{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "# Diffusion扩散模型\n",
    "\n",
    "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/application/zh_cn/generative/mindspore_diffusion.ipynb) [![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/application/zh_cn/generative/mindspore_diffusion.py) [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/tutorials/application/source_zh_cn/generative/diffusion.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本文基于[Hugging Face：The Annotated Diffusion Model](https://huggingface.co/blog/annotated-diffusion)一文翻译迁移而来，同时参考了[由浅入深了解Diffusion Model](https://zhuanlan.zhihu.com/p/525106459)一文。\n",
    "\n",
    "> 本教程在Jupyter Notebook上成功运行。如您下载本文档为Python文件，执行Python文件时，请确保执行环境安装了GUI界面。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "关于扩散模型（Diffusion Models）有很多种理解，本文的介绍是基于denoising diffusion probabilistic model （DDPM），DDPM已经在（无）条件图像/音频/视频生成领域取得了较多显著的成果，现有的比较受欢迎的的例子包括由OpenAI主导的[GLIDE](https://arxiv.org/abs/2112.10741)和[DALL-E 2](https://openai.com/dall-e-2/)、由海德堡大学主导的[潜在扩散](https://github.com/CompVis/latent-diffusion)和由Google Brain主导的[图像生成](https://imagen.research.google/)。\n",
    "\n",
    "实际上生成模型的扩散概念已经在（[Sohl-Dickstein et al., 2015](https://arxiv.org/abs/1503.03585)）中介绍过。然而，直到（[Song et al., 2019](https://arxiv.org/abs/1907.05600)）（斯坦福大学）和（[Ho et al., 2020](https://arxiv.org/abs/2006.11239)）（在Google Brain）才各自独立地改进了这种方法。\n",
    "\n",
    "本文是在Phil Wang[基于PyTorch框架的复现](https://github.com/lucidrains/denoising-diffusion-pytorch)的基础上（而它本身又是基于[TensorFlow实现](https://github.com/hojonathanho/diffusion)），迁移到MindSpore AI框架上实现的。\n",
    "\n",
    "![Image-1](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/application/source_zh_cn/generative/images/diffusion_1.png)\n",
    "\n",
    "实验中我们采用离散时间（潜在变量模型）的观点，另外，读者也可以查看有关于扩散模型的其他[几个观点](https://twitter.com/sedielem/status/1530894256168222722?s=20&t=mfv4afx1GcNQU5fZklpACw)！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实验开始之前请确保安装并导入所需的库（假设您已经安装了[MindSpore](https://mindspore.cn/install)、download、dataset、matplotlib以及tqdm）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "from functools import partial\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.auto import tqdm\n",
    "import numpy as np\n",
    "from multiprocessing import cpu_count\n",
    "from download import download\n",
    "\n",
    "import mindspore as ms\n",
    "import mindspore.nn as nn\n",
    "import mindspore.ops as ops\n",
    "from mindspore import Tensor, Parameter\n",
    "from mindspore import dtype as mstype\n",
    "from mindspore.dataset.vision import Resize, Inter, CenterCrop, ToTensor, RandomHorizontalFlip, ToPIL\n",
    "from mindspore.common.initializer import initializer\n",
    "from mindspore.amp import DynamicLossScaler\n",
    "\n",
    "ms.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 模型简介\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 什么是Diffusion Model？\n",
    "\n",
    "如果将Diffusion与其他生成模型（如Normalizing Flows、GAN或VAE）进行比较，它并没有那么复杂，它们都将噪声从一些简单分布转换为数据样本，Diffusion也是从纯噪声开始通过一个神经网络学习逐步去噪，最终得到一个实际图像。\n",
    "Diffusion对于图像的处理包括以下两个过程：\n",
    "\n",
    "- 我们选择的固定（或预定义）正向扩散过程 $q$ ：它逐渐将高斯噪声添加到图像中，直到最终得到纯噪声\n",
    "\n",
    "- 一个学习的反向去噪的扩散过程 $p_\\theta$ ：通过训练神经网络从纯噪声开始逐渐对图像去噪，直到最终得到一个实际的图像\n",
    "\n",
    "![Image-2](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/application/source_zh_cn/generative/images/diffusion_2.png)\n",
    "\n",
    "由 $t$ 索引的正向和反向过程都发生在某些有限时间步长 $T$（DDPM作者使用 $T=1000$）内。从$t=0$开始，在数据分布中采样真实图像 $\\mathbf{x}_0$（本文使用一张来自ImageNet的猫图像形象的展示了diffusion正向添加噪声的过程），正向过程在每个时间步长 $t$ 都从高斯分布中采样一些噪声，再添加到上一个时刻的图像中。假定给定一个足够大的 $T$ 和一个在每个时间步长添加噪声的良好时间表，您最终会在 $t=T$ 通过渐进的过程得到所谓的[各向同性的高斯分布](https://math.stackexchange.com/questions/1991961/gaussian-distribution-is-isotropic)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 扩散模型实现原理\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Diffusion 前向过程\n",
    "\n",
    "所谓前向过程，即向图片上加噪声的过程。虽然这个步骤无法做到图片生成，但这是理解diffusion model以及构建训练样本至关重要的一步。\n",
    "首先我们需要一个可控的损失函数，并运用神经网络对其进行优化。\n",
    "\n",
    "设 $q(x_0)$ 是真实数据分布，由于 $x_0 \\sim q(x_0)$ ，所以我们可以从这个分布中采样以获得图像 $x_0$ 。接下来我们定义前向扩散过程 $q(x_t | x_{t-1})$ ，在前向过程中我们会根据已知的方差 ${0}<\\beta_{1}<\\beta_{2}< ... <\\beta_{T}<{1}$ 在每个时间步长 t 添加高斯噪声，由于前向过程的每个时刻 t 只与时刻 t-1 有关，所以也可以看做马尔科夫过程：\n",
    "\n",
    "$$\n",
    "q(\\mathbf{x}_t | \\mathbf{x}_{t-1}) = \\mathcal{N}(\\mathbf{x}_t; \\sqrt{1 - \\beta_t} \\mathbf{x}_{t-1}, \\beta_t \\mathbf{I})\n",
    "$$\n",
    "\n",
    "回想一下，正态分布（也称为高斯分布）由两个参数定义：平均值 $\\mu$ 和方差 $\\sigma^2 \\geq 0$ 。基本上，在每个时间步长 $t$ 处的产生的每个新的（轻微噪声）图像都是从条件高斯分布中绘制的，其中\n",
    "\n",
    "$$\n",
    "q(\\mathbf{\\mu}_t) = \\sqrt{1 - \\beta_t} \\mathbf{x}_{t-1}\n",
    "$$\n",
    "\n",
    "我们可以通过采样 $\\mathbf{\\epsilon} \\sim \\mathcal{N}(\\mathbf{0}, \\mathbf{I})$ 然后设置\n",
    "\n",
    "$$\n",
    "q(\\mathbf{x}_t) = \\sqrt{1 - \\beta_t} \\mathbf{x}_{t-1} +  \\sqrt{\\beta_t} \\mathbf{\\epsilon}\n",
    "$$\n",
    "\n",
    "请注意， $\\beta_t$ 在每个时间步长 $t$ （因此是下标）不是恒定的：事实上，我们定义了一个所谓的“动态方差”的方法，使得每个时间步长的 $\\beta_t$ 可以是线性的、二次的、余弦的等（有点像动态学习率方法）。\n",
    "\n",
    "因此，如果我们适当设置时间表，从 $\\mathbf{x}_0$ 开始，我们最终得到 $\\mathbf{x}_1,  ..., \\mathbf{x}_t, ..., \\mathbf{x}_T$，即随着 $t$ 的增大 $\\mathbf{x}_t$ 会越来越接近纯噪声，而 $\\mathbf{x}_T$ 就是纯高斯噪声。\n",
    "\n",
    "那么，如果我们知道条件概率分布 $p(\\mathbf{x}_{t-1} | \\mathbf{x}_t)$ ，我们就可以反向运行这个过程：通过采样一些随机高斯噪声 $\\mathbf{x}_T$，然后逐渐去噪它，最终得到真实分布 $\\mathbf{x}_0$ 中的样本。但是，我们不知道条件概率分布 $p(\\mathbf{x}_{t-1} | \\mathbf{x}_t)$ 。这很棘手，因为需要知道所有可能图像的分布，才能计算这个条件概率。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Diffusion 逆向过程\n",
    "\n",
    "为了解决上述问题，我们将利用神经网络来近似（学习）这个条件概率分布 $p_\\theta (\\mathbf{x}_{t-1} | \\mathbf{x}_t)$ , 其中 $\\theta$ 是神经网络的参数。如果说前向过程(forward)是加噪的过程，那么逆向过程(reverse)就是diffusion的去噪推断过程，而通过神经网络学习并表示 $p_\\theta (\\mathbf{x}_{t-1} | \\mathbf{x}_t)$ 的过程就是Diffusion 逆向去噪的核心。\n",
    "\n",
    "现在，我们知道了需要一个神经网络来学习逆向过程的（条件）概率分布。我们假设这个反向过程也是高斯的，任何高斯分布都由2个参数定义：\n",
    "\n",
    "- 由 $\\mu_\\theta$ 参数化的平均值\n",
    "\n",
    "- 由 $\\mu_\\theta$ 参数化的方差\n",
    "\n",
    "综上，我们可以将逆向过程公式化为\n",
    "\n",
    "$$\n",
    "p_\\theta (\\mathbf{x}_{t-1} | \\mathbf{x}_t) = \\mathcal{N}(\\mathbf{x}_{t-1};\\mu_\\theta(\\mathbf{x}_{t},t), \\Sigma_\\theta (\\mathbf{x}_{t},t))\n",
    "$$\n",
    "\n",
    "其中平均值和方差也取决于噪声水平 $t$ ，神经网络需要通过学习来表示这些均值和方差。\n",
    "\n",
    "- 注意，DDPM的作者决定保持方差固定，让神经网络只学习（表示）这个条件概率分布的平均值 $\\mu_\\theta$ 。\n",
    "\n",
    "- 本文我们同样假设神经网络只需要学习（表示）这个条件概率分布的平均值 $\\mu_\\theta$ 。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "为了导出一个目标函数来学习反向过程的平均值，作者观察到 $q$ 和 $p_\\theta$ 的组合可以被视为变分自动编码器(VAE)。因此，变分下界（也称为ELBO）可用于最小化真值数据样本 $\\mathbf{x}_0$ 的似然负对数（有关ELBO的详细信息，请参阅VAE论文[（Kingma等人，2013年）](https://arxiv.org/abs/1312.6114)），该过程的ELBO是每个时间步长的损失之和 $L=L_0+L_1+...+L_T$ ，其中，每项的损失 $L_t$ （除了 $L_0$ ）实际上是2个高斯分布之间的KL发散，可以明确地写为相对于均值的L2-loss!\n",
    "\n",
    "如Sohl-Dickstein等人所示，构建Diffusion正向过程的直接结果是我们可以在条件是 $\\mathbf{x}_0$ （因为高斯和也是高斯）的情况下，在任意噪声水平上采样 $\\mathbf{x}_t$ ，而不需要重复应用 $q$ 去采样 $\\mathbf{x}_t$ ，这非常方便。使用\n",
    "\n",
    "$$\n",
    "\\\\\\alpha_t := 1 - \\beta_t\\\\\\\\\\bar{\\alpha}t := \\Pi_{s=1}^{t} \\alpha_s\\\\\n",
    "$$\n",
    "\n",
    "我们就有\n",
    "\n",
    "$$  \n",
    "q(\\mathbf{x}_t | \\mathbf{x}_0) = \\cal{N}(\\mathbf{x}_t; \\sqrt{\\bar{\\alpha}_t} \\mathbf{x}_0, (1- \\bar{\\alpha}_t) \\mathbf{I})\n",
    "$$\n",
    "\n",
    "这意味着我们可以采样高斯噪声并适当地缩放它，然后将其添加到 $\\mathbf{x}_0$ 中，直接获得 $\\mathbf{x}_t$ 。\n",
    "\n",
    "请注意，$\\bar{\\alpha}_t$ 是已知 $\\beta_t$ 方差计划的函数，因此也是已知的，可以预先计算。这允许我们在训练期间优化损失函数 $L$ 的随机项。或者换句话说，在训练期间随机采样 $t$ 并优化 $L_t$ 。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如Ho等人所展示的那样，这种性质的另一个优点是可以重新参数化平均值，使神经网络学习（预测）构成损失的KL项中噪声的附加噪声。这意味着我们的神经网络变成了噪声预测器，而不是（直接）均值预测器。其中，平均值可以按如下方式计算：\n",
    "\n",
    "$$ \\mathbf{\\mu}_\\theta(\\mathbf{x}_t, t) = \\frac{1}{\\sqrt{\\alpha_t}} \\left(  \\mathbf{x}_t - \\frac{\\beta_t}{\\sqrt{1- \\bar{\\alpha}_t}} \\mathbf{\\epsilon}_\\theta(\\mathbf{x}_t, t) \\right) $$\n",
    "\n",
    "最终的目标函数 ${L}_{t}$ 如下 （随机步长 t 由 $({\\epsilon} \\sim N(\\mathbf{0}, \\mathbf{I}))$ 给定）：\n",
    "\n",
    "$$ \\| \\mathbf{\\epsilon} - \\mathbf{\\epsilon}_\\theta(\\mathbf{x}_t, t) \\|^2 = \\| \\mathbf{\\epsilon} - \\mathbf{\\epsilon}_\\theta( \\sqrt{\\bar{\\alpha}_t} \\mathbf{x}_0 + \\sqrt{(1- \\bar{\\alpha}_t)  } \\mathbf{\\epsilon}, t) \\|^2$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "在这里， $\\mathbf{x}_0$ 是初始（真实，未损坏）图像， $\\mathbf{\\epsilon}$ 是在时间步长 $t$ 采样的纯噪声，$\\mathbf{\\epsilon}_\\theta (\\mathbf{x}_t, t)$是我们的神经网络。神经网络是基于真实噪声和预测高斯噪声之间的简单均方误差（MSE）进行优化的。\n",
    "\n",
    "训练算法现在如下所示：\n",
    "\n",
    "![Image-3](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/application/source_zh_cn/generative/images/diffusion_3.png)\n",
    "\n",
    "换句话说：\n",
    "\n",
    "- 我们从真实未知和可能复杂的数据分布中随机抽取一个样本 $q(\\mathbf{x}_0)$\n",
    "\n",
    "- 我们均匀地采样$1$和$T$之间的噪声水平$t$（即，随机时间步长）\n",
    "\n",
    "- 我们从高斯分布中采样一些噪声，并使用上面定义的属性在 $t$ 时间步上破坏输入\n",
    "\n",
    "- 神经网络被训练以基于损坏的图像 $\\mathbf{x}_t$ 来预测这种噪声，即基于已知的时间表 $\\mathbf{x}_t$ 上施加的噪声\n",
    "\n",
    "实际上，所有这些都是在批数据上使用随机梯度下降来优化神经网络完成的。\n",
    "\n",
    "#### U-Net神经网络预测噪声\n",
    "\n",
    "神经网络需要在特定时间步长接收带噪声的图像，并返回预测的噪声。请注意，预测噪声是与输入图像具有相同大小/分辨率的张量。因此，从技术上讲，网络接受并输出相同形状的张量。那么我们可以用什么类型的神经网络来实现呢？\n",
    "\n",
    "这里通常使用的是非常相似的[自动编码器](https://en.wikipedia.org/wiki/Autoencoder)，您可能还记得典型的\"深度学习入门\"教程。自动编码器在编码器和解码器之间有一个所谓的\"bottleneck\"层。编码器首先将图像编码为一个称为\"bottleneck\"的较小的隐藏表示，然后解码器将该隐藏表示解码回实际图像。这迫使网络只保留bottleneck层中最重要的信息。\n",
    "\n",
    "在模型结构方面，DDPM的作者选择了U-Net，出自（[Ronneberger et al.，2015](https://arxiv.org/abs/1505.04597)）（当时，它在医学图像分割方面取得了最先进的结果）。这个网络就像任何自动编码器一样，在中间由一个bottleneck组成，确保网络只学习最重要的信息。重要的是，它在编码器和解码器之间引入了残差连接，极大地改善了梯度流（灵感来自于（[He et al., 2015](https://arxiv.org/abs/1512.03385)））。\n",
    "\n",
    "![Image-4](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/application/source_zh_cn/generative/images/diffusion_4.jpg)\n",
    "\n",
    "可以看出，U-Net模型首先对输入进行下采样（即，在空间分辨率方面使输入更小），之后执行上采样。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 构建Diffusion模型\n",
    "\n",
    "下面，我们逐步构建Diffusion模型。\n",
    "\n",
    "首先，我们定义了一些帮助函数和类，这些函数和类将在实现神经网络时使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def rearrange(head, inputs):\n",
    "    b, hc, x, y = inputs.shape\n",
    "    c = hc // head\n",
    "    return inputs.reshape((b, head, c, x * y))\n",
    "\n",
    "def rsqrt(x):\n",
    "    res = ops.sqrt(x)\n",
    "    return ops.inv(res)\n",
    "\n",
    "def randn_like(x, dtype=None):\n",
    "    if dtype is None:\n",
    "        dtype = x.dtype\n",
    "    res = ops.standard_normal(x.shape).astype(dtype)\n",
    "    return res\n",
    "\n",
    "def randn(shape, dtype=None):\n",
    "    if dtype is None:\n",
    "        dtype = ms.float32\n",
    "    res = ops.standard_normal(shape).astype(dtype)\n",
    "    return res\n",
    "\n",
    "def randint(low, high, size, dtype=ms.int32):\n",
    "    res = ops.uniform(size, Tensor(low, dtype), Tensor(high, dtype), dtype=dtype)\n",
    "    return res\n",
    "\n",
    "def exists(x):\n",
    "    return x is not None\n",
    "\n",
    "def default(val, d):\n",
    "    if exists(val):\n",
    "        return val\n",
    "    return d() if callable(d) else d\n",
    "\n",
    "def _check_dtype(d1, d2):\n",
    "    if ms.float32 in (d1, d2):\n",
    "        return ms.float32\n",
    "    if d1 == d2:\n",
    "        return d1\n",
    "    raise ValueError('dtype is not supported.')\n",
    "\n",
    "class Residual(nn.Cell):\n",
    "    def __init__(self, fn):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "\n",
    "    def construct(self, x, *args, **kwargs):\n",
    "        return self.fn(x, *args, **kwargs) + x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "我们还定义了上采样和下采样操作的别名。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def Upsample(dim):\n",
    "    return nn.Conv2dTranspose(dim, dim, 4, 2, pad_mode=\"pad\", padding=1)\n",
    "\n",
    "def Downsample(dim):\n",
    "    return nn.Conv2d(dim, dim, 4, 2, pad_mode=\"pad\", padding=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 位置向量\n",
    "\n",
    "由于神经网络的参数在时间（噪声水平）上共享，作者使用正弦位置嵌入来编码$t$，灵感来自Transformer（[Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)）。对于批处理中的每一张图像，神经网络\"知道\"它在哪个特定时间步长（噪声水平）上运行。\n",
    "\n",
    "`SinusoidalPositionEmbeddings`模块采用`(batch_size, 1)`形状的张量作为输入（即批处理中几个有噪声图像的噪声水平），并将其转换为`(batch_size, dim)`形状的张量，其中`dim`是位置嵌入的尺寸。然后，我们将其添加到每个剩余块中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class SinusoidalPositionEmbeddings(nn.Cell):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "        half_dim = self.dim // 2\n",
    "        emb = math.log(10000) / (half_dim - 1)\n",
    "        emb = np.exp(np.arange(half_dim) * - emb)\n",
    "        self.emb = Tensor(emb, ms.float32)\n",
    "\n",
    "    def construct(self, x):\n",
    "        emb = x[:, None] * self.emb[None, :]\n",
    "        emb = ops.concat((ops.sin(emb), ops.cos(emb)), axis=-1)\n",
    "        return emb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### ResNet/ConvNeXT块\n",
    "\n",
    "接下来，我们定义U-Net模型的核心构建块。DDPM作者使用了一个Wide ResNet块（[Zagoruyko et al., 2016](https://arxiv.org/abs/1605.07146)），但Phil Wang决定添加ConvNeXT（[Liu et al., 2022](https://arxiv.org/abs/2201.03545)）替换ResNet，因为后者在图像领域取得了巨大成功。\n",
    "\n",
    "在最终的U-Net架构中，可以选择其中一个或另一个，本文选择ConvNeXT块构建U-Net模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Block(nn.Cell):\n",
    "    def __init__(self, dim, dim_out, groups=1):\n",
    "        super().__init__()\n",
    "        self.proj = nn.Conv2d(dim, dim_out, 3, pad_mode=\"pad\", padding=1)\n",
    "        self.proj = c(dim, dim_out, 3, padding=1, pad_mode='pad')\n",
    "        self.norm = nn.GroupNorm(groups, dim_out)\n",
    "        self.act = nn.SiLU()\n",
    "\n",
    "    def construct(self, x, scale_shift=None):\n",
    "        x = self.proj(x)\n",
    "        x = self.norm(x)\n",
    "\n",
    "        if exists(scale_shift):\n",
    "            scale, shift = scale_shift\n",
    "            x = x * (scale + 1) + shift\n",
    "\n",
    "        x = self.act(x)\n",
    "        return x\n",
    "\n",
    "class ConvNextBlock(nn.Cell):\n",
    "    def __init__(self, dim, dim_out, *, time_emb_dim=None, mult=2, norm=True):\n",
    "        super().__init__()\n",
    "        self.mlp = (\n",
    "            nn.SequentialCell(nn.GELU(), nn.Dense(time_emb_dim, dim))\n",
    "            if exists(time_emb_dim)\n",
    "            else None\n",
    "        )\n",
    "\n",
    "        self.ds_conv = nn.Conv2d(dim, dim, 7, padding=3, group=dim, pad_mode=\"pad\")\n",
    "        self.net = nn.SequentialCell(\n",
    "            nn.GroupNorm(1, dim) if norm else nn.Identity(),\n",
    "            nn.Conv2d(dim, dim_out * mult, 3, padding=1, pad_mode=\"pad\"),\n",
    "            nn.GELU(),\n",
    "            nn.GroupNorm(1, dim_out * mult),\n",
    "            nn.Conv2d(dim_out * mult, dim_out, 3, padding=1, pad_mode=\"pad\"),\n",
    "        )\n",
    "\n",
    "        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()\n",
    "\n",
    "    def construct(self, x, time_emb=None):\n",
    "        h = self.ds_conv(x)\n",
    "        if exists(self.mlp) and exists(time_emb):\n",
    "            assert exists(time_emb), \"time embedding must be passed in\"\n",
    "            condition = self.mlp(time_emb)\n",
    "            condition = condition.expand_dims(-1).expand_dims(-1)\n",
    "            h = h + condition\n",
    "\n",
    "        h = self.net(h)\n",
    "        return h + self.res_conv(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Attention模块\n",
    "\n",
    "接下来，我们定义SiLU模块，DDPM作者将其添加到卷积块之间。SiLU是著名的Transformer架构([Vaswani et al., 2017](https://arxiv.org/abs/1706.03762))，在人工智能的各个领域都取得了巨大的成功，从NLP到[蛋白质折叠](https://www.deepmind.com/blog/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology)。Phil Wang使用了两种注意力变体：一种是常规的multi-head self-attention（如Transformer中使用的），另一种是[LinearAttention](https://github.com/lucidrains/linear-attention-transformer)([Shen et al., 2018](https://arxiv.org/abs/1812.01243))，其时间和内存要求在序列长度上线性缩放，而不是在常规注意力中缩放。\n",
    "要想对Attention机制进行深入的了解，请参照Jay Allamar的[精彩的博文](https://jalammar.github.io/illustrated-transformer/)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Attention(nn.Cell):\n",
    "    def __init__(self, dim, heads=4, dim_head=32):\n",
    "        super().__init__()\n",
    "        self.scale = dim_head ** -0.5\n",
    "        self.heads = heads\n",
    "        hidden_dim = dim_head * heads\n",
    "\n",
    "        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, pad_mode='valid', has_bias=False)\n",
    "        self.to_out = nn.Conv2d(hidden_dim, dim, 1, pad_mode='valid', has_bias=True)\n",
    "        self.map = ops.Map()\n",
    "        self.partial = ops.Partial()\n",
    "\n",
    "    def construct(self, x):\n",
    "        b, _, h, w = x.shape\n",
    "        qkv = self.to_qkv(x).chunk(3, 1)\n",
    "        q, k, v = self.map(self.partial(rearrange, self.heads), qkv)\n",
    "\n",
    "        q = q * self.scale\n",
    "\n",
    "        # 'b h d i, b h d j -> b h i j'\n",
    "        sim = ops.bmm(q.swapaxes(2, 3), k)\n",
    "        attn = ops.softmax(sim, axis=-1)\n",
    "        # 'b h i j, b h d j -> b h i d'\n",
    "        out = ops.bmm(attn, v.swapaxes(2, 3))\n",
    "        out = out.swapaxes(-1, -2).reshape((b, -1, h, w))\n",
    "\n",
    "        return self.to_out(out)\n",
    "\n",
    "\n",
    "class LayerNorm(nn.Cell):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.g = Parameter(initializer('ones', (1, dim, 1, 1)), name='g')\n",
    "\n",
    "    def construct(self, x):\n",
    "        eps = 1e-5\n",
    "        var = x.var(1, keepdims=True)\n",
    "        mean = x.mean(1, keep_dims=True)\n",
    "        return (x - mean) * rsqrt((var + eps)) * self.g\n",
    "\n",
    "\n",
    "class LinearAttention(nn.Cell):\n",
    "    def __init__(self, dim, heads=4, dim_head=32):\n",
    "        super().__init__()\n",
    "        self.scale = dim_head ** -0.5\n",
    "        self.heads = heads\n",
    "        hidden_dim = dim_head * heads\n",
    "        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, pad_mode='valid', has_bias=False)\n",
    "\n",
    "        self.to_out = nn.SequentialCell(\n",
    "            nn.Conv2d(hidden_dim, dim, 1, pad_mode='valid', has_bias=True),\n",
    "            LayerNorm(dim)\n",
    "        )\n",
    "\n",
    "        self.map = ops.Map()\n",
    "        self.partial = ops.Partial()\n",
    "\n",
    "    def construct(self, x):\n",
    "        b, _, h, w = x.shape\n",
    "        qkv = self.to_qkv(x).chunk(3, 1)\n",
    "        q, k, v = self.map(self.partial(rearrange, self.heads), qkv)\n",
    "\n",
    "        q = ops.softmax(q, -2)\n",
    "        k = ops.softmax(k, -1)\n",
    "\n",
    "        q = q * self.scale\n",
    "        v = v / (h * w)\n",
    "\n",
    "        # 'b h d n, b h e n -> b h d e'\n",
    "        context = ops.bmm(k, v.swapaxes(2, 3))\n",
    "        # 'b h d e, b h d n -> b h e n'\n",
    "        out = ops.bmm(context.swapaxes(2, 3), q)\n",
    "\n",
    "        out = out.reshape((b, -1, h, w))\n",
    "        return self.to_out(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 组归一化\n",
    "\n",
    "DDPM作者将U-Net的卷积/注意层与群归一化（[Wu et al., 2018](https://arxiv.org/abs/1803.08494)）。下面，我们定义一个`PreNorm`类，将用于在注意层之前应用groupnorm。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class PreNorm(nn.Cell):\n",
    "    def __init__(self, dim, fn):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "        self.norm = nn.GroupNorm(1, dim)\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.norm(x)\n",
    "        return self.fn(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 条件U-Net\n",
    "\n",
    "我们已经定义了所有的构建块（位置嵌入、ResNet/ConvNeXT块、Attention和组归一化），现在需要定义整个神经网络了。请记住，网络 $\\mathbf{\\epsilon}_\\theta(\\mathbf{x}_t, t)$ 的工作是接收一批噪声图像+噪声水平，并输出添加到输入中的噪声。\n",
    "\n",
    "更具体的：\n",
    "网络获取了一批`(batch_size, num_channels, height, width)`形状的噪声图像和一批`(batch_size, 1)`形状的噪音水平作为输入，并返回`(batch_size, num_channels, height, width)`形状的张量。\n",
    "\n",
    "网络构建过程如下：\n",
    "\n",
    "- 首先，将卷积层应用于噪声图像批上，并计算噪声水平的位置\n",
    "\n",
    "- 接下来，应用一系列下采样级。每个下采样阶段由2个ResNet/ConvNeXT块 + groupnorm + attention + 残差连接 + 一个下采样操作组成\n",
    "\n",
    "- 在网络的中间，再次应用ResNet或ConvNeXT块，并与attention交织\n",
    "\n",
    "- 接下来，应用一系列上采样级。每个上采样级由2个ResNet/ConvNeXT块+ groupnorm + attention + 残差连接 + 一个上采样操作组成\n",
    "\n",
    "- 最后，应用ResNet/ConvNeXT块，然后应用卷积层\n",
    "\n",
    "最终，神经网络将层堆叠起来，就像它们是乐高积木一样（但重要的是[了解它们是如何工作的](http://karpathy.github.io/2019/04/25/recipe/)）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Unet(nn.Cell):\n",
    "    def __init__(\n",
    "            self,\n",
    "            dim,\n",
    "            init_dim=None,\n",
    "            out_dim=None,\n",
    "            dim_mults=(1, 2, 4, 8),\n",
    "            channels=3,\n",
    "            with_time_emb=True,\n",
    "            convnext_mult=2,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        self.channels = channels\n",
    "\n",
    "        init_dim = default(init_dim, dim // 3 * 2)\n",
    "        self.init_conv = nn.Conv2d(channels, init_dim, 7, padding=3, pad_mode=\"pad\", has_bias=True)\n",
    "\n",
    "        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]\n",
    "        in_out = list(zip(dims[:-1], dims[1:]))\n",
    "\n",
    "        block_klass = partial(ConvNextBlock, mult=convnext_mult)\n",
    "\n",
    "        if with_time_emb:\n",
    "            time_dim = dim * 4\n",
    "            self.time_mlp = nn.SequentialCell(\n",
    "                SinusoidalPositionEmbeddings(dim),\n",
    "                nn.Dense(dim, time_dim),\n",
    "                nn.GELU(),\n",
    "                nn.Dense(time_dim, time_dim),\n",
    "            )\n",
    "        else:\n",
    "            time_dim = None\n",
    "            self.time_mlp = None\n",
    "\n",
    "        self.downs = nn.CellList([])\n",
    "        self.ups = nn.CellList([])\n",
    "        num_resolutions = len(in_out)\n",
    "\n",
    "        for ind, (dim_in, dim_out) in enumerate(in_out):\n",
    "            is_last = ind >= (num_resolutions - 1)\n",
    "\n",
    "            self.downs.append(\n",
    "                nn.CellList(\n",
    "                    [\n",
    "                        block_klass(dim_in, dim_out, time_emb_dim=time_dim),\n",
    "                        block_klass(dim_out, dim_out, time_emb_dim=time_dim),\n",
    "                        Residual(PreNorm(dim_out, LinearAttention(dim_out))),\n",
    "                        Downsample(dim_out) if not is_last else nn.Identity(),\n",
    "                    ]\n",
    "                )\n",
    "            )\n",
    "\n",
    "        mid_dim = dims[-1]\n",
    "        self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim=time_dim)\n",
    "        self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))\n",
    "        self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim=time_dim)\n",
    "\n",
    "        for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):\n",
    "            is_last = ind >= (num_resolutions - 1)\n",
    "\n",
    "            self.ups.append(\n",
    "                nn.CellList(\n",
    "                    [\n",
    "                        block_klass(dim_out * 2, dim_in, time_emb_dim=time_dim),\n",
    "                        block_klass(dim_in, dim_in, time_emb_dim=time_dim),\n",
    "                        Residual(PreNorm(dim_in, LinearAttention(dim_in))),\n",
    "                        Upsample(dim_in) if not is_last else nn.Identity(),\n",
    "                    ]\n",
    "                )\n",
    "            )\n",
    "\n",
    "        out_dim = default(out_dim, channels)\n",
    "        self.final_conv = nn.SequentialCell(\n",
    "            block_klass(dim, dim), nn.Conv2d(dim, out_dim, 1)\n",
    "        )\n",
    "\n",
    "    def construct(self, x, time):\n",
    "        x = self.init_conv(x)\n",
    "\n",
    "        t = self.time_mlp(time) if exists(self.time_mlp) else None\n",
    "\n",
    "        h = []\n",
    "\n",
    "        for block1, block2, attn, downsample in self.downs:\n",
    "            x = block1(x, t)\n",
    "            x = block2(x, t)\n",
    "            x = attn(x)\n",
    "            h.append(x)\n",
    "\n",
    "            x = downsample(x)\n",
    "\n",
    "        x = self.mid_block1(x, t)\n",
    "        x = self.mid_attn(x)\n",
    "        x = self.mid_block2(x, t)\n",
    "\n",
    "        len_h = len(h) - 1\n",
    "        for block1, block2, attn, upsample in self.ups:\n",
    "            x = ops.concat((x, h[len_h]), 1)\n",
    "            len_h -= 1\n",
    "            x = block1(x, t)\n",
    "            x = block2(x, t)\n",
    "            x = attn(x)\n",
    "\n",
    "            x = upsample(x)\n",
    "        return self.final_conv(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 正向扩散\n",
    "\n",
    "我们已经知道正向扩散过程在多个时间步长$T$中，从实际分布逐渐向图像添加噪声，根据差异计划进行正向扩散。最初的DDPM作者采用了线性时间表：\n",
    "\n",
    "- 我们将正向过程方差设置为常数，从$\\beta_1 = 10^{−4}$线性增加到$\\beta_T = 0.02$。\n",
    "\n",
    "- 但是，它在（[Nichol et al., 2021](https://arxiv.org/abs/2102.09672)）中表明，当使用余弦调度时，可以获得更好的结果。\n",
    "\n",
    "下面，我们定义了$T$时间步的时间表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def linear_beta_schedule(timesteps):\n",
    "    beta_start = 0.0001\n",
    "    beta_end = 0.02\n",
    "    return np.linspace(beta_start, beta_end, timesteps).astype(np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "首先，让我们使用 $T=200$ 时间步长的线性计划，并定义我们需要的 $\\\\β_t$ 中的各种变量，例如方差 $\\bar{\\alpha}_t$ 的累积乘积。下面的每个变量都只是一维张量，存储从 $t$ 到 $T$ 的值。重要的是，我们还定义了`extract`函数，它将允许我们提取一批适当的 $t$ 索引。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 扩散200步\n",
    "timesteps = 200\n",
    "\n",
    "# 定义 beta schedule\n",
    "betas = linear_beta_schedule(timesteps=timesteps)\n",
    "\n",
    "# 定义 alphas\n",
    "alphas = 1. - betas\n",
    "alphas_cumprod = np.cumprod(alphas, axis=0)\n",
    "alphas_cumprod_prev = np.pad(alphas_cumprod[:-1], (1, 0), constant_values=1)\n",
    "\n",
    "sqrt_recip_alphas = Tensor(np.sqrt(1. / alphas))\n",
    "sqrt_alphas_cumprod = Tensor(np.sqrt(alphas_cumprod))\n",
    "sqrt_one_minus_alphas_cumprod = Tensor(np.sqrt(1. - alphas_cumprod))\n",
    "\n",
    "# 计算 q(x_{t-1} | x_t, x_0)\n",
    "posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)\n",
    "\n",
    "p2_loss_weight = (1 + alphas_cumprod / (1 - alphas_cumprod)) ** -0.\n",
    "p2_loss_weight = Tensor(p2_loss_weight)\n",
    "\n",
    "def extract(a, t, x_shape):\n",
    "    b = t.shape[0]\n",
    "    out = Tensor(a).gather(t, -1)\n",
    "    return out.reshape(b, *((1,) * (len(x_shape) - 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "我们将用猫图像说明如何在扩散过程的每个时间步骤中添加噪音。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/image_cat.zip (170 kB)\n",
      "\n",
      "file_sizes: 100%|████████████████████████████| 174k/174k [00:00<00:00, 1.45MB/s]\n",
      "Extracting zip file...\n",
      "Successfully downloaded / unzipped to ./\n"
     ]
    }
   ],
   "source": [
    "# 下载猫猫图像\n",
    "url = 'https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/image_cat.zip'\n",
    "path = download(url, './', kind=\"zip\", replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x120>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "\n",
    "image = Image.open('./image_cat/jpg/000000039769.jpg')\n",
    "base_width = 160\n",
    "image = image.resize((base_width, int(float(image.size[1]) * float(base_width / float(image.size[0])))))\n",
    "image.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "噪声被添加到mindspore张量中，而不是Pillow图像。我们将首先定义图像转换，允许我们从PIL图像转换到mindspore张量（我们可以在其上添加噪声），反之亦然。\n",
    "\n",
    "这些转换相当简单：我们首先通过除以$255$来标准化图像（使它们在 $[0,1]$ 范围内），然后确保它们在 $[-1, 1]$ 范围内。DPPM论文中有介绍到：\n",
    "\n",
    "> 假设图像数据由 $\\{0, 1, ... , 255\\}$ 中的整数组成，线性缩放为 $[−1, 1]$ ， 这确保了神经网络反向过程在从标准正常先验 $p(\\mathbf{x}_T )$开始的一致缩放输入上运行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 128, 128)\n"
     ]
    }
   ],
   "source": [
    "from mindspore.dataset import ImageFolderDataset\n",
    "\n",
    "image_size = 128\n",
    "transforms = [\n",
    "    Resize(image_size, Inter.BILINEAR),\n",
    "    CenterCrop(image_size),\n",
    "    ToTensor(),\n",
    "    lambda t: (t * 2) - 1\n",
    "]\n",
    "\n",
    "\n",
    "path = './image_cat'\n",
    "dataset = ImageFolderDataset(dataset_dir=path, num_parallel_workers=cpu_count(),\n",
    "                             extensions=['.jpg', '.jpeg', '.png', '.tiff'],\n",
    "                             num_shards=1, shard_id=0, shuffle=False, decode=True)\n",
    "dataset = dataset.project('image')\n",
    "transforms.insert(1, RandomHorizontalFlip())\n",
    "dataset_1 = dataset.map(transforms, 'image')\n",
    "dataset_2 = dataset_1.batch(1, drop_remainder=True)\n",
    "x_start = next(dataset_2.create_tuple_iterator())[0]\n",
    "print(x_start.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "我们还定义了反向变换，它接收一个包含 $[-1, 1]$ 中的张量，并将它们转回 PIL 图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "reverse_transform = [\n",
    "    lambda t: (t + 1) / 2,\n",
    "    lambda t: ops.permute(t, (1, 2, 0)), # CHW to HWC\n",
    "    lambda t: t * 255.,\n",
    "    lambda t: t.asnumpy().astype(np.uint8),\n",
    "    ToPIL()\n",
    "]\n",
    "\n",
    "def compose(transform, x):\n",
    "    for d in transform:\n",
    "        x = d(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "让我们验证一下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reverse_image = compose(reverse_transform, x_start[0])\n",
    "reverse_image.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "我们现在可以定义前向扩散过程，如本文所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def q_sample(x_start, t, noise=None):\n",
    "    if noise is None:\n",
    "        noise = randn_like(x_start)\n",
    "    return (extract(sqrt_alphas_cumprod, t, x_start.shape) * x_start +\n",
    "            extract(sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "让我们在特定的时间步长上测试它："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_noisy_image(x_start, t):\n",
    "    # 添加噪音\n",
    "    x_noisy = q_sample(x_start, t=t)\n",
    "\n",
    "    # 转换为 PIL 图像\n",
    "    noisy_image = compose(reverse_transform, x_noisy[0])\n",
    "\n",
    "    return noisy_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F54569F3950>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置 time step\n",
    "t = Tensor([40])\n",
    "noisy_image = get_noisy_image(x_start, t)\n",
    "print(noisy_image)\n",
    "noisy_image.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "让我们为不同的时间步骤可视化此情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot(imgs, with_orig=False, row_title=None, **imshow_kwargs):\n",
    "    if not isinstance(imgs[0], list):\n",
    "        imgs = [imgs]\n",
    "\n",
    "    num_rows = len(imgs)\n",
    "    num_cols = len(imgs[0]) + with_orig\n",
    "    _, axs = plt.subplots(figsize=(200, 200), nrows=num_rows, ncols=num_cols, squeeze=False)\n",
    "    for row_idx, row in enumerate(imgs):\n",
    "        row = [image] + row if with_orig else row\n",
    "        for col_idx, img in enumerate(row):\n",
    "            ax = axs[row_idx, col_idx]\n",
    "            ax.imshow(np.asarray(img), **imshow_kwargs)\n",
    "            ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])\n",
    "\n",
    "    if with_orig:\n",
    "        axs[0, 0].set(title='Original image')\n",
    "        axs[0, 0].title.set_size(8)\n",
    "    if row_title is not None:\n",
    "        for row_idx in range(num_rows):\n",
    "            axs[row_idx, 0].set(ylabel=row_title[row_idx])\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 14400x14400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot([get_noisy_image(x_start, Tensor([t])) for t in [0, 50, 100, 150, 199]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "这意味着我们现在可以定义给定模型的损失函数，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def p_losses(unet_model, x_start, t, noise=None):\n",
    "    if noise is None:\n",
    "        noise = randn_like(x_start)\n",
    "    x_noisy = q_sample(x_start=x_start, t=t, noise=noise)\n",
    "    predicted_noise = unet_model(x_noisy, t)\n",
    "\n",
    "    loss = nn.SmoothL1Loss()(noise, predicted_noise)# todo\n",
    "    loss = loss.reshape(loss.shape[0], -1)\n",
    "    loss = loss * extract(p2_loss_weight, t, loss.shape)\n",
    "    return loss.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "`denoise_model`将是我们上面定义的U-Net。我们将在真实噪声和预测噪声之间使用Huber损失。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 数据准备与处理\n",
    "\n",
    "在这里我们定义一个正则数据集。数据集可以来自简单的真实数据集的图像组成，如Fashion-MNIST、CIFAR-10或ImageNet，其中线性缩放为 $[−1, 1]$ 。\n",
    "\n",
    "每个图像的大小都会调整为相同的大小。有趣的是，图像也是随机水平翻转的。根据论文内容：我们在CIFAR10的训练中使用了随机水平翻转；我们尝试了有翻转和没有翻转的训练，并发现翻转可以稍微提高样本质量。\n",
    "\n",
    "本实验我们选用Fashion_MNIST数据集，我们使用download下载并解压Fashion_MNIST数据集到指定路径。此数据集由已经具有相同分辨率的图像组成，即28x28。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/dataset.zip (29.4 MB)\n",
      "\n",
      "file_sizes: 100%|██████████████████████████| 30.9M/30.9M [00:00<00:00, 43.4MB/s]\n",
      "Extracting zip file...\n",
      "Successfully downloaded / unzipped to ./\n"
     ]
    }
   ],
   "source": [
    "# 下载MNIST数据集\n",
    "url = 'https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/dataset.zip'\n",
    "path = download(url, './', kind=\"zip\", replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from mindspore.dataset import FashionMnistDataset\n",
    "\n",
    "image_size = 28\n",
    "channels = 1\n",
    "batch_size = 16\n",
    "\n",
    "fashion_mnist_dataset_dir = \"./dataset\"\n",
    "dataset = FashionMnistDataset(dataset_dir=fashion_mnist_dataset_dir, usage=\"train\", num_parallel_workers=cpu_count(), shuffle=True, num_shards=1, shard_id=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "接下来，我们定义一个transform操作，将在整个数据集上动态应用该操作。该操作应用一些基本的图像预处理：随机水平翻转、重新调整，最后使它们的值在 $[-1,1]$ 范围内。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "transforms = [\n",
    "    RandomHorizontalFlip(),\n",
    "    ToTensor(),\n",
    "    lambda t: (t * 2) - 1\n",
    "]\n",
    "\n",
    "\n",
    "dataset = dataset.project('image')\n",
    "dataset = dataset.shuffle(64)\n",
    "dataset = dataset.map(transforms, 'image')\n",
    "dataset = dataset.batch(16, drop_remainder=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['image'])\n"
     ]
    }
   ],
   "source": [
    "x = next(dataset.create_dict_iterator())\n",
    "print(x.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 采样\n",
    "\n",
    "由于我们将在训练期间从模型中采样（以便跟踪进度），我们定义了下面的代码。采样在本文中总结为算法2：\n",
    "\n",
    "![Image-5](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/application/source_zh_cn/generative/images/diffusion_5.png)\n",
    "\n",
    "从扩散模型生成新图像是通过反转扩散过程来实现的：我们从$T$开始，我们从高斯分布中采样纯噪声，然后使用我们的神经网络逐渐去噪（使用它所学习的条件概率），直到我们最终在时间步$t = 0$结束。如上图所示，我们可以通过使用我们的噪声预测器插入平均值的重新参数化，导出一个降噪程度较低的图像\n",
    "$\\mathbf{x}_{t-1 }$。请注意，方差是提前知道的。\n",
    "\n",
    "理想情况下，我们最终会得到一个看起来像是来自真实数据分布的图像。\n",
    "\n",
    "下面的代码实现了这一点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def p_sample(model, x, t, t_index):\n",
    "    betas_t = extract(betas, t, x.shape)\n",
    "    sqrt_one_minus_alphas_cumprod_t = extract(\n",
    "        sqrt_one_minus_alphas_cumprod, t, x.shape\n",
    "    )\n",
    "    sqrt_recip_alphas_t = extract(sqrt_recip_alphas, t, x.shape)\n",
    "    model_mean = sqrt_recip_alphas_t * (x - betas_t * model(x, t) / sqrt_one_minus_alphas_cumprod_t)\n",
    "\n",
    "    if t_index == 0:\n",
    "        return model_mean\n",
    "    posterior_variance_t = extract(posterior_variance, t, x.shape)\n",
    "    noise = randn_like(x)\n",
    "    return model_mean + ops.sqrt(posterior_variance_t) * noise\n",
    "\n",
    "def p_sample_loop(model, shape):\n",
    "    b = shape[0]\n",
    "    # 从纯噪声开始\n",
    "    img = randn(shape, dtype=None)\n",
    "    imgs = []\n",
    "\n",
    "    for i in tqdm(reversed(range(0, timesteps)), desc='sampling loop time step', total=timesteps):\n",
    "        img = p_sample(model, img, ms.numpy.full((b,), i, dtype=mstype.int32), i)\n",
    "        imgs.append(img.asnumpy())\n",
    "    return imgs\n",
    "\n",
    "def sample(model, image_size, batch_size=16, channels=3):\n",
    "    return p_sample_loop(model, shape=(batch_size, channels, image_size, image_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "请注意，上面的代码是原始实现的简化版本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 训练过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "下面，我们开始训练吧！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 定义动态学习率\n",
    "lr = nn.cosine_decay_lr(min_lr=1e-7, max_lr=1e-4, total_step=10*3750, step_per_epoch=3750, decay_epoch=10)\n",
    "\n",
    "# 定义 Unet模型\n",
    "unet_model = Unet(\n",
    "    dim=image_size,\n",
    "    channels=channels,\n",
    "    dim_mults=(1, 2, 4,)\n",
    ")\n",
    "\n",
    "name_list = []\n",
    "for (name, par) in list(unet_model.parameters_and_names()):\n",
    "    name_list.append(name)\n",
    "i = 0\n",
    "for item in list(unet_model.trainable_params()):\n",
    "    item.name = name_list[i]\n",
    "    i += 1\n",
    "\n",
    "# 定义优化器\n",
    "optimizer = nn.Adam(unet_model.trainable_params(), learning_rate=lr)\n",
    "loss_scaler = DynamicLossScaler(65536, 2, 1000)\n",
    "\n",
    "# 定义前向过程\n",
    "def forward_fn(data, t, noise=None):\n",
    "    loss = p_losses(unet_model, data, t, noise)\n",
    "    return loss\n",
    "\n",
    "# 计算梯度\n",
    "grad_fn = ms.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=False)\n",
    "\n",
    "# 梯度更新\n",
    "def train_step(data, t, noise):\n",
    "    loss, grads = grad_fn(data, t, noise)\n",
    "    optimizer(grads)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " epoch:  0  step:  0  Loss:  0.43375123\n",
      " epoch:  0  step:  500  Loss:  0.113769315\n",
      " epoch:  0  step:  1000  Loss:  0.08649178\n",
      " epoch:  0  step:  1500  Loss:  0.067664884\n",
      " epoch:  0  step:  2000  Loss:  0.07234038\n",
      " epoch:  0  step:  2500  Loss:  0.043936778\n",
      " epoch:  0  step:  3000  Loss:  0.058127824\n",
      " epoch:  0  step:  3500  Loss:  0.049789283\n",
      "training time: 922.3438229560852 s\n",
      " epoch:  1  step:  0  Loss:  0.05088563\n",
      " epoch:  1  step:  500  Loss:  0.051174678\n",
      " epoch:  1  step:  1000  Loss:  0.04455947\n",
      " epoch:  1  step:  1500  Loss:  0.055165425\n",
      " epoch:  1  step:  2000  Loss:  0.043942295\n",
      " epoch:  1  step:  2500  Loss:  0.03274461\n",
      " epoch:  1  step:  3000  Loss:  0.048117325\n",
      " epoch:  1  step:  3500  Loss:  0.063063145\n",
      "training time: 937.5596783161163 s\n",
      " epoch:  2  step:  0  Loss:  0.052893892\n",
      " epoch:  2  step:  500  Loss:  0.05721748\n",
      " epoch:  2  step:  1000  Loss:  0.057248186\n",
      " epoch:  2  step:  1500  Loss:  0.048806388\n",
      " epoch:  2  step:  2000  Loss:  0.05007638\n",
      " epoch:  2  step:  2500  Loss:  0.04337231\n",
      " epoch:  2  step:  3000  Loss:  0.043207955\n",
      " epoch:  2  step:  3500  Loss:  0.034530163\n",
      "training time: 947.6374666690826 s\n",
      " epoch:  3  step:  0  Loss:  0.04867614\n",
      " epoch:  3  step:  500  Loss:  0.051636297\n",
      " epoch:  3  step:  1000  Loss:  0.03338969\n",
      " epoch:  3  step:  1500  Loss:  0.0420174\n",
      " epoch:  3  step:  2000  Loss:  0.052145053\n",
      " epoch:  3  step:  2500  Loss:  0.03905913\n",
      " epoch:  3  step:  3000  Loss:  0.07621498\n",
      " epoch:  3  step:  3500  Loss:  0.06484105\n",
      "training time: 957.7780408859253 s\n",
      " epoch:  4  step:  0  Loss:  0.046281893\n",
      " epoch:  4  step:  500  Loss:  0.03783619\n",
      " epoch:  4  step:  1000  Loss:  0.0587488\n",
      " epoch:  4  step:  1500  Loss:  0.06974746\n",
      " epoch:  4  step:  2000  Loss:  0.04299112\n",
      " epoch:  4  step:  2500  Loss:  0.027945498\n",
      " epoch:  4  step:  3000  Loss:  0.045338146\n",
      " epoch:  4  step:  3500  Loss:  0.06362417\n",
      "training time: 955.6116819381714 s\n",
      " epoch:  5  step:  0  Loss:  0.04781142\n",
      " epoch:  5  step:  500  Loss:  0.032488734\n",
      " epoch:  5  step:  1000  Loss:  0.061507083\n",
      " epoch:  5  step:  1500  Loss:  0.039130375\n",
      " epoch:  5  step:  2000  Loss:  0.034972396\n",
      " epoch:  5  step:  2500  Loss:  0.039485026\n",
      " epoch:  5  step:  3000  Loss:  0.06690869\n",
      " epoch:  5  step:  3500  Loss:  0.05355365\n",
      "training time: 951.7758958339691 s\n",
      " epoch:  6  step:  0  Loss:  0.04807706\n",
      " epoch:  6  step:  500  Loss:  0.021469856\n",
      " epoch:  6  step:  1000  Loss:  0.035354104\n",
      " epoch:  6  step:  1500  Loss:  0.044303045\n",
      " epoch:  6  step:  2000  Loss:  0.040063944\n",
      " epoch:  6  step:  2500  Loss:  0.02970439\n",
      " epoch:  6  step:  3000  Loss:  0.041152682\n",
      " epoch:  6  step:  3500  Loss:  0.02062454\n",
      "training time: 955.2220208644867 s\n",
      " epoch:  7  step:  0  Loss:  0.029668871\n",
      " epoch:  7  step:  500  Loss:  0.028485576\n",
      " epoch:  7  step:  1000  Loss:  0.029675964\n",
      " epoch:  7  step:  1500  Loss:  0.052743085\n",
      " epoch:  7  step:  2000  Loss:  0.03664278\n",
      " epoch:  7  step:  2500  Loss:  0.04454907\n",
      " epoch:  7  step:  3000  Loss:  0.043067697\n",
      " epoch:  7  step:  3500  Loss:  0.0619511\n",
      "training time: 952.6654670238495 s\n",
      " epoch:  8  step:  0  Loss:  0.055328347\n",
      " epoch:  8  step:  500  Loss:  0.035807922\n",
      " epoch:  8  step:  1000  Loss:  0.026412832\n",
      " epoch:  8  step:  1500  Loss:  0.051044375\n",
      " epoch:  8  step:  2000  Loss:  0.05474911\n",
      " epoch:  8  step:  2500  Loss:  0.044595096\n",
      " epoch:  8  step:  3000  Loss:  0.034082986\n",
      " epoch:  8  step:  3500  Loss:  0.02653109\n",
      "training time: 961.9374921321869 s\n",
      " epoch:  9  step:  0  Loss:  0.039675284\n",
      " epoch:  9  step:  500  Loss:  0.046295933\n",
      " epoch:  9  step:  1000  Loss:  0.031403508\n",
      " epoch:  9  step:  1500  Loss:  0.028816734\n",
      " epoch:  9  step:  2000  Loss:  0.06530296\n",
      " epoch:  9  step:  2500  Loss:  0.051451046\n",
      " epoch:  9  step:  3000  Loss:  0.037913296\n",
      " epoch:  9  step:  3500  Loss:  0.030541396\n",
      "training time: 974.643147945404 s\n",
      "Training Success!\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "epochs = 10\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    begin_time = time.time()\n",
    "    for step, batch in enumerate(dataset.create_tuple_iterator()):\n",
    "        unet_model.set_train()\n",
    "        batch_size = batch[0].shape[0]\n",
    "        t = randint(0, timesteps, (batch_size,), dtype=ms.int32)\n",
    "        noise = randn_like(batch[0])\n",
    "        loss = train_step(batch[0], t, noise)\n",
    "\n",
    "        if step % 500 == 0:\n",
    "            print(\" epoch: \", epoch, \" step: \", step, \" Loss: \", loss)\n",
    "    end_time = time.time()\n",
    "    times = end_time - begin_time\n",
    "    print(\"training time:\", times, \"s\")\n",
    "    # 展示随机采样效果\n",
    "    unet_model.set_train(False)\n",
    "    samples = sample(unet_model, image_size=image_size, batch_size=64, channels=channels)\n",
    "    plt.imshow(samples[-1][5].reshape(image_size, image_size, channels), cmap=\"gray\")\n",
    "print(\"Training Success!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 推理过程（从模型中采样）\n",
    "\n",
    "要从模型中采样，我们可以只使用上面定义的采样函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4641cfe838584e8b84e3ba83a8872e2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "sampling loop time step:   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 采样64个图片\n",
    "unet_model.set_train(False)\n",
    "samples = sample(unet_model, image_size=image_size, batch_size=64, channels=channels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5175ea1690>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示一个随机效果\n",
    "random_index = 5\n",
    "plt.imshow(samples[-1][random_index].reshape(image_size, image_size, channels), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "可以看到这个模型能产生一件衣服！\n",
    "\n",
    "请注意，我们训练的数据集分辨率相当低（28x28）。\n",
    "\n",
    "我们还可以创建去噪过程的gif："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.animation as animation\n",
    "\n",
    "random_index = 53\n",
    "\n",
    "fig = plt.figure()\n",
    "ims = []\n",
    "for i in range(timesteps):\n",
    "    im = plt.imshow(samples[i][random_index].reshape(image_size, image_size, channels), cmap=\"gray\", animated=True)\n",
    "    ims.append([im])\n",
    "\n",
    "animate = animation.ArtistAnimation(fig, ims, interval=50, blit=True, repeat_delay=100)\n",
    "animate.save('diffusion.gif')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 总结\n",
    "\n",
    "请注意，DDPM论文表明扩散模型是（非）条件图像有希望生成的方向。自那以后，diffusion得到了（极大的）改进，最明显的是文本条件图像生成。下面，我们列出了一些重要的（但远非详尽无遗的）后续工作：\n",
    "\n",
    "- 改进的去噪扩散概率模型([Nichol et al., 2021](https://arxiv.org/abs/2102.09672))：发现学习条件分布的方差（除平均值外）有助于提高性能\n",
    "\n",
    "- 用于高保真图像生成的级联扩散模型([[Ho et al., 2021](https://arxiv.org/abs/2106.15282))：引入级联扩散，它包括多个扩散模型的流水线，这些模型生成分辨率提高的图像，用于高保真图像合成\n",
    "\n",
    "- 扩散模型在图像合成上击败了GANs([Dhariwal et al., 2021](https://arxiv.org/abs/2105.05233))：表明扩散模型通过改进U-Net体系结构以及引入分类器指导，可以获得优于当前最先进的生成模型的图像样本质量\n",
    "\n",
    "- 无分类器扩散指南([[Ho et al., 2021](https://openreview.net/pdf?id=qw8AKxfYbI))：表明通过使用单个神经网络联合训练条件和无条件扩散模型，不需要分类器来指导扩散模型\n",
    "\n",
    "- 具有CLIP Latents (DALL-E 2) 的分层文本条件图像生成 ([Ramesh et al., 2022](https://cdn.openai.com/papers/dall-e-2.pdf))：在将文本标题转换为CLIP图像嵌入之前使用，然后扩散模型将其解码为图像\n",
    "\n",
    "- 具有深度语言理解的真实文本到图像扩散模型（ImageGen）([Saharia et al., 2022](https://arxiv.org/abs/2205.11487))：表明将大型预训练语言模型（例如T5）与级联扩散结合起来，对于文本到图像的合成很有效\n",
    "\n",
    "请注意，此列表仅包括在撰写本文，即2022年6月7日之前的重要作品。\n",
    "\n",
    "目前，扩散模型的主要（也许唯一）缺点是它们需要多次正向传递来生成图像（对于像GAN这样的生成模型来说，情况并非如此）。然而，有[正在进行中的研究](https://arxiv.org/abs/2204.13902)表明只需要10个去噪步骤就能实现高保真生成。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考\n",
    "\n",
    "1. [The Annotated Diffusion Model](https://huggingface.co/blog/annotated-diffusion)\n",
    "\n",
    "2. [由浅入深了解Diffusion Model](https://zhuanlan.zhihu.com/p/525106459)"
   ]
  }
 ],
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    "name": "ipython",
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