DiffSynth-Studio/docs/source/finetune/train_sd_lora.md

训练 Stable Diffusion LoRA

训练脚本只需要一个文件。我们支持 CivitAI 中的主流检查点。默认情况下，我们使用基础的 Stable Diffusion v1.5。你可以从 HuggingFace 或 ModelScope 下载。你可以使用以下代码下载这个文件：

from diffsynth import download_models

download_models(["StableDiffusion_v15"])

models/stable_diffusion
├── Put Stable Diffusion checkpoints here.txt
└── v1-5-pruned-emaonly.safetensors

使用以下命令启动训练任务：

CUDA_VISIBLE_DEVICES="0" python examples/train/stable_diffusion/train_sd_lora.py \
  --pretrained_path models/stable_diffusion/v1-5-pruned-emaonly.safetensors \
  --dataset_path data/dog \
  --output_path ./models \
  --max_epochs 1 \
  --steps_per_epoch 500 \
  --height 512 \
  --width 512 \
  --center_crop \
  --precision "16-mixed" \
  --learning_rate 1e-4 \
  --lora_rank 4 \
  --lora_alpha 4 \
  --use_gradient_checkpointing

有关参数的更多信息，请使用 python examples/train/stable_diffusion/train_sd_lora.py -h 查看详细信息。

训练完成后，使用 model_manager.load_lora 加载 LoRA 以进行推理。

from diffsynth import ModelManager, SDImagePipeline
import torch

model_manager = ModelManager(torch_dtype=torch.float16, device="cuda",
                             file_path_list=["models/stable_diffusion/v1-5-pruned-emaonly.safetensors"])
model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0)
pipe = SDImagePipeline.from_model_manager(model_manager)

torch.manual_seed(0)
image = pipe(
    prompt="a dog is jumping, flowers around the dog, the background is mountains and clouds", 
    negative_prompt="bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi, extra tails",
    cfg_scale=7.5,
    num_inference_steps=100, width=512, height=512,
)
image.save("image_with_lora.jpg")