stable diffusion3微调脚本

### Stable Diffusion 3 微调脚本 对于希望对Stable Diffusion 3进行微调以适应特定数据集或改进某些方面性能的研究者来说，编写一个有效的微调脚本至关重要。下面提供了一个简化版的Python脚本用于执行此操作[^2]。 ```python import torch from diffusers import UNet2DConditionModel, DDPMScheduler, DDIMScheduler from transformers import CLIPTextModel from datasets import load_dataset from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize from torch.utils.data import DataLoader from tqdm.auto import tqdm def create_preprocessing_pipeline(resize_to=(512, 512)): """创建预处理流水线""" transforms = Compose([ Resize(resize_to), CenterCrop(resize_to), ToTensor(), Normalize([0.5], [0.5]) ]) return transforms def main(): device = "cuda" if torch.cuda.is_available() else "cpu" # 加载预训练模型组件 unet = UNet2DConditionModel.from_pretrained( "runwayml/stable-diffusion-v1-5", subfolder="unet" ).to(device) text_encoder = CLIPTextModel.from_pretrained( "openai/clip-vit-large-patch14" ).to(device) noise_scheduler = DDPMScheduler.from_config("runwayml/stable-diffusion-v1-5/scheduler") optimizer = torch.optim.AdamW(unet.parameters(), lr=5e-6) dataset = load_dataset('your_custom_dataset_name', split='train') preprocess = create_preprocessing_pipeline() def collate_fn(examples): pixel_values = torch.stack([preprocess(example["image"]) for example in examples]) input_ids = tokenizer( examples['text'], padding=True, truncation=True, max_length=77, return_tensors="pt").input_ids.to(device) return {"pixel_values": pixel_values, 'input_ids': input_ids} dataloader = DataLoader(dataset, batch_size=8, shuffle=True, collate_fn=collate_fn) num_epochs = 10 steps_per_epoch = len(dataloader) progress_bar = tqdm(range(num_epochs * steps_per_epoch)) for epoch in range(num_epochs): unet.train() for step, batch in enumerate(dataloader): with torch.no_grad(): encoder_hidden_states = text_encoder(batch['input_ids'])[0] noisy_latents = noise_scheduler.add_noise(latents=batch['pixel_values'].to(device), timesteps=torch.randint(999,(batch['pixel_values'].shape[0],)).to(device)) optimizer.zero_grad() model_pred = unet(noisy_latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample loss = F.mse_loss(model_pred.float(), target=noise.float()) loss.backward(loss) optimizer.step() progress_bar.update(1) if __name__ == "__main__": main() ``` 上述代码展示了如何加载必要的库并定义函数来设置图像转换管道、准备自定义数据集以及配置优化器和学习率调度程序。注意，在实际应用中可能还需要调整超参数和其他细节以获得最佳效果。