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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=[], preset_lora_path=None, |
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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 = SD3ImagePipeline.from_model_manager(model_manager) |
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self.pipe.scheduler.set_timesteps(1000, training=True) |
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if preset_lora_path is not None: |
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preset_lora_path = preset_lora_path.split(",") |
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for path in preset_lora_path: |
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model_manager.load_lora(path) |
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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 models, separated by comma. For example, SD3: `models/stable_diffusion_3/sd3_medium_incl_clips_t5xxlfp16.safetensors`, SD3.5-large: `models/stable_diffusion_3/text_encoders/clip_g.safetensors,models/stable_diffusion_3/text_encoders/clip_l.safetensors,models/stable_diffusion_3/text_encoders/t5xxl_fp16.safetensors,models/stable_diffusion_3/sd3.5_large.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,norm_1_a.linear,norm_1_b.linear,a_to_out,b_to_out,ff_a.0,ff_a.2,ff_b.0,ff_b.2", |
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help="Layers with LoRA modules.", |
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) |
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parser.add_argument( |
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"--preset_lora_path", |
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type=str, |
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default=None, |
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help="Preset LoRA path.", |
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) |
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parser.add_argument( |
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"--num_timesteps", |
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type=int, |
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default=1000, |
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help="Number of total timesteps. For turbo models, please set this parameter to the number of expected number of inference steps.", |
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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.split(","), |
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preset_lora_path=args.preset_lora_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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