🎨 完整的 IOPaint 项目更新

## 主要更新
- ✨ 更新所有依赖到最新稳定版本
- 📝 添加详细的项目文档和模型推荐
- 🔧 配置 VSCode Cloud Studio 预览功能
- 🐛 修复 PyTorch API 弃用警告

## 依赖更新
- diffusers: 0.27.2 → 0.35.2
- gradio: 4.21.0 → 5.46.0
- peft: 0.7.1 → 0.18.0
- Pillow: 9.5.0 → 11.3.0
- fastapi: 0.108.0 → 0.116.2

## 新增文件
- CLAUDE.md - 项目架构和开发指南
- UPGRADE_NOTES.md - 详细的升级说明
- .vscode/preview.yml - 预览配置
- .vscode/LAUNCH_GUIDE.md - 启动指南
- .gitignore - 更新的忽略规则

## 代码修复
- 修复 iopaint/model/ldm.py 中的 torch.cuda.amp.autocast() 弃用警告

## 文档更新
- README.md - 添加模型推荐和使用指南
- 完整的项目源码（iopaint/）
- Web 前端源码（web_app/）

🤖 Generated with Claude Code

iopaint/model/sdxl.py

@@ -0,0 +1,110 @@
+import os
+
+import PIL.Image
+import cv2
+import torch
+from diffusers import AutoencoderKL
+from loguru import logger
+
+from iopaint.schema import InpaintRequest, ModelType
+
+from .base import DiffusionInpaintModel
+from .helper.cpu_text_encoder import CPUTextEncoderWrapper
+from .original_sd_configs import get_config_files
+from .utils import (
+    handle_from_pretrained_exceptions,
+    get_torch_dtype,
+    enable_low_mem,
+    is_local_files_only,
+)
+
+
+class SDXL(DiffusionInpaintModel):
+    name = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
+    pad_mod = 8
+    min_size = 512
+    lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
+    model_id_or_path = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
+
+    def init_model(self, device: torch.device, **kwargs):
+        from diffusers.pipelines import StableDiffusionXLInpaintPipeline
+
+        use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
+
+        if self.model_info.model_type == ModelType.DIFFUSERS_SDXL:
+            num_in_channels = 4
+        else:
+            num_in_channels = 9
+
+        if os.path.isfile(self.model_id_or_path):
+            self.model = StableDiffusionXLInpaintPipeline.from_single_file(
+                self.model_id_or_path,
+                torch_dtype=torch_dtype,
+                num_in_channels=num_in_channels,
+                load_safety_checker=False,
+                original_config_file=get_config_files()['xl'],
+            )
+        else:
+            model_kwargs = {
+                **kwargs.get("pipe_components", {}),
+                "local_files_only": is_local_files_only(**kwargs),
+            }
+            if "vae" not in model_kwargs:
+                vae = AutoencoderKL.from_pretrained(
+                    "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
+                )
+                model_kwargs["vae"] = vae
+            self.model = handle_from_pretrained_exceptions(
+                StableDiffusionXLInpaintPipeline.from_pretrained,
+                pretrained_model_name_or_path=self.model_id_or_path,
+                torch_dtype=torch_dtype,
+                variant="fp16",
+                **model_kwargs
+            )
+
+        enable_low_mem(self.model, kwargs.get("low_mem", False))
+
+        if kwargs.get("cpu_offload", False) and use_gpu:
+            logger.info("Enable sequential cpu offload")
+            self.model.enable_sequential_cpu_offload(gpu_id=0)
+        else:
+            self.model = self.model.to(device)
+            if kwargs["sd_cpu_textencoder"]:
+                logger.info("Run Stable Diffusion TextEncoder on CPU")
+                self.model.text_encoder = CPUTextEncoderWrapper(
+                    self.model.text_encoder, torch_dtype
+                )
+                self.model.text_encoder_2 = CPUTextEncoderWrapper(
+                    self.model.text_encoder_2, torch_dtype
+                )
+
+        self.callback = kwargs.pop("callback", None)
+
+    def forward(self, image, mask, config: InpaintRequest):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W, 1] 255 means area to repaint
+        return: BGR IMAGE
+        """
+        self.set_scheduler(config)
+
+        img_h, img_w = image.shape[:2]
+
+        output = self.model(
+            image=PIL.Image.fromarray(image),
+            prompt=config.prompt,
+            negative_prompt=config.negative_prompt,
+            mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
+            num_inference_steps=config.sd_steps,
+            strength=0.999 if config.sd_strength == 1.0 else config.sd_strength,
+            guidance_scale=config.sd_guidance_scale,
+            output_type="np",
+            callback_on_step_end=self.callback,
+            height=img_h,
+            width=img_w,
+            generator=torch.manual_seed(config.sd_seed),
+        ).images[0]
+
+        output = (output * 255).round().astype("uint8")
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return output