0522-1710

diffusion.ipynb

@@ -33,7 +33,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0e9b206a3f14edfb9b9b320f025bea4",
+       "model_id": "b24ea18addd84753a509dd05eeb11cfe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -65,10 +65,10 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import random\n",
-    "from abc import ABC, abstractmethod\n",
+    "# from abc import ABC, abstractmethod\n",
     "import torch.nn.functional as F\n",
     "import math\n",
-    "from PIL import Image\n",
+    "# from PIL import Image\n",
     "import os\n",
     "from torch.utils.tensorboard import SummaryWriter\n",
     "import copy\n",
@@ -273,7 +273,7 @@
     "\n",
     "    n_epoch = 10#2#5#25 # 120\n",
     "    num_timesteps = 1000#1000 # 1000, 500; DDPM time steps\n",
-    "    batch_size = 10#10#20#2#100 # 10\n",
+    "    batch_size = 10#20#2#100 # 10\n",
     "    # n_sample = 24 # 64, the number of samples in sampling process\n",
     "    n_param = 2\n",
     "    guide_w = 0#-1#0#-1#0.1#[0,0.1] #[0,0.5,2] strength of generative guidance\n",
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -523,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -550,14 +550,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "images rescaled to [-1.0, 1.213691234588623]\n",
-      "params rescaled to [0.0, 0.9993793236536365]\n"
+      "images rescaled to [-1.0, 1.1875841617584229]\n",
+      "params rescaled to [0.0, 0.9999290410760016]\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98e98851f3c048eb8b588dd32898638c",
+       "model_id": "6795b3f3a0ed4c999019c8be5af4e578",
        "version_major": 2,
        "version_minor": 0
       },
@@ -571,7 +571,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9efae14d2adb4637a92954b667d9a1ab",
+       "model_id": "54cb12b82b434237af28cbb72f7c071b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -585,7 +585,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "346038f7596b437b99f752c941597e39",
+       "model_id": "649033a8004c459f8fe892f52a355d7d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -599,7 +599,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cb8a836369064316b50efa17163b63e0",
+       "model_id": "be5b7b4a88894d769b06dc0cd3daeed5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -613,7 +613,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc0791f12b1b4297875b0afae9471f56",
+       "model_id": "d9ab19a2535d45ea8f795ace2466bbe2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -627,7 +627,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "83d26bd75d1548168cae8bc6cc7e7c7e",
+       "model_id": "f6f6969943344210b1c530cbced3ea20",
        "version_major": 2,
        "version_minor": 0
       },
@@ -641,7 +641,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eae9deee76ae42b8a433b4b7e0b9407f",
+       "model_id": "3924f748eccd4bc6aa6bf5c271f9d8a9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -655,7 +655,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ccb3309aecb14b11aa9fae260497a5f1",
+       "model_id": "07ca3e6371d843fa8a05dc490bcc136a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -669,7 +669,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "95dbf4f3e68b48daa1f6029a32407077",
+       "model_id": "af2aa62cfad948589415315abedb32f1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -683,7 +683,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5227fb9c32c64d0aa72146d76620d219",
+       "model_id": "e23db426060c41b490fb09b6412ac258",
        "version_major": 2,
        "version_minor": 0
       },
@@ -735,6 +735,13 @@
     "ddpm21cm.sample(\"./outputs/model_state_09.pth\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,