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58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
from diffsynth import ModelManager, SD3ImagePipeline
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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",
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):
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super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing)
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# Load models
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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 = SD3ImagePipeline.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(self.pipe.denoising_model(), lora_rank=lora_rank, lora_alpha=lora_alpha, lora_target_modules=lora_target_modules, init_lora_weights=init_lora_weights)
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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_3/sd3_medium_incl_clips.safetensors` or `models/stable_diffusion_3/sd3_medium_incl_clips_t5xxlfp16.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="a_to_qkv,b_to_qkv",
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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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lora_target_modules=args.lora_target_modules
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)
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launch_training_task(model, args)
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