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sd and sdxl training

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mi804
2026-04-24 10:19:58 +08:00
parent a8a0f082bb
commit 5cdab9ed01
17 changed files with 543 additions and 26 deletions
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examples/stable_diffusion_xl/model_inference/StableDiffusionXL-T2I.py → examples/stable_diffusion_xl/model_inference/StableDiffusionXL.py
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examples/stable_diffusion_xl/model_inference_low_vram/StableDiffusionXL-T2I.py → examples/stable_diffusion_xl/model_inference_low_vram/StableDiffusionXL.py
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examples/stable_diffusion_xl/model_training/lora/StableDiffusionXL-test.sh Normal file
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# 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"

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examples/stable_diffusion_xl/model_training/lora/StableDiffusionXL.sh Normal file
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# 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"

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examples/stable_diffusion_xl/model_training/train.py Normal file
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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_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)

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examples/stable_diffusion_xl/model_training/validate_full/StableDiffusionXL.py Normal file
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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")

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examples/stable_diffusion_xl/model_training/validate_lora/StableDiffusionXL.py Normal file
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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")