🎨 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/kandinsky.py

@@ -0,0 +1,65 @@
+import PIL.Image
+import cv2
+import numpy as np
+import torch
+
+from iopaint.const import KANDINSKY22_NAME
+from .base import DiffusionInpaintModel
+from iopaint.schema import InpaintRequest
+from .utils import get_torch_dtype, enable_low_mem, is_local_files_only
+
+
+class Kandinsky(DiffusionInpaintModel):
+    pad_mod = 64
+    min_size = 512
+
+    def init_model(self, device: torch.device, **kwargs):
+        from diffusers import AutoPipelineForInpainting
+
+        use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
+
+        model_kwargs = {
+            "torch_dtype": torch_dtype,
+            "local_files_only": is_local_files_only(**kwargs),
+        }
+        self.model = AutoPipelineForInpainting.from_pretrained(
+            self.name, **model_kwargs
+        ).to(device)
+        enable_low_mem(self.model, kwargs.get("low_mem", False))
+
+        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)
+
+        generator = torch.manual_seed(config.sd_seed)
+        mask = mask.astype(np.float32) / 255
+        img_h, img_w = image.shape[:2]
+
+        # kandinsky 没有 strength
+        output = self.model(
+            prompt=config.prompt,
+            negative_prompt=config.negative_prompt,
+            image=PIL.Image.fromarray(image),
+            mask_image=mask[:, :, 0],
+            height=img_h,
+            width=img_w,
+            num_inference_steps=config.sd_steps,
+            guidance_scale=config.sd_guidance_scale,
+            output_type="np",
+            callback_on_step_end=self.callback,
+            generator=generator,
+        ).images[0]
+
+        output = (output * 255).round().astype("uint8")
+        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+        return output
+
+
+class Kandinsky22(Kandinsky):
+    name = KANDINSKY22_NAME