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237 lines
10 KiB
Python
237 lines
10 KiB
Python
from diffsynth import ModelManager, FluxImagePipeline
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from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task
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from diffsynth.models.lora import FluxLoRAConverter
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import torch, os, argparse
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import lightning as pl
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from diffsynth.data.image_pulse import SingleTaskDataset, MultiTaskDataset
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from diffsynth.pipelines.flux_image import lets_dance_flux
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from diffsynth.models.flux_reference_embedder import FluxReferenceEmbedder
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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="kaiming", pretrained_lora_path=None,
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state_dict_converter=None, quantize = None
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):
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super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing, state_dict_converter=state_dict_converter)
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# Load models
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model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device)
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if quantize is None:
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model_manager.load_models(pretrained_weights)
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else:
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model_manager.load_models(pretrained_weights[1:])
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model_manager.load_model(pretrained_weights[0], torch_dtype=quantize)
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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.pipe = FluxImagePipeline.from_model_manager(model_manager)
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self.pipe.reference_embedder = FluxReferenceEmbedder()
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self.pipe.reference_embedder.init()
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if quantize is not None:
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self.pipe.dit.quantize()
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self.pipe.scheduler.set_timesteps(1000, training=True)
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self.freeze_parameters()
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self.pipe.reference_embedder.requires_grad_(True)
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self.pipe.reference_embedder.train()
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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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# state_dict_converter=FluxLoRAConverter.align_to_diffsynth_format
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# )
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def training_step(self, batch, batch_idx):
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# Data
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text, image = batch["instruction"], batch["image_2"]
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image_ref = batch["image_1"]
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# Prepare input parameters
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self.pipe.device = self.device
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prompt_emb = self.pipe.encode_prompt(text, positive=True)
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if "latents" in batch:
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latents = batch["latents"].to(dtype=self.pipe.torch_dtype, device=self.device)
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else:
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latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device))
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noise = torch.randn_like(latents)
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timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,))
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timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device)
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extra_input = self.pipe.prepare_extra_input(latents)
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noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep)
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training_target = self.pipe.scheduler.training_target(latents, noise, timestep)
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# Reference image
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hidden_states_ref = self.pipe.vae_encoder(image_ref.to(dtype=self.pipe.torch_dtype, device=self.device))
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# Compute loss
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noise_pred = lets_dance_flux(
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self.pipe.denoising_model(),
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reference_embedder=self.pipe.reference_embedder,
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hidden_states_ref=hidden_states_ref,
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hidden_states=noisy_latents, timestep=timestep, **prompt_emb, **extra_input,
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use_gradient_checkpointing=self.use_gradient_checkpointing
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)
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loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
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loss = loss * self.pipe.scheduler.training_weight(timestep)
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# Record log
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self.log("train_loss", loss, prog_bar=True)
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return loss
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def configure_optimizers(self):
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trainable_modules = filter(lambda p: p.requires_grad, self.pipe.parameters())
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optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate)
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return optimizer
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def on_save_checkpoint(self, checkpoint):
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checkpoint.clear()
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trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.named_parameters()))
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trainable_param_names = set([named_param[0] for named_param in trainable_param_names])
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state_dict = self.pipe.state_dict()
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lora_state_dict = {}
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for name, param in state_dict.items():
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if name in trainable_param_names:
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lora_state_dict[name] = param
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if self.state_dict_converter is not None:
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lora_state_dict = self.state_dict_converter(lora_state_dict, alpha=self.lora_alpha)
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checkpoint.update(lora_state_dict)
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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_text_encoder_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 text encoder model. For example, `models/FLUX/FLUX.1-dev/text_encoder/model.safetensors`.",
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)
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parser.add_argument(
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"--pretrained_text_encoder_2_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 t5 text encoder model. For example, `models/FLUX/FLUX.1-dev/text_encoder_2`.",
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)
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parser.add_argument(
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"--pretrained_dit_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 dit model. For example, `models/FLUX/FLUX.1-dev/flux1-dev.safetensors`.",
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)
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parser.add_argument(
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"--pretrained_vae_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 vae model. For example, `models/FLUX/FLUX.1-dev/ae.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,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp",
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help="Layers with LoRA modules.",
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)
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parser.add_argument(
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"--align_to_opensource_format",
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default=False,
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action="store_true",
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help="Whether to export lora files aligned with other opensource format.",
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)
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parser.add_argument(
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"--quantize",
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type=str,
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default=None,
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choices=["float8_e4m3fn"],
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help="Whether to use quantization when training the model, and in which format.",
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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_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={"32": torch.float32, "bf16": torch.bfloat16}.get(args.precision, torch.float16),
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pretrained_weights=[args.pretrained_dit_path, args.pretrained_text_encoder_path, args.pretrained_text_encoder_2_path, args.pretrained_vae_path],
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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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lora_target_modules=args.lora_target_modules,
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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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state_dict_converter=FluxLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else None,
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quantize={"float8_e4m3fn": torch.float8_e4m3fn}.get(args.quantize, None),
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)
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# dataset and data loader
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dataset = MultiTaskDataset(
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dataset_list=[
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SingleTaskDataset(
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"/shark/zhongjie/data/image_pulse_datasets/task1/data/dataset_change_add_remove",
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metadata_path="/shark/zhongjie/data/image_pulse_datasets/task1/data/metadata/20250411_dataset_change_add_remove.json",
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height=512, width=512,
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),
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SingleTaskDataset(
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"/shark/zhongjie/data/image_pulse_datasets/task1/data/dataset_zoomin_zoomout",
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metadata_path="/shark/zhongjie/data/image_pulse_datasets/task1/data/metadata/20250411_dataset_zoomin_zoomout.json",
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height=512, width=512,
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),
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SingleTaskDataset(
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"/shark/zhongjie/data/image_pulse_datasets/task1/data/dataset_style_transfer",
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keys=(("image_1", "image_4", "editing_instruction"), ("image_4", "image_1", "reverse_editing_instruction")),
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metadata_path="/shark/zhongjie/data/image_pulse_datasets/task1/data/metadata/20250411_dataset_style_transfer.json",
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height=512, width=512,
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),
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SingleTaskDataset(
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"/shark/zhongjie/data/image_pulse_datasets/task1/data/dataset_faceid",
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metadata_path="/shark/zhongjie/data/image_pulse_datasets/task1/data/metadata/20250411_dataset_faceid.json",
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height=512, width=512,
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),
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],
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dataset_weight=(4, 2, 2, 1),
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steps_per_epoch=args.steps_per_epoch,
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)
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train_loader = torch.utils.data.DataLoader(
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dataset,
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shuffle=True,
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batch_size=args.batch_size,
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num_workers=args.dataloader_num_workers
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)
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# train
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trainer = pl.Trainer(
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max_epochs=args.max_epochs,
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accelerator="gpu",
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devices="auto",
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precision=args.precision,
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strategy=args.training_strategy,
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default_root_dir=args.output_path,
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accumulate_grad_batches=args.accumulate_grad_batches,
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callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)],
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logger=None,
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)
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trainer.fit(model=model, train_dataloaders=train_loader)
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