update

diffusion_course/unit1/01_introduction_to_diffusers_CN.ipynb

@@ -541,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -549,19 +549,7 @@
     "id": "-yX-MZhSsxwp",
     "outputId": "f8efea0d-41e6-4674-c09f-d905d6cd05dc"
    },
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'torchvision'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_dataset\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m transforms\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torchvision'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torchvision\n",
     "from datasets import load_dataset\n",
@@ -710,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -719,19 +707,7 @@
     "id": "oP-rFQUzdx9h",
     "outputId": "dea1ec0a-9a08-433a-a8d4-9f8731e2e3ea"
    },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'plt' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mplot(noise_scheduler\u001b[38;5;241m.\u001b[39malphas_cumprod\u001b[38;5;241m.\u001b[39mcpu() \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m0.5\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m$\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124msqrt\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124malpha}_t}}$\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot((\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m noise_scheduler\u001b[38;5;241m.\u001b[39malphas_cumprod\u001b[38;5;241m.\u001b[39mcpu()) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m0.5\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m$\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124msqrt\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m(1 - \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124malpha}_t)}$\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend(fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx-large\u001b[39m\u001b[38;5;124m\"\u001b[39m);\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(noise_scheduler.alphas_cumprod.cpu() ** 0.5, label=r\"${\\sqrt{\\bar{\\alpha}_t}}$\")\n",
     "plt.plot((1 - noise_scheduler.alphas_cumprod.cpu()) ** 0.5, label=r\"$\\sqrt{(1 - \\bar{\\alpha}_t)}$\")\n",
@@ -854,23 +830,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "id": "fRGXiotOs4Mc"
    },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'image_size' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[5], line 5\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdiffusers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m UNet2DModel\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Create a model\u001b[39;00m\n\u001b[0;32m      4\u001b[0m model \u001b[38;5;241m=\u001b[39m UNet2DModel(\n\u001b[1;32m----> 5\u001b[0m     sample_size\u001b[38;5;241m=\u001b[39m\u001b[43mimage_size\u001b[49m,  \u001b[38;5;66;03m# the target image resolution\u001b[39;00m\n\u001b[0;32m      6\u001b[0m     in_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,  \u001b[38;5;66;03m# the number of input channels, 3 for RGB images\u001b[39;00m\n\u001b[0;32m      7\u001b[0m     out_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,  \u001b[38;5;66;03m# the number of output channels\u001b[39;00m\n\u001b[0;32m      8\u001b[0m     layers_per_block\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,  \u001b[38;5;66;03m# how many ResNet layers to use per UNet block\u001b[39;00m\n\u001b[0;32m      9\u001b[0m     block_out_channels\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m64\u001b[39m, \u001b[38;5;241m128\u001b[39m, \u001b[38;5;241m128\u001b[39m, \u001b[38;5;241m256\u001b[39m),  \u001b[38;5;66;03m# More channels -> more parameters\u001b[39;00m\n\u001b[0;32m     10\u001b[0m     down_block_types\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m     11\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownBlock2D\u001b[39m\u001b[38;5;124m\"\u001b[39m,  \u001b[38;5;66;03m# a regular ResNet downsampling block\u001b[39;00m\n\u001b[0;32m     12\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownBlock2D\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     13\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttnDownBlock2D\u001b[39m\u001b[38;5;124m\"\u001b[39m,  \u001b[38;5;66;03m# a ResNet downsampling block with spatial self-attention\u001b[39;00m\n\u001b[0;32m     14\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttnDownBlock2D\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     15\u001b[0m     ),\n\u001b[0;32m     16\u001b[0m     up_block_types\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m     17\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttnUpBlock2D\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     18\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttnUpBlock2D\u001b[39m\u001b[38;5;124m\"\u001b[39m,  \u001b[38;5;66;03m# a ResNet upsampling block with spatial self-attention\u001b[39;00m\n\u001b[0;32m     19\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUpBlock2D\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     20\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUpBlock2D\u001b[39m\u001b[38;5;124m\"\u001b[39m,  \u001b[38;5;66;03m# a regular ResNet upsampling block\u001b[39;00m\n\u001b[0;32m     21\u001b[0m     ),\n\u001b[0;32m     22\u001b[0m )\n\u001b[0;32m     23\u001b[0m model\u001b[38;5;241m.\u001b[39mto(device);\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'image_size' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from diffusers import UNet2DModel\n",
     "\n",
@@ -1838,7 +1802,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.6 (default, Sep 26 2022, 11:37:49) \n[Clang 14.0.0 (clang-1400.0.29.202)]"
   },
   "vscode": {
    "interpreter": {

diffusion_course/unit1/01_introduction_to_diffusers_CN.md

@@ -276,20 +276,6 @@ train_dataloader = torch.utils.data.DataLoader(
 )
 ```
 
-
-    ---------------------------------------------------------------------------
-
-    ModuleNotFoundError                       Traceback (most recent call last)
-
-    Cell In[1], line 1
-    ----> 1 import torchvision
-          2 from datasets import load_dataset
-          3 from torchvision import transforms
-
-
-    ModuleNotFoundError: No module named 'torchvision'
-
-
 我们可以从中取出一批图像数据来看一看他们是什么样子:
 
 
@@ -351,20 +337,6 @@ plt.plot((1 - noise_scheduler.alphas_cumprod.cpu()) ** 0.5, label=r"$\sqrt{(1 -
 plt.legend(fontsize="x-large");
 ```
 
-
-    ---------------------------------------------------------------------------
-
-    NameError                                 Traceback (most recent call last)
-
-    Cell In[4], line 1
-    ----> 1 plt.plot(noise_scheduler.alphas_cumprod.cpu() ** 0.5, label=r"${\sqrt{\bar{\alpha}_t}}$")
-          2 plt.plot((1 - noise_scheduler.alphas_cumprod.cpu()) ** 0.5, label=r"$\sqrt{(1 - \bar{\alpha}_t)}$")
-          3 plt.legend(fontsize="x-large");
-
-
-    NameError: name 'plt' is not defined
-
-
 **练习:** 你可以探索一下使用不同的beta_start时曲线是如何变化的，beta_end 与 beta_schedule可以通过以下注释内容来修改:
 
 
@@ -447,39 +419,6 @@ model = UNet2DModel(
 model.to(device);
 ```
 
-
-    ---------------------------------------------------------------------------
-
-    NameError                                 Traceback (most recent call last)
-
-    Cell In[5], line 5
-          1 from diffusers import UNet2DModel
-          3 # Create a model
-          4 model = UNet2DModel(
-    ----> 5     sample_size=image_size,  # the target image resolution
-          6     in_channels=3,  # the number of input channels, 3 for RGB images
-          7     out_channels=3,  # the number of output channels
-          8     layers_per_block=2,  # how many ResNet layers to use per UNet block
-          9     block_out_channels=(64, 128, 128, 256),  # More channels -> more parameters
-         10     down_block_types=(
-         11         "DownBlock2D",  # a regular ResNet downsampling block
-         12         "DownBlock2D",
-         13         "AttnDownBlock2D",  # a ResNet downsampling block with spatial self-attention
-         14         "AttnDownBlock2D",
-         15     ),
-         16     up_block_types=(
-         17         "AttnUpBlock2D",
-         18         "AttnUpBlock2D",  # a ResNet upsampling block with spatial self-attention
-         19         "UpBlock2D",
-         20         "UpBlock2D",  # a regular ResNet upsampling block
-         21     ),
-         22 )
-         23 model.to(device);
-
-
-    NameError: name 'image_size' is not defined
-
-
 当在处理更高分辨率的输入时，你可能想用更多层的下、上采样模块，让注意力层只聚焦在最低分辨率（最底）层来减少内存消耗。我们在之后会讨论该如何实验来找到最适用与你手头场景的配置方法。
 
 我们可以通过输入一批数据和随机的迭代周期数来看输出是否与输入尺寸相同：