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
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
import cv2
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import numpy as np
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from loguru import logger
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from iopaint.helper import download_model
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from iopaint.plugins.base_plugin import BasePlugin
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from iopaint.schema import RunPluginRequest
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class GFPGANPlugin(BasePlugin):
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name = "GFPGAN"
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support_gen_image = True
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def __init__(self, device, upscaler=None):
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super().__init__()
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from .gfpganer import MyGFPGANer
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url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
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model_md5 = "94d735072630ab734561130a47bc44f8"
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model_path = download_model(url, model_md5)
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logger.info(f"GFPGAN model path: {model_path}")
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# Use GFPGAN for face enhancement
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self.face_enhancer = MyGFPGANer(
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model_path=model_path,
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upscale=1,
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arch="clean",
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channel_multiplier=2,
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device=device,
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bg_upsampler=upscaler.model if upscaler is not None else None,
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)
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self.face_enhancer.face_helper.face_det.mean_tensor.to(device)
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self.face_enhancer.face_helper.face_det = (
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self.face_enhancer.face_helper.face_det.to(device)
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)
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def gen_image(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
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weight = 0.5
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bgr_np_img = cv2.cvtColor(rgb_np_img, cv2.COLOR_RGB2BGR)
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logger.info(f"GFPGAN input shape: {bgr_np_img.shape}")
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_, _, bgr_output = self.face_enhancer.enhance(
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bgr_np_img,
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has_aligned=False,
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only_center_face=False,
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paste_back=True,
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weight=weight,
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)
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logger.info(f"GFPGAN output shape: {bgr_output.shape}")
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# try:
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# if scale != 2:
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# interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
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# h, w = img.shape[0:2]
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# output = cv2.resize(
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# output,
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# (int(w * scale / 2), int(h * scale / 2)),
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# interpolation=interpolation,
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# )
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# except Exception as error:
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# print("wrong scale input.", error)
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return bgr_output
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