mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
282 lines
8.9 KiB
Plaintext
282 lines
8.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ycYWcsEKc6w7"
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},
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"source": [
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"# Stable Diffusion AI Notebook (Release 2.0.0)\n",
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"\n",
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"<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
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"#### Instructions:\n",
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"1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
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"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",
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"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",
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"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",
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"4. To quit Dream bot use `q` command. <br> \n",
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"---\n",
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"<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",
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"<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",
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"##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
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"---\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dr32VLxlnouf"
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},
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"source": [
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"## ◢ Installation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "a2Z5Qu_o8VtQ"
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},
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"outputs": [],
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"source": [
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"#@title 1. Check current GPU assigned\n",
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"!nvidia-smi -L\n",
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "vbI9ZsQHzjqF"
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},
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"outputs": [],
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"source": [
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"#@title 2. Download stable-diffusion Repository\n",
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"from os.path import exists\n",
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"\n",
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"!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
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"%cd /content/InvokeAI/\n",
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"!git checkout --quiet tags/v2.0.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "QbXcGXYEFSNB"
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},
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"outputs": [],
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"source": [
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"#@title 3. Install dependencies\n",
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"import gc\n",
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"\n",
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"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements.txt\n",
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"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-lin-win-colab-CUDA.txt\n",
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"!pip install colab-xterm\n",
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"!pip install -r requirements-lin-win-colab-CUDA.txt\n",
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"!pip install clean-fid torchtext\n",
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"!pip install transformers\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "8rSMhgnAttQa"
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},
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"outputs": [],
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"source": [
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"#@title 4. Restart Runtime\n",
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"exit()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "ChIDWxLVHGGJ"
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},
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"outputs": [],
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"source": [
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"#@title 5. Load small ML models required\n",
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"import gc\n",
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"%cd /content/InvokeAI/\n",
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"!python scripts/preload_models.py\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "795x1tMoo8b1"
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},
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"source": [
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"## ◢ Configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "YEWPV-sF1RDM"
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},
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"outputs": [],
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"source": [
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"#@title 6. Mount google Drive\n",
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "zRTJeZ461WGu"
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},
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"outputs": [],
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"source": [
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"#@title 7. Drive Path to model\n",
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"#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
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"#@markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
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"#@markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
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"from os.path import exists\n",
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"\n",
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"model_path = \"\" #@param {type:\"string\"}\n",
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"if exists(model_path):\n",
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" print(\"✅ Valid directory\")\n",
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"else: \n",
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" print(\"❌ File doesn't exist\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "UY-NNz4I8_aG"
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},
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"outputs": [],
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"source": [
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"#@title 8. Symlink to model\n",
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"\n",
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"from os.path import exists\n",
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"import os \n",
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"\n",
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"# Folder creation if it doesn't exist\n",
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"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
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" print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
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"else:\n",
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" %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
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" print(\"✅ Dir stable-diffusion-v1 created\")\n",
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"\n",
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"# Symbolic link if it doesn't exist\n",
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"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
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" print(\"❗ Symlink already created\")\n",
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"else: \n",
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" src = model_path\n",
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" dst = '/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt'\n",
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" os.symlink(src, dst) \n",
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" print(\"✅ Symbolic link created successfully\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Mc28N0_NrCQH"
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},
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"source": [
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"## ◢ Execution"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "ir4hCrMIuUpl"
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},
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"outputs": [],
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"source": [
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"#@title 9. Run Terminal and Execute Dream bot\n",
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"#@markdown <font color=\"blue\">Steps:</font> <br>\n",
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"#@markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
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"#@markdown 2. After initialized you'll see `Dream>` line.<br>\n",
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"#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
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"#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
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"\n",
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"%load_ext colabxterm\n",
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"%xterm\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "qnLohSHmKoGk"
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},
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"outputs": [],
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"source": [
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"#@title 10. Show the last 15 generated images\n",
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"import glob\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.image as mpimg\n",
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"%matplotlib inline\n",
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"\n",
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"images = []\n",
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"for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
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" images.append(mpimg.imread(img_path))\n",
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"\n",
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"images = images[:15] \n",
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"\n",
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"plt.figure(figsize=(20,10))\n",
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"\n",
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"columns = 5\n",
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"for i, image in enumerate(images):\n",
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" ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
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" ax.axes.xaxis.set_visible(False)\n",
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" ax.axes.yaxis.set_visible(False)\n",
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" ax.axis('off')\n",
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" plt.imshow(image)\n",
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" gc.collect()\n",
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"\n"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"private_outputs": true,
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"provenance": []
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},
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"gpuClass": "standard",
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"kernelspec": {
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"display_name": "Python 3.10.4 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.10.4"
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},
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"vscode": {
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"interpreter": {
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"hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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