sd and sdxl training

examples/stable_diffusion/model_training/lora/StableDiffusion-test.sh

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+# Dataset: data/diffsynth_example_dataset/stable_diffusion/StableDiffusion/
+# Debug test: num_epochs=1, dataset_repeat=1 for quick validation
+
+# ===== 固定参数（无需修改） =====
+accelerate launch examples/stable_diffusion/model_training/train.py \
+    --learning_rate 1e-4 --num_epochs 1 \
+    --lora_rank 32 \
+    --use_gradient_checkpointing --find_unused_parameters \
+    --dataset_base_path "./data/diffsynth_example_dataset/stable_diffusion/StableDiffusion" \
+    --dataset_metadata_path "./data/diffsynth_example_dataset/stable_diffusion/StableDiffusion/metadata.csv" \
+    --model_id_with_origin_paths "AI-ModelScope/stable-diffusion-v1-5:text_encoder/model.safetensors,AI-ModelScope/stable-diffusion-v1-5:unet/diffusion_pytorch_model.safetensors,AI-ModelScope/stable-diffusion-v1-5:vae/diffusion_pytorch_model.safetensors" \
+    --lora_base_model "unet" \
+    --remove_prefix_in_ckpt "pipe.unet." \
+    --max_pixels 262144 \
+    --height 512 --width 512 \
+    --dataset_repeat 1 \
+    --output_path "./models/train/StableDiffusion_lora_debug" \
+    --lora_target_modules "to_q,to_k,to_v,to_out.0" \
+    --data_file_keys "image"

examples/stable_diffusion/model_training/lora/StableDiffusion.sh

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+# Dataset: data/diffsynth_example_dataset/stable_diffusion/StableDiffusion/
+# Download: modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "stable_diffusion/StableDiffusion/*" --local_dir ./data/diffsynth_example_dataset
+
+# ===== 固定参数（无需修改） =====
+accelerate launch examples/stable_diffusion/model_training/train.py \
+    --learning_rate 1e-4 --num_epochs 5 \
+    --lora_rank 32 \
+    --use_gradient_checkpointing --find_unused_parameters \
+    --dataset_base_path "./data/diffsynth_example_dataset/stable_diffusion/StableDiffusion" \
+    --dataset_metadata_path "./data/diffsynth_example_dataset/stable_diffusion/StableDiffusion/metadata.csv" \
+    --model_id_with_origin_paths "AI-ModelScope/stable-diffusion-v1-5:text_encoder/model.safetensors,AI-ModelScope/stable-diffusion-v1-5:unet/diffusion_pytorch_model.safetensors,AI-ModelScope/stable-diffusion-v1-5:vae/diffusion_pytorch_model.safetensors" \
+    --lora_base_model "unet" \
+    --remove_prefix_in_ckpt "pipe.unet." \
+    --max_pixels 262144 \
+    --height 512 --width 512 \
+    --dataset_repeat 50 \
+    --output_path "./models/train/StableDiffusion_lora" \
+    --lora_target_modules "to_q,to_k,to_v,to_out.0" \
+    --data_file_keys "image"

examples/stable_diffusion/model_training/train.py

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+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 import StableDiffusionPipeline, ModelConfig
+from diffsynth.diffusion import *
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+class StableDiffusionTrainingModule(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__()
+        # ===== 解析模型配置 =====
+        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-v1-5", origin_file_pattern="tokenizer/"))
+        # ===== 构建 Pipeline =====
+        self.pipe = StableDiffusionPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config)
+        # ===== 拆分 Pipeline Units =====
+        self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
+
+        # ===== 切换到训练模式 =====
+        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 =====
+        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,
+            "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),
+        }
+
+    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 =====
+        inputs_shared = {
+            # ===== 核心字段映射 =====
+            "input_image": data["image"],
+            "height": data["image"].size[1],
+            "width": data["image"].size[0],
+            # ===== 框架控制参数 =====
+            "cfg_scale": 1,
+            "rand_device": self.pipe.device,
+            "use_gradient_checkpointing": self.use_gradient_checkpointing,
+            "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
+        }
+        # ===== 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:
+            inputs = self.pipe.unit_runner(unit, self.pipe, *inputs)
+        loss = self.task_to_loss[self.task](self.pipe, *inputs)
+        return loss
+
+
+def stable_diffusion_parser():
+    parser = argparse.ArgumentParser(description="Stable Diffusion training.")
+    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("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
+    return parser
+
+
+if __name__ == "__main__":
+    parser = stable_diffusion_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,
+        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=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))),
+            ]),
+        },
+    )
+    # ===== TrainingModule =====
+    model = StableDiffusionTrainingModule(
+        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="cpu" if args.initialize_model_on_cpu else 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,
+        "sft": launch_training_task,
+        "sft:train": launch_training_task,
+    }
+    launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)

examples/stable_diffusion/model_training/validate_full/StableDiffusion.py

@@ -0,0 +1,17 @@
+from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline, ModelConfig
+import torch
+
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    torch_dtype=torch.float32,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/"),
+)
+prompt = "dog, white and brown dog, sitting on wall, under pink flowers"
+image = pipe(prompt=prompt, seed=42, rand_device="cuda", num_inference_steps=50, cfg_scale=7.5)
+image.save("image.jpg")

examples/stable_diffusion/model_training/validate_lora/StableDiffusion.py

@@ -0,0 +1,18 @@
+from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline, ModelConfig
+import torch
+
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    torch_dtype=torch.float32,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/"),
+)
+pipe.load_lora(pipe.unet, "./models/train/StableDiffusion_lora/epoch-4.safetensors")
+prompt = "dog, white and brown dog, sitting on wall, under pink flowers"
+image = pipe(prompt=prompt, seed=42, rand_device="cuda", num_inference_steps=50, cfg_scale=7.5)
+image.save("image.jpg")

examples/stable_diffusion_xl/model_training/lora/StableDiffusionXL-test.sh

@@ -0,0 +1,21 @@
+# Dataset: data/diffsynth_example_dataset/stable_diffusion_xl/StableDiffusionXL/
+# Debug test: num_epochs=1, dataset_repeat=1 for quick validation
+
+# ===== 固定参数（无需修改） =====
+accelerate launch examples/stable_diffusion_xl/model_training/train.py \
+    --learning_rate 1e-4 --num_epochs 1 \
+    --lora_rank 32 \
+    --use_gradient_checkpointing --find_unused_parameters \
+    --dataset_base_path "./data/diffsynth_example_dataset/stable_diffusion_xl/StableDiffusionXL" \
+    --dataset_metadata_path "./data/diffsynth_example_dataset/stable_diffusion_xl/StableDiffusionXL/metadata.csv" \
+    --model_id_with_origin_paths "AI-ModelScope/stable-diffusion-xl-base-1.0:text_encoder/model.safetensors,AI-ModelScope/stable-diffusion-xl-base-1.0:text_encoder_2/model.safetensors,AI-ModelScope/stable-diffusion-xl-base-1.0:unet/diffusion_pytorch_model.safetensors,AI-ModelScope/stable-diffusion-xl-base-1.0:vae/diffusion_pytorch_model.safetensors" \
+    --tokenizer_path "AI-ModelScope/stable-diffusion-xl-base-1.0:tokenizer/" \
+    --tokenizer_2_path "AI-ModelScope/stable-diffusion-xl-base-1.0:tokenizer_2/" \
+    --lora_base_model "unet" \
+    --remove_prefix_in_ckpt "pipe.unet." \
+    --max_pixels 1048576 \
+    --height 1024 --width 1024 \
+    --dataset_repeat 1 \
+    --output_path "./models/train/StableDiffusionXL_lora_debug" \
+    --lora_target_modules "to_q,to_k,to_v,to_out.0" \
+    --data_file_keys "image"

examples/stable_diffusion_xl/model_training/lora/StableDiffusionXL.sh

@@ -0,0 +1,21 @@
+# Dataset: data/diffsynth_example_dataset/stable_diffusion_xl/StableDiffusionXL/
+# Download: modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "stable_diffusion_xl/StableDiffusionXL/*" --local_dir ./data/diffsynth_example_dataset
+
+# ===== 固定参数（无需修改） =====
+accelerate launch examples/stable_diffusion_xl/model_training/train.py \
+    --learning_rate 1e-4 --num_epochs 5 \
+    --lora_rank 32 \
+    --use_gradient_checkpointing --find_unused_parameters \
+    --dataset_base_path "./data/diffsynth_example_dataset/stable_diffusion_xl/StableDiffusionXL" \
+    --dataset_metadata_path "./data/diffsynth_example_dataset/stable_diffusion_xl/StableDiffusionXL/metadata.csv" \
+    --model_id_with_origin_paths "AI-ModelScope/stable-diffusion-xl-base-1.0:text_encoder/model.safetensors,AI-ModelScope/stable-diffusion-xl-base-1.0:text_encoder_2/model.safetensors,AI-ModelScope/stable-diffusion-xl-base-1.0:unet/diffusion_pytorch_model.safetensors,AI-ModelScope/stable-diffusion-xl-base-1.0:vae/diffusion_pytorch_model.safetensors" \
+    --tokenizer_path "AI-ModelScope/stable-diffusion-xl-base-1.0:tokenizer/" \
+    --tokenizer_2_path "AI-ModelScope/stable-diffusion-xl-base-1.0:tokenizer_2/" \
+    --lora_base_model "unet" \
+    --remove_prefix_in_ckpt "pipe.unet." \
+    --max_pixels 1048576 \
+    --height 1024 --width 1024 \
+    --dataset_repeat 50 \
+    --output_path "./models/train/StableDiffusionXL_lora" \
+    --lora_target_modules "to_q,to_k,to_v,to_out.0" \
+    --data_file_keys "image"

examples/stable_diffusion_xl/model_training/train.py

@@ -0,0 +1,174 @@
+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"
+
+
+class StableDiffusionXLTrainingModule(DiffusionTrainingModule):
+    def __init__(
+        self,
+        model_paths=None, model_id_with_origin_paths=None,
+        tokenizer_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__()
+        # ===== 解析模型配置 =====
+        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 =====
+        self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
+
+        # ===== 切换到训练模式 =====
+        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 =====
+        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,
+            "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),
+        }
+
+    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 =====
+            "input_image": data["image"],
+            "height": height,
+            "width": width,
+            # ===== 框架控制参数 =====
+            "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:
+            inputs = self.pipe.unit_runner(unit, self.pipe, *inputs)
+        loss = self.task_to_loss[self.task](self.pipe, *inputs)
+        return loss
+
+
+def stable_diffusion_xl_parser():
+    parser = argparse.ArgumentParser(description="Stable Diffusion XL training.")
+    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()
+    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,
+        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=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))),
+            ]),
+        },
+    )
+    # ===== 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,
+        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="cpu" if args.initialize_model_on_cpu else 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,
+        "sft": launch_training_task,
+        "sft:train": launch_training_task,
+    }
+    launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)

examples/stable_diffusion_xl/model_training/validate_full/StableDiffusionXL.py

@@ -0,0 +1,19 @@
+from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline, ModelConfig
+import torch
+
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    torch_dtype=torch.float32,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder_2/model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"),
+    tokenizer_2_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"),
+)
+prompt = "dog, white and brown dog, sitting on wall, under pink flowers"
+image = pipe(prompt=prompt, seed=42, rand_device="cuda", num_inference_steps=50, cfg_scale=5.0)
+image.save("image.jpg")

examples/stable_diffusion_xl/model_training/validate_lora/StableDiffusionXL.py

@@ -0,0 +1,20 @@
+from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline, ModelConfig
+import torch
+
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    torch_dtype=torch.float32,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder_2/model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"),
+    tokenizer_2_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"),
+)
+pipe.load_lora(pipe.unet, "./models/train/StableDiffusionXL_lora/epoch-4.safetensors")
+prompt = "dog, white and brown dog, sitting on wall, under pink flowers"
+image = pipe(prompt=prompt, seed=42, rand_device="cuda", num_inference_steps=50, cfg_scale=5.0)
+image.save("image.jpg")