import torch, os, json from diffsynth import load_state_dict from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig from diffsynth.pipelines.flux_image_new import ControlNetInput from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_data_process_task, qwen_image_parser from diffsynth.trainers.unified_dataset import UnifiedDataset os.environ["TOKENIZERS_PARALLELISM"] = "false" class QwenImageTrainingModule(DiffusionTrainingModule): def __init__( self, model_paths=None, model_id_with_origin_paths=None, tokenizer_path=None, processor_path=None, trainable_models=None, lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, use_gradient_checkpointing=True, use_gradient_checkpointing_offload=False, extra_inputs=None, enable_fp8_training=False, ): super().__init__() # Load models offload_dtype = torch.float8_e4m3fn if enable_fp8_training else None model_configs = [] if model_paths is not None: model_paths = json.loads(model_paths) model_configs += [ModelConfig(path=path, offload_dtype=offload_dtype) for path in model_paths] if model_id_with_origin_paths is not None: model_id_with_origin_paths = model_id_with_origin_paths.split(",") model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1], offload_dtype=offload_dtype) for i in model_id_with_origin_paths] tokenizer_config = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path) processor_config = ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/") if processor_path is None else ModelConfig(processor_path) self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, tokenizer_config=tokenizer_config, processor_config=processor_config) # Enable FP8 if enable_fp8_training: self.pipe._enable_fp8_lora_training(torch.float8_e4m3fn) # Reset training scheduler (do it in each training step) self.pipe.scheduler.set_timesteps(1000, training=True) # Freeze untrainable models self.pipe.freeze_except([] if trainable_models is None else trainable_models.split(",")) # Add LoRA to the base models if lora_base_model is not None: model = self.add_lora_to_model( getattr(self.pipe, lora_base_model), target_modules=lora_target_modules.split(","), lora_rank=lora_rank, upcast_dtype=self.pipe.torch_dtype, ) if lora_checkpoint is not None: state_dict = load_state_dict(lora_checkpoint) state_dict = self.mapping_lora_state_dict(state_dict) load_result = model.load_state_dict(state_dict, strict=False) print(f"LoRA checkpoint loaded: {lora_checkpoint}, total {len(state_dict)} keys") if len(load_result[1]) > 0: print(f"Warning, LoRA key mismatch! Unexpected keys in LoRA checkpoint: {load_result[1]}") setattr(self.pipe, lora_base_model, model) # Store 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 [] def forward_preprocess(self, data): # CFG-sensitive parameters inputs_posi = {"prompt": data["prompt"]} inputs_nega = {"negative_prompt": ""} # CFG-unsensitive parameters 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], # 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, "edit_image_auto_resize": True, } # Extra inputs controlnet_input, blockwise_controlnet_input = {}, {} for extra_input in self.extra_inputs: if extra_input.startswith("blockwise_controlnet_"): blockwise_controlnet_input[extra_input.replace("blockwise_controlnet_", "")] = data[extra_input] elif extra_input.startswith("controlnet_"): controlnet_input[extra_input.replace("controlnet_", "")] = data[extra_input] else: inputs_shared[extra_input] = data[extra_input] if len(controlnet_input) > 0: inputs_shared["controlnet_inputs"] = [ControlNetInput(**controlnet_input)] if len(blockwise_controlnet_input) > 0: inputs_shared["blockwise_controlnet_inputs"] = [ControlNetInput(**blockwise_controlnet_input)] # Pipeline units will automatically process the input parameters. for unit in self.pipe.units: inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega) return {**inputs_shared, **inputs_posi} def forward(self, data, inputs=None): if inputs is None: inputs = self.forward_preprocess(data) return inputs if __name__ == "__main__": parser = qwen_image_parser() args = parser.parse_args() dataset = UnifiedDataset( base_path=args.dataset_base_path, metadata_path=args.dataset_metadata_path, repeat=1, # Set repeat = 1 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 = QwenImageTrainingModule( model_paths=args.model_paths, model_id_with_origin_paths=args.model_id_with_origin_paths, tokenizer_path=args.tokenizer_path, processor_path=args.processor_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, use_gradient_checkpointing=args.use_gradient_checkpointing, use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, extra_inputs=args.extra_inputs, enable_fp8_training=args.enable_fp8_training, ) model_logger = ModelLogger(args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt) launch_data_process_task( dataset, model, model_logger, num_workers=args.dataset_num_workers, )