SDXL controlnet canny 模型训练

### 使用Canny边缘检测作为ControlNet模块进行SDXL模型训练的方法 #### 准备工作 为了使用Canny边缘检测作为ControlNet模块来增强Stable Diffusion XL (SDXL) 的图像生成能力，需先完成环境配置和依赖库的安装。确保已按照官方文档中的说明设置好Python虚拟环境并安装必要的包[^1]。 #### 加载预训练模型 加载用于实验的基础模型`controlnet-canny-sdxl-1.0.safetensors`文件以及对应的Diffusers管道组件。这一步骤对于初始化项目至关重要，因为这些权重决定了网络架构及其初始性能水平[^2]。 ```python from diffusers import StableDiffusionControlNetPipeline, ControlNetModel import torch # Load the pre-trained model and pipeline components. controlnet = ControlNetModel.from_pretrained( "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16 ) pipeline = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ).to("cuda") ``` #### 数据集准备与处理 构建适合于训练的数据集，并对其进行适当转换以便输入到神经网络中。通常情况下会采用自定义Dataset类实现数据读取逻辑，在此过程中可以应用OpenCV或其他计算机视觉工具来进行图片预处理操作，比如调整大小、裁剪等。特别需要注意的是要提取每张原图对应的Canny边缘特征图作为额外条件信息传递给ControlNet层。 ```python import cv2 import numpy as np from PIL import Image from torchvision.transforms.functional import to_tensor class CustomImageDataset(torch.utils.data.Dataset): def __init__(self, image_paths): self.image_paths = image_paths def __len__(self): return len(self.image_paths) def __getitem__(self, idx): img_path = self.image_paths[idx] # Read an RGB image from file path using OpenCV bgr_img = cv2.imread(img_path) rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB) # Convert it into grayscale for edge detection with Canny algorithm gray_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2GRAY) edges = cv2.Canny(gray_img, threshold1=100, threshold2=200) # Normalize both images between [-1, 1], convert them to tensors tensor_rgb = to_tensor(Image.fromarray(np.uint8(rgb_img))).float() tensor_edges = to_tensor(Image.fromarray(edges)).float() return {"image": tensor_rgb * 2 - 1., "conditioning_image": tensor_edges * 2 - 1.} ``` #### 训练流程设计 设定超参数如批次大小(batch size)，迭代次数(epochs)，优化器(optimizer settings)等；编写损失函数计算部分以指导反向传播更新权值的过程。考虑到任务特性可能还需要加入正则化项防止过拟合现象发生。此外，定期保存checkpoint有助于后续恢复中断的任务继续执行下去。 ```python optimizer = torch.optim.AdamW(pipeline.unet.parameters(), lr=5e-6) loss_fn = torch.nn.MSELoss(reduction='mean') for epoch in range(num_epochs): running_loss = [] for batch_idx, data in enumerate(dataloader): optimizer.zero_grad(set_to_none=True) prompt_embeds = pipeline.text_encoder(data["prompt"])[0] noise_pred_clean = pipeline.unet( data['image'].half().to('cuda'), timestep=timesteps[batch_idx].repeat(BATCH_SIZE), encoder_hidden_states=prompt_embeds.half().to('cuda') ).sample conditioning_zero_out = torch.zeros_like(data['conditioning_image']).half().to('cuda') noise_pred_inpaint = pipeline.unet( data['image'].half().to('cuda'), timestep=timesteps[batch_idx].repeat(BATCH_SIZE), encoder_hidden_states=prompt_embeds.half().to('cuda'), down_block_additional_residuals=[ state[half_key].clone() for half_key in down_keys], mid_block_additional_residual=state[mid_key].clone(), ).sample loss = F.mse_loss(noise_pred_clean, target=data['target'], reduction="none").mean([1, 2, 3]).mean((0)) + \ config.control_alpha * F.mse_loss(noise_pred_inpaint, target=data['target'], reduction="none").mean([1, 2, 3]).mean((0)) loss.backward() optimizer.step() running_loss.append(loss.item()) avg_epoch_loss = sum(running_loss)/len(running_loss) print(f'Epoch {epoch}, Loss: {avg_epoch_loss}') ```