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
22 lines
753 B
Python
22 lines
753 B
Python
import torch
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import numpy as np
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def append_dims(x, target_dims):
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"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
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From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
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dims_to_append = target_dims - x.ndim
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if dims_to_append < 0:
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raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
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return x[(...,) + (None,) * dims_to_append]
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def norm_thresholding(x0, value):
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s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
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return x0 * (value / s)
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def spatial_norm_thresholding(x0, value):
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# b c h w
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s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
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return x0 * (value / s) |