mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-13 17:33:31 +03:00
634 lines
28 KiB
Plaintext
634 lines
28 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": "S5RoIM-5IPZJ"
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},
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"source": [
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"[](https://colab.research.google.com/github/Sygil-Dev/sygil-webui/blob/main/Web_based_UI_for_Stable_Diffusion_colab.ipynb)"
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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": "5-Bx4AsEoPU-"
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},
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"source": [
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"# README"
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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": "z4kQYMPQn4d-"
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},
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"source": [
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"###<center>Web-based UI for Stable Diffusion</center>\n",
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"\n",
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"## Created by [Sygil-Dev](https://github.com/Sygil-Dev)\n",
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"\n",
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"## [Visit Sygil-Dev's Discord Server](https://discord.gg/gyXNe4NySY) [](https://discord.gg/gyXNe4NySY)\n",
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"\n",
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"## Installation instructions for:\n",
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"\n",
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"- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/1.windows-installation.html)**\n",
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"- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/2.linux-installation.html)**\n",
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"\n",
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"### Want to ask a question or request a feature?\n",
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"\n",
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"Come to our [Discord Server](https://discord.gg/gyXNe4NySY) or use [Discussions](https://github.com/Sygil-Dev/sygil-webui/discussions).\n",
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"\n",
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"## Documentation\n",
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"\n",
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"[Documentation is located here](https://sygil-dev.github.io/sygil-webui/)\n",
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"\n",
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"## Want to contribute?\n",
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"\n",
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"Check the [Contribution Guide](CONTRIBUTING.md)\n",
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"\n",
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"[Sygil-Dev](https://github.com/Sygil-Dev) main devs:\n",
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"\n",
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"*  [hlky](https://github.com/hlky)\n",
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"* [ZeroCool940711](https://github.com/ZeroCool940711)\n",
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"* [codedealer](https://github.com/codedealer)\n",
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"\n",
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"### Project Features:\n",
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"\n",
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"* Two great Web UI's to choose from: Streamlit or Gradio\n",
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"\n",
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"* No more manually typing parameters, now all you have to do is write your prompt and adjust sliders\n",
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"\n",
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"* Built-in image enhancers and upscalers, including GFPGAN and realESRGAN\n",
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"\n",
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"* Run additional upscaling models on CPU to save VRAM\n",
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"\n",
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"* Textual inversion 🔥: [info](https://textual-inversion.github.io/) - requires enabling, see [here](https://github.com/hlky/sd-enable-textual-inversion), script works as usual without it enabled\n",
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"\n",
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"* Advanced img2img editor with Mask and crop capabilities\n",
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"\n",
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"* Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)\n",
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"\n",
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"* More diffusion samplers 🔥🔥: A great collection of samplers to use, including:\n",
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" \n",
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" - `k_euler` (Default)\n",
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" - `k_lms`\n",
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" - `k_euler_a`\n",
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" - `k_dpm_2`\n",
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" - `k_dpm_2_a`\n",
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" - `k_heun`\n",
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" - `PLMS`\n",
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" - `DDIM`\n",
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"\n",
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"* Loopback ➿: Automatically feed the last generated sample back into img2img\n",
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"\n",
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"* Prompt Weighting 🏋️: Adjust the strength of different terms in your prompt\n",
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"\n",
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"* Selectable GPU usage with `--gpu <id>`\n",
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"\n",
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"* Memory Monitoring 🔥: Shows VRAM usage and generation time after outputting\n",
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"\n",
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"* Word Seeds 🔥: Use words instead of seed numbers\n",
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"\n",
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"* CFG: Classifier free guidance scale, a feature for fine-tuning your output\n",
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"\n",
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"* Automatic Launcher: Activate conda and run Stable Diffusion with a single command\n",
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"\n",
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"* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB\n",
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"\n",
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"* Prompt validation: If your prompt is too long, you will get a warning in the text output field\n",
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"\n",
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"* Copy-paste generation parameters: A text output provides generation parameters in an easy to copy-paste form for easy sharing.\n",
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"\n",
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"* Correct seeds for batches: If you use a seed of 1000 to generate two batches of two images each, four generated images will have seeds: `1000, 1001, 1002, 1003`.\n",
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"\n",
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"* Prompt matrix: Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.\n",
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"\n",
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"* Loopback for Image2Image: A checkbox for img2img allowing to automatically feed output image as input for the next batch. Equivalent to saving output image, and replacing input image with it.\n",
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"\n",
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"# Stable Diffusion Web UI\n",
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"\n",
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"A fully-integrated and easy way to work with Stable Diffusion right from a browser window.\n",
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"\n",
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"## Streamlit\n",
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"\n",
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"\n",
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"\n",
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"**Features:**\n",
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"\n",
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"- Clean UI with an easy to use design, with support for widescreen displays.\n",
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"- Dynamic live preview of your generations\n",
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"- Easily customizable presets right from the WebUI (Coming Soon!)\n",
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"- An integrated gallery to show the generations for a prompt or session (Coming soon!)\n",
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"- Better optimization VRAM usage optimization, less errors for bigger generations.\n",
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"- Text2Video - Generate video clips from text prompts right from the WEb UI (WIP)\n",
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"- Concepts Library - Run custom embeddings others have made via textual inversion.\n",
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"- Actively being developed with new features being added and planned - Stay Tuned!\n",
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"- Streamlit is now the new primary UI for the project moving forward.\n",
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"- *Currently in active development and still missing some of the features present in the Gradio Interface.*\n",
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"\n",
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"Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.\n",
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"\n",
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"## Gradio\n",
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"\n",
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"\n",
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"\n",
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"**Features:**\n",
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"\n",
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"- Older UI design that is fully functional and feature complete.\n",
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"- Has access to all upscaling models, including LSDR.\n",
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"- Dynamic prompt entry automatically changes your generation settings based on `--params` in a prompt.\n",
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"- Includes quick and easy ways to send generations to Image2Image or the Image Lab for upscaling.\n",
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"- *Note, the Gradio interface is no longer being actively developed and is only receiving bug fixes.*\n",
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"\n",
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"Please see the [Gradio Documentation](docs/5.gradio-interface.md) to learn more.\n",
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"\n",
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"## Image Upscalers\n",
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"\n",
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"---\n",
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"\n",
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"### GFPGAN\n",
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"\n",
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"\n",
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"\n",
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"Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strong the effect is.\n",
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"\n",
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"If you want to use GFPGAN to improve generated faces, you need to install it separately.\n",
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"Download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it\n",
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"into the `/sygil-webui/models/gfpgan` directory.\n",
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"\n",
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"### RealESRGAN\n",
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"\n",
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"\n",
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"\n",
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"Lets you double the resolution of generated images. There is a checkbox in every tab to use RealESRGAN, and you can choose between the regular upscaler and the anime version.\n",
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"There is also a separate tab for using RealESRGAN on any picture.\n",
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"\n",
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"Download [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) and [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth).\n",
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"Put them into the `sygil-webui/models/realesrgan` directory.\n",
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"\n",
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"\n",
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"\n",
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"### LSDR\n",
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"\n",
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"Download **LDSR** [project.yaml](https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1) and [model last.cpkt](https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1). Rename last.ckpt to model.ckpt and place both under `sygil-webui/models/ldsr/`\n",
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"\n",
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"### GoBig, and GoLatent *(Currently on the Gradio version Only)*\n",
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"\n",
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"More powerful upscalers that uses a seperate Latent Diffusion model to more cleanly upscale images.\n",
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"\n",
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"\n",
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"\n",
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"Please see the [Image Enhancers Documentation](docs/6.image_enhancers.md) to learn more.\n",
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"\n",
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"-----\n",
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"\n",
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"### *Original Information From The Stable Diffusion Repo*\n",
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"\n",
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"# Stable Diffusion\n",
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"\n",
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"*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*\n",
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"\n",
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"[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>\n",
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"[Robin Rombach](https://github.com/rromb)\\*,\n",
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"[Andreas Blattmann](https://github.com/ablattmann)\\*,\n",
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"[Dominik Lorenz](https://github.com/qp-qp)\\,\n",
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"[Patrick Esser](https://github.com/pesser),\n",
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"[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>\n",
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"\n",
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"**CVPR '22 Oral**\n",
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"\n",
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"which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).\n",
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"\n",
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"[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion\n",
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"model.\n",
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"Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.\n",
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"Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),\n",
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"this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.\n",
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"With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.\n",
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"See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).\n",
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"\n",
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"## Stable Diffusion v1\n",
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"\n",
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"Stable Diffusion v1 refers to a specific configuration of the model\n",
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"architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet\n",
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"and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and\n",
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"then finetuned on 512x512 images.\n",
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"\n",
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"*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present\n",
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"in its training data.\n",
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"Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).\n",
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"\n",
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"## Comments\n",
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"\n",
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"- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)\n",
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" and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).\n",
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" Thanks for open-sourcing!\n",
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"\n",
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"- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).\n",
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"\n",
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"## BibTeX\n",
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"\n",
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"```\n",
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"@misc{rombach2021highresolution,\n",
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" title={High-Resolution Image Synthesis with Latent Diffusion Models},\n",
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" author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},\n",
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" year={2021},\n",
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" eprint={2112.10752},\n",
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" archivePrefix={arXiv},\n",
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" primaryClass={cs.CV}\n",
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"}\n",
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"\n",
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"```"
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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": "iegma7yteERV"
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},
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"source": [
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"# Config options for Colab instance\n",
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"> Before running, make sure GPU backend is enabled. (Unless you plan on generating with Stable Horde)\n",
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">> Runtime -> Change runtime type -> Hardware Accelerator -> GPU (Make sure to save)"
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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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"id": "OXn96M9deVtF"
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},
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"outputs": [],
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"source": [
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"#@title { display-mode: \"form\" }\n",
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"#@markdown WebUI repo (and branch)\n",
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"repo_name = \"Sygil-Dev/sygil-webui\" #@param {type:\"string\"}\n",
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"repo_branch = \"dev\" #@param {type:\"string\"}\n",
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"\n",
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"#@markdown Mount Google Drive\n",
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"mount_google_drive = True #@param {type:\"boolean\"}\n",
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"save_outputs_to_drive = True #@param {type:\"boolean\"}\n",
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"#@markdown Folder in Google Drive to search for custom models\n",
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"MODEL_DIR = \"sygil-webui/models\" #@param {type:\"string\"}\n",
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"\n",
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"#@markdown Folder in Google Drive to look for custom config file (streamlit.yaml)\n",
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"CONFIG_DIR = \"sygil-webui\" #@param {type:\"string\"}\n",
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"\n",
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"#@markdown Enter auth token from Huggingface.co\n",
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"#@markdown >(required for downloading stable diffusion model.)\n",
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"HF_TOKEN = \"\" #@param {type:\"string\"}\n",
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"\n",
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"#@markdown Select which models to prefetch\n",
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"STABLE_DIFFUSION = True #@param {type:\"boolean\"}\n",
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"WAIFU_DIFFUSION = False #@param {type:\"boolean\"}\n",
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"TRINART_SD = False #@param {type:\"boolean\"}\n",
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"SD_WD_LD_TRINART_MERGED = False #@param {type:\"boolean\"}\n",
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"GFPGAN = True #@param {type:\"boolean\"}\n",
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"REALESRGAN = True #@param {type:\"boolean\"}\n",
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"LDSR = True #@param {type:\"boolean\"}\n",
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"BLIP_MODEL = False #@param {type:\"boolean\"}\n",
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"\n",
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"#@markdown Save models to Google Drive for faster loading in future (Be warned! Make sure you have enough space!)\n",
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"SAVE_MODELS = False #@param {type:\"boolean\"}"
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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": "IZjJSr-WPNxB"
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},
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"source": [
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"# Setup\n",
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"\n",
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">Runtime will crash when installing conda. This is normal as we are forcing a restart of the runtime from code.\n",
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"\n",
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">Just hit \"Run All\" again. 😑"
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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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"id": "eq0-E5mjSpmP"
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},
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"outputs": [],
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"source": [
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"#@title Make sure we have access to GPU backend\n",
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"!nvidia-smi -L"
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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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"id": "cDu33xkdJ5mD"
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},
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"outputs": [],
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"source": [
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"#@title Install miniConda (mamba)\n",
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"!pip install condacolab\n",
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"import condacolab\n",
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"condacolab.install_from_url(\"https://github.com/conda-forge/miniforge/releases/download/4.14.0-0/Mambaforge-4.14.0-0-Linux-x86_64.sh\")\n",
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"\n",
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"import condacolab\n",
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"condacolab.check()\n",
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"# The runtime will crash here!!! Don't panic! We planned for this remember?"
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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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"id": "pZHGf03Vp305"
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},
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"outputs": [],
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"source": [
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"#@title Clone webUI repo and download font\n",
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"import os\n",
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"REPO_URL = os.path.join('https://github.com', repo_name)\n",
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"PATH_TO_REPO = os.path.join('/content', repo_name.split('/')[1])\n",
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"!git clone {REPO_URL}\n",
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"%cd {PATH_TO_REPO}\n",
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"!git checkout {repo_branch}\n",
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"!git pull"
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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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"id": "dmN2igp5Yk3z"
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},
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"outputs": [],
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"source": [
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"#@title Install dependencies\n",
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"!mamba install cudatoolkit=11.3 git numpy=1.22.3 pip=20.3 python=3.8.5 pytorch=1.11.0 scikit-image=0.19.2 torchvision=0.12.0 -y\n",
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"!python --version\n",
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"!pip install -r requirements.txt"
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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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"id": "Nxaxfgo_F8Am"
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},
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"outputs": [],
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"source": [
|
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"#@title Install localtunnel to openGoogle's ports\n",
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"!npm install localtunnel"
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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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"id": "pcSWo9Zkzbsf"
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},
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"outputs": [],
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"source": [
|
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"#@title Mount Google Drive (if selected)\n",
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"if mount_google_drive:\n",
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" # Mount google drive to store outputs.\n",
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" from google.colab import drive\n",
|
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" drive.mount('/content/drive/', force_remount=True)\n",
|
|
"\n",
|
|
"if save_outputs_to_drive:\n",
|
|
" # Make symlink to redirect downloads\n",
|
|
" OUTPUT_PATH = os.path.join('/content/drive/MyDrive', repo_name.split('/')[1], 'outputs')\n",
|
|
" os.makedirs(OUTPUT_PATH, exist_ok=True)\n",
|
|
" os.symlink(OUTPUT_PATH, os.path.join(PATH_TO_REPO, 'outputs'), target_is_directory=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "vMdmh81J70yA"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Pre-fetch models\n",
|
|
"%cd {PATH_TO_REPO}\n",
|
|
"# make list of models we want to download\n",
|
|
"model_list = {\n",
|
|
" 'stable_diffusion': f'{STABLE_DIFFUSION}',\n",
|
|
" 'waifu_diffusion': f'{WAIFU_DIFFUSION}',\n",
|
|
" 'trinart_stable_diffusion': f'{TRINART_SD}',\n",
|
|
" 'sd_wd_ld_trinart_merged': f'{SD_WD_LD_TRINART_MERGED}',\n",
|
|
" 'gfpgan': f'{GFPGAN}',\n",
|
|
" 'realesrgan': f'{REALESRGAN}',\n",
|
|
" 'ldsr': f'{LDSR}',\n",
|
|
" 'blip_model': f'{BLIP_MODEL}'}\n",
|
|
"download_list = {k for (k,v) in model_list.items() if v == 'True'}\n",
|
|
"\n",
|
|
"# get model info (file name, download link, save location)\n",
|
|
"import yaml\n",
|
|
"from pprint import pprint\n",
|
|
"with open('configs/webui/webui_streamlit.yaml') as f:\n",
|
|
" dataMap = yaml.safe_load(f)\n",
|
|
"models = dataMap['model_manager']['models']\n",
|
|
"existing_models = []\n",
|
|
"\n",
|
|
"# copy script from model manager\n",
|
|
"import requests, time, shutil\n",
|
|
"from requests.auth import HTTPBasicAuth\n",
|
|
"\n",
|
|
"if MODEL_DIR != \"\":\n",
|
|
" MODEL_DIR = os.path.join('/content/drive/MyDrive', MODEL_DIR)\n",
|
|
"else:\n",
|
|
" MODEL_DIR = '/content/drive/MyDrive'\n",
|
|
"\n",
|
|
"def download_file(file_name, file_path, file_url):\n",
|
|
" os.makedirs(file_path, exist_ok=True)\n",
|
|
" link_path = os.path.join(MODEL_DIR, file_name)\n",
|
|
" full_path = os.path.join(file_path, file_name)\n",
|
|
" if os.path.exists(link_path):\n",
|
|
" print( file_name + \" found in Google Drive\")\n",
|
|
" if not os.path.exists(full_path):\n",
|
|
" print( \" creating symlink...\")\n",
|
|
" os.symlink(link_path, full_path)\n",
|
|
" else:\n",
|
|
" print( \" symlink already exists\")\n",
|
|
" elif not os.path.exists(full_path):\n",
|
|
" print( \"Downloading \" + file_name + \"...\", end=\"\" )\n",
|
|
" token = None\n",
|
|
" if \"huggingface.co\" in file_url:\n",
|
|
" token = HTTPBasicAuth('token', HF_TOKEN)\n",
|
|
" try:\n",
|
|
" with requests.get(file_url, auth = token, stream=True) as r:\n",
|
|
" starttime = time.time()\n",
|
|
" r.raise_for_status()\n",
|
|
" with open(full_path, 'wb') as f:\n",
|
|
" for chunk in r.iter_content(chunk_size=8192):\n",
|
|
" f.write(chunk)\n",
|
|
" if ((time.time() - starttime) % 60.0) > 2 :\n",
|
|
" starttime = time.time()\n",
|
|
" print( \".\", end=\"\" )\n",
|
|
" print( \"done\" )\n",
|
|
" print( \" \" + file_name + \" downloaded to \\'\" + file_path + \"\\'\" )\n",
|
|
" if SAVE_MODELS and os.path.exists(MODEL_DIR):\n",
|
|
" shutil.copy2(full_path,MODEL_DIR)\n",
|
|
" print( \" Saved \" + file_name + \" to \" + MODEL_DIR)\n",
|
|
" except:\n",
|
|
" print( \"Failed to download \" + file_name + \".\" )\n",
|
|
" return\n",
|
|
" else:\n",
|
|
" print( full_path + \" already exists.\" )\n",
|
|
" existing_models.append(file_name)\n",
|
|
"\n",
|
|
"# download models in list\n",
|
|
"for model in download_list:\n",
|
|
" model_name = models[model]['model_name']\n",
|
|
" file_info = models[model]['files']\n",
|
|
" for file in file_info:\n",
|
|
" file_name = file_info[file]['file_name']\n",
|
|
" file_url = file_info[file]['download_link']\n",
|
|
" if 'save_location' in file_info[file]:\n",
|
|
" file_path = file_info[file]['save_location']\n",
|
|
" else:\n",
|
|
" file_path = models[model]['save_location']\n",
|
|
" download_file(file_name, file_path, file_url)\n",
|
|
"\n",
|
|
"# add custom models not in list\n",
|
|
"CUSTOM_MODEL_DIR = os.path.join(PATH_TO_REPO, 'models/custom')\n",
|
|
"if os.path.exists(MODEL_DIR):\n",
|
|
" custom_models = os.listdir(MODEL_DIR)\n",
|
|
" custom_models = [m for m in custom_models if os.path.isfile(MODEL_DIR + '/' + m)]\n",
|
|
" os.makedirs(CUSTOM_MODEL_DIR, exist_ok=True)\n",
|
|
" print( \"Custom model(s) found: \" )\n",
|
|
" for m in custom_models:\n",
|
|
" if m in existing_models:\n",
|
|
" continue\n",
|
|
" full_path = os.path.join(CUSTOM_MODEL_DIR, m)\n",
|
|
" if not os.path.exists(full_path):\n",
|
|
" print( \" \" + m )\n",
|
|
" os.symlink(os.path.join(MODEL_DIR , m), full_path)\n",
|
|
"\n",
|
|
"# get custom config file if it exists\n",
|
|
"if CONFIG_DIR != \"\":\n",
|
|
" CONFIG_FILE = os.path.join('/content/drive/MyDrive', CONFIG_DIR, 'userconfig_streamlit.yaml')\n",
|
|
" config_location = os.path.join(PATH_TO_REPO, 'configs/webui/userconfig_streamlit.yaml')\n",
|
|
" if os.path.exists(CONFIG_FILE) and not os.path.exists(config_location):\n",
|
|
" os.symlink(CONFIG_DIR, config_location)\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "pjIjiCuJysJI"
|
|
},
|
|
"source": [
|
|
"# Launch the web ui server\n",
|
|
"### (optional) JS to prevent idle timeout:\n",
|
|
"Press 'F12' OR ('CTRL' + 'SHIFT' + 'I') OR right click on this website -> inspect. Then click on the console tab and paste in the following code.\n",
|
|
"```js,\n",
|
|
"function ClickConnect(){\n",
|
|
"console.log(\"Working\");\n",
|
|
"document.querySelector(\"colab-toolbar-button#connect\").click()\n",
|
|
"}\n",
|
|
"setInterval(ClickConnect,60000)\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "-WknaU7uu_q6"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Press play on the music player to keep the tab alive (Uses only 13MB of data)\n",
|
|
"%%html\n",
|
|
"<b>Press play on the music player to keep the tab alive, then start your generation below (Uses only 13MB of data)</b><br/>\n",
|
|
"<audio src=\"https://henk.tech/colabkobold/silence.m4a\" controls>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "5whXm2nfSZ39"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Run localtunnel and start Streamlit server. ('Ctrl' + 'left click') on link in the 'link.txt' file. (/content/link.txt)\n",
|
|
"!npx localtunnel --port 8501 &>/content/link.txt &\n",
|
|
"!streamlit run scripts/webui_streamlit.py --theme.base dark --server.headless true 2>&1 | tee -a /content/log.txt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "QhazvrFG97zX"
|
|
},
|
|
"source": [
|
|
"Run Streamlit through cloudflare."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "jjjjjjjjjjjjjj"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#@title Run Streamlit through cloudflare.\n",
|
|
"!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n",
|
|
"!dpkg -i cloudflared-linux-amd64.deb\n",
|
|
"\n",
|
|
"import subprocess\n",
|
|
"import threading\n",
|
|
"import time\n",
|
|
"import socket\n",
|
|
"import urllib.request\n",
|
|
"\n",
|
|
"def iframe_thread(port):\n",
|
|
" while True:\n",
|
|
" time.sleep(0.5)\n",
|
|
" sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
|
|
" result = sock.connect_ex(('127.0.0.1', port))\n",
|
|
" if result == 0:\n",
|
|
" break\n",
|
|
" sock.close()\n",
|
|
"\n",
|
|
" p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
|
|
" for line in p.stderr:\n",
|
|
" l = line.decode()\n",
|
|
" if \"trycloudflare.com \" in l:\n",
|
|
" print(\"This is the URL to access Sygil WebUI:\", l[l.find(\"http\"):], end='')\n",
|
|
"\n",
|
|
"\n",
|
|
"threading.Thread(target=iframe_thread, daemon=True, args=(8501,)).start()\n",
|
|
"\n",
|
|
"!streamlit run scripts/webui_streamlit.py --theme.base dark --server.headless true"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"accelerator": "GPU",
|
|
"colab": {
|
|
"collapsed_sections": [
|
|
"5-Bx4AsEoPU-",
|
|
"xMWVQOg0G1Pj"
|
|
],
|
|
"private_outputs": true,
|
|
"provenance": []
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"name": "python"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|