Merge branch 'main' into bugfix/convert-script

invokeai/app/invocations/params.py

@@ -6,8 +6,7 @@ from pydantic import Field
 
 from invokeai.app.invocations.prompt import PromptOutput
 
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
-                             InvocationConfig, InvocationContext)
 from .math import FloatOutput, IntOutput
 
 # Pass-through parameter nodes - used by subgraphs
@@ -68,6 +67,7 @@ class ParamStringInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> StringOutput:
         return StringOutput(text=self.text)
 
+
 class ParamPromptInvocation(BaseInvocation):
     """A prompt input parameter"""
 
@@ -80,4 +80,4 @@ class ParamPromptInvocation(BaseInvocation):
         }
 
     def invoke(self, context: InvocationContext) -> PromptOutput:
-        return PromptOutput(prompt=self.prompt)
+        return PromptOutput(prompt=self.prompt)

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -139,8 +139,19 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   useHotkeys('s', handleUseSeed, [imageDTO]);
 
   const handleUsePrompt = useCallback(() => {
-    recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
+    recallBothPrompts(
-  }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
+      metadata?.positive_prompt,
+      metadata?.negative_prompt,
+      metadata?.positive_style_prompt,
+      metadata?.negative_style_prompt
+    );
+  }, [
+    metadata?.negative_prompt,
+    metadata?.positive_prompt,
+    metadata?.positive_style_prompt,
+    metadata?.negative_style_prompt,
+    recallBothPrompts,
+  ]);
 
   useHotkeys('p', handleUsePrompt, [imageDTO]);
 

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -102,8 +102,19 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
 
   // Recall parameters handlers
   const handleRecallPrompt = useCallback(() => {
-    recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
+    recallBothPrompts(
-  }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
+      metadata?.positive_prompt,
+      metadata?.negative_prompt,
+      metadata?.positive_style_prompt,
+      metadata?.negative_style_prompt
+    );
+  }, [
+    metadata?.negative_prompt,
+    metadata?.positive_prompt,
+    metadata?.positive_style_prompt,
+    metadata?.negative_style_prompt,
+    recallBothPrompts,
+  ]);
 
   const handleRecallSeed = useCallback(() => {
     recallSeed(metadata?.seed);

invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts

@@ -1,5 +1,15 @@
 import { useAppToaster } from 'app/components/Toaster';
 import { useAppDispatch } from 'app/store/storeHooks';
+import {
+  refinerModelChanged,
+  setNegativeStylePromptSDXL,
+  setPositiveStylePromptSDXL,
+  setRefinerAestheticScore,
+  setRefinerCFGScale,
+  setRefinerScheduler,
+  setRefinerStart,
+  setRefinerSteps,
+} from 'features/sdxl/store/sdxlSlice';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { UnsafeImageMetadata } from 'services/api/endpoints/images';
@@ -22,6 +32,10 @@ import {
   isValidMainModel,
   isValidNegativePrompt,
   isValidPositivePrompt,
+  isValidSDXLNegativeStylePrompt,
+  isValidSDXLPositiveStylePrompt,
+  isValidSDXLRefinerAestheticScore,
+  isValidSDXLRefinerStart,
   isValidScheduler,
   isValidSeed,
   isValidSteps,
@@ -74,17 +88,34 @@ export const useRecallParameters = () => {
    * Recall both prompts with toast
    */
   const recallBothPrompts = useCallback(
-    (positivePrompt: unknown, negativePrompt: unknown) => {
+    (
+      positivePrompt: unknown,
+      negativePrompt: unknown,
+      positiveStylePrompt: unknown,
+      negativeStylePrompt: unknown
+    ) => {
       if (
         isValidPositivePrompt(positivePrompt) ||
-        isValidNegativePrompt(negativePrompt)
+        isValidNegativePrompt(negativePrompt) ||
+        isValidSDXLPositiveStylePrompt(positiveStylePrompt) ||
+        isValidSDXLNegativeStylePrompt(negativeStylePrompt)
       ) {
         if (isValidPositivePrompt(positivePrompt)) {
           dispatch(setPositivePrompt(positivePrompt));
         }
+
         if (isValidNegativePrompt(negativePrompt)) {
           dispatch(setNegativePrompt(negativePrompt));
         }
+
+        if (isValidSDXLPositiveStylePrompt(positiveStylePrompt)) {
+          dispatch(setPositiveStylePromptSDXL(positiveStylePrompt));
+        }
+
+        if (isValidSDXLPositiveStylePrompt(negativeStylePrompt)) {
+          dispatch(setNegativeStylePromptSDXL(negativeStylePrompt));
+        }
+
         parameterSetToast();
         return;
       }
@@ -123,6 +154,36 @@ export const useRecallParameters = () => {
     [dispatch, parameterSetToast, parameterNotSetToast]
   );
 
+  /**
+   * Recall SDXL Positive Style Prompt with toast
+   */
+  const recallSDXLPositiveStylePrompt = useCallback(
+    (positiveStylePrompt: unknown) => {
+      if (!isValidSDXLPositiveStylePrompt(positiveStylePrompt)) {
+        parameterNotSetToast();
+        return;
+      }
+      dispatch(setPositiveStylePromptSDXL(positiveStylePrompt));
+      parameterSetToast();
+    },
+    [dispatch, parameterSetToast, parameterNotSetToast]
+  );
+
+  /**
+   * Recall SDXL Negative Style Prompt with toast
+   */
+  const recallSDXLNegativeStylePrompt = useCallback(
+    (negativeStylePrompt: unknown) => {
+      if (!isValidSDXLNegativeStylePrompt(negativeStylePrompt)) {
+        parameterNotSetToast();
+        return;
+      }
+      dispatch(setNegativeStylePromptSDXL(negativeStylePrompt));
+      parameterSetToast();
+    },
+    [dispatch, parameterSetToast, parameterNotSetToast]
+  );
+
   /**
    * Recall seed with toast
    */
@@ -271,6 +332,14 @@ export const useRecallParameters = () => {
         steps,
         width,
         strength,
+        positive_style_prompt,
+        negative_style_prompt,
+        refiner_model,
+        refiner_cfg_scale,
+        refiner_steps,
+        refiner_scheduler,
+        refiner_aesthetic_store,
+        refiner_start,
       } = metadata;
 
       if (isValidCfgScale(cfg_scale)) {
@@ -304,6 +373,38 @@ export const useRecallParameters = () => {
         dispatch(setImg2imgStrength(strength));
       }
 
+      if (isValidSDXLPositiveStylePrompt(positive_style_prompt)) {
+        dispatch(setPositiveStylePromptSDXL(positive_style_prompt));
+      }
+
+      if (isValidSDXLNegativeStylePrompt(negative_style_prompt)) {
+        dispatch(setNegativeStylePromptSDXL(negative_style_prompt));
+      }
+
+      if (isValidMainModel(refiner_model)) {
+        dispatch(refinerModelChanged(refiner_model));
+      }
+
+      if (isValidSteps(refiner_steps)) {
+        dispatch(setRefinerSteps(refiner_steps));
+      }
+
+      if (isValidCfgScale(refiner_cfg_scale)) {
+        dispatch(setRefinerCFGScale(refiner_cfg_scale));
+      }
+
+      if (isValidScheduler(refiner_scheduler)) {
+        dispatch(setRefinerScheduler(refiner_scheduler));
+      }
+
+      if (isValidSDXLRefinerAestheticScore(refiner_aesthetic_store)) {
+        dispatch(setRefinerAestheticScore(refiner_aesthetic_store));
+      }
+
+      if (isValidSDXLRefinerStart(refiner_start)) {
+        dispatch(setRefinerStart(refiner_start));
+      }
+
       allParameterSetToast();
     },
     [allParameterNotSetToast, allParameterSetToast, dispatch]
@@ -313,6 +414,8 @@ export const useRecallParameters = () => {
     recallBothPrompts,
     recallPositivePrompt,
     recallNegativePrompt,
+    recallSDXLPositiveStylePrompt,
+    recallSDXLNegativeStylePrompt,
     recallSeed,
     recallCfgScale,
     recallModel,

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -310,6 +310,39 @@ export type PrecisionParam = z.infer<typeof zPrecision>;
 export const isValidPrecision = (val: unknown): val is PrecisionParam =>
   zPrecision.safeParse(val).success;
 
+/**
+ * Zod schema for SDXL refiner aesthetic score parameter
+ */
+export const zSDXLRefinerAestheticScore = z.number().min(1).max(10);
+/**
+ * Type alias for SDXL refiner aesthetic score parameter, inferred from its zod schema
+ */
+export type SDXLRefinerAestheticScoreParam = z.infer<
+  typeof zSDXLRefinerAestheticScore
+>;
+/**
+ * Validates/type-guards a value as a SDXL refiner aesthetic score parameter
+ */
+export const isValidSDXLRefinerAestheticScore = (
+  val: unknown
+): val is SDXLRefinerAestheticScoreParam =>
+  zSDXLRefinerAestheticScore.safeParse(val).success;
+
+/**
+ * Zod schema for SDXL start parameter
+ */
+export const zSDXLRefinerstart = z.number().min(0).max(1);
+/**
+ * Type alias for SDXL start, inferred from its zod schema
+ */
+export type SDXLRefinerStartParam = z.infer<typeof zSDXLRefinerstart>;
+/**
+ * Validates/type-guards a value as a SDXL refiner aesthetic score parameter
+ */
+export const isValidSDXLRefinerStart = (
+  val: unknown
+): val is SDXLRefinerStartParam => zSDXLRefinerstart.safeParse(val).success;
+
 // /**
 //  * Zod schema for BaseModelType
 //  */

invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatButton.tsx

@@ -21,8 +21,8 @@ export default function ParamSDXLConcatButton() {
 
   return (
     <IAIIconButton
-      aria-label="Concat"
+      aria-label="Concatenate Prompt & Style"
-      tooltip="Concatenates Basic Prompt with Style (Recommended)"
+      tooltip="Concatenate Prompt & Style"
       variant="outline"
       isChecked={shouldConcatSDXLStylePrompt}
       onClick={handleShouldConcatPromptChange}

notebooks/Stable_Diffusion_AI_Notebook.ipynb

@@ -1,281 +1,283 @@
 {
-  "cells": [
+ "cells": [
-    {
+  {
-      "cell_type": "markdown",
+   "cell_type": "markdown",
-      "metadata": {
+   "metadata": {
-        "id": "ycYWcsEKc6w7"
+    "id": "ycYWcsEKc6w7"
-      },
+   },
-      "source": [
+   "source": [
-        "# Stable Diffusion AI Notebook (Release 2.0.0)\n",
+    "# Stable Diffusion AI Notebook (Release 2.0.0)\n",
-        "\n",
+    "\n",
-        "<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
+    "<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
-        "#### Instructions:\n",
+    "#### Instructions:\n",
-        "1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
+    "1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
-        "2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll need to enter `python scripts/dream.py` command to run Dream bot.<br> \n",
+    "2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll need to enter `python scripts/dream.py` command to run Dream bot.<br> \n",
-        "3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
+    "3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
-        "3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also show last generated images in cell #10.\n",
+    "3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also show last generated images in cell #10.\n",
-        "4. To quit Dream bot use `q` command. <br> \n",
+    "4. To quit Dream bot use `q` command. <br> \n",
-        "---\n",
+    "---\n",
-        "<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
+    "<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
-        "<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
+    "<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
-        "##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
+    "##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
-        "---\n"
+    "---\n"
-      ]
+   ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dr32VLxlnouf"
-      },
-      "source": [
-        "## ◢ Installation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "a2Z5Qu_o8VtQ"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 1. Check current GPU assigned\n",
-        "!nvidia-smi -L\n",
-        "!nvidia-smi"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "vbI9ZsQHzjqF"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 2. Download stable-diffusion Repository\n",
-        "from os.path import exists\n",
-        "\n",
-        "!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
-        "%cd /content/InvokeAI/\n",
-        "!git checkout --quiet tags/v2.0.0"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "QbXcGXYEFSNB"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 3. Install dependencies\n",
-        "import gc\n",
-        "\n",
-        "!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-base.txt\n",
-        "!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-win-colab-cuda.txt\n",
-        "!pip install colab-xterm\n",
-        "!pip install -r requirements-lin-win-colab-CUDA.txt\n",
-        "!pip install clean-fid torchtext\n",
-        "!pip install transformers\n",
-        "gc.collect()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "8rSMhgnAttQa"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 4. Restart Runtime\n",
-        "exit()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "ChIDWxLVHGGJ"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 5. Load small ML models required\n",
-        "import gc\n",
-        "%cd /content/InvokeAI/\n",
-        "!python scripts/preload_models.py\n",
-        "gc.collect()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "795x1tMoo8b1"
-      },
-      "source": [
-        "## ◢ Configuration"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "YEWPV-sF1RDM"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 6. Mount google Drive\n",
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "zRTJeZ461WGu"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 7. Drive Path to model\n",
-        "#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
-        "#@markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
-        "#@markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
-        "from os.path import exists\n",
-        "\n",
-        "model_path = \"\" #@param {type:\"string\"}\n",
-        "if exists(model_path):\n",
-        "    print(\"✅ Valid directory\")\n",
-        "else: \n",
-        "    print(\"❌ File doesn't exist\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "UY-NNz4I8_aG"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 8. Symlink to model\n",
-        "\n",
-        "from os.path import exists\n",
-        "import os \n",
-        "\n",
-        "# Folder creation if it doesn't exist\n",
-        "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
-        "     print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
-        "else:\n",
-        "    %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
-        "    print(\"✅ Dir stable-diffusion-v1 created\")\n",
-        "\n",
-        "# Symbolic link if it doesn't exist\n",
-        "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
-        "    print(\"❗ Symlink already created\")\n",
-        "else: \n",
-        "    src = model_path\n",
-        "    dst = '/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt'\n",
-        "    os.symlink(src, dst) \n",
-        "    print(\"✅ Symbolic link created successfully\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Mc28N0_NrCQH"
-      },
-      "source": [
-        "## ◢ Execution"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "ir4hCrMIuUpl"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 9. Run Terminal and Execute Dream bot\n",
-        "#@markdown <font color=\"blue\">Steps:</font> <br>\n",
-        "#@markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
-        "#@markdown 2. After initialized you'll see `Dream>` line.<br>\n",
-        "#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
-        "#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
-        "\n",
-        "%load_ext colabxterm\n",
-        "%xterm\n",
-        "gc.collect()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "qnLohSHmKoGk"
-      },
-      "outputs": [],
-      "source": [
-        "#@title 10. Show the last 15 generated images\n",
-        "import glob\n",
-        "import matplotlib.pyplot as plt\n",
-        "import matplotlib.image as mpimg\n",
-        "%matplotlib inline\n",
-        "\n",
-        "images = []\n",
-        "for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
-        "    images.append(mpimg.imread(img_path))\n",
-        "\n",
-        "images = images[:15] \n",
-        "\n",
-        "plt.figure(figsize=(20,10))\n",
-        "\n",
-        "columns = 5\n",
-        "for i, image in enumerate(images):\n",
-        "    ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
-        "    ax.axes.xaxis.set_visible(False)\n",
-        "    ax.axes.yaxis.set_visible(False)\n",
-        "    ax.axis('off')\n",
-        "    plt.imshow(image)\n",
-        "    gc.collect()\n",
-        "\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "collapsed_sections": [],
-      "private_outputs": true,
-      "provenance": []
-    },
-    "gpuClass": "standard",
-    "kernelspec": {
-      "display_name": "Python 3.9.12 64-bit",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python",
-      "version": "3.9.12"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "4e870c5c5fe42db7e2c5647ae5af656ff3391bf8c2b729cbf7fa0e16ca8cb5af"
-      }
-    }
   },
-  "nbformat": 4,
+  {
-  "nbformat_minor": 0
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dr32VLxlnouf"
+   },
+   "source": [
+    "## ◢ Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "a2Z5Qu_o8VtQ"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 1. Check current GPU assigned\n",
+    "!nvidia-smi -L\n",
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "vbI9ZsQHzjqF"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 2. Download stable-diffusion Repository\n",
+    "from os.path import exists\n",
+    "\n",
+    "!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
+    "%cd /content/InvokeAI/\n",
+    "!git checkout --quiet tags/v2.0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "QbXcGXYEFSNB"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 3. Install dependencies\n",
+    "import gc\n",
+    "\n",
+    "!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-base.txt\n",
+    "!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-win-colab-cuda.txt\n",
+    "!pip install colab-xterm\n",
+    "!pip install -r requirements-lin-win-colab-CUDA.txt\n",
+    "!pip install clean-fid torchtext\n",
+    "!pip install transformers\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "8rSMhgnAttQa"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 4. Restart Runtime\n",
+    "exit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "ChIDWxLVHGGJ"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 5. Load small ML models required\n",
+    "import gc\n",
+    "\n",
+    "%cd /content/InvokeAI/\n",
+    "!python scripts/preload_models.py\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "795x1tMoo8b1"
+   },
+   "source": [
+    "## ◢ Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "YEWPV-sF1RDM"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 6. Mount google Drive\n",
+    "from google.colab import drive\n",
+    "\n",
+    "drive.mount(\"/content/drive\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "zRTJeZ461WGu"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 7. Drive Path to model\n",
+    "# @markdown Path should start with /content/drive/path-to-your-file <br>\n",
+    "# @markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
+    "# @markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
+    "from os.path import exists\n",
+    "\n",
+    "model_path = \"\"  # @param {type:\"string\"}\n",
+    "if exists(model_path):\n",
+    "    print(\"✅ Valid directory\")\n",
+    "else:\n",
+    "    print(\"❌ File doesn't exist\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "UY-NNz4I8_aG"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 8. Symlink to model\n",
+    "\n",
+    "from os.path import exists\n",
+    "import os\n",
+    "\n",
+    "# Folder creation if it doesn't exist\n",
+    "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
+    "    print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
+    "else:\n",
+    "    %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
+    "    print(\"✅ Dir stable-diffusion-v1 created\")\n",
+    "\n",
+    "# Symbolic link if it doesn't exist\n",
+    "if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
+    "    print(\"❗ Symlink already created\")\n",
+    "else:\n",
+    "    src = model_path\n",
+    "    dst = \"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"\n",
+    "    os.symlink(src, dst)\n",
+    "    print(\"✅ Symbolic link created successfully\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Mc28N0_NrCQH"
+   },
+   "source": [
+    "## ◢ Execution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "ir4hCrMIuUpl"
+   },
+   "outputs": [],
+   "source": [
+    "# @title 9. Run Terminal and Execute Dream bot\n",
+    "# @markdown <font color=\"blue\">Steps:</font> <br>\n",
+    "# @markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
+    "# @markdown 2. After initialized you'll see `Dream>` line.<br>\n",
+    "# @markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
+    "# @markdown 4. To quit Dream bot use: `q` command.<br>\n",
+    "\n",
+    "%load_ext colabxterm\n",
+    "%xterm\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "qnLohSHmKoGk"
+   },
+   "outputs": [],
+   "source": [
+    "#@title 10. Show the last 15 generated images\n",
+    "import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "%matplotlib inline\n",
+    "\n",
+    "images = []\n",
+    "for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
+    "    images.append(mpimg.imread(img_path))\n",
+    "\n",
+    "images = images[:15] \n",
+    "\n",
+    "plt.figure(figsize=(20,10))\n",
+    "\n",
+    "columns = 5\n",
+    "for i, image in enumerate(images):\n",
+    "    ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
+    "    ax.axes.xaxis.set_visible(False)\n",
+    "    ax.axes.yaxis.set_visible(False)\n",
+    "    ax.axis('off')\n",
+    "    plt.imshow(image)\n",
+    "    gc.collect()\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "private_outputs": true,
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3.9.12 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4e870c5c5fe42db7e2c5647ae5af656ff3391bf8c2b729cbf7fa0e16ca8cb5af"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }

scripts/orig_scripts/latent_imagenet_diffusion.ipynb

@@ -52,17 +52,17 @@
      "name": "stdout",
      "text": [
       "Cloning into 'latent-diffusion'...\n",
-      "remote: Enumerating objects: 992, done.\u001B[K\n",
+      "remote: Enumerating objects: 992, done.\u001b[K\n",
-      "remote: Counting objects: 100% (695/695), done.\u001B[K\n",
+      "remote: Counting objects: 100% (695/695), done.\u001b[K\n",
-      "remote: Compressing objects: 100% (397/397), done.\u001B[K\n",
+      "remote: Compressing objects: 100% (397/397), done.\u001b[K\n",
-      "remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001B[K\n",
+      "remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001b[K\n",
       "Receiving objects: 100% (992/992), 30.78 MiB | 29.43 MiB/s, done.\n",
       "Resolving deltas: 100% (510/510), done.\n",
       "Cloning into 'taming-transformers'...\n",
-      "remote: Enumerating objects: 1335, done.\u001B[K\n",
+      "remote: Enumerating objects: 1335, done.\u001b[K\n",
-      "remote: Counting objects: 100% (525/525), done.\u001B[K\n",
+      "remote: Counting objects: 100% (525/525), done.\u001b[K\n",
-      "remote: Compressing objects: 100% (493/493), done.\u001B[K\n",
+      "remote: Compressing objects: 100% (493/493), done.\u001b[K\n",
-      "remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001B[K\n",
+      "remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001b[K\n",
       "Receiving objects: 100% (1335/1335), 412.35 MiB | 30.53 MiB/s, done.\n",
       "Resolving deltas: 100% (267/267), done.\n",
       "Obtaining file:///content/taming-transformers\n",
@@ -73,23 +73,24 @@
       "Installing collected packages: taming-transformers\n",
       "  Running setup.py develop for taming-transformers\n",
       "Successfully installed taming-transformers-0.0.1\n",
-      "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
       "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
-      "arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001B[0m\n"
+      "arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "#@title Installation\n",
+    "# @title Installation\n",
     "!git clone https://github.com/CompVis/latent-diffusion.git\n",
     "!git clone https://github.com/CompVis/taming-transformers\n",
     "!pip install -e ./taming-transformers\n",
     "!pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops\n",
     "\n",
     "import sys\n",
+    "\n",
     "sys.path.append(\".\")\n",
-    "sys.path.append('./taming-transformers')\n",
+    "sys.path.append(\"./taming-transformers\")\n",
-    "from taming.models import vqgan "
+    "from taming.models import vqgan"
    ]
   },
   {
@@ -104,11 +105,11 @@
   {
    "cell_type": "code",
    "source": [
-    "#@title Download\n",
+    "# @title Download\n",
-    "%cd latent-diffusion/ \n",
+    "%cd latent-diffusion/\n",
     "\n",
     "!mkdir -p models/ldm/cin256-v2/\n",
-    "!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt "
+    "!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt"
    ],
    "metadata": {
     "colab": {
@@ -203,7 +204,7 @@
   {
    "cell_type": "code",
    "source": [
-    "#@title loading utils\n",
+    "# @title loading utils\n",
     "import torch\n",
     "from omegaconf import OmegaConf\n",
     "\n",
@@ -212,7 +213,7 @@
     "\n",
     "def load_model_from_config(config, ckpt):\n",
     "    print(f\"Loading model from {ckpt}\")\n",
-    "    pl_sd = torch.load(ckpt)#, map_location=\"cpu\")\n",
+    "    pl_sd = torch.load(ckpt)  # , map_location=\"cpu\")\n",
     "    sd = pl_sd[\"state_dict\"]\n",
     "    model = instantiate_from_config(config.model)\n",
     "    m, u = model.load_state_dict(sd, strict=False)\n",
@@ -222,7 +223,7 @@
     "\n",
     "\n",
     "def get_model():\n",
-    "    config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\")  \n",
+    "    config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\")\n",
     "    model = load_model_from_config(config, \"models/ldm/cin256-v2/model.ckpt\")\n",
     "    return model"
    ],
@@ -276,18 +277,18 @@
   {
    "cell_type": "code",
    "source": [
-    "import numpy as np \n",
+    "import numpy as np\n",
     "from PIL import Image\n",
     "from einops import rearrange\n",
     "from torchvision.utils import make_grid\n",
     "\n",
     "\n",
-    "classes = [25, 187, 448, 992]   # define classes to be sampled here\n",
+    "classes = [25, 187, 448, 992]  # define classes to be sampled here\n",
     "n_samples_per_class = 6\n",
     "\n",
     "ddim_steps = 20\n",
     "ddim_eta = 0.0\n",
-    "scale = 3.0   # for unconditional guidance\n",
+    "scale = 3.0  # for unconditional guidance\n",
     "\n",
     "\n",
     "all_samples = list()\n",
@@ -295,36 +296,39 @@
     "with torch.no_grad():\n",
     "    with model.ema_scope():\n",
     "        uc = model.get_learned_conditioning(\n",
-    "            {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)}\n",
+    "            {model.cond_stage_key: torch.tensor(n_samples_per_class * [1000]).to(model.device)}\n",
-    "            )\n",
+    "        )\n",
-    "        \n",
+    "\n",
     "        for class_label in classes:\n",
-    "            print(f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\")\n",
+    "            print(\n",
-    "            xc = torch.tensor(n_samples_per_class*[class_label])\n",
+    "                f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\"\n",
+    "            )\n",
+    "            xc = torch.tensor(n_samples_per_class * [class_label])\n",
     "            c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)})\n",
-    "            \n",
+    "\n",
-    "            samples_ddim, _ = sampler.sample(S=ddim_steps,\n",
+    "            samples_ddim, _ = sampler.sample(\n",
-    "                                             conditioning=c,\n",
+    "                S=ddim_steps,\n",
-    "                                             batch_size=n_samples_per_class,\n",
+    "                conditioning=c,\n",
-    "                                             shape=[3, 64, 64],\n",
+    "                batch_size=n_samples_per_class,\n",
-    "                                             verbose=False,\n",
+    "                shape=[3, 64, 64],\n",
-    "                                             unconditional_guidance_scale=scale,\n",
+    "                verbose=False,\n",
-    "                                             unconditional_conditioning=uc, \n",
+    "                unconditional_guidance_scale=scale,\n",
-    "                                             eta=ddim_eta)\n",
+    "                unconditional_conditioning=uc,\n",
+    "                eta=ddim_eta,\n",
+    "            )\n",
     "\n",
     "            x_samples_ddim = model.decode_first_stage(samples_ddim)\n",
-    "            x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, \n",
+    "            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
-    "                                         min=0.0, max=1.0)\n",
     "            all_samples.append(x_samples_ddim)\n",
     "\n",
     "\n",
     "# display as grid\n",
     "grid = torch.stack(all_samples, 0)\n",
-    "grid = rearrange(grid, 'n b c h w -> (n b) c h w')\n",
+    "grid = rearrange(grid, \"n b c h w -> (n b) c h w\")\n",
     "grid = make_grid(grid, nrow=n_samples_per_class)\n",
     "\n",
     "# to image\n",
-    "grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()\n",
+    "grid = 255.0 * rearrange(grid, \"c h w -> h w c\").cpu().numpy()\n",
     "Image.fromarray(grid.astype(np.uint8))"
    ],
    "metadata": {