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from diffsynth import ModelManager, SDXLImagePipeline |
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from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task |
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import torch, os, argparse |
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os.environ["TOKENIZERS_PARALLELISM"] = "True" |
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class LightningModel(LightningModelForT2ILoRA): |
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def __init__( |
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self, |
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torch_dtype=torch.float16, pretrained_weights=[], |
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learning_rate=1e-4, use_gradient_checkpointing=True, |
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lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, |
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): |
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super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing) |
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model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) |
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model_manager.load_models(pretrained_weights) |
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self.pipe = SDXLImagePipeline.from_model_manager(model_manager) |
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self.pipe.scheduler.set_timesteps(1000) |
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self.freeze_parameters() |
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self.add_lora_to_model( |
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self.pipe.denoising_model(), |
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lora_rank=lora_rank, |
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lora_alpha=lora_alpha, |
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lora_target_modules=lora_target_modules, |
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init_lora_weights=init_lora_weights, |
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pretrained_lora_path=pretrained_lora_path, |
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) |
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def parse_args(): |
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parser = argparse.ArgumentParser(description="Simple example of a training script.") |
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parser.add_argument( |
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"--pretrained_path", |
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type=str, |
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default=None, |
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required=True, |
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help="Path to pretrained model. For example, `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`.", |
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) |
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parser.add_argument( |
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"--lora_target_modules", |
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type=str, |
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default="to_q,to_k,to_v,to_out", |
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help="Layers with LoRA modules.", |
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) |
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parser = add_general_parsers(parser) |
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args = parser.parse_args() |
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return args |
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if __name__ == '__main__': |
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args = parse_args() |
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model = LightningModel( |
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torch_dtype=torch.float32 if args.precision == "32" else torch.float16, |
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pretrained_weights=[args.pretrained_path], |
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learning_rate=args.learning_rate, |
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use_gradient_checkpointing=args.use_gradient_checkpointing, |
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lora_rank=args.lora_rank, |
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lora_alpha=args.lora_alpha, |
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init_lora_weights=args.init_lora_weights, |
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pretrained_lora_path=args.pretrained_lora_path, |
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lora_target_modules=args.lora_target_modules |
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) |
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launch_training_task(model, args) |
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