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55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
import torch, os, json
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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from diffsynth.trainers.utils import DiffusionTrainingModule, VideoDataset, launch_training_task, wan_parser
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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class WanTrainingModule(DiffusionTrainingModule):
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def __init__(self, model_paths, task="train_lora", lora_target_modules="q,k,v,o,ffn.0,ffn.2", lora_rank=32):
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super().__init__()
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self.pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cpu",
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model_configs=[ModelConfig(path=path) for path in model_paths],
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)
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self.pipe.scheduler.set_timesteps(1000, training=True)
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if task == "train_lora":
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self.pipe.freeze_except([])
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self.pipe.dit = self.add_lora_to_model(self.pipe.dit, target_modules=lora_target_modules.split(","), lora_rank=lora_rank)
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else:
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self.pipe.freeze_except(["dit"])
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def forward_preprocess(self, data):
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inputs_posi = {"prompt": data["prompt"]}
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inputs_nega = {}
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inputs_shared = {
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"input_image": data["video"][0],
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"input_video": data["video"],
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"height": data["video"][0].size[1],
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"width": data["video"][0].size[0],
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"num_frames": len(data["video"]),
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# Please do not modify the following parameters.
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"cfg_scale": 1,
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"tiled": False,
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"rand_device": self.pipe.device,
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"use_gradient_checkpointing": True,
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"cfg_merge": False,
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}
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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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def forward(self, data):
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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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return loss
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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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dataset = VideoDataset(args=args)
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model = WanTrainingModule(json.loads(args.model_paths), task=args.task, lora_target_modules=args.lora_target_modules, lora_rank=args.lora_rank)
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launch_training_task(model, dataset, args=args)
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