import torch, os, argparse, accelerate from diffsynth.core import UnifiedDataset from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline from diffsynth.diffusion import * os.environ["TOKENIZERS_PARALLELISM"] = "false" class LingBotVideoTrainingModule(DiffusionTrainingModule): def __init__( self, model_paths=None, model_id_with_origin_paths=None, processor_path=None, trainable_models=None, lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, preset_lora_path=None, preset_lora_model=None, use_gradient_checkpointing=True, use_gradient_checkpointing_offload=False, first_frame_as_condition=False, extra_inputs=None, fp8_models=None, offload_models=None, quant_options=None, resume_from_checkpoint=None, remove_prefix_in_ckpt=None, device="cpu", task="sft", max_timestep_boundary=1.0, min_timestep_boundary=0.0, ): super().__init__() # Load models. The Qwen3-VL processor (tokenizer + image/video processor) is # passed separately via `processor_config`, mirroring the inference pipeline. model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, quant_options=quant_options, device=device) processor_config = self.parse_path_or_model_id(processor_path) self.pipe = LingBotVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, processor_config=processor_config) self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model) self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt) # Training mode: FlowMatchScheduler (Wan template) runs the full 1000-step # schedule and the SFT loss samples a random timestep. Attention-only LoRA is the # default; pass --lora_target_modules "to_q,to_k,to_v,to_out" to skip MoE/router. self.switch_pipe_to_training_mode( self.pipe, trainable_models, lora_base_model, lora_target_modules, lora_rank, lora_checkpoint, preset_lora_path, preset_lora_model, task=task, ) # Store other configs self.use_gradient_checkpointing = use_gradient_checkpointing self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload self.first_frame_as_condition = first_frame_as_condition self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] self.fp8_models = fp8_models self.task = task self.task_to_loss = { "sft:data_process": lambda pipe, *args: args, "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), } self.max_timestep_boundary = max_timestep_boundary self.min_timestep_boundary = min_timestep_boundary def get_pipeline_inputs(self, data): # The metadata "prompt" column stores the serialised structured-JSON caption # (see rewrite_captions.py); the pipeline's PromptEmbedder consumes it as-is. inputs_posi = {"prompt": data["prompt"]} inputs_nega = {} inputs_shared = { # Assume you are using this pipeline for inference, # please fill in the input parameters. "input_video": data["video"], "height": data["video"][0].size[1], "width": data["video"][0].size[0], "num_frames": len(data["video"]), # Please do not modify the following parameters # unless you clearly know what this will cause. "cfg_scale": 1, "seed": None, "rand_device": self.pipe.device, "use_gradient_checkpointing": self.use_gradient_checkpointing, "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, "max_timestep_boundary": self.max_timestep_boundary, "min_timestep_boundary": self.min_timestep_boundary, } if self.first_frame_as_condition: # Image-to-video (TI2V) LoRA: condition on the clip's own first frame. The # ImageEmbedder unit VAE-encodes it to first_frame_latents and feeds it to the # text encoder as vlm_image; FlowMatchSFTLoss then pins that clean latent into # the first temporal slot and excludes it from the loss (see diffsynth/diffusion/ # loss.py). No separate condition column or core change is needed. If your # dataset instead ships a distinct condition frame, drop this flag and pass it via # --extra_inputs input_image (with the column added to --data_file_keys). inputs_shared["input_image"] = data["video"][0] inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared) return inputs_shared, inputs_posi, inputs_nega def forward(self, data, inputs=None): if inputs is None: inputs = self.get_pipeline_inputs(data) inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) for unit in self.pipe.units: inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) loss = self.task_to_loss[self.task](self.pipe, *inputs) return loss def lingbot_video_parser(): parser = argparse.ArgumentParser(description="Simple example of a LingBot-Video training script.") parser = add_general_config(parser) parser = add_video_size_config(parser) parser.add_argument("--processor_path", type=str, default=None, help="Path to the Qwen3-VL processor directory (or `model_id:origin_file_pattern`). Used to tokenize prompts.") parser.add_argument("--first_frame_as_condition", default=False, action="store_true", help="Image-to-video (TI2V) LoRA: condition on each clip's own first frame (pinned as a clean latent and excluded from the loss).") parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (fraction of the training schedule, in [0, 1]).") parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (fraction of the training schedule, in [0, 1]).") parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.") return parser if __name__ == "__main__": parser = lingbot_video_parser() args = parser.parse_args() accelerator = accelerate.Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)], ) 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 = LingBotVideoTrainingModule( model_paths=args.model_paths, model_id_with_origin_paths=args.model_id_with_origin_paths, 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, preset_lora_path=args.preset_lora_path, preset_lora_model=args.preset_lora_model, use_gradient_checkpointing=args.use_gradient_checkpointing, use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, first_frame_as_condition=args.first_frame_as_condition, extra_inputs=args.extra_inputs, fp8_models=args.fp8_models, offload_models=args.offload_models, quant_options=args.quant_options, resume_from_checkpoint=args.resume_from_checkpoint, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, task=args.task, device="cpu" if (args.initialize_model_on_cpu or args.enable_model_cpu_offload) else accelerator.device, max_timestep_boundary=args.max_timestep_boundary, min_timestep_boundary=args.min_timestep_boundary, ) model_logger = ModelLogger( args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, enable_tensorboard_log=args.enable_tensorboard_log, enable_swanlab_log=args.enable_swanlab_log, swanlab_project=args.swanlab_project, enable_wandb_log=args.enable_wandb_log, wandb_project=args.wandb_project, enable_csv_log=args.enable_csv_log, ) launcher_map = { "sft:data_process": launch_data_process_task, "sft": launch_training_task, "sft:train": launch_training_task, } launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)