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
81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
import torch
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from torch import nn
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class LitEma(nn.Module):
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def __init__(self, model, decay=0.9999, use_num_upates=True):
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super().__init__()
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if decay < 0.0 or decay > 1.0:
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raise ValueError('Decay must be between 0 and 1')
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self.m_name2s_name = {}
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self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
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self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
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else torch.tensor(-1, dtype=torch.int))
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for name, p in model.named_parameters():
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if p.requires_grad:
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# remove as '.'-character is not allowed in buffers
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s_name = name.replace('.', '')
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self.m_name2s_name.update({name: s_name})
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self.register_buffer(s_name, p.clone().detach().data)
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self.collected_params = []
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def reset_num_updates(self):
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del self.num_updates
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self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
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def forward(self, model):
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decay = self.decay
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if self.num_updates >= 0:
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self.num_updates += 1
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decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
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one_minus_decay = 1.0 - decay
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with torch.no_grad():
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m_param = dict(model.named_parameters())
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shadow_params = dict(self.named_buffers())
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for key in m_param:
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if m_param[key].requires_grad:
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sname = self.m_name2s_name[key]
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shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
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shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
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else:
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assert not key in self.m_name2s_name
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def copy_to(self, model):
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m_param = dict(model.named_parameters())
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shadow_params = dict(self.named_buffers())
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for key in m_param:
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if m_param[key].requires_grad:
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m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
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else:
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assert not key in self.m_name2s_name
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def store(self, parameters):
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"""
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Save the current parameters for restoring later.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; the parameters to be
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temporarily stored.
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"""
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self.collected_params = [param.clone() for param in parameters]
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def restore(self, parameters):
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"""
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Restore the parameters stored with the `store` method.
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Useful to validate the model with EMA parameters without affecting the
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original optimization process. Store the parameters before the
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`copy_to` method. After validation (or model saving), use this to
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restore the former parameters.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; the parameters to be
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updated with the stored parameters.
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"""
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for c_param, param in zip(self.collected_params, parameters):
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param.data.copy_(c_param.data)
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