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https://github.com/modelscope/DiffSynth-Studio.git
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DiffSynth-Studio 2.0 major update
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@@ -1,52 +1,82 @@
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import torch, os, json
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from diffsynth import load_state_dict
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, wan_parser
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from diffsynth.trainers.unified_dataset import UnifiedDataset, LoadVideo, LoadAudio, ImageCropAndResize, ToAbsolutePath
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import torch, os, argparse, accelerate, warnings
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from diffsynth.core import UnifiedDataset
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from diffsynth.core.data.operators import LoadVideo, LoadAudio, ImageCropAndResize, ToAbsolutePath
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from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
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from diffsynth.diffusion import *
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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class WanTrainingModule(DiffusionTrainingModule):
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def __init__(
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self,
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model_paths=None, model_id_with_origin_paths=None, audio_processor_config=None,
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model_paths=None, model_id_with_origin_paths=None,
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tokenizer_path=None, audio_processor_path=None,
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trainable_models=None,
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lora_base_model=None, lora_target_modules="q,k,v,o,ffn.0,ffn.2", lora_rank=32, lora_checkpoint=None,
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lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
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preset_lora_path=None, preset_lora_model=None,
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use_gradient_checkpointing=True,
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use_gradient_checkpointing_offload=False,
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extra_inputs=None,
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fp8_models=None,
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offload_models=None,
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device="cpu",
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task="sft",
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max_timestep_boundary=1.0,
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min_timestep_boundary=0.0,
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):
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super().__init__()
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# Warning
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if not use_gradient_checkpointing:
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warnings.warn("Gradient checkpointing is detected as disabled. To prevent out-of-memory errors, the training framework will forcibly enable gradient checkpointing.")
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use_gradient_checkpointing = True
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# Load models
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model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=False)
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if audio_processor_config is not None:
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audio_processor_config = ModelConfig(model_id=audio_processor_config.split(":")[0], origin_file_pattern=audio_processor_config.split(":")[1])
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self.pipe = WanVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, audio_processor_config=audio_processor_config)
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model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
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tokenizer_config = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/") if tokenizer_path is None else ModelConfig(tokenizer_path)
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audio_processor_config = ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/") if audio_processor_path is None else ModelConfig(audio_processor_path)
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self.pipe = WanVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, audio_processor_config=audio_processor_config)
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self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
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# Training mode
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self.switch_pipe_to_training_mode(
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self.pipe, trainable_models,
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lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint,
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enable_fp8_training=False,
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lora_base_model, lora_target_modules, lora_rank, lora_checkpoint,
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preset_lora_path, preset_lora_model,
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task=task,
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)
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# Store other configs
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self.use_gradient_checkpointing = use_gradient_checkpointing
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self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
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self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
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self.fp8_models = fp8_models
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self.task = task
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self.task_to_loss = {
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"sft:data_process": lambda pipe, *args: args,
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"direct_distill:data_process": lambda pipe, *args: args,
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"sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
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"sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
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"direct_distill": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi),
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"direct_distill:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi),
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}
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self.max_timestep_boundary = max_timestep_boundary
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self.min_timestep_boundary = min_timestep_boundary
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def forward_preprocess(self, data):
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# CFG-sensitive parameters
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def parse_extra_inputs(self, data, extra_inputs, inputs_shared):
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for extra_input in extra_inputs:
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if extra_input == "input_image":
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inputs_shared["input_image"] = data["video"][0]
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elif extra_input == "end_image":
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inputs_shared["end_image"] = data["video"][-1]
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elif extra_input == "reference_image" or extra_input == "vace_reference_image":
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inputs_shared[extra_input] = data[extra_input][0]
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else:
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inputs_shared[extra_input] = data[extra_input]
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return inputs_shared
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def get_pipeline_inputs(self, data):
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inputs_posi = {"prompt": data["prompt"]}
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inputs_nega = {}
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# CFG-unsensitive parameters
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inputs_shared = {
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# Assume you are using this pipeline for inference,
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# please fill in the input parameters.
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@@ -66,34 +96,37 @@ class WanTrainingModule(DiffusionTrainingModule):
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"max_timestep_boundary": self.max_timestep_boundary,
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"min_timestep_boundary": self.min_timestep_boundary,
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}
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# Extra inputs
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for extra_input in self.extra_inputs:
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if extra_input == "input_image":
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inputs_shared["input_image"] = data["video"][0]
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elif extra_input == "end_image":
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inputs_shared["end_image"] = data["video"][-1]
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elif extra_input == "reference_image" or extra_input == "vace_reference_image":
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inputs_shared[extra_input] = data[extra_input][0]
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else:
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inputs_shared[extra_input] = data[extra_input]
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# Pipeline units will automatically process the input parameters.
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for unit in self.pipe.units:
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inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
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return {**inputs_shared, **inputs_posi}
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inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
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return inputs_shared, inputs_posi, inputs_nega
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def forward(self, data, inputs=None):
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if inputs is None: inputs = self.forward_preprocess(data)
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models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
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loss = self.pipe.training_loss(**models, **inputs)
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if inputs is None: inputs = self.get_pipeline_inputs(data)
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inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
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for unit in self.pipe.units:
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inputs = self.pipe.unit_runner(unit, self.pipe, *inputs)
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loss = self.task_to_loss[self.task](self.pipe, *inputs)
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return loss
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def wan_parser():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser = add_general_config(parser)
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parser = add_video_size_config(parser)
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parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.")
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parser.add_argument("--audio_processor_path", type=str, default=None, help="Path to the audio processor. If provided, the processor will be used for Wan2.2-S2V model.")
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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).")
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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).")
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parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
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return parser
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if __name__ == "__main__":
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parser = wan_parser()
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args = parser.parse_args()
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accelerator = accelerate.Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)],
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)
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dataset = UnifiedDataset(
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base_path=args.dataset_base_path,
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metadata_path=args.dataset_metadata_path,
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@@ -118,19 +151,35 @@ if __name__ == "__main__":
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model = WanTrainingModule(
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model_paths=args.model_paths,
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model_id_with_origin_paths=args.model_id_with_origin_paths,
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audio_processor_config=args.audio_processor_config,
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tokenizer_path=args.tokenizer_path,
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audio_processor_path=args.audio_processor_path,
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trainable_models=args.trainable_models,
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lora_base_model=args.lora_base_model,
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lora_target_modules=args.lora_target_modules,
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lora_rank=args.lora_rank,
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lora_checkpoint=args.lora_checkpoint,
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preset_lora_path=args.preset_lora_path,
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preset_lora_model=args.preset_lora_model,
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use_gradient_checkpointing=args.use_gradient_checkpointing,
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use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
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extra_inputs=args.extra_inputs,
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fp8_models=args.fp8_models,
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offload_models=args.offload_models,
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task=args.task,
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device="cpu" if args.initialize_model_on_cpu else accelerator.device,
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max_timestep_boundary=args.max_timestep_boundary,
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min_timestep_boundary=args.min_timestep_boundary,
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)
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model_logger = ModelLogger(
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args.output_path,
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remove_prefix_in_ckpt=args.remove_prefix_in_ckpt
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remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
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)
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launch_training_task(dataset, model, model_logger, args=args)
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launcher_map = {
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"sft:data_process": launch_data_process_task,
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"direct_distill:data_process": launch_data_process_task,
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"sft": launch_training_task,
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"sft:train": launch_training_task,
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"direct_distill": launch_training_task,
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"direct_distill:train": launch_training_task,
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}
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launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)
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