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
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
|
|
# This source code is licensed under the license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from typing import Type
|
|
|
|
|
|
class MLPBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
embedding_dim: int,
|
|
mlp_dim: int,
|
|
act: Type[nn.Module] = nn.GELU,
|
|
) -> None:
|
|
super().__init__()
|
|
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
|
|
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
|
|
self.act = act()
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
return self.lin2(self.act(self.lin1(x)))
|
|
|
|
|
|
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
|
|
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
|
|
class LayerNorm2d(nn.Module):
|
|
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
|
|
super().__init__()
|
|
self.weight = nn.Parameter(torch.ones(num_channels))
|
|
self.bias = nn.Parameter(torch.zeros(num_channels))
|
|
self.eps = eps
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
u = x.mean(1, keepdim=True)
|
|
s = (x - u).pow(2).mean(1, keepdim=True)
|
|
x = (x - u) / torch.sqrt(s + self.eps)
|
|
x = self.weight[:, None, None] * x + self.bias[:, None, None]
|
|
return x
|