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
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
import torch
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import numpy as np
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class AbstractDistribution:
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def sample(self):
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raise NotImplementedError()
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def mode(self):
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raise NotImplementedError()
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class DiracDistribution(AbstractDistribution):
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def __init__(self, value):
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self.value = value
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def sample(self):
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return self.value
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def mode(self):
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return self.value
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class DiagonalGaussianDistribution(object):
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def __init__(self, parameters, deterministic=False):
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self.parameters = parameters
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
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self.deterministic = deterministic
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self.std = torch.exp(0.5 * self.logvar)
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self.var = torch.exp(self.logvar)
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if self.deterministic:
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self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
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def sample(self):
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x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
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return x
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def kl(self, other=None):
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if self.deterministic:
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return torch.Tensor([0.])
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else:
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if other is None:
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return 0.5 * torch.sum(torch.pow(self.mean, 2)
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+ self.var - 1.0 - self.logvar,
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dim=[1, 2, 3])
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else:
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return 0.5 * torch.sum(
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torch.pow(self.mean - other.mean, 2) / other.var
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+ self.var / other.var - 1.0 - self.logvar + other.logvar,
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dim=[1, 2, 3])
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def nll(self, sample, dims=[1,2,3]):
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if self.deterministic:
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return torch.Tensor([0.])
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logtwopi = np.log(2.0 * np.pi)
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return 0.5 * torch.sum(
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logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
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dim=dims)
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def mode(self):
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return self.mean
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def normal_kl(mean1, logvar1, mean2, logvar2):
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"""
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source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
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Compute the KL divergence between two gaussians.
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Shapes are automatically broadcasted, so batches can be compared to
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scalars, among other use cases.
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"""
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tensor = None
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for obj in (mean1, logvar1, mean2, logvar2):
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if isinstance(obj, torch.Tensor):
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tensor = obj
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break
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assert tensor is not None, "at least one argument must be a Tensor"
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# Force variances to be Tensors. Broadcasting helps convert scalars to
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# Tensors, but it does not work for torch.exp().
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logvar1, logvar2 = [
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x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
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for x in (logvar1, logvar2)
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]
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return 0.5 * (
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-1.0
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+ logvar2
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- logvar1
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+ torch.exp(logvar1 - logvar2)
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+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
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)
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