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‎Stable_Diffusion.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4",
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"private_outputs": true,
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"authorship_tag": "ABX9TyOiUWRZBuOHzRrD41UDa1vL",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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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": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/ruchira-net/css-loader/blob/master/Stable_Diffusion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "3he-zWjkTirt"
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},
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"execution_count": null,
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"outputs": []
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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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"id": "aSGhPjR3Er-N"
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},
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"outputs": [],
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"source": [
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"#@title 👇 Installing dependencies { display-mode: \"form\" }\n",
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"#@markdown ---\n",
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"#@markdown Make sure to select **GPU** as the runtime type:<br/>\n",
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"#@markdown *Runtime->Change Runtime Type->Under Hardware accelerator, select GPU*\n",
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"#@markdown\n",
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"#@markdown ---\n",
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"\n",
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"!pip -q install torch torchvision tor diffusers transformers accelerate scipy safetensors xformers mediapy ipywidgets==7.7.1\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 👇 Selecting Model { form-width: \"20%\", display-mode: \"form\" }\n",
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"#@markdown ---\n",
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"#@markdown - **Select Model** - A list of Stable Diffusion models to choose from.\n",
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"#@markdown - **Select Sampler** - A list of schedulers to choose from. Default is EulerAncestralScheduler.\n",
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"#@markdown - **Safety Checker** - Enable/Disable uncensored content\n",
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"#@markdown\n",
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"#@markdown ---\n",
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"\n",
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"from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler\n",
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"from diffusers.models import AutoencoderKL\n",
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"import torch\n",
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"import ipywidgets as widgets\n",
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"import importlib\n",
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"from PIL import Image\n",
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"\n",
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"#Enable third party widget support\n",
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"from google.colab import output\n",
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"output.enable_custom_widget_manager()\n",
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"\n",
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"#Pipe\n",
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"pipe = None\n",
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"\n",
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"#Models\n",
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"select_model = widgets.Dropdown(\n",
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" options=[\n",
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" (\"Dreamlike Photoreal 2.0\" , \"stabilityai/stable-diffusion-xl-base-1.0\")\n",
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" ],\n",
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" description=\"Select Model:\"\n",
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")\n",
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"\n",
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"#Schedulers\n",
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"select_sampler = widgets.Dropdown(\n",
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" options=[\n",
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" \"EulerAncestralDiscreteScheduler\",\n",
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" \"EulerDiscreteScheduler\",\n",
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" \"UniPCMultistepScheduler\",\n",
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" \"DDIMScheduler\"\n",
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" ],\n",
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" description=\"Select Schedular:\"\n",
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")\n",
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"select_sampler.style.description_width = \"auto\"\n",
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"\n",
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"#Safety Checker\n",
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"safety_check = widgets.Checkbox(\n",
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" value=False,\n",
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" description=\"Enable Safety Check\",\n",
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" layout=widgets.Layout(margin=\"0px 0px 0px -85px\")\n",
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")\n",
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"\n",
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"#Output\n",
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"out = widgets.Output()\n",
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"\n",
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"#Apply Settings\n",
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"apply_btn = widgets.Button(\n",
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" description=\"Apply\",\n",
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" button_style=\"info\"\n",
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")\n",
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"\n",
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"\n",
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"#Get scheduler\n",
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"def get_scheduler(name):\n",
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"\n",
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" match name:\n",
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"\n",
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" case \"EulerAncestralDiscreteScheduler\":\n",
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" return EulerAncestralDiscreteScheduler.from_pretrained(select_model.value, subfolder=\"scheduler\")\n",
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"\n",
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"#Run pipeline\n",
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"def pipeline(p):\n",
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"\n",
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" global pipe\n",
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"\n",
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" out.clear_output()\n",
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" apply_btn.disabled = True\n",
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"\n",
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" with out:\n",
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"\n",
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" print(\"Running, please wait...\")\n",
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"\n",
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" pipe = StableDiffusionPipeline.from_pretrained(\n",
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" select_model.value,\n",
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" scheduler=get_scheduler(select_sampler.value),\n",
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" torch_dtype=torch.float16,\n",
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" vae=AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16).to(\"cuda\")\n",
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" ).to(\"cuda\")\n",
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"\n",
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" pipe.safety_checker = None\n",
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"\n",
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" pipe.enable_xformers_memory_efficient_attention()\n",
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"\n",
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" print(\"Finished!\")\n",
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"\n",
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" apply_btn.disabled = False\n",
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"\n",
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"\n",
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"#Display\n",
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"apply_btn.on_click(pipeline)\n",
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"\n",
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"widgets.VBox(\n",
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" [\n",
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" widgets.HTML(value=\"<h2>Configure Pipeline</h2>\"),\n",
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" select_model, select_sampler, safety_check, apply_btn, out\n",
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" ]\n",
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")\n",
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"\n",
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"#call pipeline method from here\n",
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"pipeline(None)"
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],
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"metadata": {
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"id": "b_0UKcp3E9no"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%env PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True"
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],
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"metadata": {
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"id": "ef0CwmfkmKOg"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 👇 Generating Images { form-width: \"20%\", display-mode: \"form\" }\n",
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"#@markdown ---\n",
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"#@markdown - **Prompt** - Description of the image\n",
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"#@markdown - **Negative Prompt** - Things you don't want to see or ignore in the image\n",
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"#@markdown - **Steps** - Number of denoising steps. Higher steps may lead to better results but takes longer time to generate the image. Default is `30`.\n",
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"#@markdown - **CFG** - Guidance scale ranging from `0` to `20`. Lower values allow the AI to be more creative and less strict at following the prompt. Default is `7.5`.\n",
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"#@markdown - **Seed** - A random value that controls image generation. The same seed and prompt produce the same images. Set `-1` for using random seed values.\n",
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"#@markdown ---\n",
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"import ipywidgets as widgets, mediapy, random\n",
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"import IPython.display\n",
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"\n",
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"\n",
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"#PARAMETER WIDGETS\n",
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"width = \"300px\"\n",
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"\n",
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"prompt = widgets.Textarea(\n",
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" value=\"(photorealistic (HDR):2), (gorgeous:1.1), (only one:1.4) \",\n",
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" placeholder=\"Enter prompt\",\n",
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" #description=\"Prompt:\",\n",
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" rows=5,\n",
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" layout=widgets.Layout(width=\"600px\")\n",
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")\n",
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"\n",
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"camera = widgets.Dropdown(\n",
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" options=[('leica summicron 35mm f2.0', ' leica summicron 35mm f2.0'), ('sony a7ii', ' sony a7ii'), ('kodak potra', ' kodak potra')],\n",
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" value=' leica summicron 35mm f2.0',\n",
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" description=\"Camera:\",\n",
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" layout=widgets.Layout(width=width)\n",
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")\n",
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"\n",
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"lighting = widgets.Dropdown(\n",
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" options=[('romantic lighting', ' romantic lighting,'), ('dramatic lighting', ' dramatic lighting,')],\n",
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" value=' dramatic lighting,',\n",
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" description=\"Lighting:\",\n",
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" layout=widgets.Layout(width=width)\n",
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")\n",
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"\n",
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"neg_prompt = widgets.Textarea(\n",
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" value=\"disfigured, bad lips, ugly lips, bad nose, ugly nose, bad face, ugly face, bad mouth, ugly mouth, beard, bad teeth, ugly teeth, ((grayscale)), cross-eyed, uneven eyes, bad ears, ugly ears, deformed, malformed limbs, bad anatomy, bad hands, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, worst face, three crus, extra crus, fused crus, worst feet, three feet, fused feet, fused thigh, three thigh, fused thigh, extra thigh, worst thigh, missing fingers, extra fingers, ugly fingers, long fingers, horn, extra eyes, huge eyes, 2girl, amputation, disconnected limbs, cartoon, cg, 3d, anime, unreal, animate, masculine, fat, chubby, (tamil:1.7), (red dot:2), (red mark:1.9), (pottu:1.8), (bindi:1.6), paintings, sketches, lowres, ((monochrome)), heavy make-up, (worst quality:1.6), (low quality:1.5), body hair, jewellary, topless, multiple people, blurry, duplicate bodies\",\n",
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" placeholder=\"Enter negative prompt\",\n",
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" #description=\"Negative Prompt:\",\n",
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" rows=5,\n",
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" layout=widgets.Layout(width=\"600px\")\n",
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")\n",
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"\n",
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"num_images = widgets.IntText(\n",
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" value=4,\n",
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" description=\"Images:\",\n",
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" layout=widgets.Layout(width=width),\n",
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")\n",
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"\n",
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"steps = widgets.IntText(\n",
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" value=35,\n",
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" description=\"Steps:\",\n",
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" layout=widgets.Layout(width=width)\n",
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")\n",
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"\n",
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"CFG = widgets.FloatText(\n",
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" value=7,\n",
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" description=\"CFG:\",\n",
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" layout=widgets.Layout(width=width)\n",
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")\n",
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"\n",
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"img_height = widgets.Dropdown(\n",
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" options=[('512px', 512), ('768px', 768), ('720px', 720)],\n",
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" value=720,\n",
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" description=\"Height:\",\n",
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" layout=widgets.Layout(width=width)\n",
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")\n",
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"\n",
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"img_width = widgets.Dropdown(\n",
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" options=[('512px', 512), ('768px', 768), ('1280px', 1280)],\n",
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" value=1280,\n",
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" description=\"Width:\",\n",
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" layout=widgets.Layout(width=width)\n",
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")\n",
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"\n",
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"random_seed = widgets.IntText(\n",
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" value=-1,\n",
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" description=\"Seed:\",\n",
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" layout=widgets.Layout(width=width),\n",
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" disabled=False\n",
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")\n",
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"\n",
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"generate = widgets.Button(\n",
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" description=\"Generate\",\n",
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" disabled=False,\n",
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" button_style=\"primary\"\n",
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")\n",
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"\n",
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"display_imgs = widgets.Output()\n",
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"\n",
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"\n",
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"#RUN\n",
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"def generate_img(i):\n",
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"\n",
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" #Clear output\n",
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" display_imgs.clear_output()\n",
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" generate.disabled = True\n",
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"\n",
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" #Calculate seed\n",
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" seed = random.randint(0, 2147483647) if random_seed.value == -1 else random_seed.value\n",
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"\n",
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" with display_imgs:\n",
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"\n",
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" print(f\"Prompt:\\n{prompt.value + lighting.value + camera.value}\")\n",
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"\n",
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" images = pipe(\n",
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" prompt.value + lighting.value + camera.value,\n",
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" height = img_height.value,\n",
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" width = img_width.value,\n",
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" num_inference_steps = steps.value,\n",
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" guidance_scale = CFG.value,\n",
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" num_images_per_prompt = num_images.value,\n",
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" negative_prompt = neg_prompt.value,\n",
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" generator = torch.Generator(\"cuda\").manual_seed(seed),\n",
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" ).images\n",
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"\n",
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" mediapy.show_images(images)\n",
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"\n",
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" print(f\"Seed:\\n{seed}\")\n",
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"\n",
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" generate.disabled = False\n",
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"\n",
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"#Display\n",
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"generate.on_click(generate_img)\n",
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"\n",
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"widgets.VBox(\n",
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" [\n",
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" widgets.AppLayout(\n",
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" header=widgets.VBox(\n",
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" [camera, lighting]\n",
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" ),\n",
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" left_sidebar=widgets.VBox(\n",
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" [num_images, steps, CFG, img_height, img_width, random_seed]\n",
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" ),\n",
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" center=widgets.VBox(\n",
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" [prompt, neg_prompt, generate]\n",
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" ),\n",
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" right_sidebar=None,\n",
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" footer=None\n",
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" ),\n",
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" display_imgs\n",
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" ]\n",
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")"
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],
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"metadata": {
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"id": "-g-Q5UGcGATS"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}

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