached_data_files(self, path):
+        for file_name in os.listdir(path):
+            subpath = os.path.join(path, file_name)
+            if os.path.isdir(subpath):
+                self.search_for_cached_data_files(subpath)
+            elif subpath.endswith(".pth"):
+                self.cached_data.append(subpath)
+    
+    def load_metadata(self, metadata_path):
+        if metadata_path is None:
+            print("No metadata_path. Searching for cached data files.")
+            self.search_for_cached_data_files(self.base_path)
+            print(f"{len(self.cached_data)} cached data files found.")
+        elif metadata_path.endswith(".json"):
+            with open(metadata_path, "r") as f:
+                metadata = json.load(f)
+            self.data = metadata
+        elif metadata_path.endswith(".jsonl"):
+            metadata = []
+            with open(metadata_path, 'r') as f:
+                for line in f:
+                    metadata.append(json.loads(line.strip()))
+            self.data = metadata
+        else:
+            metadata = pandas.read_csv(metadata_path)
+            self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
+
+    def __getitem__(self, data_id):
+        if self.load_from_cache:
+            data = self.cached_data[data_id % len(self.cached_data)]
+            data = self.cached_data_operator(data)
+        else:
+            data = self.data[data_id % len(self.data)].copy()
+            for key in self.data_file_keys:
+                if key in data:
+                    if key in self.special_operator_map:
+                        data[key] = self.special_operator_map[key]
+                    elif key in self.data_file_keys:
+                        data[key] = self.main_data_operator(data[key])
+        return data
+
+    def __len__(self):
+        if self.load_from_cache:
+            return len(self.cached_data) * self.repeat
+        else:
+            return len(self.data) * self.repeat
+        
+    def check_data_equal(self, data1, data2):
+        # Debug only
+        if len(data1) != len(data2):
+            return False
+        for k in data1:
+            if data1[k] != data2[k]:
+                return False
+        return True

diffsynth/trainers/utils.py

@@ -417,6 +417,13 @@ class DiffusionTrainingModule(torch.nn.Module):
                 state_dict_[name] = param
             state_dict = state_dict_
         return state_dict
+    
+    
+    def transfer_data_to_device(self, data, device):
+        for key in data:
+            if isinstance(data[key], torch.Tensor):
+                data[key] = data[key].to(device)
+        return data
 
 
 
@@ -484,7 +491,10 @@ def launch_training_task(
         for data in tqdm(dataloader):
             with accelerator.accumulate(model):
                 optimizer.zero_grad()
-                loss = model(data)
+                if dataset.load_from_cache:
+                    loss = model({}, inputs=data)
+                else:
+                    loss = model(data)
                 accelerator.backward(loss)
                 optimizer.step()
                 model_logger.on_step_end(accelerator, model, save_steps)
@@ -494,16 +504,24 @@ def launch_training_task(
     model_logger.on_training_end(accelerator, model, save_steps)
 
 
-def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"):
+def launch_data_process_task(
-    dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0])
+    dataset: torch.utils.data.Dataset,
+    model: DiffusionTrainingModule,
+    model_logger: ModelLogger,
+    num_workers: int = 8,
+):
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
     accelerator = Accelerator()
     model, dataloader = accelerator.prepare(model, dataloader)
-    os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True)
+    
-    for data_id, data in enumerate(tqdm(dataloader)):
+    for data_id, data in tqdm(enumerate(dataloader)):
-        with torch.no_grad():
+        with accelerator.accumulate(model):
-            inputs = model.forward_preprocess(data)
+            with torch.no_grad():
-            inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs}
+                folder = os.path.join(model_logger.output_path, str(accelerator.process_index))
-            torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth"))
+                os.makedirs(folder, exist_ok=True)
+                save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth")
+                data = model(data)
+                torch.save(data, save_path)
 
 
 

examples/flux/model_training/train.py

@@ -1,8 +1,9 @@
 import torch, os, json
 from diffsynth import load_state_dict
 from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
-from diffsynth.trainers.utils import DiffusionTrainingModule, ImageDataset, ModelLogger, launch_training_task, flux_parser
+from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, flux_parser
 from diffsynth.models.lora import FluxLoRAConverter
+from diffsynth.trainers.unified_dataset import UnifiedDataset
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
@@ -106,7 +107,20 @@ class FluxTrainingModule(DiffusionTrainingModule):
 if __name__ == "__main__":
     parser = flux_parser()
     args = parser.parse_args()
-    dataset = ImageDataset(args=args)
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=args.dataset_repeat,
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_image_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+        )
+    )
     model = FluxTrainingModule(
         model_paths=args.model_paths,
         model_id_with_origin_paths=args.model_id_with_origin_paths,

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

@@ -0,0 +1,25 @@
+accelerate launch examples/qwen_image/model_training/train_data_process.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --output_path "./models/train/Qwen-Image_lora_cache" \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8
+
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path models/train/Qwen-Image_lora_cache \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image_lora" \
+  --lora_base_model "dit" \
+  --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 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --enable_fp8_training

examples/qwen_image/model_training/train.py

@@ -2,7 +2,8 @@ import torch, os, json
 from diffsynth import load_state_dict
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
 from diffsynth.pipelines.flux_image_new import ControlNetInput
-from diffsynth.trainers.utils import DiffusionTrainingModule, ImageDataset, ModelLogger, launch_training_task, qwen_image_parser
+from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, qwen_image_parser
+from diffsynth.trainers.unified_dataset import UnifiedDataset
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
@@ -110,6 +111,7 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
     
     def forward(self, data, inputs=None):
         if inputs is None: inputs = self.forward_preprocess(data)
+        else: inputs = self.transfer_data_to_device(inputs, self.pipe.device)
         models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
         loss = self.pipe.training_loss(**models, **inputs)
         return loss
@@ -119,7 +121,20 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
 if __name__ == "__main__":
     parser = qwen_image_parser()
     args = parser.parse_args()
-    dataset = ImageDataset(args=args)
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=args.dataset_repeat,
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_image_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+        )
+    )
     model = QwenImageTrainingModule(
         model_paths=args.model_paths,
         model_id_with_origin_paths=args.model_id_with_origin_paths,

examples/qwen_image/model_training/train_data_process.py

@@ -0,0 +1,154 @@
+import torch, os, json
+from diffsynth import load_state_dict
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from diffsynth.pipelines.flux_image_new import ControlNetInput
+from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_data_process_task, qwen_image_parser
+from diffsynth.trainers.unified_dataset import UnifiedDataset
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+
+class QwenImageTrainingModule(DiffusionTrainingModule):
+    def __init__(
+        self,
+        model_paths=None, model_id_with_origin_paths=None,
+        tokenizer_path=None, processor_path=None,
+        trainable_models=None,
+        lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
+        use_gradient_checkpointing=True,
+        use_gradient_checkpointing_offload=False,
+        extra_inputs=None,
+        enable_fp8_training=False,
+    ):
+        super().__init__()
+        # Load models
+        offload_dtype = torch.float8_e4m3fn if enable_fp8_training else None
+        model_configs = []
+        if model_paths is not None:
+            model_paths = json.loads(model_paths)
+            model_configs += [ModelConfig(path=path, offload_dtype=offload_dtype) for path in model_paths]
+        if model_id_with_origin_paths is not None:
+            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
+            model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1], offload_dtype=offload_dtype) for i in model_id_with_origin_paths]
+
+        tokenizer_config = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path)
+        processor_config = ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/") if processor_path is None else ModelConfig(processor_path)
+        self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, tokenizer_config=tokenizer_config, processor_config=processor_config)
+        
+        # Enable FP8
+        if enable_fp8_training:
+            self.pipe._enable_fp8_lora_training(torch.float8_e4m3fn)
+        
+        # Reset training scheduler (do it in each training step)
+        self.pipe.scheduler.set_timesteps(1000, training=True)
+        
+        # Freeze untrainable models
+        self.pipe.freeze_except([] if trainable_models is None else trainable_models.split(","))
+        
+        # Add LoRA to the base models
+        if lora_base_model is not None:
+            model = self.add_lora_to_model(
+                getattr(self.pipe, lora_base_model),
+                target_modules=lora_target_modules.split(","),
+                lora_rank=lora_rank,
+                upcast_dtype=self.pipe.torch_dtype,
+            )
+            if lora_checkpoint is not None:
+                state_dict = load_state_dict(lora_checkpoint)
+                state_dict = self.mapping_lora_state_dict(state_dict)
+                load_result = model.load_state_dict(state_dict, strict=False)
+                print(f"LoRA checkpoint loaded: {lora_checkpoint}, total {len(state_dict)} keys")
+                if len(load_result[1]) > 0:
+                    print(f"Warning, LoRA key mismatch! Unexpected keys in LoRA checkpoint: {load_result[1]}")
+            setattr(self.pipe, lora_base_model, model)
+            
+        # Store other configs
+        self.use_gradient_checkpointing = use_gradient_checkpointing
+        self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
+        self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
+
+    
+    def forward_preprocess(self, data):
+        # CFG-sensitive parameters
+        inputs_posi = {"prompt": data["prompt"]}
+        inputs_nega = {"negative_prompt": ""}
+        
+        # CFG-unsensitive parameters
+        inputs_shared = {
+            # Assume you are using this pipeline for inference,
+            # please fill in the input parameters.
+            "input_image": data["image"],
+            "height": data["image"].size[1],
+            "width": data["image"].size[0],
+            # Please do not modify the following parameters
+            # unless you clearly know what this will cause.
+            "cfg_scale": 1,
+            "rand_device": self.pipe.device,
+            "use_gradient_checkpointing": self.use_gradient_checkpointing,
+            "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
+            "edit_image_auto_resize": True,
+        }
+        
+        # Extra inputs
+        controlnet_input, blockwise_controlnet_input = {}, {}
+        for extra_input in self.extra_inputs:
+            if extra_input.startswith("blockwise_controlnet_"):
+                blockwise_controlnet_input[extra_input.replace("blockwise_controlnet_", "")] = data[extra_input]
+            elif extra_input.startswith("controlnet_"):
+                controlnet_input[extra_input.replace("controlnet_", "")] = data[extra_input]
+            else:
+                inputs_shared[extra_input] = data[extra_input]
+        if len(controlnet_input) > 0:
+            inputs_shared["controlnet_inputs"] = [ControlNetInput(**controlnet_input)]
+        if len(blockwise_controlnet_input) > 0:
+            inputs_shared["blockwise_controlnet_inputs"] = [ControlNetInput(**blockwise_controlnet_input)]
+        
+        # Pipeline units will automatically process the input parameters.
+        for unit in self.pipe.units:
+            inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
+        return {**inputs_shared, **inputs_posi}
+    
+    
+    def forward(self, data, inputs=None):
+        if inputs is None: inputs = self.forward_preprocess(data)
+        return inputs
+
+
+
+if __name__ == "__main__":
+    parser = qwen_image_parser()
+    args = parser.parse_args()
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=1, # Set repeat = 1
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_image_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+        )
+    )
+    model = QwenImageTrainingModule(
+        model_paths=args.model_paths,
+        model_id_with_origin_paths=args.model_id_with_origin_paths,
+        tokenizer_path=args.tokenizer_path,
+        processor_path=args.processor_path,
+        trainable_models=args.trainable_models,
+        lora_base_model=args.lora_base_model,
+        lora_target_modules=args.lora_target_modules,
+        lora_rank=args.lora_rank,
+        lora_checkpoint=args.lora_checkpoint,
+        use_gradient_checkpointing=args.use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
+        extra_inputs=args.extra_inputs,
+        enable_fp8_training=args.enable_fp8_training,
+    )
+    model_logger = ModelLogger(args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt)
+    launch_data_process_task(
+        dataset, model, model_logger,
+        num_workers=args.dataset_num_workers,
+    )

examples/wanvideo/model_training/train.py

@@ -1,7 +1,8 @@
 import torch, os, json
 from diffsynth import load_state_dict
 from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
-from diffsynth.trainers.utils import DiffusionTrainingModule, VideoDataset, ModelLogger, launch_training_task, wan_parser
+from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, wan_parser
+from diffsynth.trainers.unified_dataset import UnifiedDataset
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
@@ -112,7 +113,23 @@ class WanTrainingModule(DiffusionTrainingModule):
 if __name__ == "__main__":
     parser = wan_parser()
     args = parser.parse_args()
-    dataset = VideoDataset(args=args)
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=args.dataset_repeat,
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_video_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+            num_frames=args.num_frames,
+            time_division_factor=4,
+            time_division_remainder=1,
+        ),
+    )
     model = WanTrainingModule(
         model_paths=args.model_paths,
         model_id_with_origin_paths=args.model_id_with_origin_paths,