update with save_model

README.md

@@ -11,6 +11,16 @@ tags:
 base_model: runwayml/stable-diffusion-v1-5
 inference: true
 instance_prompt: A mushroom in [V] style
+widget:
+- text: ' '
+  output:
+    url: image_0.png
+- text: ' '
+  output:
+    url: image_1.png
+- text: ' '
+  output:
+    url: image_2.png
 ---
 
 <!-- This model card has been generated automatically according to the information the training script had access to. You

test-model-card-template-dreambooth-sd15-lora-adv.ipynb

@@ -33,20 +33,17 @@
     "    repo_folder=None,\n",
     "    vae_path=None,\n",
     "):\n",
-    "    img_str = \"widget:\\n\"\n",
-    "    lora = \"lora\" if not use_dora else \"dora\"\n",
-    "    for i, image in enumerate(images):\n",
-    "        image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n",
-    "        img_str += f\"\"\"\n",
-    "        - text: '{validation_prompt if validation_prompt else ' ' }'\n",
-    "          output:\n",
-    "            url:\n",
-    "                \"image_{i}.png\"\n",
-    "        \"\"\"\n",
-    "    if not images:\n",
-    "        img_str += f\"\"\"\n",
-    "        - text: '{instance_prompt}'\n",
-    "        \"\"\"\n",
+    "    widget_dict = []\n",
+    "    if images is not None:\n",
+    "        for i, image in enumerate(images):\n",
+    "            image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n",
+    "            widget_dict.append(\n",
+    "                {\"text\": validation_prompt if validation_prompt else \" \", \"output\": {\"url\": f\"image_{i}.png\"}}\n",
+    "            )\n",
+    "    else:\n",
+    "        widget_dict.append(\n",
+    "            {\"text\": instance_prompt}\n",
+    "        )\n",
     "    embeddings_filename = f\"{repo_folder}_emb\"\n",
     "    instance_prompt_webui = re.sub(r\"<s\\d+>\", \"\", re.sub(r\"<s\\d+>\", embeddings_filename, instance_prompt, count=1))\n",
     "    ti_keys = \", \".join(f'\"{match}\"' for match in re.findall(r\"<s\\d+>\", instance_prompt))\n",
@@ -137,6 +134,7 @@
     "        prompt=instance_prompt,\n",
     "        model_description=model_description,\n",
     "        inference=True,\n",
+    "        widget=widget_dict,\n",
     "    )\n",
     "\n",
     "    tags = [\"text-to-image\", \n",
@@ -155,7 +153,20 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "load_or_create_model_card() got an unexpected keyword argument 'widget_dict'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 8\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdiffusers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_image\n\u001b[1;32m      3\u001b[0m images \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      4\u001b[0m     load_image(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/A\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m20mushroom\u001b[39m\u001b[38;5;132;01m%20i\u001b[39;00m\u001b[38;5;124mn\u001b[39m\u001b[38;5;132;01m%20%\u001b[39;00m\u001b[38;5;124m5BV\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m5D\u001b[39m\u001b[38;5;132;01m%20s\u001b[39;00m\u001b[38;5;124mtyle.png\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m      6\u001b[0m ]\n\u001b[0;32m----> 8\u001b[0m \u001b[43msave_model_card\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_dora\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mabby101/test\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43mimages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbase_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrunwayml/stable-diffusion-v1-5\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43minstance_prompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mA mushroom in [V] style\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[2], line 112\u001b[0m, in \u001b[0;36msave_model_card\u001b[0;34m(repo_id, use_dora, images, base_model, train_text_encoder, train_text_encoder_ti, token_abstraction_dict, instance_prompt, validation_prompt, repo_folder, vae_path)\u001b[0m\n\u001b[1;32m     61\u001b[0m                 trigger_str \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;124mto trigger concept `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` → use `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtokens\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` in your prompt \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m     64\u001b[0m     model_description \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;124m# SD1.5 LoRA DreamBooth - \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\n\u001b[1;32m     66\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    110\u001b[0m \n\u001b[1;32m    111\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m--> 112\u001b[0m     model_card \u001b[38;5;241m=\u001b[39m \u001b[43mload_or_create_model_card\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    113\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_id_or_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    114\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfrom_training\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    115\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlicense\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mopenrail++\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    116\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbase_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    117\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minstance_prompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_description\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_description\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    119\u001b[0m \u001b[43m        \u001b[49m\u001b[43minference\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    120\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwidget_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwidget_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    121\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    123\u001b[0m     tags \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext-to-image\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m    124\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdiffusers\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m    125\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdiffusers-training\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstable-diffusion\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m    129\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstable-diffusion-diffusers\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    130\u001b[0m     model_card \u001b[38;5;241m=\u001b[39m populate_model_card(model_card, tags\u001b[38;5;241m=\u001b[39mtags)\n",
+      "\u001b[0;31mTypeError\u001b[0m: load_or_create_model_card() got an unexpected keyword argument 'widget_dict'"
+     ]
+    }
+   ],
    "source": [
     "from diffusers.utils import load_image\n",
     "\n",
@@ -176,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {