Upload sd_token_similarity_calculator.ipynb

sd_token_similarity_calculator.ipynb

@@ -123,7 +123,7 @@
         },
         "outputId": "e335f5da-b26d-4eea-f854-fd646444ea14"
       },
-      "execution_count": 15,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -279,6 +279,48 @@
       },
       "execution_count": null,
       "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "\n",
+        "This is how the notebook works:\n",
+        "\n",
+        "Similiar vectors = similiar output in the SD 1.5 / SDXL / FLUX model\n",
+        "\n",
+        "CLIP converts the prompt text to vectors (“tensors”) , with float32 values usually ranging from -1 to 1\n",
+        "\n",
+        "Dimensions are [ 1x768 ] tensors for SD 1.5 , and a [ 1x768 , 1x1024 ] tensor for SDXL and FLUX.\n",
+        "\n",
+        "The SD models and FLUX converts these vectors to an image.\n",
+        "\n",
+        "This notebook takes an input string , tokenizes it and matches the first token against the 49407 token vectors in the vocab.json : https://huggingface.co/black-forest-labs/FLUX.1-dev/tree/main/tokenizer\n",
+        "\n",
+        "It finds the “most similiar tokens” in the list. Similarity is the theta angle between the token vectors.\n",
+        "\n",
+        "\n",
+        "<div>\n",
+        "<img src=\"https://huggingface.co/datasets/codeShare/sd_tokens/resolve/main/cosine.jpeg\" width=\"300\"/>\n",
+        "</div>\n",
+        "\n",
+        "The angle is calculated using cosine similarity , where 1 = 100% similarity (parallell vectors) , and 0 = 0% similarity (perpendicular vectors).\n",
+        "\n",
+        "Negative similarity is also possible.\n",
+        "\n",
+        "So if you are bored of prompting “girl” and want something similiar you can run this notebook and use the “chick</w>” token at 21.88% similarity , for example\n",
+        "\n",
+        "You can also run a mixed search , like “cute+girl”/2 , where for example “kpop</w>” has a 16.71% similarity\n",
+        "\n",
+        "Sidenote: Prompt weights like (banana:1.2) will scale the magnitude of the corresponding 1x768 tensor(s) by 1.2 .\n",
+        "\n",
+        "Source: https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted_prompts*\n",
+        "\n",
+        "So TLDR; vector direction = “what to generate” , vector magnitude = “prompt weights”"
+      ],
+      "metadata": {
+        "id": "njeJx_nSSA8H"
+      }
     }
   ]
 }