1b87a98261
## 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
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
import torch
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import PIL
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import cv2
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from PIL import Image
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import numpy as np
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from iopaint.helper import pad_img_to_modulo
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def make_canny_control_image(image: np.ndarray) -> Image:
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canny_image = cv2.Canny(image, 100, 200)
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canny_image = canny_image[:, :, None]
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canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
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canny_image = PIL.Image.fromarray(canny_image)
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control_image = canny_image
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return control_image
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def make_openpose_control_image(image: np.ndarray) -> Image:
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from controlnet_aux import OpenposeDetector
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processor = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
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control_image = processor(image, hand_and_face=True)
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return control_image
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def resize_image(input_image, resolution):
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H, W, C = input_image.shape
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H = float(H)
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W = float(W)
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k = float(resolution) / min(H, W)
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H *= k
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W *= k
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H = int(np.round(H / 64.0)) * 64
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W = int(np.round(W / 64.0)) * 64
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img = cv2.resize(
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input_image,
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(W, H),
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interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA,
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)
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return img
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def make_depth_control_image(image: np.ndarray) -> Image:
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from controlnet_aux import MidasDetector
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midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
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origin_height, origin_width = image.shape[:2]
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pad_image = pad_img_to_modulo(image, mod=64, square=False, min_size=512)
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depth_image = midas(pad_image)
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depth_image = depth_image[0:origin_height, 0:origin_width]
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depth_image = depth_image[:, :, None]
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depth_image = np.concatenate([depth_image, depth_image, depth_image], axis=2)
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control_image = PIL.Image.fromarray(depth_image)
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return control_image
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def make_inpaint_control_image(image: np.ndarray, mask: np.ndarray) -> torch.Tensor:
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"""
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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"""
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image = image.astype(np.float32) / 255.0
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image[mask[:, :, -1] > 128] = -1.0 # set as masked pixel
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image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return image
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