update sd training scripts

examples/stable_diffusion_xl/model_training/train.py

@@ -1,6 +1,5 @@
 import torch, os, argparse, accelerate
 from diffsynth.core import UnifiedDataset
-from diffsynth.core.data.operators import ToAbsolutePath, LoadImage, ImageCropAndResize, RouteByType, SequencialProcess
 from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline, ModelConfig
 from diffsynth.diffusion import *
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -10,7 +9,7 @@ class StableDiffusionXLTrainingModule(DiffusionTrainingModule):
     def __init__(
         self,
         model_paths=None, model_id_with_origin_paths=None,
-        tokenizer_path=None, tokenizer_2_path=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,
@@ -23,75 +22,56 @@ class StableDiffusionXLTrainingModule(DiffusionTrainingModule):
         task="sft",
     ):
         super().__init__()
-        # ===== 解析模型配置 =====
+        # Load models
         model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
-        # ===== Tokenizer 配置 =====
-        tokenizer_config = self.parse_path_or_model_id(tokenizer_path, default_value=ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"))
-        tokenizer_2_config = self.parse_path_or_model_id(tokenizer_2_path, default_value=ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"))
-        # ===== 构建 Pipeline =====
-        self.pipe = StableDiffusionXLPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, tokenizer_2_config=tokenizer_2_config)
-        # ===== 拆分 Pipeline Units =====
+        tokenizer_config = self.parse_path_or_model_id(tokenizer_path, ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"))
+        tokenizer_2_config = self.parse_path_or_model_id(tokenizer_path, ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"))
+        self.pipe = StableDiffusionXLPipeline.from_pretrained(torch_dtype=torch.float32, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, tokenizer_2_config=tokenizer_2_config)
         self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
 
-        # ===== 切换到训练模式 =====
+        # 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,
         )
-
-        # ===== 其他配置 =====
+        
+        # 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
-        # ===== 任务模式路由 =====
         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):
-        # ===== 正向提示词 =====
         inputs_posi = {"prompt": data["prompt"]}
-        # ===== 负向提示词：训练不需要 =====
         inputs_nega = {"negative_prompt": ""}
-        # ===== 共享参数 =====
-        height = data["image"].size[1]
-        width = data["image"].size[0]
         inputs_shared = {
-            # ===== 核心字段映射 =====
+            # Assume you are using this pipeline for inference,
+            # please fill in the input parameters.
             "input_image": data["image"],
-            "height": height,
-            "width": width,
-            # ===== 框架控制参数 =====
+            "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,
         }
-        # ===== SDXL 特有：add_time_ids (micro-conditioning) =====
-        # 在 __call__ 中计算，但训练不跑 __call__，所以在这里注入
-        text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim
-        add_time_ids = [height, width, 0, 0, height, width]
-        expected_add_embed_dim = self.pipe.unet.add_embedding.linear_1.in_features
-        addition_time_embed_dim = self.pipe.unet.add_time_proj.num_channels
-        passed_add_embed_dim = addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
-        if expected_add_embed_dim != passed_add_embed_dim:
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, "
-                f"but a vector of {passed_add_embed_dim} was created."
-            )
-        inputs_posi["add_time_ids"] = torch.tensor([add_time_ids], dtype=self.pipe.torch_dtype, device=self.pipe.device)
-        # ===== 额外字段注入 =====
         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):
-        # ===== 标准实现，不要修改 =====
         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:
@@ -100,25 +80,22 @@ class StableDiffusionXLTrainingModule(DiffusionTrainingModule):
         return loss
 
 
-def stable_diffusion_xl_parser():
-    parser = argparse.ArgumentParser(description="Stable Diffusion XL training.")
+def 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.")
     parser.add_argument("--tokenizer_2_path", type=str, default=None, help="Path to tokenizer 2.")
-    parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
     return parser
 
 
 if __name__ == "__main__":
-    parser = stable_diffusion_xl_parser()
+    parser = parser()
     args = parser.parse_args()
-    # ===== Accelerator 配置 =====
     accelerator = accelerate.Accelerator(
         gradient_accumulation_steps=args.gradient_accumulation_steps,
         kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)],
     )
-    # ===== 数据集定义 =====
     dataset = UnifiedDataset(
         base_path=args.dataset_base_path,
         metadata_path=args.dataset_metadata_path,
@@ -129,22 +106,14 @@ if __name__ == "__main__":
             max_pixels=args.max_pixels,
             height=args.height,
             width=args.width,
-            height_division_factor=8,
-            width_division_factor=8,
-        ),
-        special_operator_map={
-            "image": RouteByType(operator_map=[
-                (str, ToAbsolutePath(args.dataset_base_path) >> LoadImage() >> ImageCropAndResize(args.height, args.width, args.max_pixels, 8, 8)),
-                (list, SequencialProcess(ToAbsolutePath(args.dataset_base_path) >> LoadImage(convert_RGB=False, convert_RGBA=True) >> ImageCropAndResize(args.height, args.width, args.max_pixels, 8, 8))),
-            ]),
-        },
+            height_division_factor=32,
+            width_division_factor=32,
+        )
     )
-    # ===== TrainingModule =====
     model = StableDiffusionXLTrainingModule(
         model_paths=args.model_paths,
         model_id_with_origin_paths=args.model_id_with_origin_paths,
         tokenizer_path=args.tokenizer_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,
@@ -158,17 +127,18 @@ if __name__ == "__main__":
         fp8_models=args.fp8_models,
         offload_models=args.offload_models,
         task=args.task,
-        device="cpu" if args.initialize_model_on_cpu else accelerator.device,
+        device=accelerator.device,
     )
-    # ===== ModelLogger =====
     model_logger = ModelLogger(
         args.output_path,
         remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
     )
-    # ===== 任务路由 =====
     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)