| import torch, os, argparse, accelerate |
| from diffsynth.core import UnifiedDataset |
| from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig |
| from diffsynth.diffusion import * |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" |
|
|
|
|
| class FluxTrainingModule(DiffusionTrainingModule): |
| def __init__( |
| self, |
| model_paths=None, model_id_with_origin_paths=None, |
| tokenizer_1_path=None, tokenizer_2_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__() |
| |
| model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device) |
| tokenizer_1_config = ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="tokenizer/") if tokenizer_1_path is None else ModelConfig(tokenizer_1_path) |
| tokenizer_2_config = ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="tokenizer_2/") if tokenizer_2_path is None else ModelConfig(tokenizer_2_path) |
| self.pipe = FluxImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_1_config=tokenizer_1_config, tokenizer_2_config=tokenizer_2_config) |
| self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model) |
|
|
| |
| 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, |
| ) |
| |
| |
| 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), |
| } |
| |
| def get_pipeline_inputs(self, data): |
| inputs_posi = {"prompt": data["prompt"]} |
| inputs_nega = {"negative_prompt": ""} |
| inputs_shared = { |
| |
| |
| "input_image": data["image"], |
| "height": data["image"].size[1], |
| "width": data["image"].size[0], |
| |
| |
| "cfg_scale": 1, |
| "embedded_guidance": 1, |
| "t5_sequence_length": 512, |
| "tiled": False, |
| "rand_device": self.pipe.device, |
| "use_gradient_checkpointing": self.use_gradient_checkpointing, |
| "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, |
| } |
| 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 flux_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_1_path", type=str, default=None, help="Path to CLIP tokenizer.") |
| parser.add_argument("--tokenizer_2_path", type=str, default=None, help="Path to T5 tokenizer.") |
| parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.") |
| return parser |
|
|
|
|
| def convert_lora_format(state_dict, alpha=None): |
| prefix_rename_dict = { |
| "single_blocks": "lora_unet_single_blocks", |
| "blocks": "lora_unet_double_blocks", |
| } |
| middle_rename_dict = { |
| "norm.linear": "modulation_lin", |
| "to_qkv_mlp": "linear1", |
| "proj_out": "linear2", |
| "norm1_a.linear": "img_mod_lin", |
| "norm1_b.linear": "txt_mod_lin", |
| "attn.a_to_qkv": "img_attn_qkv", |
| "attn.b_to_qkv": "txt_attn_qkv", |
| "attn.a_to_out": "img_attn_proj", |
| "attn.b_to_out": "txt_attn_proj", |
| "ff_a.0": "img_mlp_0", |
| "ff_a.2": "img_mlp_2", |
| "ff_b.0": "txt_mlp_0", |
| "ff_b.2": "txt_mlp_2", |
| } |
| suffix_rename_dict = { |
| "lora_B.weight": "lora_up.weight", |
| "lora_A.weight": "lora_down.weight", |
| } |
| state_dict_ = {} |
| for name, param in state_dict.items(): |
| names = name.split(".") |
| if names[-2] != "lora_A" and names[-2] != "lora_B": |
| names.pop(-2) |
| prefix = names[0] |
| middle = ".".join(names[2:-2]) |
| suffix = ".".join(names[-2:]) |
| block_id = names[1] |
| if middle not in middle_rename_dict: |
| continue |
| rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix] |
| state_dict_[rename] = param |
| if rename.endswith("lora_up.weight"): |
| lora_alpha = alpha if alpha is not None else param.shape[-1] |
| state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((lora_alpha,))[0] |
| return state_dict_ |
|
|
|
|
| if __name__ == "__main__": |
| parser = flux_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 = FluxTrainingModule( |
| model_paths=args.model_paths, |
| model_id_with_origin_paths=args.model_id_with_origin_paths, |
| tokenizer_1_path=args.tokenizer_1_path, |
| tokenizer_2_path=args.tokenizer_2_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, |
| state_dict_converter=convert_lora_format if args.align_to_opensource_format else lambda x:x, |
| ) |
| 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, |
| } |
| launcher_map[args.task](accelerator, dataset, model, model_logger, args=args) |
|
|
