import torch, os, argparse, accelerate, copy from diffsynth.core import UnifiedDataset from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig from diffsynth.pipelines.z_image import ZImageUnit_Image2LoRAEncode, ZImageUnit_Image2LoRADecode, ZImageUnit_Image2LoRATraining from diffsynth.diffusion import * os.environ["TOKENIZERS_PARALLELISM"] = "false" class ZImageTrainingModule(DiffusionTrainingModule): def __init__( self, model_paths=None, model_id_with_origin_paths=None, tokenizer_path=None, trainable_models=None, lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, preset_lora_path=None, preset_lora_model=None, use_gradient_checkpointing=True, use_gradient_checkpointing_offload=False, extra_inputs=None, fp8_models=None, offload_models=None, device="cpu", task="sft", ): super().__init__() # Load models vram_config = { "offload_dtype": torch.bfloat16, "offload_device": device, "onload_dtype": torch.bfloat16, "onload_device": device, "preparing_dtype": torch.bfloat16, "preparing_device": device, "computation_dtype": torch.bfloat16, "computation_device": device, } self.pipe = ZImagePipeline.from_pretrained( torch_dtype=torch.bfloat16, device=device, model_configs=[ ModelConfig(model_id="Tongyi-MAI/Z-Image-Base-1211_Temp", origin_file_pattern="transformer/*.safetensors", **vram_config), ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"), ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors"), ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors"), ModelConfig(model_paths), ], tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"), ) self.pipe.vram_management_enabled = False self.pipe.units = self.pipe.units + [ ZImageUnit_Image2LoRAEncode(), ZImageUnit_Image2LoRADecode(), ZImageUnit_Image2LoRATraining(), ] self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model) # Training mode self.switch_pipe_to_training_mode( self.pipe, trainable_models, lora_base_model, lora_target_modules, lora_rank, lora_checkpoint, preset_lora_path, preset_lora_model, task=task, ) # Other configs self.use_gradient_checkpointing = use_gradient_checkpointing self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] self.fp8_models = fp8_models self.task = task self.task_to_loss = { "sft:data_process": lambda pipe, *args: args, "direct_distill:data_process": lambda pipe, *args: args, "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi), "direct_distill": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), "direct_distill:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi), } if task == "trajectory_imitation": # This is an experimental feature. # We may remove it in the future. self.loss_fn = TrajectoryImitationLoss() self.task_to_loss["trajectory_imitation"] = self.loss_fn self.pipe_teacher = copy.deepcopy(self.pipe) self.pipe_teacher.requires_grad_(False) def get_pipeline_inputs(self, data): inputs_posi = {"prompt": data["prompt"]} inputs_nega = {"negative_prompt": ""} inputs_shared = { # Assume you are using this pipeline for inference, # please fill in the input parameters. "input_image": data["image"], "height": data["image"].size[1], "width": data["image"].size[0], "image2lora_images": data["image"], # Please do not modify the following parameters # unless you clearly know what this will cause. "cfg_scale": 1, "rand_device": self.pipe.device, "use_gradient_checkpointing": self.use_gradient_checkpointing, "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, } if self.task == "trajectory_imitation": inputs_shared["cfg_scale"] = 2 inputs_shared["teacher"] = self.pipe_teacher inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared) return inputs_shared, inputs_posi, inputs_nega def forward(self, data, inputs=None): if inputs is None: inputs = self.get_pipeline_inputs(data) inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) for unit in self.pipe.units: inputs = self.pipe.unit_runner(unit, self.pipe, *inputs) loss = self.task_to_loss[self.task](self.pipe, *inputs) return loss def z_image_parser(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser = add_general_config(parser) parser = add_image_size_config(parser) parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.") return parser if __name__ == "__main__": parser = z_image_parser() args = parser.parse_args() accelerator = accelerate.Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)], ) dataset = UnifiedDataset( base_path=args.dataset_base_path, metadata_path=args.dataset_metadata_path, repeat=args.dataset_repeat, data_file_keys=args.data_file_keys.split(","), main_data_operator=UnifiedDataset.default_image_operator( base_path=args.dataset_base_path, max_pixels=args.max_pixels, height=args.height, width=args.width, height_division_factor=16, width_division_factor=16, ) ) model = ZImageTrainingModule( model_paths=args.model_paths, model_id_with_origin_paths=args.model_id_with_origin_paths, tokenizer_path=args.tokenizer_path, trainable_models=args.trainable_models, lora_base_model=args.lora_base_model, lora_target_modules=args.lora_target_modules, lora_rank=args.lora_rank, lora_checkpoint=args.lora_checkpoint, preset_lora_path=args.preset_lora_path, preset_lora_model=args.preset_lora_model, use_gradient_checkpointing=args.use_gradient_checkpointing, use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, extra_inputs=args.extra_inputs, fp8_models=args.fp8_models, offload_models=args.offload_models, task=args.task, device=accelerator.device, ) model_logger = ModelLogger( args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, ) launcher_map = { "sft:data_process": launch_data_process_task, "direct_distill:data_process": launch_data_process_task, "sft": launch_training_task, "sft:train": launch_training_task, "direct_distill": launch_training_task, "direct_distill:train": launch_training_task, "trajectory_imitation": launch_training_task, } launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)