from diffsynth import ModelManager, SD3ImagePipeline from peft import LoraConfig, inject_adapter_in_model from torchvision import transforms from PIL import Image import lightning as pl import pandas as pd import torch, os, argparse os.environ["TOKENIZERS_PARALLELISM"] = "True" class TextImageDataset(torch.utils.data.Dataset): def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False): self.steps_per_epoch = steps_per_epoch metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv")) self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]] self.text = metadata["text"].to_list() self.image_processor = transforms.Compose( [ transforms.Resize(max(height, width), interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)), transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def __getitem__(self, index): data_id = torch.randint(0, len(self.path), (1,))[0] data_id = (data_id + index) % len(self.path) # For fixed seed. text = self.text[data_id] image = Image.open(self.path[data_id]).convert("RGB") image = self.image_processor(image) return {"text": text, "image": image} def __len__(self): return self.steps_per_epoch class LightningModel(pl.LightningModule): def __init__(self, torch_dtype=torch.float16, learning_rate=1e-4, pretrained_weights=[], lora_rank=4, lora_alpha=4, use_gradient_checkpointing=True): super().__init__() # Load models model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) model_manager.load_models(pretrained_weights) self.pipe = SD3ImagePipeline.from_model_manager(model_manager) # Freeze parameters self.pipe.text_encoder_1.requires_grad_(False) self.pipe.text_encoder_2.requires_grad_(False) if self.pipe.text_encoder_3 is not None: self.pipe.text_encoder_3.requires_grad_(False) self.pipe.dit.requires_grad_(False) self.pipe.vae_decoder.requires_grad_(False) self.pipe.vae_encoder.requires_grad_(False) self.pipe.text_encoder_1.eval() self.pipe.text_encoder_2.eval() if self.pipe.text_encoder_3 is not None: self.pipe.text_encoder_3.eval() self.pipe.dit.train() self.pipe.vae_decoder.eval() self.pipe.vae_encoder.eval() # Add LoRA to DiT lora_config = LoraConfig( r=lora_rank, lora_alpha=lora_alpha, init_lora_weights="gaussian", target_modules=["a_to_qkv", "b_to_qkv"], ) self.pipe.dit = inject_adapter_in_model(lora_config, self.pipe.dit) for param in self.pipe.dit.parameters(): # Upcast LoRA parameters into fp32 if param.requires_grad: param.data = param.to(torch.float32) # Set other parameters self.learning_rate = learning_rate self.use_gradient_checkpointing = use_gradient_checkpointing self.pipe.scheduler.set_timesteps(1000) def training_step(self, batch, batch_idx): # Data text, image = batch["text"], batch["image"] # Prepare input parameters self.pipe.device = self.device prompt_emb, pooled_prompt_emb = self.pipe.prompter.encode_prompt( self.pipe.text_encoder_1, self.pipe.text_encoder_2, self.pipe.text_encoder_3, text, device=self.device ) latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device)) noise = torch.randn_like(latents) timestep_id = torch.randint(0, 1000, (1,)) timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device) noisy_latents = self.pipe.scheduler.add_noise(latents, noise, self.pipe.scheduler.timesteps[timestep_id]) training_target = self.pipe.scheduler.training_target(latents, noise, timestep) # Compute loss noise_pred = self.pipe.dit(noisy_latents, timestep, prompt_emb, pooled_prompt_emb, use_gradient_checkpointing=self.use_gradient_checkpointing) loss = torch.nn.functional.mse_loss(noise_pred, training_target) # Record log self.log("train_loss", loss, prog_bar=True) return loss def configure_optimizers(self): trainable_modules = filter(lambda p: p.requires_grad, self.pipe.dit.parameters()) optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) return optimizer def on_save_checkpoint(self, checkpoint): checkpoint.clear() trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.dit.named_parameters())) trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) state_dict = self.pipe.dit.state_dict() for name, param in state_dict.items(): if name in trainable_param_names: checkpoint[name] = param def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--pretrained_path", type=str, default=None, required=True, 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`.", ) parser.add_argument( "--dataset_path", type=str, default=None, required=True, help="The path of the Dataset.", ) parser.add_argument( "--output_path", type=str, default="./", help="Path to save the model.", ) parser.add_argument( "--steps_per_epoch", type=int, default=500, help="Number of steps per epoch.", ) parser.add_argument( "--height", type=int, default=1024, help="Image height.", ) parser.add_argument( "--width", type=int, default=1024, help="Image width.", ) parser.add_argument( "--center_crop", default=False, action="store_true", help=( "Whether to center crop the input images to the resolution. If not set, the images will be randomly" " cropped. The images will be resized to the resolution first before cropping." ), ) parser.add_argument( "--random_flip", default=False, action="store_true", help="Whether to randomly flip images horizontally", ) parser.add_argument( "--batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", ) parser.add_argument( "--precision", type=str, default="16-mixed", choices=["32", "16", "16-mixed"], help="Training precision", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Learning rate.", ) parser.add_argument( "--lora_rank", type=int, default=4, help="The dimension of the LoRA update matrices.", ) parser.add_argument( "--lora_alpha", type=float, default=4.0, help="The weight of the LoRA update matrices.", ) parser.add_argument( "--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.", ) parser.add_argument( "--accumulate_grad_batches", type=int, default=1, help="The number of batches in gradient accumulation.", ) parser.add_argument( "--training_strategy", type=str, default="auto", choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], help="Training strategy", ) parser.add_argument( "--max_epochs", type=int, default=1, help="Number of epochs.", ) args = parser.parse_args() return args if __name__ == '__main__': # args args = parse_args() # dataset and data loader dataset = TextImageDataset( args.dataset_path, steps_per_epoch=args.steps_per_epoch * args.batch_size, height=args.height, width=args.width, center_crop=args.center_crop, random_flip=args.random_flip ) train_loader = torch.utils.data.DataLoader( dataset, shuffle=True, batch_size=args.batch_size, num_workers=args.dataloader_num_workers ) # model model = LightningModel( pretrained_weights=[args.pretrained_path], torch_dtype=torch.float32 if args.precision == "32" else torch.float16, learning_rate=args.learning_rate, lora_rank=args.lora_rank, lora_alpha=args.lora_alpha, use_gradient_checkpointing=args.use_gradient_checkpointing ) # train trainer = pl.Trainer( max_epochs=args.max_epochs, accelerator="gpu", devices="auto", precision=args.precision, strategy=args.training_strategy, default_root_dir=args.output_path, accumulate_grad_batches=args.accumulate_grad_batches, callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)] ) trainer.fit(model=model, train_dataloaders=train_loader)