Merge branch 'dev' into dependabot/pip/dev/fairscale-0.4.12

.idea/.gitignore

@@ -1,11 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml
-
-*.pyc
-.idea

.streamlit/config.toml

@@ -34,7 +34,6 @@ maxMessageSize = 200
 enableWebsocketCompression = false
 
 [browser]
-serverAddress = "localhost"
 gatherUsageStats = false
 serverPort = 8501
 

README.md

@@ -1,41 +1,45 @@
 # <center>Web-based UI for Stable Diffusion</center>
 
-## Created by [sd-webui](https://github.com/sd-webui)
+## Created by [Sygil.Dev](https://github.com/sygil-dev)
 
-## [Visit sd-webui's Discord Server](https://discord.gg/gyXNe4NySY) [![Discord Server](https://user-images.githubusercontent.com/5977640/190528254-9b5b4423-47ee-4f24-b4f9-fd13fba37518.png)](https://discord.gg/gyXNe4NySY)
+## [Join us at Sygil.Dev's Discord Server](https://discord.gg/gyXNe4NySY) [![Discord Server](https://user-images.githubusercontent.com/5977640/190528254-9b5b4423-47ee-4f24-b4f9-fd13fba37518.png)](https://discord.gg/gyXNe4NySY)
 
 ## Installation instructions for:
-- **[Windows](https://sd-webui.github.io/stable-diffusion-webui/docs/1.windows-installation.html)** 
+
-- **[Linux](https://sd-webui.github.io/stable-diffusion-webui/docs/2.linux-installation.html)**
+- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/1.windows-installation.html)** 
+- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/2.linux-installation.html)**
 
 ### Want to ask a question or request a feature?
 
-Come to our [Discord Server](https://discord.gg/gyXNe4NySY) or use [Discussions](https://github.com/sd-webui/stable-diffusion-webui/discussions).
+Come to our [Discord Server](https://discord.gg/gyXNe4NySY) or use [Discussions](https://github.com/sygil-dev/sygil-webui/discussions).
 
 ## Documentation
 
-[Documentation is located here](https://sd-webui.github.io/stable-diffusion-webui/)
+[Documentation is located here](https://sygil-dev.github.io/sygil-webui/)
 
 ## Want to contribute?
 
 Check the [Contribution Guide](CONTRIBUTING.md)
 
-[sd-webui](https://github.com/sd-webui) is:
+[Sygil-Dev](https://github.com/Sygil-Dev) main devs:
+
 * ![hlky's avatar](https://avatars.githubusercontent.com/u/106811348?s=40&v=4) [hlky](https://github.com/hlky)
 * ![ZeroCool940711's avatar](https://avatars.githubusercontent.com/u/5977640?s=40&v=4)[ZeroCool940711](https://github.com/ZeroCool940711)
 * ![codedealer's avatar](https://avatars.githubusercontent.com/u/4258136?s=40&v=4)[codedealer](https://github.com/codedealer)
 
 ### Project Features:
 
-* Two great Web UI's to choose from: Streamlit or Gradio
-* No more manually typing parameters, now all you have to do is write your prompt and adjust sliders
 * Built-in image enhancers and upscalers, including GFPGAN and realESRGAN
+
+* Generator Preview: See your image as its being made
+
 * Run additional upscaling models on CPU to save VRAM
-* 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
+
-* Advanced img2img editor with Mask and crop capabilities
+* Textual inversion: [Reaserch Paper](https://textual-inversion.github.io/) 
-* Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)
+
-* More diffusion samplers 🔥🔥: A great collection of samplers to use, including:
+* K-Diffusion Samplers: A great collection of samplers to use, including:
-  - `k_euler` (Default)
+  
+  - `k_euler`
   - `k_lms`
   - `k_euler_a`
   - `k_dpm_2`
@@ -44,57 +48,78 @@ Check the [Contribution Guide](CONTRIBUTING.md)
   - `PLMS`
   - `DDIM`
 
-* Loopback ➿: Automatically feed the last generated sample back into img2img
+* Loopback: Automatically feed the last generated sample back into img2img
-* Prompt Weighting 🏋️: Adjust the strength of different terms in your prompt
+
-* Selectable GPU usage with `--gpu <id>`
+* Prompt Weighting & Negative Prompts: Gain more control over your creations
-* Memory Monitoring 🔥: Shows VRAM usage and generation time after outputting
+
-* Word Seeds 🔥: Use words instead of seed numbers
+* Selectable GPU usage from Settings tab
-* CFG: Classifier free guidance scale, a feature for fine-tuning your output
+
-* Automatic Launcher: Activate conda and run Stable Diffusion with a single command
+* Word Seeds: Use words instead of seed numbers
-* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB
+
+* Automated Launcher: Activate conda and run Stable Diffusion with a single command
+
+* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB (with *optimized* mode enabled in Settings)
+
 * Prompt validation: If your prompt is too long, you will get a warning in the text output field
-* Copy-paste generation parameters: A text output provides generation parameters in an easy to copy-paste form for easy sharing.
+
-* 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`.
+* Sequential 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`.
+
 * Prompt matrix: Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.
-* 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.
 
+* [Gradio] Advanced img2img editor with Mask and crop capabilities
 
-# Stable Diffusion Web UI
+* [Gradio] Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)
-A fully-integrated and easy way to work with Stable Diffusion right from a browser window.
+
+# SD WebUI
+
+An easy way to work with Stable Diffusion right from your browser.
 
 ## Streamlit
 
 ![](images/streamlit/streamlit-t2i.png)
 
 **Features:**
-- Clean UI with an easy to use design, with support for widescreen displays.
+
-- Dynamic live preview of your generations
+- Clean UI with an easy to use design, with support for widescreen displays
-- Easily customizable presets right from the WebUI (Coming Soon!)
+- *Dynamic live preview* of your generations
-- An integrated gallery to show the generations for a prompt or session (Coming soon!)
+- Easily customizable defaults, right from the WebUI's Settings tab
-- Better optimization VRAM usage optimization, less errors for bigger generations.
+- An integrated gallery to show the generations for a prompt
-- Text2Video - Generate video clips from text prompts right from the WEb UI (WIP)
+- *Optimized VRAM* usage for bigger generations or usage on lower end GPUs
-- Concepts Library - Run custom embeddings others have made via textual inversion.
+- *Text to Video:* Generate video clips from text prompts right from the WebUI (WIP)
-- Actively being developed with new features being added and planned - Stay Tuned!
+- Image to Text: Use [CLIP Interrogator](https://github.com/pharmapsychotic/clip-interrogator) to interrogate an image and get a prompt that you can use to generate a similar image using Stable Diffusion.
-- Streamlit is now the new primary UI for the project moving forward.
+- *Concepts Library:* Run custom embeddings others have made via textual inversion.
-- *Currently in active development and still missing some of the features present in the Gradio Interface.*
+- Textual Inversion training: Train your own embeddings on any photo you want and use it on your prompt.
+- **Currently in development: [Stable Horde](https://stablehorde.net/) integration; ImgLab, batch inputs, & mask editor from Gradio
+
+**Prompt Weights & Negative Prompts:**
+
+To give a token (tag recognized by the AI) a specific or increased weight (emphasis), add `:0.##` to the prompt, where `0.##` is a decimal that will specify the weight of all tokens before the colon.
+Ex: `cat:0.30, dog:0.70` or `guy riding a bicycle :0.7, incoming car :0.30`
+
+Negative prompts can be added by using  `###` , after which any tokens will be seen as negative. 
+Ex: `cat playing with string ### yarn` will negate `yarn` from the generated image. 
+
+Negatives are a very powerful tool to get rid of contextually similar or related topics, but **be careful when adding them since the AI might see connections you can't**, and end up outputting gibberish
+
+**Tip:* Try using the same seed with different prompt configurations or weight values see how the AI understands them, it can lead to prompts that are more well-tuned and less prone to error.
 
 Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.
 
-
+## Gradio [Legacy]
-## Gradio
 
 ![](images/gradio/gradio-t2i.png)
 
 **Features:**
-- Older UI design that is fully functional and feature complete.
+
+- Older UI that is functional and feature complete.
 - Has access to all upscaling models, including LSDR.
 - Dynamic prompt entry automatically changes your generation settings based on `--params` in a prompt.
 - Includes quick and easy ways to send generations to Image2Image or the Image Lab for upscaling.
-- *Note, the Gradio interface is no longer being actively developed and is only receiving bug fixes.*
+
+**Note: the Gradio interface is no longer being actively developed by Sygil.Dev and is only receiving bug fixes.**
 
 Please see the [Gradio Documentation](docs/5.gradio-interface.md) to learn more.
 
-
 ## Image Upscalers
 
 ---
@@ -106,8 +131,8 @@ Please see the [Gradio Documentation](docs/5.gradio-interface.md) to learn more.
 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.
 
 If you want to use GFPGAN to improve generated faces, you need to install it separately.
-Download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and put it
+Download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it
-into the `/stable-diffusion-webui/models/gfpgan` directory. 
+into the `/sygil-webui/models/gfpgan` directory. 
 
 ### RealESRGAN
 
@@ -117,20 +142,24 @@ Lets you double the resolution of generated images. There is a checkbox in every
 There is also a separate tab for using RealESRGAN on any picture.
 
 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).
-Put them into the `stable-diffusion-webui/models/realesrgan` directory. 
+Put them into the `sygil-webui/models/realesrgan` directory. 
 
-### GoBig, LSDR, and GoLatent *(Currently Gradio Only)*
+### LSDR
+
+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/`
+
+### GoBig, and GoLatent *(Currently on the Gradio version Only)*
 
 More powerful upscalers that uses a seperate Latent Diffusion model to more cleanly upscale images.
 
-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 stable-diffusion-webui/models/ldsr/
+Please see the [Image Enhancers Documentation](docs/6.image_enhancers.md) to learn more.
-
-Please see the [Image Enhancers Documentation](docs/5.image_enhancers.md) to learn more.
 
 -----
 
-### *Original Information From The Stable Diffusion Repo*
+### *Original Information From The Stable Diffusion Repo:*
+
 # Stable Diffusion
+
 *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:*
 
 [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
@@ -144,7 +173,6 @@ Please see the [Image Enhancers Documentation](docs/5.image_enhancers.md) to lea
 
 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/).
 
-
 [Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
 model.
 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. 
@@ -172,7 +200,6 @@ Thanks for open-sourcing!
 
 - The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). 
 
-
 ## BibTeX
 
 ```
@@ -184,7 +211,4 @@ Thanks for open-sourcing!
       archivePrefix={arXiv},
       primaryClass={cs.CV}
 }
-
 ```
-
-

Web_based_UI_for_Stable_Diffusion_colab.ipynb

@@ -0,0 +1,554 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "private_outputs": true,
+      "provenance": [],
+      "collapsed_sections": [
+        "5-Bx4AsEoPU-",
+        "xMWVQOg0G1Pj"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Sygil-Dev/sygil-webui/blob/dev/Web_based_UI_for_Stable_Diffusion_colab.ipynb)"
+      ],
+      "metadata": {
+        "id": "S5RoIM-5IPZJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# README"
+      ],
+      "metadata": {
+        "id": "5-Bx4AsEoPU-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###<center>Web-based UI for Stable Diffusion</center>\n",
+        "\n",
+        "## Created by [Sygil-Dev](https://github.com/Sygil-Dev)\n",
+        "\n",
+        "## [Visit Sygil-Dev's Discord Server](https://discord.gg/gyXNe4NySY) [![Discord Server](https://user-images.githubusercontent.com/5977640/190528254-9b5b4423-47ee-4f24-b4f9-fd13fba37518.png)](https://discord.gg/gyXNe4NySY)\n",
+        "\n",
+        "## Installation instructions for:\n",
+        "\n",
+        "- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/1.windows-installation.html)** \n",
+        "- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/2.linux-installation.html)**\n",
+        "\n",
+        "### Want to ask a question or request a feature?\n",
+        "\n",
+        "Come to our [Discord Server](https://discord.gg/gyXNe4NySY) or use [Discussions](https://github.com/Sygil-Dev/sygil-webui/discussions).\n",
+        "\n",
+        "## Documentation\n",
+        "\n",
+        "[Documentation is located here](https://sygil-dev.github.io/sygil-webui/)\n",
+        "\n",
+        "## Want to contribute?\n",
+        "\n",
+        "Check the [Contribution Guide](CONTRIBUTING.md)\n",
+        "\n",
+        "[Sygil-Dev](https://github.com/Sygil-Dev) main devs:\n",
+        "\n",
+        "* ![hlky's avatar](https://avatars.githubusercontent.com/u/106811348?s=40&v=4) [hlky](https://github.com/hlky)\n",
+        "* ![ZeroCool940711's avatar](https://avatars.githubusercontent.com/u/5977640?s=40&v=4)[ZeroCool940711](https://github.com/ZeroCool940711)\n",
+        "* ![codedealer's avatar](https://avatars.githubusercontent.com/u/4258136?s=40&v=4)[codedealer](https://github.com/codedealer)\n",
+        "\n",
+        "### Project Features:\n",
+        "\n",
+        "* Two great Web UI's to choose from: Streamlit or Gradio\n",
+        "\n",
+        "* No more manually typing parameters, now all you have to do is write your prompt and adjust sliders\n",
+        "\n",
+        "* Built-in image enhancers and upscalers, including GFPGAN and realESRGAN\n",
+        "\n",
+        "* Run additional upscaling models on CPU to save VRAM\n",
+        "\n",
+        "* 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",
+        "\n",
+        "* Advanced img2img editor with Mask and crop capabilities\n",
+        "\n",
+        "* Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)\n",
+        "\n",
+        "* More diffusion samplers 🔥🔥: A great collection of samplers to use, including:\n",
+        "  \n",
+        "  - `k_euler` (Default)\n",
+        "  - `k_lms`\n",
+        "  - `k_euler_a`\n",
+        "  - `k_dpm_2`\n",
+        "  - `k_dpm_2_a`\n",
+        "  - `k_heun`\n",
+        "  - `PLMS`\n",
+        "  - `DDIM`\n",
+        "\n",
+        "* Loopback ➿: Automatically feed the last generated sample back into img2img\n",
+        "\n",
+        "* Prompt Weighting 🏋️: Adjust the strength of different terms in your prompt\n",
+        "\n",
+        "* Selectable GPU usage with `--gpu <id>`\n",
+        "\n",
+        "* Memory Monitoring 🔥: Shows VRAM usage and generation time after outputting\n",
+        "\n",
+        "* Word Seeds 🔥: Use words instead of seed numbers\n",
+        "\n",
+        "* CFG: Classifier free guidance scale, a feature for fine-tuning your output\n",
+        "\n",
+        "* Automatic Launcher: Activate conda and run Stable Diffusion with a single command\n",
+        "\n",
+        "* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB\n",
+        "\n",
+        "* Prompt validation: If your prompt is too long, you will get a warning in the text output field\n",
+        "\n",
+        "* Copy-paste generation parameters: A text output provides generation parameters in an easy to copy-paste form for easy sharing.\n",
+        "\n",
+        "* 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",
+        "\n",
+        "* Prompt matrix: Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.\n",
+        "\n",
+        "* 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",
+        "\n",
+        "# Stable Diffusion Web UI\n",
+        "\n",
+        "A fully-integrated and easy way to work with Stable Diffusion right from a browser window.\n",
+        "\n",
+        "## Streamlit\n",
+        "\n",
+        "![](https://github.com/aedhcarrick/sygil-webui/blob/patch-2/images/streamlit/streamlit-t2i.png?raw=1)\n",
+        "\n",
+        "**Features:**\n",
+        "\n",
+        "- Clean UI with an easy to use design, with support for widescreen displays.\n",
+        "- Dynamic live preview of your generations\n",
+        "- Easily customizable presets right from the WebUI (Coming Soon!)\n",
+        "- An integrated gallery to show the generations for a prompt or session (Coming soon!)\n",
+        "- Better optimization VRAM usage optimization, less errors for bigger generations.\n",
+        "- Text2Video - Generate video clips from text prompts right from the WEb UI (WIP)\n",
+        "- Concepts Library - Run custom embeddings others have made via textual inversion.\n",
+        "- Actively being developed with new features being added and planned - Stay Tuned!\n",
+        "- Streamlit is now the new primary UI for the project moving forward.\n",
+        "- *Currently in active development and still missing some of the features present in the Gradio Interface.*\n",
+        "\n",
+        "Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.\n",
+        "\n",
+        "## Gradio\n",
+        "\n",
+        "![](https://github.com/aedhcarrick/sygil-webui/blob/patch-2/images/gradio/gradio-t2i.png?raw=1)\n",
+        "\n",
+        "**Features:**\n",
+        "\n",
+        "- Older UI design that is fully functional and feature complete.\n",
+        "- Has access to all upscaling models, including LSDR.\n",
+        "- Dynamic prompt entry automatically changes your generation settings based on `--params` in a prompt.\n",
+        "- Includes quick and easy ways to send generations to Image2Image or the Image Lab for upscaling.\n",
+        "- *Note, the Gradio interface is no longer being actively developed and is only receiving bug fixes.*\n",
+        "\n",
+        "Please see the [Gradio Documentation](docs/5.gradio-interface.md) to learn more.\n",
+        "\n",
+        "## Image Upscalers\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### GFPGAN\n",
+        "\n",
+        "![](https://github.com/aedhcarrick/sygil-webui/blob/patch-2/images/GFPGAN.png?raw=1)\n",
+        "\n",
+        "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",
+        "\n",
+        "If you want to use GFPGAN to improve generated faces, you need to install it separately.\n",
+        "Download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it\n",
+        "into the `/sygil-webui/models/gfpgan` directory. \n",
+        "\n",
+        "### RealESRGAN\n",
+        "\n",
+        "![](https://github.com/aedhcarrick/sygil-webui/blob/patch-2/images/RealESRGAN.png?raw=1)\n",
+        "\n",
+        "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",
+        "There is also a separate tab for using RealESRGAN on any picture.\n",
+        "\n",
+        "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",
+        "Put them into the `sygil-webui/models/realesrgan` directory. \n",
+        "\n",
+        "\n",
+        "\n",
+        "### LSDR\n",
+        "\n",
+        "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",
+        "\n",
+        "### GoBig, and GoLatent *(Currently on the Gradio version Only)*\n",
+        "\n",
+        "More powerful upscalers that uses a seperate Latent Diffusion model to more cleanly upscale images.\n",
+        "\n",
+        "\n",
+        "\n",
+        "Please see the [Image Enhancers Documentation](docs/6.image_enhancers.md) to learn more.\n",
+        "\n",
+        "-----\n",
+        "\n",
+        "### *Original Information From The Stable Diffusion Repo*\n",
+        "\n",
+        "# Stable Diffusion\n",
+        "\n",
+        "*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",
+        "\n",
+        "[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>\n",
+        "[Robin Rombach](https://github.com/rromb)\\*,\n",
+        "[Andreas Blattmann](https://github.com/ablattmann)\\*,\n",
+        "[Dominik Lorenz](https://github.com/qp-qp)\\,\n",
+        "[Patrick Esser](https://github.com/pesser),\n",
+        "[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>\n",
+        "\n",
+        "**CVPR '22 Oral**\n",
+        "\n",
+        "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",
+        "\n",
+        "[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion\n",
+        "model.\n",
+        "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",
+        "Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), \n",
+        "this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.\n",
+        "With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.\n",
+        "See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).\n",
+        "\n",
+        "## Stable Diffusion v1\n",
+        "\n",
+        "Stable Diffusion v1 refers to a specific configuration of the model\n",
+        "architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet\n",
+        "and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and \n",
+        "then finetuned on 512x512 images.\n",
+        "\n",
+        "*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present\n",
+        "in its training data. \n",
+        "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",
+        "\n",
+        "## Comments\n",
+        "\n",
+        "- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)\n",
+        "  and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). \n",
+        "  Thanks for open-sourcing!\n",
+        "\n",
+        "- 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",
+        "\n",
+        "## BibTeX\n",
+        "\n",
+        "```\n",
+        "@misc{rombach2021highresolution,\n",
+        "      title={High-Resolution Image Synthesis with Latent Diffusion Models}, \n",
+        "      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},\n",
+        "      year={2021},\n",
+        "      eprint={2112.10752},\n",
+        "      archivePrefix={arXiv},\n",
+        "      primaryClass={cs.CV}\n",
+        "}\n",
+        "\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "z4kQYMPQn4d-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Config options for Colab instance\n",
+        "> Before running, make sure GPU backend is enabled. (Unless you plan on generating with Stable Horde)\n",
+        ">> Runtime -> Change runtime type -> Hardware Accelerator -> GPU (Make sure to  save)"
+      ],
+      "metadata": {
+        "id": "iegma7yteERV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown WebUI repo (and branch)\n",
+        "repo_name = \"Sygil-Dev/sygil-webui\" #@param {type:\"string\"}\n",
+        "repo_branch = \"dev\" #@param {type:\"string\"}\n",
+        "\n",
+        "#@markdown Mount Google Drive\n",
+        "mount_google_drive = True #@param {type:\"boolean\"}\n",
+        "save_outputs_to_drive = True #@param {type:\"boolean\"}\n",
+        "#@markdown Folder in Google Drive to search for custom models\n",
+        "MODEL_DIR = \"\" #@param {type:\"string\"}\n",
+        "\n",
+        "#@markdown Enter auth token from Huggingface.co\n",
+        "#@markdown >(required for downloading stable diffusion model.)\n",
+        "HF_TOKEN = \"\" #@param {type:\"string\"}\n",
+        "\n",
+        "#@markdown Select which models to prefetch\n",
+        "STABLE_DIFFUSION = True #@param {type:\"boolean\"}\n",
+        "WAIFU_DIFFUSION = False #@param {type:\"boolean\"}\n",
+        "TRINART_SD = False #@param {type:\"boolean\"}\n",
+        "SD_WD_LD_TRINART_MERGED = False #@param {type:\"boolean\"}\n",
+        "GFPGAN = True #@param {type:\"boolean\"}\n",
+        "REALESRGAN = True #@param {type:\"boolean\"}\n",
+        "LDSR = True #@param {type:\"boolean\"}\n",
+        "BLIP_MODEL = False #@param {type:\"boolean\"}\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "OXn96M9deVtF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Setup\n",
+        "\n",
+        ">Runtime will crash when installing conda. This is normal as we are forcing a restart of the runtime from code.\n",
+        "\n",
+        ">Just hit \"Run All\" again. 😑"
+      ],
+      "metadata": {
+        "id": "IZjJSr-WPNxB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eq0-E5mjSpmP"
+      },
+      "source": [
+        "#@title Make sure we have access to GPU backend\n",
+        "!nvidia-smi -L"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Install miniConda (mamba)\n",
+        "!pip install condacolab\n",
+        "import condacolab\n",
+        "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",
+        "\n",
+        "import condacolab\n",
+        "condacolab.check()\n",
+        "# The runtime will crash here!!! Don't panic! We planned for this remember?"
+      ],
+      "metadata": {
+        "id": "cDu33xkdJ5mD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Clone webUI repo and download font\n",
+        "import os\n",
+        "REPO_URL = os.path.join('https://github.com', repo_name)\n",
+        "PATH_TO_REPO = os.path.join('/content', repo_name.split('/')[1])\n",
+        "!git clone {REPO_URL}\n",
+        "%cd {PATH_TO_REPO}\n",
+        "!git checkout {repo_branch}\n",
+        "!git pull\n",
+        "!wget -O arial.ttf https://github.com/matomo-org/travis-scripts/blob/master/fonts/Arial.ttf?raw=true"
+      ],
+      "metadata": {
+        "id": "pZHGf03Vp305"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Install dependencies\n",
+        "!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",
+        "!python --version\n",
+        "!pip install -r requirements.txt"
+      ],
+      "metadata": {
+        "id": "dmN2igp5Yk3z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Install localtunnel to openGoogle's ports\n",
+        "!npm install localtunnel"
+      ],
+      "metadata": {
+        "id": "Nxaxfgo_F8Am"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Mount Google Drive (if selected)\n",
+        "if mount_google_drive:\n",
+        "    # Mount google drive to store outputs.\n",
+        "    from google.colab import drive\n",
+        "    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"
+      ],
+      "metadata": {
+        "id": "pcSWo9Zkzbsf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "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",
+        "\n",
+        "# copy script from model manager\n",
+        "import requests, time\n",
+        "from requests.auth import HTTPBasicAuth\n",
+        "\n",
+        "def download_file(file_name, file_path, file_url):\n",
+        "    os.makedirs(file_path, exist_ok=True)\n",
+        "    if os.path.exists(os.path.join(MODEL_DIR , file_name)):\n",
+        "        print( file_name + \"found in Google Drive\")\n",
+        "        print( \"Creating symlink...\")\n",
+        "        os.symlink(os.path.join(MODEL_DIR , file_name), os.path.join(file_path, file_name))\n",
+        "    elif not os.path.exists(os.path.join(file_path , file_name)):\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(os.path.join(file_path, file_name), '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",
+        "        except:\n",
+        "            print( \"Failed to download \" + file_name + \".\" )\n",
+        "    else:\n",
+        "        print( file_name + \" already exists.\" )\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 MODEL_DIR != \"\":\n",
+        "    MODEL_DIR = os.path.join('/content/drive/MyDrive', MODEL_DIR)\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",
+        "            print( \"  \" + m )\n",
+        "            os.symlink(os.path.join(MODEL_DIR , m), os.path.join(CUSTOM_MODEL_DIR, m))\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "vMdmh81J70yA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "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",
+        "```"
+      ],
+      "metadata": {
+        "id": "pjIjiCuJysJI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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>"
+      ],
+      "metadata": {
+        "id": "-WknaU7uu_q6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "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"
+      ],
+      "metadata": {
+        "id": "5whXm2nfSZ39"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

configs/webui/webui.yaml

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

configs/webui/webui_streamlit.yaml

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -19,14 +19,16 @@
 # You may add overrides in a file named "userconfig_streamlit.yaml" in this folder, which can contain any subset
 # of the properties below.
 general:
+    version: 1.24.6
     streamlit_telemetry: False
     default_theme: dark
-    huggingface_token: ""
+    huggingface_token: ''
+    stable_horde_api: '0000000000'
     gpu: 0
     outdir: outputs
-    default_model: "Stable Diffusion v1.4" 
+    default_model: "Stable Diffusion v1.5"
     default_model_config: "configs/stable-diffusion/v1-inference.yaml"
-    default_model_path: "models/ldm/stable-diffusion-v1/model.ckpt"
+    default_model_path: "models/ldm/stable-diffusion-v1/Stable Diffusion v1.5.ckpt"
     use_sd_concepts_library: True
     sd_concepts_library_folder: "models/custom/sd-concepts-library"
     GFPGAN_dir: "./models/gfpgan"
@@ -38,17 +40,19 @@ general:
     upscaling_method: "RealESRGAN"
     outdir_txt2img: outputs/txt2img
     outdir_img2img: outputs/img2img
+    outdir_img2txt: outputs/img2txt
     gfpgan_cpu: False
     esrgan_cpu: False
     extra_models_cpu: False
     extra_models_gpu: False
     gfpgan_gpu: 0
     esrgan_gpu: 0
+    keep_all_models_loaded: False
     save_metadata: True
     save_format: "png"
     skip_grid: False
     skip_save: False
-    grid_format: "jpg:95"
+    grid_quality: 95
     n_rows: -1
     no_verify_input: False
     no_half: False
@@ -62,6 +66,13 @@ general:
     update_preview: True
     update_preview_frequency: 10
 
+admin:
+    hide_server_setting: False
+    hide_browser_setting: False
+
+debug:
+    enable_hydralit: False
+
 txt2img:
     prompt:
     width:
@@ -79,7 +90,6 @@ txt2img:
     cfg_scale:
         value:      7.5
         min_value:  1.0
-        max_value:  30.0
         step:       0.5
 
     seed: ""
@@ -126,8 +136,8 @@ txt2img:
     write_info_files: True
 
 txt2vid:
-    default_model: "CompVis/stable-diffusion-v1-4"
+    default_model: "runwayml/stable-diffusion-v1-5"
-    custom_models_list: ["CompVis/stable-diffusion-v1-4"]
+    custom_models_list: ["runwayml/stable-diffusion-v1-5", "CompVis/stable-diffusion-v1-4", "hakurei/waifu-diffusion"]
     prompt:
     width:
         value:      512
@@ -144,7 +154,6 @@ txt2vid:
     cfg_scale:
         value:      7.5
         min_value:  1.0
-        max_value:  30.0
         step:       0.5
 
     batch_count:
@@ -175,9 +184,11 @@ txt2vid:
     normalize_prompt_weights: True
     save_individual_images: True
     save_video: True
+    save_video_on_stop: False
     group_by_prompt: True
     write_info_files: True
     do_loop: False
+    use_lerp_for_text: False
     save_as_jpg: False
     use_GFPGAN: False
     use_RealESRGAN: False
@@ -193,20 +204,20 @@ txt2vid:
 
     beta_start:
         value:      0.00085
-        min_value:  0.0001
+        min_value:  0.00010
-        max_value:  0.0300
+        max_value:  0.03000
-        step:       0.0001
+        step:       0.00010
         format:     "%.5f"
 
     beta_end:
-        value:      0.012
+        value:      0.01200
-        min_value:  0.0001
+        min_value:  0.00010
-        max_value:  0.0300
+        max_value:  0.03000
-        step:       0.0001
+        step:       0.00010
         format:     "%.5f"
 
     beta_scheduler_type: "scaled_linear"
-    max_frames: 100
+    max_duration_in_seconds: 30
 
     LDSR_config:
         sampling_steps: 50
@@ -224,7 +235,8 @@ img2img:
         step:       0.01
     # 0: Keep masked area
     # 1: Regenerate only masked area
-    mask_mode: 0
+    mask_mode: 1
+    noise_mode: "Matched Noise"
     mask_restore: False
     # 0: Just resize
     # 1: Crop and resize
@@ -248,7 +260,6 @@ img2img:
     cfg_scale:
         value:      7.5
         min_value:  1.0
-        max_value:  30.0
         step:       0.5
 
     batch_count:
@@ -271,9 +282,8 @@ img2img:
 
     find_noise_steps:
         value:      100
-        min_value:  0
+        min_value:  100
-        max_value:  500
+        step:       100
-        step:       10
 
     LDSR_config:
         sampling_steps: 50
@@ -300,7 +310,7 @@ img2img:
     write_info_files: True
 
 img2txt:
-    batch_size: 420
+    batch_size: 2000
     blip_image_eval_size: 512
     keep_all_models_loaded: False
 
@@ -311,7 +321,7 @@ gfpgan:
   strength: 100
 
 textual_inversion:
-    pretrained_model_name_or_path: "models/diffusers/stable-diffusion-v1-4"
+    pretrained_model_name_or_path: "models/diffusers/stable-diffusion-v1-5"
     tokenizer_name: "models/clip-vit-large-patch14"
 
 
@@ -321,12 +331,12 @@ daisi_app:
 model_manager:
     models:
         stable_diffusion:
-            model_name:     "Stable Diffusion v1.4"
+            model_name:     "Stable Diffusion v1.5"
             save_location:  "./models/ldm/stable-diffusion-v1"
             files:
                 model_ckpt:
-                    file_name:  "model.ckpt"
+                    file_name:  "Stable Diffusion v1.5.ckpt"
-                    download_link:  "https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media"
+                    download_link:  "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt"
 
         gfpgan:
             model_name:     "GFPGAN"
@@ -358,12 +368,12 @@ model_manager:
 
 
         waifu_diffusion:
-            model_name:     "Waifu Diffusion v1.2"
+            model_name:     "Waifu Diffusion v1.3"
             save_location:  "./models/custom"
             files:
                 waifu_diffusion:
-                    file_name: "waifu-diffusion.ckpt"
+                    file_name: "Waifu-Diffusion-v1-3 Full ema.ckpt"
-                    download_link:  "https://huggingface.co/crumb/pruned-waifu-diffusion/resolve/main/model-pruned.ckpt"
+                    download_link:  "https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-full.ckpt"
 
 
         trinart_stable_diffusion:
@@ -374,13 +384,21 @@ model_manager:
                     file_name: "trinart.ckpt"
                     download_link:  "https://huggingface.co/naclbit/trinart_stable_diffusion_v2/resolve/main/trinart2_step95000.ckpt"
 
+        sd_wd_ld_trinart_merged:
+            model_name:     "SD1.5-WD1.3-LD-Trinart-Merged"
+            save_location:  "./models/custom"
+            files:
+                sd_wd_ld_trinart_merged:
+                    file_name: "SD1.5-WD1.3-LD-Trinart-Merged.ckpt"
+                    download_link:  "https://huggingface.co/ZeroCool94/sd1.5-wd1.3-ld-trinart-merged/resolve/main/SD1.5-WD1.3-LD-Trinart-Merged.ckpt"
+
         stable_diffusion_concept_library:
             model_name:     "Stable Diffusion Concept Library"
             save_location:  "./models/custom/sd-concepts-library/"
             files:
                 concept_library:
                     file_name: ""
-                    download_link:  "https://github.com/sd-webui/sd-concepts-library"
+                    download_link:  "https://github.com/Sygil-Dev/sd-concepts-library"
 
         blip_model:
             model_name:     "Blip Model"
@@ -401,4 +419,3 @@ model_manager:
                 ldsr_model:
                     file_name:     "model.ckpt"
                     download_link:  "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
-    

daisi_app.py

@@ -5,11 +5,11 @@ print (os.getcwd)
 
 try:
 	with open("environment.yaml") as file_handle:
-		environment_data = yaml.load(file_handle, Loader=yaml.FullLoader)
+		environment_data = yaml.safe_load(file_handle, Loader=yaml.FullLoader)
 except FileNotFoundError:
 	try:
 		with open(os.path.join("..", "environment.yaml")) as file_handle:
-			environment_data = yaml.load(file_handle, Loader=yaml.FullLoader)
+			environment_data = yaml.safe_load(file_handle, Loader=yaml.FullLoader)
 	except:
 		pass
 

data/img2txt/subreddits.txt

@@ -0,0 +1,160 @@
+/r/ImaginaryAetherpunk
+/r/ImaginaryAgriculture
+/r/ImaginaryAirships
+/r/ImaginaryAliens
+/r/ImaginaryAngels
+/r/ImaginaryAnimals
+/r/ImaginaryArchers
+/r/ImaginaryArchitecture
+/r/ImaginaryArmor
+/r/ImaginaryArtisans
+/r/ImaginaryAssassins
+/r/ImaginaryAstronauts
+/r/ImaginaryAsylums
+/r/ImaginaryAutumnscapes
+/r/ImaginaryAviation
+/r/ImaginaryAzeroth
+/r/ImaginaryBattlefields
+/r/ImaginaryBeasts
+/r/ImaginaryBehemoths
+/r/ImaginaryBodyscapes
+/r/ImaginaryBooks
+/r/ImaginaryCanyons
+/r/ImaginaryCarnage
+/r/ImaginaryCastles
+/r/ImaginaryCaves
+/r/ImaginaryCentaurs
+/r/ImaginaryCharacters
+/r/ImaginaryCityscapes
+/r/ImaginaryClerics
+/r/ImaginaryCowboys
+/r/ImaginaryCrawlers
+/r/ImaginaryCultists
+/r/ImaginaryCybernetics
+/r/ImaginaryCyberpunk
+/r/ImaginaryDarkSouls
+/r/ImaginaryDemons
+/r/ImaginaryDerelicts
+/r/ImaginaryDeserts
+/r/ImaginaryDieselpunk
+/r/ImaginaryDinosaurs
+/r/ImaginaryDragons
+/r/ImaginaryDruids
+/r/ImaginaryDwarves
+/r/ImaginaryDwellings
+/r/ImaginaryElementals
+/r/ImaginaryElves
+/r/ImaginaryExplosions
+/r/ImaginaryFactories
+/r/ImaginaryFaeries
+/r/ImaginaryFallout
+/r/ImaginaryFamilies
+/r/ImaginaryFashion
+/r/ImaginaryFood
+/r/ImaginaryForests
+/r/ImaginaryFutureWar
+/r/ImaginaryFuturism
+/r/ImaginaryGardens
+/r/ImaginaryGatherings
+/r/ImaginaryGiants
+/r/ImaginaryGlaciers
+/r/ImaginaryGnomes
+/r/ImaginaryGoblins
+/r/ImaginaryHellscapes
+/r/ImaginaryHistory
+/r/ImaginaryHorrors
+/r/ImaginaryHumans
+/r/ImaginaryHybrids
+/r/ImaginaryIcons
+/r/ImaginaryImmortals
+/r/ImaginaryInteriors
+/r/ImaginaryIslands
+/r/ImaginaryJedi
+/r/ImaginaryKanto
+/r/ImaginaryKnights
+/r/ImaginaryLakes
+/r/ImaginaryLandscapes
+/r/ImaginaryLesbians
+/r/ImaginaryLeviathans
+/r/ImaginaryLovers
+/r/ImaginaryMarvel
+/r/ImaginaryMeIRL
+/r/ImaginaryMechs
+/r/ImaginaryMen
+/r/ImaginaryMerchants
+/r/ImaginaryMerfolk
+/r/ImaginaryMiddleEarth
+/r/ImaginaryMindscapes
+/r/ImaginaryMonsterBoys
+/r/ImaginaryMonsterGirls
+/r/ImaginaryMonsters
+/r/ImaginaryMonuments
+/r/ImaginaryMountains
+/r/ImaginaryMovies
+/r/ImaginaryMythology
+/r/ImaginaryNatives
+/r/ImaginaryNecronomicon
+/r/ImaginaryNightscapes
+/r/ImaginaryNinjas
+/r/ImaginaryNobles
+/r/ImaginaryNomads
+/r/ImaginaryOrcs
+/r/ImaginaryPathways
+/r/ImaginaryPirates
+/r/ImaginaryPolice
+/r/ImaginaryPolitics
+/r/ImaginaryPortals
+/r/ImaginaryPrisons
+/r/ImaginaryPropaganda
+/r/ImaginaryRivers
+/r/ImaginaryRobotics
+/r/ImaginaryRuins
+/r/ImaginaryScholars
+/r/ImaginaryScience
+/r/ImaginarySeascapes
+/r/ImaginarySkyscapes
+/r/ImaginarySlavery
+/r/ImaginarySoldiers
+/r/ImaginarySpirits
+/r/ImaginarySports
+/r/ImaginarySpringscapes
+/r/ImaginaryStarscapes
+/r/ImaginaryStarships
+/r/ImaginaryStatues
+/r/ImaginarySteampunk
+/r/ImaginarySummerscapes
+/r/ImaginarySwamps
+/r/ImaginaryTamriel
+/r/ImaginaryTaverns
+/r/ImaginaryTechnology
+/r/ImaginaryTemples
+/r/ImaginaryTowers
+/r/ImaginaryTrees
+/r/ImaginaryTrolls
+/r/ImaginaryUndead
+/r/ImaginaryUnicorns
+/r/ImaginaryVampires
+/r/ImaginaryVehicles
+/r/ImaginaryVessels
+/r/ImaginaryVikings
+/r/ImaginaryVillages
+/r/ImaginaryVolcanoes
+/r/ImaginaryWTF
+/r/ImaginaryWalls
+/r/ImaginaryWarhammer
+/r/ImaginaryWarriors
+/r/ImaginaryWarships
+/r/ImaginaryWastelands
+/r/ImaginaryWaterfalls
+/r/ImaginaryWaterscapes
+/r/ImaginaryWeaponry
+/r/ImaginaryWeather
+/r/ImaginaryWerewolves
+/r/ImaginaryWesteros
+/r/ImaginaryWildlands
+/r/ImaginaryWinterscapes
+/r/ImaginaryWitcher
+/r/ImaginaryWitches
+/r/ImaginaryWizards
+/r/ImaginaryWorldEaters
+/r/ImaginaryWorlds

data/img2txt/techniques.txt

@@ -0,0 +1,63 @@
+Fine Art
+Diagrammatic
+Geometric
+Architectural
+Analytic
+3D
+Anamorphic
+Pencil
+Color Pencil
+Charcoal
+Graphite
+Chalk
+Pen
+Ink
+Crayon
+Pastel
+Sand
+Beach Art
+Rangoli
+Mehndi
+Flower
+Food Art
+Tattoo
+Digital
+Pixel
+Embroidery 
+Line
+Pointillism
+Single Color
+Stippling
+Contour
+Hatching
+Scumbling
+Scribble
+Geometric Portait
+Triangulation
+Caricature
+Photorealism
+Photo realistic
+Doodling
+Wordtoons
+Cartoon
+Anime
+Manga
+Graffiti
+Typography
+Calligraphy
+Mosaic
+Figurative
+Anatomy
+Life
+Still life
+Portrait
+Landscape
+Perspective
+Funny
+Surreal
+Wall Mural
+Street
+Realistic
+Photo Realistic
+Hyper Realistic
+Doodle

data/tags/config.json

@@ -0,0 +1,26 @@
+{
+	"tagFile": "danbooru.csv",
+	"maxResults": 5,
+	"replaceUnderscores": true,
+	"escapeParentheses": true,
+	"colors": {
+		"danbooru": {
+			"0": ["lightblue", "dodgerblue"],
+			"1": ["indianred", "firebrick"],
+			"3": ["violet", "darkorchid"],
+			"4": ["lightgreen", "darkgreen"],
+			"5": ["orange", "darkorange"]
+		},
+		"e621": {
+			"-1": ["red", "maroon"],
+			"0": ["lightblue", "dodgerblue"],
+			"1": ["gold", "goldenrod"],
+			"3": ["violet", "darkorchid"],
+			"4": ["lightgreen", "darkgreen"],
+			"5": ["tomato", "darksalmon"],
+			"6": ["red", "maroon"],
+			"7": ["whitesmoke", "black"],
+			"8": ["seagreen", "darkseagreen"]
+		}
+	}
+}

db.json

@@ -0,0 +1,467 @@
+{
+  "stable_diffusion": {
+    "name": "stable_diffusion",
+    "type": "ckpt",
+    "description": "Generalist AI image generating model. The baseline for all finetuned models.",
+    "version": "1.5",
+    "style": "generalist",
+    "nsfw": false,
+    "download_all": true,
+    "requires": [
+      "clip-vit-large-patch14"
+    ],
+    "config": {
+      "files": [
+        {
+          "path": "models/ldm/stable-diffusion-v1/model_1_5.ckpt"
+        },
+        {
+          "path": "configs/stable-diffusion/v1-inference.yaml"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "model_1_5.ckpt",
+          "file_path": "models/ldm/stable-diffusion-v1",
+          "file_url": "https://{username}:{password}@huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt",
+          "hf_auth": true
+        }
+      ]
+    },
+    "available": false
+  },  
+  "stable_diffusion_1.4": {
+    "name": "stable_diffusion",
+    "type": "ckpt",
+    "description": "Generalist AI image generating model. The baseline for all finetuned models.",
+    "version": "1.4",
+    "style": "generalist",
+    "nsfw": false,
+    "download_all": true,
+    "requires": [
+      "clip-vit-large-patch14"
+    ],
+    "config": {
+      "files": [
+        {
+          "path": "models/ldm/stable-diffusion-v1/model.ckpt",
+          "md5sum": "c01059060130b8242849d86e97212c84"
+        },
+        {
+          "path": "configs/stable-diffusion/v1-inference.yaml"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "model.ckpt",
+          "file_path": "models/ldm/stable-diffusion-v1",
+          "file_url": "https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media"
+        }
+      ],
+      "alt_download": [
+        {
+          "file_name": "model.ckpt",
+          "file_path": "models/ldm/stable-diffusion-v1",
+          "file_url": "https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt",
+          "hf_auth": true
+        }
+      ]
+    },
+    "available": false
+  },
+  "waifu_diffusion": {
+    "name": "waifu_diffusion",
+    "type": "ckpt",
+    "description": "Anime styled generations.",
+    "version": "1.3",
+    "style": "anime",
+    "nsfw": false,
+    "download_all": true,
+    "requires": [
+      "clip-vit-large-patch14"
+    ],
+    "config": {
+      "files": [
+        {
+          "path": "models/custom/waifu-diffusion.ckpt",
+          "md5sum": "a2aa170e3f513b32a3fd8841656e0123"
+        },
+        {
+          "path": "configs/stable-diffusion/v1-inference.yaml"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "waifu-diffusion.ckpt",
+          "file_path": "models/custom",
+          "file_url": "https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-full.ckpt"
+        }
+      ]
+    },
+    "available": false
+  },
+  "Furry Epoch": {
+    "name": "Furry Epoch",
+    "type": "ckpt",
+    "description": "Furry styled generations.",
+    "version": "4",
+    "style": "furry",
+    "nsfw": false,
+    "download_all": false,
+    "requires": [
+      "clip-vit-large-patch14"
+    ],
+    "config": {
+      "files": [
+        {
+          "path": "models/custom/furry-diffusion.ckpt",
+          "md5sum": "f8ef45a295ef4966682f6e8fc2c6830d"
+        },
+        {
+          "path": "configs/stable-diffusion/v1-inference.yaml"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "furry-diffusion.ckpt",
+          "file_path": "models/custom",
+          "file_url": "https://sexy.canine.wf/file/furry-ckpt/furry_epoch4.ckpt"
+        }
+      ]
+    },
+    "available": false
+  },
+  "Yiffy": {
+    "name": "Yiffy",
+    "type": "ckpt",
+    "description": "Furry styled generations.",
+    "version": "18",
+    "style": "furry",
+    "nsfw": false,
+    "download_all": true,
+    "requires": [
+      "clip-vit-large-patch14"
+    ],
+    "config": {
+      "files": [
+        {
+          "path": "models/custom/yiffy.ckpt",
+          "md5sum": "dbe25794e24af183565dc45e9ec99713"
+        },
+        {
+          "path": "configs/stable-diffusion/v1-inference.yaml"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "yiffy.ckpt",
+          "file_path": "models/custom",
+          "file_url": "https://sexy.canine.wf/file/yiffy-ckpt/yiffy-e18.ckpt"
+        }
+      ]
+    },
+    "available": false
+  },
+  "Zack3D": {
+    "name": "Zack3D",
+    "type": "ckpt",
+    "description": "Kink/NSFW oriented furry styled generations.",
+    "version": "1",
+    "style": "furry",
+    "nsfw": true,
+    "download_all": true,
+    "requires": [
+      "clip-vit-large-patch14"
+    ],
+    "config": {
+      "files": [
+        {
+          "path": "models/custom/Zack3D.ckpt",
+          "md5sum": "aa944b1ecdaac60113027a0fdcda4f1b"
+        },
+        {
+          "path": "configs/stable-diffusion/v1-inference.yaml"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "Zack3D.ckpt",
+          "file_path": "models/custom",
+          "file_url": "https://sexy.canine.wf/file/furry-ckpt/Zack3D_Kinky-v1.ckpt"
+        }
+      ]
+    },
+    "available": false
+  },
+  "trinart": {
+    "name": "trinart",
+    "type": "ckpt",
+    "description": "Manga styled generations.",
+    "version": "1",
+    "style": "anime",
+    "nsfw": false,
+    "download_all": true,
+    "requires": [
+      "clip-vit-large-patch14"
+    ],
+    "config": {
+      "files": [
+        {
+          "path": "models/custom/trinart.ckpt"
+        },
+        {
+          "path": "configs/stable-diffusion/v1-inference.yaml"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "trinart.ckpt",
+          "file_path": "models/custom",
+          "file_url": "https://huggingface.co/naclbit/trinart_stable_diffusion_v2/resolve/main/trinart2_step95000.ckpt"
+        }
+      ]
+    },
+    "available": false
+  },
+  "RealESRGAN_x4plus": {
+    "name": "RealESRGAN_x4plus",
+    "type": "realesrgan",
+    "description": "Upscaler.",
+    "version": "0.1.0",
+    "style": "generalist",
+    "config": {
+      "files": [
+        {
+          "path": "models/realesrgan/RealESRGAN_x4plus.pth"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "RealESRGAN_x4plus.pth",
+          "file_path": "models/realesrgan",
+          "file_url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
+        }
+      ]
+    },
+    "available": false
+  },
+  "RealESRGAN_x4plus_anime_6B": {
+    "name": "RealESRGAN_x4plus_anime_6B",
+    "type": "realesrgan",
+    "description": "Anime focused upscaler.",
+    "version": "0.2.2.4",
+    "style": "anime",
+    "config": {
+      "files": [
+        {
+          "path": "models/realesrgan/RealESRGAN_x4plus_anime_6B.pth"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "RealESRGAN_x4plus_anime_6B.pth",
+          "file_path": "models/realesrgan",
+          "file_url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
+        }
+      ]
+    },
+    "available": false
+  },
+  "GFPGAN": {
+    "name": "GFPGAN",
+    "type": "gfpgan",
+    "description": "Face correction.",
+    "version": "1.4",
+    "style": "generalist",
+    "config": {
+      "files": [
+        {
+          "path": "models/gfpgan/GFPGANv1.4.pth"
+        },
+        {
+          "path": "gfpgan/weights/detection_Resnet50_Final.pth"
+        },
+        {
+          "path": "gfpgan/weights/parsing_parsenet.pth"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "GFPGANv1.4.pth",
+          "file_path": "models/gfpgan",
+          "file_url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
+        },
+        {
+          "file_name": "detection_Resnet50_Final.pth",
+          "file_path": "./gfpgan/weights",
+          "file_url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth"
+        },
+        {
+          "file_name": "parsing_parsenet.pth",
+          "file_path": "./gfpgan/weights",
+          "file_url": "https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth"
+        }
+      ]
+    },
+    "available": false
+  },
+  "LDSR": {
+    "name": "LDSR",
+    "type": "ckpt",
+    "description": "Upscaler.",
+    "version": "1",
+    "style": "generalist",
+    "nsfw": false,
+    "download_all": true,
+    "config": {
+      "files": [
+        {
+          "path": "models/ldsr/model.ckpt"
+        },
+        {
+          "path": "models/ldsr/project.yaml"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "model.ckpt",
+          "file_path": "models/ldsr",
+          "file_url": "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
+        },
+        {
+          "file_name": "project.yaml",
+          "file_path": "models/ldsr",
+          "file_url": "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+        }
+      ]
+    },
+    "available": false
+  },
+  "BLIP": {
+    "name": "BLIP",
+    "type": "blip",
+    "config": {
+      "files": [
+        {
+          "path": "models/blip/model__base_caption.pth"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "model__base_caption.pth",
+          "file_path": "models/blip",
+          "file_url": "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth"
+        }
+      ]
+    },
+    "available": false
+  },
+  "ViT-L/14": {
+    "name": "ViT-L/14",
+    "type": "clip",
+    "config": {
+      "files": [
+        {
+          "path": "models/clip/ViT-L-14.pt"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "ViT-L-14.pt",
+          "file_path": "./models/clip",
+          "file_url": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"
+        }
+      ]
+    },
+    "available": false
+  },
+  "ViT-g-14": {
+    "name": "ViT-g-14",
+    "pretrained_name":  "laion2b_s12b_b42k",
+    "type": "open_clip",
+    "config": {
+      "files": [
+        {
+          "path": "models/clip/models--laion--CLIP-ViT-g-14-laion2B-s12B-b42K/"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "main",
+          "file_path": "./models/clip/models--laion--CLIP-ViT-g-14-laion2B-s12B-b42K/refs",
+          "file_content": "b36bdd32483debcf4ed2f918bdae1d4a46ee44b8"
+        },
+        {
+          "file_name": "6aac683f899159946bc4ca15228bb7016f3cbb1a2c51f365cba0b23923f344da",
+          "file_path": "./models/clip/models--laion--CLIP-ViT-g-14-laion2B-s12B-b42K/blobs",
+          "file_url": "https://huggingface.co/laion/CLIP-ViT-g-14-laion2B-s12B-b42K/resolve/main/open_clip_pytorch_model.bin"
+        },
+        {
+          "file_name": "open_clip_pytorch_model.bin",
+          "file_path": "./models/clip/models--laion--CLIP-ViT-g-14-laion2B-s12B-b42K/snapshots/b36bdd32483debcf4ed2f918bdae1d4a46ee44b8",
+          "symlink": "./models/clip/models--laion--CLIP-ViT-g-14-laion2B-s12B-b42K/blobs/6aac683f899159946bc4ca15228bb7016f3cbb1a2c51f365cba0b23923f344da"
+        }
+      ]
+    },
+    "available": false
+  },
+  "ViT-H-14": {
+    "name": "ViT-H-14",
+    "pretrained_name":  "laion2b_s32b_b79k",
+    "type": "open_clip",
+    "config": {
+      "files": [
+        {
+          "path": "models/clip/models--laion--CLIP-ViT-H-14-laion2B-s32B-b79K/"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "main",
+          "file_path": "./models/clip/models--laion--CLIP-ViT-H-14-laion2B-s32B-b79K/refs",
+          "file_content": "58a1e03a7acfacbe6b95ebc24ae0394eda6a14fc"
+        },
+        {
+          "file_name": "9a78ef8e8c73fd0df621682e7a8e8eb36c6916cb3c16b291a082ecd52ab79cc4",
+          "file_path": "./models/clip/models--laion--CLIP-ViT-H-14-laion2B-s32B-b79K/blobs",
+          "file_url": "https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.bin"
+        },
+        {
+          "file_name": "open_clip_pytorch_model.bin",
+          "file_path": "./models/clip/models--laion--CLIP-ViT-H-14-laion2B-s32B-b79K/snapshots/58a1e03a7acfacbe6b95ebc24ae0394eda6a14fc",
+          "symlink": "./models/clip/models--laion--CLIP-ViT-H-14-laion2B-s32B-b79K/blobs/9a78ef8e8c73fd0df621682e7a8e8eb36c6916cb3c16b291a082ecd52ab79cc4"
+        }
+      ]
+    },
+    "available": false
+  },
+  "diffusers_stable_diffusion": {
+    "name": "diffusers_stable_diffusion",
+    "type": "diffusers",
+    "requires": [
+      "clip-vit-large-patch14"
+    ],
+    "config": {
+      "files": [
+        {
+          "path": "models/diffusers/"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "diffusers_stable_diffusion",
+          "file_url": "https://{username}:{password}@huggingface.co/CompVis/stable-diffusion-v1-4.git",
+          "git": true,
+          "hf_auth": true,
+          "post_process": [
+            {
+              "delete": "models/diffusers/stable-diffusion-v1-4/.git"
+            }
+          ]
+        }
+      ]
+      
+    },
+    "available": false
+  }
+}

db_dep.json

@@ -0,0 +1,98 @@
+{
+  "sd-concepts-library": {
+    "type": "dependency",
+    "optional": true,
+    "config": {
+      "files": [
+        {
+          "path": "models/custom/sd-concepts-library/"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "sd-concepts-library",
+          "file_path": "./models/custom/sd-concepts-library/",
+          "file_url": "https://github.com/sd-webui/sd-concepts-library/archive/refs/heads/main.zip",
+          "unzip": true,
+          "move_subfolder": "sd-concepts-library"
+        }
+      ]
+    },
+    "available": false
+  },
+  "clip-vit-large-patch14": {
+    "type": "dependency",
+    "optional": false,
+    "config": {
+      "files": [
+        {
+          "path": "models/clip-vit-large-patch14/config.json"
+        },
+        {
+          "path": "models/clip-vit-large-patch14/merges.txt"
+        },
+        {
+          "path": "models/clip-vit-large-patch14/preprocessor_config.json"
+        },
+        {
+          "path": "models/clip-vit-large-patch14/pytorch_model.bin"
+        },
+        {
+          "path": "models/clip-vit-large-patch14/special_tokens_map.json"
+        },
+        {
+          "path": "models/clip-vit-large-patch14/tokenizer.json"
+        },
+        {
+          "path": "models/clip-vit-large-patch14/tokenizer_config.json"
+        },
+        {
+          "path": "models/clip-vit-large-patch14/vocab.json"
+        }
+      ],
+      "download": [
+        {
+          "file_name": "config.json",
+          "file_path": "models/clip-vit-large-patch14",
+          "file_url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/config.json"
+        },
+        {
+          "file_name": "merges.txt",
+          "file_path": "models/clip-vit-large-patch14",
+          "file_url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/merges.txt"
+        },
+        {
+          "file_name": "preprocessor_config.json",
+          "file_path": "models/clip-vit-large-patch14",
+          "file_url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/preprocessor_config.json"
+        },
+        {
+          "file_name": "pytorch_model.bin",
+          "file_path": "models/clip-vit-large-patch14",
+          "file_url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin"
+        },
+        {
+          "file_name": "special_tokens_map.json",
+          "file_path": "models/clip-vit-large-patch14",
+          "file_url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/special_tokens_map.json"
+        },
+        {
+          "file_name": "tokenizer.json",
+          "file_path": "models/clip-vit-large-patch14",
+          "file_url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/tokenizer.json"
+        },
+        {
+          "file_name": "tokenizer_config.json",
+          "file_path": "models/clip-vit-large-patch14",
+          "file_url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/tokenizer_config.json"
+        },
+        {
+          "file_name": "vocab.json",
+          "file_path": "models/clip-vit-large-patch14",
+          "file_url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/vocab.json"
+        }
+      ]
+    },
+    "available": false
+  }
+}

docs/1.windows-installation.md

@@ -1,10 +1,12 @@
 ---
+
 title: Windows Installation
 ---
-<!--
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
 
-Copyright 2022 sd-webui team.
+<!--
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
+
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
@@ -20,6 +22,7 @@ along with this program.  If not, see <http://www.gnu.org/licenses/>.
 -->
 
 # Initial Setup
+
 > This is a windows guide. [To install on Linux, see this page.](2.linux-installation.md)
 
 ## Pre requisites
@@ -30,19 +33,18 @@ along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
 ![CleanShot 2022-08-31 at 16 29 48@2x](https://user-images.githubusercontent.com/463317/187796320-e6edbb39-dff1-46a2-a1a1-c4c1875d414c.jpg)
 
-
 * Download Miniconda3:
   [https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe](https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe) Get this installed so that you have access to the Miniconda3 Prompt Console.
 
 * Open Miniconda3 Prompt from your start menu after it has been installed
 
-* _(Optional)_ Create a new text file in your root directory `/stable-diffusion-webui/custom-conda-path.txt` that contains the path to your relevant Miniconda3, for example `C:\Users\<username>\miniconda3` (replace `<username>` with your own username). This is required if you have more than 1 miniconda installation or are using custom installation location. 
+* _(Optional)_ Create a new text file in your root directory `/sygil-webui/custom-conda-path.txt` that contains the path to your relevant Miniconda3, for example `C:\Users\<username>\miniconda3` (replace `<username>` with your own username). This is required if you have more than 1 miniconda installation or are using custom installation location. 
 
 ## Cloning the repo
 
-Type `git clone https://github.com/sd-webui/stable-diffusion-webui.git` into the prompt. 
+Type `git clone https://github.com/Sygil-Dev/sygil-webui.git` into the prompt. 
 
-This will create the `stable-diffusion-webui` directory in your Windows user folder. 
+This will create the `sygil-webui` directory in your Windows user folder. 
 ![CleanShot 2022-08-31 at 16 31 20@2x](https://user-images.githubusercontent.com/463317/187796462-29e5bafd-bbc1-4a48-adc8-7eccc174cb62.jpg)
 
 --- 
@@ -51,32 +53,27 @@ Once a repo has been cloned, updating it is as easy as typing `git pull` inside
 
 ![CleanShot 2022-08-31 at 16 36 34@2x](https://user-images.githubusercontent.com/463317/187796970-db94402f-717b-43a8-9c85-270c0cd256c3.jpg)
 
-
 * Next you are going to want to create a Hugging Face account: [https://huggingface.co/](https://huggingface.co/) 
 
-
 * After you have signed up, and are signed in go to this link and click on Authorize: [https://huggingface.co/CompVis/stable-diffusion-v-1-4-original](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)  
 
-
 * After you have authorized your account, go to this link to download the model weights for version 1.4 of the model, future versions will be released in the same way, and updating them will be a similar process : 
   [https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt)
 
-
+* Download the model into this directory: `C:\Users\<username>\sygil-webui\models\ldm\stable-diffusion-v1`
-* Download the model into this directory: `C:\Users\<username>\stable-diffusion-webui\models\ldm\stable-diffusion-v1`
-
 
 * Rename `sd-v1-4.ckpt` to `model.ckpt` once it is inside the stable-diffusion-v1 folder.
 
+* Since we are already in our sygil-webui folder in Miniconda, our next step is to create the environment Stable Diffusion needs to work. 
 
-* Since we are already in our stable-diffusion-webui folder in Miniconda, our next step is to create the environment Stable Diffusion needs to work. 
+* _(Optional)_ If you already have an environment set up for an installation of Stable Diffusion named ldm open up the `environment.yaml` file in `\sygil-webui\` change the environment name inside of it from `ldm` to `ldo`
-
-
-* _(Optional)_ If you already have an environment set up for an installation of Stable Diffusion named ldm open up the `environment.yaml` file in `\stable-diffusion-webui\` change the environment name inside of it from `ldm` to `ldo`
 
 --- 
 
 ## First run
-* `webui.cmd` at the root folder (`\stable-diffusion-webui\`) is your main script that you'll always run. It has the functions to automatically do the followings:
+
+* `webui.cmd` at the root folder (`\sygil-webui\`) is your main script that you'll always run. It has the functions to automatically do the followings:
+  
   * Create conda env 
   * Install and update requirements
   * Run the relauncher and webui.py script for gradio UI options 
@@ -95,34 +92,36 @@ Once a repo has been cloned, updating it is as easy as typing `git pull` inside
 
 * You should be able to see progress in your `webui.cmd` window. The [http://localhost:7860/](http://localhost:7860/) will be automatically updated to show the final image once progress reach 100%
 
-* Images created with the web interface will be saved to `\stable-diffusion-webui\outputs\` in their respective folders alongside `.yaml` text files with all of the details of your prompts for easy referencing later. Images will also be saved with their seed and numbered so that they can be cross referenced with their `.yaml` files easily. 
+* Images created with the web interface will be saved to `\sygil-webui\outputs\` in their respective folders alongside `.yaml` text files with all of the details of your prompts for easy referencing later. Images will also be saved with their seed and numbered so that they can be cross referenced with their `.yaml` files easily. 
 
 --- 
 
 ### Optional additional models
 
-There are three more models that we need to download in order to get the most out of the functionality offered by sd-webui.
+There are three more models that we need to download in order to get the most out of the functionality offered by Sygil-Dev.
 
 > The models are placed inside `src` folder. If you don't have `src` folder inside your root directory it means that you haven't installed the dependencies for your environment yet. [Follow this step](#first-run) before proceeding.
 
 ### GFPGAN
+
 1. If you want to use GFPGAN to improve generated faces, you need to install it separately.
-1. Download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it
+2. Download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it into the `/sygil-webui/models/gfpgan` directory. 
-into the `/stable-diffusion-webui/models/gfpgan` directory. 
 
 ### RealESRGAN
+
 1. 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).
-1. Put them into the `stable-diffusion-webui/models/realesrgan` directory. 
+2. Put them into the `sygil-webui/models/realesrgan` directory. 
 
 ### LDSR
-1. Detailed instructions [here](https://github.com/Hafiidz/latent-diffusion). Brief instruction as follows.
-1. Git clone [Hafiidz/latent-diffusion](https://github.com/Hafiidz/latent-diffusion) into your `/stable-diffusion-webui/src/` folder.
-1. Run `/stable-diffusion-webui/models/ldsr/download_model.bat` to automatically download and rename the models.
-1. Wait until it is done and you can confirm by confirming two new files in `stable-diffusion-webui/models/ldsr/`
-1. _(Optional)_ If there are no files there, you can manually 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). 
-1. Rename last.ckpt to model.ckpt and place both under `stable-diffusion-webui/models/ldsr/`.
-1. Refer to [here](https://github.com/sd-webui/stable-diffusion-webui/issues/488) for any issue.
 
+1. Detailed instructions [here](https://github.com/Hafiidz/latent-diffusion). Brief instruction as follows.
+2. Git clone [Hafiidz/latent-diffusion](https://github.com/Hafiidz/latent-diffusion) into your `/sygil-webui/src/` folder.
+3. Run `/sygil-webui/models/ldsr/download_model.bat` to automatically download and rename the models.
+4. Wait until it is done and you can confirm by confirming two new files in `sygil-webui/models/ldsr/`
+5. _(Optional)_ If there are no files there, you can manually 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). 
+6. Rename last.ckpt to model.ckpt and place both under `sygil-webui/models/ldsr/`.
+7. Refer to [here](https://github.com/Sygil-Dev/sygil-webui/issues/488) for any issue.
 
 # Credits
-> Modified by [Hafiidz](https://github.com/Hafiidz) with helps from sd-webui discord and team.
+
+> Modified by [Hafiidz](https://github.com/Hafiidz) with helps from Sygil-Dev discord and team.

docs/2.linux-installation.md

@@ -2,9 +2,9 @@
 title: Linux Installation
 ---
 <!--
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
@@ -42,9 +42,9 @@ along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
 **Step 3:** Make the script executable by opening the directory in your Terminal and typing `chmod +x linux-sd.sh`, or whatever you named this file as.
 
-**Step 4:** Run the script with `./linux-sd.sh`, it will begin by cloning the [WebUI Github Repo](https://github.com/sd-webui/stable-diffusion-webui) to the directory the script is located in. This folder will be named `stable-diffusion-webui`.  
+**Step 4:** Run the script with `./linux-sd.sh`, it will begin by cloning the [WebUI Github Repo](https://github.com/Sygil-Dev/sygil-webui) to the directory the script is located in. This folder will be named `sygil-webui`.  
 
-**Step 5:** The script will pause and ask that you move/copy the downloaded 1.4 AI models to the `stable-diffusion-webui` folder. Press Enter once you have done so to continue.
+**Step 5:** The script will pause and ask that you move/copy the downloaded 1.4 AI models to the `sygil-webui` folder. Press Enter once you have done so to continue.
 
 **If you are running low on storage space, you can just move the 1.4 AI models file directly to this directory, it will not be deleted, simply moved and renamed. However my personal suggestion is to just **copy** it to the repo folder, in case you desire to delete and rebuild your Stable Diffusion build again.**
 
@@ -76,7 +76,7 @@ The user will have the ability to set these to yes or no using the menu choices.
 		- Uses An Older Interface Style
 		- Will Not Receive Major Updates
 
-**Step 9:** If everything has gone successfully, either a new browser window will open with the Streamlit version, or you should see `Running on local URL:  http://localhost:7860/` in your Terminal if you launched the Gradio Interface version. Generated images will be located in the `outputs` directory inside of `stable-diffusion-webui`. Enjoy the definitive Stable Diffusion WebUI experience on Linux! :)
+**Step 9:** If everything has gone successfully, either a new browser window will open with the Streamlit version, or you should see `Running on local URL:  http://localhost:7860/` in your Terminal if you launched the Gradio Interface version. Generated images will be located in the `outputs` directory inside of `sygil-webui`. Enjoy the definitive Stable Diffusion WebUI experience on Linux! :)
 
 ## Ultimate Stable Diffusion Customizations
 
@@ -87,7 +87,7 @@ If the user chooses to Customize their setup, then they will be presented with t
 - Update the Stable Diffusion WebUI fork from the GitHub Repo
 - Customize the launch arguments for Gradio Interface version of Stable Diffusion (See Above)
 
-### Refer back to the original [WebUI Github Repo](https://github.com/sd-webui/stable-diffusion-webui) for useful tips and links to other resources that can improve your Stable Diffusion experience
+### Refer back to the original [WebUI Github Repo](https://github.com/Sygil-Dev/sygil-webui) for useful tips and links to other resources that can improve your Stable Diffusion experience
 
 ## Planned Additions
 - Investigate ways to handle Anaconda automatic installation on a user's system.

docs/3.docker-guide.md

@@ -2,7 +2,7 @@
 title: Running Stable Diffusion WebUI Using Docker
 ---
 <!--
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
 Copyright 2022 sd-webui team.
 This program is free software: you can redistribute it and/or modify
@@ -19,6 +19,34 @@ You should have received a copy of the GNU Affero General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 -->
 
+## Running prebuilt image
+
+The easiest way to run Stable Diffusion WebUI is to use the prebuilt image from Docker Hub.
+
+```bash
+docker pull hlky/sd-webui:runpod
+```
+This image has all the necessary models baked in. It is quite large but streamlines the process of managing the various models and simplifies the user experience.
+
+Alternatively you can pull:
+```bash
+docker pull hlky/sd-webui:latest
+```
+This image includes the babrebones environment to run the Web UI. The models will be downloaded during the installation process. You will have to take care of the volume for the `sd/models` directory.
+
+
+It is recommended that you run the `runpod` version.
+You can run the image using the following command:
+```bash
+docker container run --rm -d -p 8501:8501 -e STREAMLIT_SERVER_HEADLESS=true -e "WEBUI_SCRIPT=webui_streamlit.py" -e "VALIDATE_MODELS=false" -v "${PWD}/outputs:/sd/outputs" --gpus all hlky/sd-webui:runpod
+```
+
+> Note: if you are running it on runpod it only supports one volume mount which is used for your outputs.
+
+> Note: if you are running it on your local machine the output directory will be created in the current directory from where you run this command.
+
+## Building the image
+
 This Docker environment is intended to speed up development and testing of Stable Diffusion WebUI features. Use of a container image format allows for packaging and isolation of Stable Diffusion / WebUI's dependencies separate from the Host environment.
 
 You can use this Dockerfile to build a Docker image and run Stable Diffusion WebUI locally.
@@ -41,7 +69,7 @@ Additional Requirements:
 
 Other Notes:
 * "Optional" packages commonly used with Stable Diffusion WebUI workflows such as, RealESRGAN, GFPGAN, will be installed by default.
-* An older version of running Stable Diffusion WebUI using Docker exists here: https://github.com/sd-webui/stable-diffusion-webui/discussions/922
+* An older version of running Stable Diffusion WebUI using Docker exists here: https://github.com/Sygil-Dev/sygil-webui/discussions/922
 
 ### But what about AMD?
 There is tentative support for AMD GPUs through docker which can be enabled via `docker-compose.amd.yml`,
@@ -63,7 +91,7 @@ in your `.profile` or through a tool like `direnv`
 
 ### Clone Repository
 * Clone this repository to your host machine:
-    * `git clone https://github.com/sd-webui/stable-diffusion-webui.git`
+    * `git clone https://github.com/Sygil-Dev/sygil-webui.git`
 * If you plan to use Docker Compose to run the image in a container (most users), create an `.env_docker` file using the example file:
     * `cp .env_docker.example .env_docker`
     * Edit `.env_docker` using the text editor of your choice.
@@ -77,7 +105,7 @@ The default `docker-compose.yml` file  will create a Docker container instance n
 * Create an instance of the Stable Diffusion WebUI image as a Docker container:
     * `docker compose up`
 * During the first run, the container image will be build containing all of the dependencies necessary to run Stable Diffusion. This build process will take several minutes to complete
-    * After the image build has completed, you will have a docker image for running the Stable Diffusion WebUI tagged `stable-diffusion-webui:dev`
+    * After the image build has completed, you will have a docker image for running the Stable Diffusion WebUI tagged `sygil-webui:dev`
 
 (Optional) Daemon mode:
 * You can start the container in "daemon" mode by applying the `-d` option:  `docker compose up -d`. This will run the server in the background so you can close your console window without losing your work.
@@ -132,9 +160,9 @@ You will need to re-download all associated model files/weights used by Stable D
     * `docker exec -it st-webui /bin/bash`
     * `docker compose exec stable-diffusion bash`
 * To start a container using the Stable Diffusion WebUI Docker image without Docker Compose, you can do so with the following command:
-    * `docker run --rm -it --entrypoint /bin/bash stable-diffusion-webui:dev`
+    * `docker run --rm -it --entrypoint /bin/bash sygil-webui:dev`
 * To start a container, with mapped ports, GPU resource access, and a local directory bound as a container volume, you can do so with the following command:
-    * `docker run --rm -it -p 8501:8501 -p 7860:7860 --gpus all -v $(pwd):/sd --entrypoint /bin/bash stable-diffusion-webui:dev`
+    * `docker run --rm -it -p 8501:8501 -p 7860:7860 --gpus all -v $(pwd):/sd --entrypoint /bin/bash sygil-webui:dev`
 
 ---
 

docs/4.streamlit-interface.md

@@ -2,9 +2,9 @@
 title: Streamlit Web UI Interface
 ---
 <!--
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
@@ -94,7 +94,7 @@ Streamlit Image2Image allows for you to take an image, be it generated by Stable
 
 The Concept Library allows for the easy usage of custom textual inversion models. These models may be loaded into `models/custom/sd-concepts-library` and will appear in the Concepts Library in Streamlit. To use one of these custom models in a prompt, either copy it using the button on the model, or type `<model-name>` in the prompt where you wish to use it.
 
-Please see the [Concepts Library](https://github.com/sd-webui/stable-diffusion-webui/blob/master/docs/7.concepts-library.md) section to learn more about how to use these tools.
+Please see the [Concepts Library](https://github.com/Sygil-Dev/sygil-webui/blob/master/docs/7.concepts-library.md) section to learn more about how to use these tools.
 
 ## Textual Inversion
 ---

docs/44.competition.md

@@ -0,0 +1,92 @@
+# Textual inversion usage competition
+
+We are hosting a competition where the community can showcase their most inventive use of textual inversion concepts in text-to-image or text-to-video.
+
+Our compute cluster; `Nataili`, currently comprises of 3 nodes, two have 3090, the other has 2 x A5000.
+
+We estimate `Nataili` can handle 12 concepts per hour, and we can add more workers if there is high demand.
+
+Hopefully demand will be high, we want to train **hundreds** of new concepts!
+
+# Schedule
+
+2022/10/20 - Stage 1 begins, train concept command opened for usage
+
+2022/10/22 12AM UTC - Stage 2 begins, text to image command opened for usage
+
+2022/10/22 12PM UTC - Stage 1 ends, train concept command closed
+
+2022/10/24 12PM UTC - Stage 2 ends, no more entries will be accepted
+
+2022/10/24 6-12PM UTC - Winners announced
+
+
+# What does `most inventive use` mean?
+
+Whatever you want it to mean! be creative! experiment! 
+
+There are several categories we will look at:
+
+* anything that's particularly creative, ~ artistic ~ or a e s t h e t i c
+
+![20221019203426_00000](https://user-images.githubusercontent.com/106811348/197045193-d6f9c56b-9989-4f1c-b42a-bb02d62d77cd.png)
+
+* composition; meaning anything related to how big things are, their position, the angle, etc
+
+* styling; 
+
+![image](https://user-images.githubusercontent.com/106811348/197045629-029ba6f5-1f79-475c-9ce7-969aaf3d253b.png)
+
+* `The Sims(TM): Stable Diffusion edition`
+
+## So I can trai-
+
+* Yes, as long as it's sfw
+
+## `The Sims(TM): Stable Diffusion edition` ?
+
+For this event the theme is “The Sims: Stable Diffusion edition”. 
+
+So we have selected a subset of [products from Amazon Berkely Objects dataset](https://github.com/sd-webui/abo).
+
+Any other object is welcome too these are just a good source of data for this part of the competition.
+
+Each product has images from multiple angles, the train concept command accepts up to 10 images, so choose the angles and modify backgrounds, experiment!
+
+The goal with this category is to generate an image using the trained object, and the other categories apply, your imagination is the only limit! style a couch, try to make a BIG couch, try to make a couch on top of a mountain, try to make a vaporwave couch, anything!
+
+# How do I train a concept using the discord bot?
+
+Type `/trainconcept` then press tab to go through the fields
+
+`Concept name` is just a name for your concept, it doesn't have to be a single word
+
+`Placeholder` is what you will use in prompts to represent your concept
+Add `<` and `>` so it is unique, multiple words should be hyphenated
+
+`Initializer` is used as the starting point for training your concept, so this should be a single word that represents your concept
+
+Minimum 2 images. Squareish aspect ratios work best
+
+![Untitled-2](https://user-images.githubusercontent.com/106811348/197035834-cc973e29-31f8-48de-be2d-788fbe938b2e.png)
+![image](https://user-images.githubusercontent.com/106811348/197035870-b91ef2a8-0ffd-47e1-a8df-9600df26cd6b.png)
+
+# How do I use the trained concept?
+
+## Prompting with concepts
+
+When your concept is trained you can use it in prompts.
+
+`a cute <nvidiafu> as an astronaut`:
+
+![image](https://user-images.githubusercontent.com/106811348/197037250-044ea241-72a5-4caa-b772-35034245b4b6.png)
+
+or `a green <green-couch> sitting on top of a floor, a 3D render, trending on polycount, minimalism, rendered in cinema4d`:
+
+![image](https://user-images.githubusercontent.com/106811348/197037344-7ce72188-9129-4ba2-8a28-cba5fd664a9c.png)
+
+## Using concepts in the webui
+
+The discord bot will give you a link to a `.zip` file, download this, extract it, and put the folder in `stable-diffusion-webui/models/custom/sd-concepts-library`
+
+![image](https://user-images.githubusercontent.com/106811348/197037892-ce53bea4-d1db-4b25-bb7c-7dfe4d71b2b1.png)

docs/5.gradio-interface.md

@@ -2,9 +2,9 @@
 title: Gradio Web UI Interface
 ---
 <!--
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or

docs/6.image_enhancers.md

@@ -2,9 +2,9 @@
 title: Upscalers
 ---
 <!--
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
@@ -32,7 +32,7 @@ GFPGAN is designed to help restore faces in Stable Diffusion outputs. If you hav
 
 If you want to use GFPGAN to improve generated faces, you need to download the models for it seperately if you are on Windows or doing so manually on Linux.
 Download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and put it
-into the `/stable-diffusion-webui/models/gfpgan` directory after you have setup the conda environment for the first time.
+into the `/sygil-webui/models/gfpgan` directory after you have setup the conda environment for the first time.
 
 ## RealESRGAN
 ---
@@ -42,7 +42,7 @@ RealESRGAN is a 4x upscaler built into both versions of the Web UI interface. It
 
 If you want to use RealESRGAN to upscale your images, you need to download the models for it seperately if you are on Windows or doing so manually on Linux.
 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).
-Put them into the `stable-diffusion-webui/models/realesrgan` directory after you have setup the conda environment for the first time.
+Put them into the `sygil-webui/models/realesrgan` directory after you have setup the conda environment for the first time.
 
 ## GoBig (Gradio only currently)
 ---
@@ -57,7 +57,7 @@ To use GoBig, you will need to download the RealESRGAN models as directed above.
 LSDR is a 4X upscaler with high VRAM usage that uses a Latent Diffusion model to upscale the image. This will accentuate the details of an image, but won't change the composition. This might introduce sharpening, but it is great for textures or compositions with plenty of details. However, it is slower and will use more VRAM.
 
 If you want to use LSDR to upscale your images, you need to download the models for it seperately if you are on Windows or doing so manually on Linux.
-Download the 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 in the `stable-diffusion-webui/models/ldsr` directory after you have setup the conda environment for the first time.  
+Download the 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 in the `sygil-webui/models/ldsr` directory after you have setup the conda environment for the first time.  
 
 ## GoLatent (Gradio only currently)
 ---

docs/7.concepts-library.md

@@ -1,7 +1,7 @@
 <!--
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or

docs/8.custom-models.md

@@ -2,9 +2,9 @@
 title: Custom models
 ---
 <!--
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or

entrypoint.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -111,7 +111,7 @@ if [[ -e "${MODEL_DIR}/sd-concepts-library" ]]; then
 else
     # concept library does not exist, clone
     cd ${MODEL_DIR}
-    git clone https://github.com/sd-webui/sd-concepts-library.git
+    git clone https://github.com/Sygil-Dev/sd-concepts-library.git
 fi
 # create directory and link concepts library
 mkdir -p ${SCRIPT_DIR}/models/custom

environment.yaml

@@ -1,7 +1,7 @@
 name: ldm
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -29,52 +29,5 @@ dependencies:
   - scikit-image=0.19.2
   - torchvision=0.12.0
   - pip:
-    - -e .
+    - -r requirements.txt
-    - -e git+https://github.com/CompVis/taming-transformers#egg=taming-transformers
-    - -e git+https://github.com/openai/CLIP#egg=clip
-    - -e git+https://github.com/hlky/k-diffusion-sd#egg=k_diffusion
-    - -e git+https://github.com/devilismyfriend/latent-diffusion#egg=latent-diffusion
-    - accelerate==0.12.0
-    - albumentations==0.4.3
-    - basicsr>=1.3.4.0
-    - diffusers==0.3.0
-    - einops==0.3.1
-    - facexlib>=0.2.3
-    - ftfy==6.1.1
-    - fairscale==0.4.4
-    - gradio==3.1.6
-    - gfpgan==1.3.8
-    - hydralit_components==1.0.10
-    - hydralit==1.0.14
-    - imageio-ffmpeg==0.4.2
-    - imageio==2.9.0
-    - kornia==0.6
-    - loguru
-    - omegaconf==2.1.1
-    - opencv-python-headless==4.6.0.66
-    - open-clip-torch==2.0.2
-    - pandas==1.4.3
-    - piexif==1.1.3
-    - pudb==2019.2
-    - pynvml==11.4.1
-    - python-slugify>=6.1.2
-    - pytorch-lightning==1.4.2
-    - retry>=0.9.2
-    - regex
-    - realesrgan==0.3.0
-    - streamlit==1.13.0
-    - streamlit-on-Hover-tabs==1.0.1
-    - streamlit-option-menu==0.3.2
-    - streamlit_nested_layout
-    - streamlit-server-state==0.14.2
-    - streamlit-tensorboard==0.0.2
-    - test-tube>=0.7.5
-    - tensorboard==2.10.1
-    - timm==0.6.7
-    - torch-fidelity==0.3.0
-    - torchmetrics==0.6.0
-    - transformers==4.19.2
-    - tensorflow==2.10.0
-    - tqdm==4.64.0
-    - wget
 

frontend/css/no_progress_bar.css

@@ -1,7 +1,7 @@
 /*
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or

frontend/css/streamlit.main.css

@@ -1,7 +1,7 @@
 /*
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
@@ -26,10 +26,11 @@ button[data-baseweb="tab"] {
 }
 
 /* Image Container (only appear after run finished)//center the image, especially better looks in wide screen */
-.css-du1fp8 {
+.css-1kyxreq{
 	justify-content: center;
 }
 
+
 /* Streamlit header */
 .css-1avcm0n {
   background-color: transparent;
@@ -135,6 +136,7 @@ div.gallery:hover {
 /********************************************************************
     Hide anchor links on titles
 *********************************************************************/
+/*
 .css-15zrgzn {
 	display: none
 	}
@@ -144,3 +146,33 @@ div.gallery:hover {
 .css-jn99sy {
 	display: none
 	}
+	
+/* Make the text area widget have a similar height as the text input field */
+.st-dy{
+	height: 54px;
+	min-height: 25px;
+}
+.css-17useex{
+	gap: 3px;
+	
+}
+
+/* Remove some empty spaces to make the UI more compact. */
+.css-18e3th9{
+	padding-left: 10px;
+	padding-right: 30px;
+	position: unset !important; /* Fixes the layout/page going up when an expander or another item is expanded and then collapsed */ 
+}
+.css-k1vhr4{
+	padding-top: initial;
+}
+.css-ret2ud{
+	padding-left: 10px;
+	padding-right: 30px;
+	gap: initial;
+	display: initial;
+}
+
+.css-w5z5an{
+	gap: 1px;
+}

frontend/css/styles.css

@@ -1,7 +1,7 @@
 /*
-This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-Copyright 2022 sd-webui team.
+Copyright 2022 Sygil-Dev team.
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
@@ -88,3 +88,11 @@ input[type=number]:disabled { -moz-appearance: textfield; }
     /* fix buttons layouts */
 
 }
+
+/* Gradio 3.4 FIXES */
+#prompt_row button {
+    max-width: 20ch;
+}
+#text2img_col2 {
+    flex-grow: 2 !important;
+}

frontend/css_and_js.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

frontend/frontend.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -65,7 +65,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
 
                         txt2img_dimensions_info_text_box = gr.Textbox(
                             label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)")
-                    with gr.Column():
+                    with gr.Column(elem_id="text2img_col2"):
                         with gr.Box():
                             output_txt2img_gallery = gr.Gallery(label="Images", elem_id="txt2img_gallery_output").style(
                                 grid=[4, 4])
@@ -312,7 +312,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
                                                         label='Batch count (how many batches of images to generate)',
                                                         value=img2img_defaults['n_iter'])
                         img2img_dimensions_info_text_box = gr.Textbox(
-                            label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)")
+                            label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)", lines="2")
                     with gr.Column():
                         img2img_steps = gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps",
                                                   value=img2img_defaults['ddim_steps'])
@@ -499,11 +499,11 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
                             if GFPGAN is None:
                                 gr.HTML("""
     <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
-        <p><b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a></p>
+        <p><b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/Sygil-Dev/sygil-webui'>Github</a></p>
     </div>
     """)
                                 # gr.Markdown("")
-                                # gr.Markdown("<b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a>")
+                                # gr.Markdown("<b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/Sygil-Dev/sygil-webui'>Github</a>")
                             with gr.Column():
                                 gr.Markdown("<b>GFPGAN Settings</b>")
                                 imgproc_gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001,
@@ -517,7 +517,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
                             else:
                                 gr.HTML("""
     <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
-        <p><b> Please download LDSR to activate more upscale features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a></p>
+        <p><b> Please download LDSR to activate more upscale features</b>, instructions are available at the <a href='https://github.com/Sygil-Dev/sygil-webui'>Github</a></p>
     </div>
     """)
                                 upscaleModes = ['RealESRGAN', 'GoBig']
@@ -627,7 +627,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
                                             # seperator
                                             gr.HTML("""
         <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
-            <p><b> Please download RealESRGAN to activate upscale features</b>, instructions are available at the <a href='https://github.com/hlky/stable-diffusion-webui'>Github</a></p>
+            <p><b> Please download RealESRGAN to activate upscale features</b>, instructions are available at the <a href='https://github.com/Sygil-Dev/sygil-webui'>Github</a></p>
         </div>
         """)
             imgproc_toggles.change(fn=uifn.toggle_options_gfpgan, inputs=[imgproc_toggles], outputs=[gfpgan_group])
@@ -860,9 +860,9 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, imgproc=lambda
         """
         gr.HTML("""
     <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
-        <p>For help and advanced usage guides, visit the <a href="https://github.com/hlky/stable-diffusion-webui/wiki" target="_blank">Project Wiki</a></p>
+        <p>For help and advanced usage guides, visit the <a href="https://github.com/Sygil-Dev/sygil-webui/wiki" target="_blank">Project Wiki</a></p>
-        <p>Stable Diffusion WebUI is an open-source project. You can find the latest stable builds on the <a href="https://github.com/hlky/stable-diffusion" target="_blank">main repository</a>.
+        <p>Stable Diffusion WebUI is an open-source project. You can find the latest stable builds on the <a href="https://github.com/Sygil-Dev/stable-diffusion" target="_blank">main repository</a>.
-        If you would like to contribute to development or test bleeding edge builds, you can visit the <a href="https://github.com/hlky/stable-diffusion-webui" target="_blank">developement repository</a>.</p>
+        If you would like to contribute to development or test bleeding edge builds, you can visit the <a href="https://github.com/Sygil-Dev/sygil-webui" target="_blank">developement repository</a>.</p>
         <p>Device ID {current_device_index}: {current_device_name}<br/>{total_device_count} total devices</p>
     </div>
     """.format(current_device_name=torch.cuda.get_device_name(), current_device_index=torch.cuda.current_device(), total_device_count=torch.cuda.device_count()))

frontend/image_metadata.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

frontend/job_manager.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

frontend/ui_functions.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

horde_bridge.cmd

@@ -1,7 +1,7 @@
 @echo off
-:: This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+:: This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-:: Copyright 2022 sd-webui team.
+:: Copyright 2022 Sygil-Dev team.
 :: This program is free software: you can redistribute it and/or modify
 :: it under the terms of the GNU Affero General Public License as published by
 :: the Free Software Foundation, either version 3 of the License, or
@@ -58,20 +58,23 @@ IF "%v_conda_path%"=="" (
 
 :CONDA_FOUND
 echo Stashing local changes and pulling latest update...
+git status --porcelain=1 -uno | findstr . && set "HasChanges=1" || set "HasChanges=0"
 call git stash
 call git pull
+IF "%HasChanges%" == "0" GOTO SKIP_RESTORE
+
 set /P restore="Do you want to restore changes you made before updating? (Y/N): "
 IF /I "%restore%" == "N" (
-  echo Removing changes please wait...
+  echo Removing changes...
   call git stash drop
-  echo Changes removed, press any key to continue...
+  echo "Changes removed"
-  pause >nul
 ) ELSE IF /I "%restore%" == "Y" (
-  echo Restoring changes, please wait...
+  echo Restoring changes...
   call git stash pop --quiet
-  echo Changes restored, press any key to continue...
+  echo "Changes restored"
-  pause >nul
 )
+
+:SKIP_RESTORE
 call "%v_conda_path%\Scripts\activate.bat"
 
 for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml')  DO set v_cur_hash=%%a

horde_bridge.sh

@@ -1,7 +1,7 @@
 #!/bin/bash -i
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -30,7 +30,7 @@ LSDR_CONFIG="https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
 LSDR_MODEL="https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
 REALESRGAN_MODEL="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
 REALESRGAN_ANIME_MODEL="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
-SD_CONCEPT_REPO="https://github.com/sd-webui/sd-concepts-library/archive/refs/heads/main.zip"
+SD_CONCEPT_REPO="https://github.com/Sygil-Dev/sd-concepts-library/archive/refs/heads/main.zip"
 
 
 if [[ -f $ENV_MODIFED_FILE ]]; then 
@@ -91,7 +91,7 @@ sd_model_loading () {
         printf "AI Model already in place. Continuing...\n\n"
     else
         printf "\n\n########## MOVE MODEL FILE ##########\n\n"
-        printf "Please download the 1.4 AI Model from Huggingface (or another source) and place it inside of the stable-diffusion-webui folder\n\n"
+        printf "Please download the 1.4 AI Model from Huggingface (or another source) and place it inside of the sygil-webui folder\n\n"
         read -p "Once you have sd-v1-4.ckpt in the project root, Press Enter...\n\n"
         
         # Check to make sure checksum of models is the original one from HuggingFace and not a fake model set
@@ -162,7 +162,7 @@ start_initialization () {
         echo "Your model file does not exist! Place it in 'models/ldm/stable-diffusion-v1' with the name 'model.ckpt'."
         exit 1
     fi
-    printf "\nStarting Stable Horde Bridg: Please Wait...\n"; python scripts/relauncher.py --bridge -v "$@"; break;
+    printf "\nStarting Stable Horde Bridge: Please Wait...\n"; python scripts/relauncher.py --bridge -v "$@"; break;
 
 }
 

installer/create_installers.sh

@@ -0,0 +1,28 @@
+#!/bin/bash
+
+# For developers only! Not for users!
+# This creates the installer zip files that will be distributed to users
+# It packs install.{sh,bat} along with a readme, and ensures that the user
+# has the install script inside a new empty folder (after unzipping),
+# otherwise the git repo will extract into whatever folder the script is in.
+
+cd "$(dirname "${BASH_SOURCE[0]}")"
+
+# make the installer zip for linux and mac
+rm -rf sygil
+mkdir -p sygil
+cp install.sh sygil
+cp readme.txt sygil
+
+zip -r sygil-linux.zip sygil
+zip -r sygil-mac.zip sygil
+
+# make the installer zip for windows
+rm -rf sygil
+mkdir -p sygil
+cp install.bat sygil
+cp readme.txt sygil
+
+zip -r sygil-windows.zip sygil
+
+echo "The installer zips are ready to be distributed.."

installer/install.bat

@@ -0,0 +1,96 @@
+@echo off
+
+@rem This script will install git and conda (if not found on the PATH variable)
+@rem  using micromamba (an 8mb static-linked single-file binary, conda replacement).
+@rem For users who already have git and conda, this step will be skipped.
+
+@rem Then, it'll run the webui.cmd file to continue with the installation as usual.
+
+@rem This enables a user to install this project without manually installing conda and git.
+
+echo "Installing Sygil WebUI.."
+echo.
+
+@rem config
+set MAMBA_ROOT_PREFIX=%cd%\installer_files\mamba
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
+set REPO_URL=https://github.com/Sygil-Dev/sygil-webui.git
+@rem Change the download URL to Sygil repo's release URL
+@rem We need to mirror micromamba.exe, because the official download URL uses tar.bz2 compression
+@rem  which Windows can't unzip natively.
+@rem https://mamba.readthedocs.io/en/latest/installation.html#windows
+set umamba_exists=F
+
+@rem figure out whether git and conda needs to be installed
+if exist "%INSTALL_ENV_DIR%" set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
+
+set PACKAGES_TO_INSTALL=
+
+call conda --version >.tmp1 2>.tmp2
+if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda
+
+call git --version >.tmp1 2>.tmp2
+if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
+
+call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version >.tmp1 2>.tmp2
+if "%ERRORLEVEL%" EQU "0" set umamba_exists=T
+
+@rem (if necessary) install git and conda into a contained environment
+if "%PACKAGES_TO_INSTALL%" NEQ "" (
+    @rem download micromamba
+    if "%umamba_exists%" == "F" (
+        echo "Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to %MAMBA_ROOT_PREFIX%\micromamba.exe"
+
+        mkdir "%MAMBA_ROOT_PREFIX%"
+        call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > "%MAMBA_ROOT_PREFIX%\micromamba.exe"
+
+        @rem test the mamba binary
+        echo Micromamba version:
+        call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version
+    )
+
+    @rem create the installer env
+    if not exist "%INSTALL_ENV_DIR%" (
+        call "%MAMBA_ROOT_PREFIX%\micromamba.exe" create -y --prefix "%INSTALL_ENV_DIR%"
+    )
+
+    echo "Packages to install:%PACKAGES_TO_INSTALL%"
+
+    call "%MAMBA_ROOT_PREFIX%\micromamba.exe" install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
+
+    if not exist "%INSTALL_ENV_DIR%" (
+        echo "There was a problem while installing%PACKAGES_TO_INSTALL% using micromamba. Cannot continue."
+        pause
+        exit /b
+    )
+)
+
+set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
+
+@rem get the repo (and load into the current directory)
+if not exist ".git" (
+    call git config --global init.defaultBranch master
+    call git init
+    call git remote add origin %REPO_URL%
+    call git fetch
+    call git checkout origin/master -ft
+)
+
+@rem activate the base env
+call conda activate
+
+@rem make the models dir
+mkdir models\ldm\stable-diffusion-v1
+
+@rem install the project
+call webui.cmd
+
+@rem finally, tell the user that they need to download the ckpt
+echo.
+echo "Now you need to install the weights for the stable diffusion model."
+echo "Please follow the steps related to models weights at https://sd-webui.github.io/stable-diffusion-webui/docs/1.windows-installation.html#cloning-the-repo to complete the installation"
+
+@rem it would be nice if the weights downloaded automatically, and didn't need the user to do this manually.
+
+pause

installer/install.sh

@@ -0,0 +1,90 @@
+#!/bin/bash
+
+# This script will install git and conda (if not found on the PATH variable)
+#  using micromamba (an 8mb static-linked single-file binary, conda replacement).
+# For users who already have git and conda, this step will be skipped.
+
+# Then, it'll run the webui.cmd file to continue with the installation as usual.
+
+# This enables a user to install this project without manually installing conda and git.
+
+cd "$(dirname "${BASH_SOURCE[0]}")"
+
+echo "Installing Sygil WebUI.."
+echo ""
+
+OS_ARCH=$(uname -m)
+case "${OS_ARCH}" in
+    x86_64*)    OS_ARCH="64";;
+    arm64*)     OS_ARCH="aarch64";;
+    *)          echo "Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64" && exit
+esac
+
+# config
+export MAMBA_ROOT_PREFIX="$(pwd)/installer_files/mamba"
+INSTALL_ENV_DIR="$(pwd)/installer_files/env"
+MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/linux-${OS_ARCH}/latest"
+umamba_exists="F"
+
+# figure out whether git and conda needs to be installed
+if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
+
+PACKAGES_TO_INSTALL=""
+
+if ! hash "conda" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda"; fi
+if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
+
+if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi
+
+# (if necessary) install git and conda into a contained environment
+if [ "$PACKAGES_TO_INSTALL" != "" ]; then
+    # download micromamba
+    if [ "$umamba_exists" == "F" ]; then
+        echo "Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to $MAMBA_ROOT_PREFIX/micromamba"
+
+        mkdir -p "$MAMBA_ROOT_PREFIX"
+        curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvj bin/micromamba -O > "$MAMBA_ROOT_PREFIX/micromamba"
+
+        chmod u+x "$MAMBA_ROOT_PREFIX/micromamba"
+
+        # test the mamba binary
+        echo "Micromamba version:"
+        "$MAMBA_ROOT_PREFIX/micromamba" --version
+    fi
+
+    # create the installer env
+    if [ ! -e "$INSTALL_ENV_DIR" ]; then
+        "$MAMBA_ROOT_PREFIX/micromamba" create -y --prefix "$INSTALL_ENV_DIR"
+    fi
+
+    echo "Packages to install:$PACKAGES_TO_INSTALL"
+
+    "$MAMBA_ROOT_PREFIX/micromamba" install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge $PACKAGES_TO_INSTALL
+
+    if [ ! -e "$INSTALL_ENV_DIR" ]; then
+        echo "There was a problem while initializing micromamba. Cannot continue."
+        exit
+    fi
+fi
+
+if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
+
+CONDA_BASEPATH=$(conda info --base)
+source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
+
+conda activate
+
+# run the installer script for linux
+curl "https://raw.githubusercontent.com/JoshuaKimsey/Linux-StableDiffusion-Script/main/linux-sd.sh" > linux-sd.sh
+chmod u+x linux-sd.sh
+
+./linux-sd.sh
+
+# tell the user that they need to download the ckpt
+WEIGHTS_DOC_URL="https://sd-webui.github.io/stable-diffusion-webui/docs/2.linux-installation.html#initial-start-guide"
+
+echo ""
+echo "Now you need to install the weights for the stable diffusion model."
+echo "Please follow the steps at $WEIGHTS_DOC_URL to complete the installation"
+
+# it would be nice if the weights downloaded automatically, and didn't need the user to do this manually.

installer/readme.txt

@@ -0,0 +1,11 @@
+Sygil WebUI
+
+Project homepage: https://github.com/Sygil-Dev/sygil-webui
+
+Installation on Windows:
+ Please double-click the 'install.bat' file (while keeping it inside the sygil folder).
+
+Installation on Linux:
+ Please open the terminal, and run './install.sh' (while keeping it inside the sygil folder).
+
+After installation, please run the 'webui.cmd' file (on Windows) or 'webui.sh' file (on Linux/Mac) to start Sygil.

ldm/models/diffusion/kdiffusion.py

@@ -0,0 +1,55 @@
+import k_diffusion as K
+import torch
+import torch.nn as nn
+
+class KDiffusionSampler:
+    def __init__(self, m, sampler, callback=None):
+        self.model = m
+        self.model_wrap = K.external.CompVisDenoiser(m)
+        self.schedule = sampler
+        self.generation_callback = callback
+    def get_sampler_name(self):
+        return self.schedule
+    def sample(self, S, conditioning, unconditional_guidance_scale, unconditional_conditioning, x_T):
+        sigmas = self.model_wrap.get_sigmas(S)
+        x = x_T * sigmas[0]
+        model_wrap_cfg = CFGDenoiser(self.model_wrap)
+        samples_ddim = None
+        samples_ddim = K.sampling.__dict__[f'sample_{self.schedule}'](
+            model_wrap_cfg, x, sigmas,
+            extra_args={'cond': conditioning, 'uncond': unconditional_conditioning,'cond_scale': unconditional_guidance_scale},
+            disable=False, callback=self.generation_callback)
+        #
+        return samples_ddim, None
+class CFGMaskedDenoiser(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
+        x_in = x
+        x_in = torch.cat([x_in] * 2)
+        sigma_in = torch.cat([sigma] * 2)
+        cond_in = torch.cat([uncond, cond])
+        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+        denoised = uncond + (cond - uncond) * cond_scale
+
+        if mask is not None:
+            assert x0 is not None
+            img_orig = x0
+            mask_inv = 1. - mask
+            denoised = (img_orig * mask_inv) + (mask * denoised)
+
+        return denoised
+
+class CFGDenoiser(nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+
+    def forward(self, x, sigma, uncond, cond, cond_scale):
+        x_in = torch.cat([x] * 2)
+        sigma_in = torch.cat([sigma] * 2)
+        cond_in = torch.cat([uncond, cond])
+        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+        return uncond + (cond - uncond) * cond_scale

requirements.txt

@@ -1,31 +1,28 @@
-transformers==4.19.2 # do not change
+-e .
-diffusers==0.3.0
+
-invisible-watermark==0.1.5
-pytorch_lightning==1.7.7
-open-clip-torch
-loguru
-taming-transformers-rom1504==0.0.6  # required by ldm
-wget
 # See: https://github.com/CompVis/taming-transformers/issues/176
 # -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers # required by ldm
 # Note: taming package needs to be installed with -e option
+-e git+https://github.com/CompVis/taming-transformers#egg=taming-transformers
+invisible-watermark==0.1.5
+taming-transformers-rom1504==0.0.6  # required by ldm
 
+# Note: K-diffusion brings in CLIP 1.0 as a dependency automatically; will create a dependency resolution conflict when explicitly specified together
+git+https://github.com/openai/CLIP.git@main#egg=clip
 
 git+https://github.com/crowsonkb/k-diffusion.git
-# Note: K-diffusion brings in CLIP 1.0 as a dependency automatically; will create a dependency resolution conflict when explicitly specified together
+# git+https://github.com/hlky/k-diffusion-sd#egg=k_diffusion
-# git+https://github.com/openai/CLIP.git@main#egg=clip
 
 # Dependencies required for Stable Diffusion UI
 pynvml==11.4.1
 omegaconf==2.2.3
 
-Jinja2==3.1.2  # Jinja2 is required by Gradio
 # Note: Jinja2 3.x major version required due to breaking changes found in markupsafe==2.1.1; 2.0.1 is incompatible with other upstream dependencies
 # see https://github.com/pallets/markupsafe/issues/304
-
+Jinja2==3.1.2  # Jinja2 is required by Gradio
 
 # Environment Dependencies for WebUI (gradio)
-gradio==3.4
+gradio==3.4.1
 
 # Environment Dependencies for WebUI (streamlit)
 streamlit==1.13.0
@@ -34,8 +31,24 @@ streamlit-option-menu==0.3.2
 streamlit_nested_layout==0.1.1
 streamlit-server-state==0.14.2
 streamlit-tensorboard==0.0.2
+streamlit-elements==0.1.* # used for the draggable dashboard and new UI design (WIP)
+streamlit-ace==0.1.1 # used to replace the text area on the prompt and also for the code editor tool.
 hydralit==1.0.14
 hydralit_components==1.0.10
+stqdm==0.0.4
+uvicorn
+fastapi
+jsonmerge==1.8.
+matplotlib==3.6.
+resize-right==0.0.2
+torchdiffeq==0.2.3
+
+# txt2vid
+diffusers==0.6.0
+librosa==0.9.2
+
+# img2img inpainting
+streamlit-drawable-canvas==0.9.2
 
 # Img2text
 ftfy==6.1.1
@@ -45,11 +58,31 @@ timm==0.6.7
 tqdm==4.64.0
 tensorboard==2.10.1
 
-
 # Other
-retry==0.9.2  # used by sdutils
+retry==0.9.2  # used by sd_utils
-python-slugify==6.1.2  # used by sdutils
+python-slugify==6.1.2  # used by sd_utils
-piexif==1.1.3  # used by sdutils
+piexif==1.1.3  # used by sd_utils
+pywebview==3.6.3 # used by streamlit_webview.py
+
+accelerate==0.12.0
+albumentations==0.4.3
+einops==0.3.1
+facexlib>=0.2.3
+imageio-ffmpeg==0.4.2
+imageio==2.9.0
+kornia==0.6
+loguru
+opencv-python-headless==4.6.0.66
+open-clip-torch==2.0.2
+pandas==1.4.3
+pudb==2019.2
+pytorch-lightning==1.7.7
+realesrgan==0.3.0
+test-tube>=0.7.5
+timm==0.6.7
+torch-fidelity==0.3.0
+transformers==4.19.2 # do not change
+wget
 
 # Optional packages commonly used with Stable Diffusion workflow
 
@@ -57,11 +90,14 @@ piexif==1.1.3  # used by sdutils
 basicsr==1.4.2  # required by RealESRGAN
 gfpgan==1.3.8  # GFPGAN
 realesrgan==0.3.0  # RealESRGAN brings in GFPGAN as a requirement
--e git+https://github.com/devilismyfriend/latent-diffusion#egg=latent-diffusion  #ldsr
+-e git+https://github.com/devilismyfriend/latent-diffusion#egg=latent-diffusion
 
 ## for monocular depth estimation 
 tensorflow==2.10.0
 
+# Unused Packages: No current usage but will be used in the future.
+
 
 # Orphaned Packages:  No usage found
 
+

runpod_entrypoint.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

scripts/APIServer.py

@@ -0,0 +1,36 @@
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
+
+# Copyright 2022 Sygil-Dev team.
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+# base webui import and utils.
+#from sd_utils import *
+from sd_utils import *
+# streamlit imports
+
+#streamlit components section
+
+#other imports
+import os, time, requests
+import sys
+#from fastapi import FastAPI
+#import uvicorn
+
+# Temp imports
+
+# end of imports
+#---------------------------------------------------------------------------------------------------------------
+
+
+def layout():
+    st.info("Under Construction. :construction_worker:")

scripts/ModelManager.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -19,6 +19,9 @@ from sd_utils import *
 
 
 #other imports
+from requests.auth import HTTPBasicAuth
+from requests import HTTPError
+from stqdm import stqdm
 
 # Temp imports
 
@@ -28,19 +31,40 @@ from sd_utils import *
 def download_file(file_name, file_path, file_url):
     if not os.path.exists(file_path):
         os.makedirs(file_path)
-    if not os.path.exists(file_path + '/' + file_name):
+
+    if not os.path.exists(os.path.join(file_path , file_name)):
         print('Downloading ' + file_name + '...')
         # TODO - add progress bar in streamlit
         # download file with `requests``
-        with requests.get(file_url, stream=True) as r:
+        if file_name == "Stable Diffusion v1.5":
+            if "huggingface_token" not in st.session_state or st.session_state["defaults"].general.huggingface_token == "None":
+                if "progress_bar_text" in st.session_state:
+                    st.session_state["progress_bar_text"].error(
+                        "You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token."
+                                    )
+                raise OSError("You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token.")
+
+        try:
+            with requests.get(file_url, auth = HTTPBasicAuth('token', st.session_state.defaults.general.huggingface_token) if "huggingface.co" in file_url else None, stream=True) as r:
                 r.raise_for_status()
-            with open(file_path + '/' + file_name, 'wb') as f:
+                with open(os.path.join(file_path, file_name), 'wb') as f:
-                for chunk in r.iter_content(chunk_size=8192):
+                    for chunk in stqdm(r.iter_content(chunk_size=8192), backend=True, unit="kb"):
                         f.write(chunk)
+        except HTTPError as e:
+            if "huggingface.co" in file_url:
+                if "resolve"in file_url:
+                    repo_url = file_url.split("resolve")[0]
+
+                    st.session_state["progress_bar_text"].error(
+                        f"You need to accept the license for the model in order to be able to download it. "
+                        f"Please visit {repo_url} and accept the lincense there, then try again to download the model.")
+
+            logger.error(e)
 
     else:
         print(file_name + ' already exists.')
 
+
 def download_model(models, model_name):
     """ Download all files from model_list[model_name] """
     for file in models[model_name]:
@@ -51,8 +75,8 @@ def download_model(models, model_name):
 def layout():
     #search = st.text_input(label="Search", placeholder="Type the name of the model you want to search for.", help="")
 
-    colms = st.columns((1, 3, 5, 5))
+    colms = st.columns((1, 3, 3, 5, 5))
-    columns = ["№",'Model Name','Save Location','Download Link']
+    columns = ["№", 'Model Name', 'Save Location', "Download", 'Download Link']
 
     models = st.session_state["defaults"].model_manager.models
 
@@ -61,7 +85,7 @@ def layout():
         col.write(field_name)
 
     for x, model_name in enumerate(models):
-        col1, col2, col3, col4 = st.columns((1, 3, 4, 6))
+        col1, col2, col3, col4, col5 = st.columns((1, 3, 3, 3, 6))
         col1.write(x)  # index
         col2.write(models[model_name]['model_name'])
         col3.write(models[model_name]['save_location'])
@@ -69,16 +93,16 @@ def layout():
             files_exist = 0
             for file in models[model_name]['files']:
                 if "save_location" in models[model_name]['files'][file]:
-                    os.path.exists(models[model_name]['files'][file]['save_location'] + '/' + models[model_name]['files'][file]['file_name'])
+                    os.path.exists(os.path.join(models[model_name]['files'][file]['save_location'] , models[model_name]['files'][file]['file_name']))
                     files_exist += 1
-                elif os.path.exists(models[model_name]['save_location'] + '/' + models[model_name]['files'][file]['file_name']):
+                elif os.path.exists(os.path.join(models[model_name]['save_location'] , models[model_name]['files'][file]['file_name'])):
                     files_exist += 1
             files_needed = []
             for file in models[model_name]['files']:
                 if "save_location" in models[model_name]['files'][file]:
-                    if not os.path.exists(models[model_name]['files'][file]['save_location'] + '/' + models[model_name]['files'][file]['file_name']):
+                    if not os.path.exists(os.path.join(models[model_name]['files'][file]['save_location'] , models[model_name]['files'][file]['file_name'])):
                         files_needed.append(file)
-                elif not os.path.exists(models[model_name]['save_location'] + '/' + models[model_name]['files'][file]['file_name']):
+                elif not os.path.exists(os.path.join(models[model_name]['save_location'] , models[model_name]['files'][file]['file_name'])):
                     files_needed.append(file)
             if len(files_needed) > 0:
                 if st.button('Download', key=models[model_name]['model_name'], help='Download ' + models[model_name]['model_name']):
@@ -87,7 +111,10 @@ def layout():
                             download_file(models[model_name]['files'][file]['file_name'], models[model_name]['files'][file]['save_location'], models[model_name]['files'][file]['download_link'])
                         else:
                             download_file(models[model_name]['files'][file]['file_name'], models[model_name]['save_location'], models[model_name]['files'][file]['download_link'])
+                    st.experimental_rerun()
                 else:
                     st.empty()
             else:
                 st.write('✅')
+
+        #

scripts/Settings.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -28,8 +28,9 @@ from omegaconf import OmegaConf
 # end of imports
 # ---------------------------------------------------------------------------------------------------------------
 
+@logger.catch(reraise=True)
 def layout():
-	st.header("Settings")
+    #st.header("Settings")
 
     with st.form("Settings"):
         general_tab, txt2img_tab, img2img_tab, img2txt_tab, txt2vid_tab, image_processing, textual_inversion_tab, concepts_library_tab = st.tabs(
@@ -47,10 +48,9 @@ def layout():
 
                 device_list.append(f"{id}: {name} ({human_readable_size(total_memory, decimal_places=0)})")
 
-			
             with col1:
                 st.title("General")
-				st.session_state['defaults'].general.gpu = int(st.selectbox("GPU", device_list,
+                st.session_state['defaults'].general.gpu = int(st.selectbox("GPU", device_list, index=st.session_state['defaults'].general.gpu,
                                                                             help=f"Select which GPU to use. Default: {device_list[0]}").split(":")[0])
 
                 st.session_state['defaults'].general.outdir = str(st.text_input("Output directory", value=st.session_state['defaults'].general.outdir,
@@ -61,7 +61,7 @@ def layout():
                 custom_models_available()
 
                 if server_state["CustomModel_available"]:
-					st.session_state.default_model = st.selectbox("Default Model:", server_state["custom_models"],
+                    st.session_state.defaults.general.default_model = st.selectbox("Default Model:", server_state["custom_models"],
                                                                   index=server_state["custom_models"].index(st.session_state['defaults'].general.default_model),
                                                                   help="Select the model you want to use. If you have placed custom models \
                                                                   on your 'models/custom' folder they will be shown here as well. The model name that will be shown here \
@@ -69,7 +69,7 @@ def layout():
                                                                   it is recommended to give the .ckpt file a name that \
                                                                   will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
                 else:
-						st.session_state.default_model = st.selectbox("Default Model:", [st.session_state['defaults'].general.default_model],
+                    st.session_state.defaults.general.default_model = st.selectbox("Default Model:", [st.session_state['defaults'].general.default_model],
                                                                   help="Select the model you want to use. If you have placed custom models \
                                                                   on your 'models/custom' folder they will be shown here as well. \
                                                                   The model name that will be shown here is the same as the name\
@@ -93,7 +93,8 @@ def layout():
                                                                                      index=RealESRGAN_model_list.index(st.session_state['defaults'].general.RealESRGAN_model),
                                                                                      help="Default RealESRGAN model. Default: 'RealESRGAN_x4plus'")
                 Upscaler_list = ["RealESRGAN", "LDSR"]
-				st.session_state['defaults'].general.upscaling_method = st.selectbox("Upscaler", Upscaler_list, index=Upscaler_list.index(st.session_state['defaults'].general.upscaling_method), help="Default upscaling method. Default: 'RealESRGAN'")
+                st.session_state['defaults'].general.upscaling_method = st.selectbox("Upscaler", Upscaler_list, index=Upscaler_list.index(
+                    st.session_state['defaults'].general.upscaling_method), help="Default upscaling method. Default: 'RealESRGAN'")
 
             with col2:
                 st.title("Performance")
@@ -157,9 +158,11 @@ def layout():
                 # in steps will be shown, this is helpful to reduce the negative effect this option has on performance. \
                 # Default: True")
                 st.session_state["defaults"].general.update_preview = True
-				st.session_state["defaults"].general.update_preview_frequency = int(st.text_input("Update Preview Frequency", value=st.session_state['defaults'].general.update_preview_frequency,
+                st.session_state["defaults"].general.update_preview_frequency = st.number_input("Update Preview Frequency",
+                                                                                                min_value=0,
+                                                                                                value=st.session_state['defaults'].general.update_preview_frequency,
                                                                                                 help="Specify the frequency at which the image is updated in steps, this is helpful to reduce the \
-																								  negative effect updating the preview image has on performance. Default: 10"))					
+                                                                                                negative effect updating the preview image has on performance. Default: 10")
 
             with col3:
                 st.title("Others")
@@ -178,21 +181,23 @@ def layout():
 
                 st.session_state["defaults"].general.save_metadata = st.checkbox("Save Metadata", value=st.session_state['defaults'].general.save_metadata,
                                                                                  help="Save metadata on the output image. Default: True")
-				save_format_list = ["png"]
+                save_format_list = ["png","jpg", "jpeg","webp"]
                 st.session_state["defaults"].general.save_format = st.selectbox("Save Format", save_format_list, index=save_format_list.index(st.session_state['defaults'].general.save_format),
                                                                                 help="Format that will be used whens saving the output images. Default: 'png'")
 
                 st.session_state["defaults"].general.skip_grid = st.checkbox("Skip Grid", value=st.session_state['defaults'].general.skip_grid,
                                                                              help="Skip saving the grid output image. Default: False")
                 if not st.session_state["defaults"].general.skip_grid:
-					st.session_state["defaults"].general.grid_format = st.text_input("Grid Format", value=st.session_state['defaults'].general.grid_format,
+
-																					 help="Format for saving the grid output image. Default: 'jpg:95'")
+
+                    st.session_state["defaults"].general.grid_quality = st.number_input("Grid Quality", value=st.session_state['defaults'].general.grid_quality,
+                                                                                        help="Format for saving the grid output image. Default: 95")
 
                 st.session_state["defaults"].general.skip_save = st.checkbox("Skip Save", value=st.session_state['defaults'].general.skip_save,
                                                                              help="Skip saving the output image. Default: False")
 
-				st.session_state["defaults"].general.n_rows = int(st.text_input("Number of Grid Rows", value=st.session_state['defaults'].general.n_rows,
+                st.session_state["defaults"].general.n_rows = st.number_input("Number of Grid Rows", value=st.session_state['defaults'].general.n_rows,
-																				 help="Number of rows the grid wil have when saving the grid output image. Default: '-1'"))
+                                                                              help="Number of rows the grid wil have when saving the grid output image. Default: '-1'")
 
                 st.session_state["defaults"].general.no_verify_input = st.checkbox("Do not Verify Input", value=st.session_state['defaults'].general.no_verify_input,
                                                                                    help="Do not verify input to check if it's too long. Default: False")
@@ -200,20 +205,68 @@ def layout():
                 st.session_state["defaults"].daisi_app.running_on_daisi_io = st.checkbox("Running on Daisi.io?", value=st.session_state['defaults'].daisi_app.running_on_daisi_io,
                                                                                          help="Specify if we are running on app.Daisi.io . Default: False")
 
-				
-				
             with col4:
                 st.title("Streamlit Config")
 
-				st.session_state["defaults"].general.streamlit_telemetry = st.checkbox("Enable Telemetry", value=st.session_state['defaults'].general.streamlit_telemetry,
-																					help="Enables or Disables streamlit telemetry. Default: False")
-				st.session_state["streamlit_config"]["browser"]["gatherUsageStats"] = st.session_state["defaults"].general.streamlit_telemetry
-				
                 default_theme_list = ["light", "dark"]
                 st.session_state["defaults"].general.default_theme = st.selectbox("Default Theme", default_theme_list, index=default_theme_list.index(st.session_state['defaults'].general.default_theme),
                                                                                   help="Defaut theme to use as base for streamlit. Default: dark")
                 st.session_state["streamlit_config"]["theme"]["base"] = st.session_state["defaults"].general.default_theme
 
+
+                if not st.session_state['defaults'].admin.hide_server_setting:
+                    with st.expander("Server", True):
+
+                        st.session_state["streamlit_config"]['server']['headless'] = st.checkbox("Run Headless", help="If false, will attempt to open a browser window on start.  \
+                                                                                                 Default: false unless (1) we are on a Linux box where DISPLAY is unset, \
+                                                                                                 or (2) we are running in the Streamlit Atom plugin.")
+
+                        st.session_state["streamlit_config"]['server']['port'] = st.number_input("Port", value=st.session_state["streamlit_config"]['server']['port'],
+                                                                                                 help="The port where the server will listen for browser connections. Default: 8501")
+
+                        st.session_state["streamlit_config"]['server']['baseUrlPath'] = st.text_input("Base Url Path", value=st.session_state["streamlit_config"]['server']['baseUrlPath'],
+                                                                                                 help="The base path for the URL where Streamlit should be served from. Default: '' ")
+
+                        st.session_state["streamlit_config"]['server']['enableCORS'] = st.checkbox("Enable CORS", value=st.session_state['streamlit_config']['server']['enableCORS'],
+                                                                                                   help="Enables support for Cross-Origin Request Sharing (CORS) protection, for added security. \
+                                                                                                   Due to conflicts between CORS and XSRF, if `server.enableXsrfProtection` is on and `server.enableCORS` \
+                                                                                                   is off at the same time, we will prioritize `server.enableXsrfProtection`. Default: true")
+
+                        st.session_state["streamlit_config"]['server']['enableXsrfProtection'] = st.checkbox("Enable Xsrf Protection",
+                                                                                                             value=st.session_state['streamlit_config']['server']['enableXsrfProtection'],
+                                                                                                             help="Enables support for Cross-Site Request Forgery (XSRF) protection, \
+                                                                                                             for added security. Due to conflicts between CORS and XSRF, \
+                                                                                                             if `server.enableXsrfProtection` is on and `server.enableCORS` is off at \
+                                                                                                             the same time, we will prioritize `server.enableXsrfProtection`. Default: true")
+
+                        st.session_state["streamlit_config"]['server']['maxUploadSize'] = st.number_input("Max Upload Size", value=st.session_state["streamlit_config"]['server']['maxUploadSize'],
+                                                                                                 help="Max size, in megabytes, for files uploaded with the file_uploader. Default: 200")
+
+                        st.session_state["streamlit_config"]['server']['maxMessageSize'] = st.number_input("Max Message Size", value=st.session_state["streamlit_config"]['server']['maxUploadSize'],
+                                                                                                 help="Max size, in megabytes, of messages that can be sent via the WebSocket connection. Default: 200")
+
+                        st.session_state["streamlit_config"]['server']['enableWebsocketCompression'] = st.checkbox("Enable Websocket Compression",
+                                                                                                                   value=st.session_state["streamlit_config"]['server']['enableWebsocketCompression'],
+                                                                                                                   help=" Enables support for websocket compression. Default: false")
+                if not st.session_state['defaults'].admin.hide_browser_setting:
+                    with st.expander("Browser", expanded=True):
+                        st.session_state["streamlit_config"]['browser']['serverAddress'] = st.text_input("Server Address",
+                                                                                                       value=st.session_state["streamlit_config"]['browser']['serverAddress'] if "serverAddress" in st.session_state["streamlit_config"] else "localhost",
+                                                                                                       help="Internet address where users should point their browsers in order \
+                                                                                                       to connect to the app. Can be IP address or DNS name and path.\
+                                                                                                       This is used to: - Set the correct URL for CORS and XSRF protection purposes. \
+                                                                                                       - Show the URL on the terminal - Open the browser. Default: 'localhost'")
+
+                        st.session_state["defaults"].general.streamlit_telemetry = st.checkbox("Enable Telemetry", value=st.session_state['defaults'].general.streamlit_telemetry,
+                                                                                               help="Enables or Disables streamlit telemetry. Default: False")
+                        st.session_state["streamlit_config"]["browser"]["gatherUsageStats"] = st.session_state["defaults"].general.streamlit_telemetry
+
+                        st.session_state["streamlit_config"]['browser']['serverPort'] = st.number_input("Server Port", value=st.session_state["streamlit_config"]['browser']['serverPort'],
+                                                                                                 help="Port where users should point their browsers in order to connect to the app. \
+                                                                                                 This is used to: - Set the correct URL for CORS and XSRF protection purposes. \
+                                                                                                 - Show the URL on the terminal - Open the browser \
+                                                                                                 Default: whatever value is set in server.port.")
+
             with col5:
                 st.title("Huggingface")
                 st.session_state["defaults"].general.huggingface_token = st.text_input("Huggingface Token", value=st.session_state['defaults'].general.huggingface_token, type="password",
@@ -222,6 +275,15 @@ def layout():
                                                                                        and WILL NOT be share with us or anyone. You can get your access token \
                                                                                        at https://huggingface.co/settings/tokens. Default: None")
 
+                st.title("Stable Horde")
+                st.session_state["defaults"].general.stable_horde_api = st.text_input("Stable Horde Api", value=st.session_state["defaults"].general.stable_horde_api, type="password",
+                                                                                      help="First Register an account at https://stablehorde.net/register which will generate for you \
+                                                                                      an API key. Store that key somewhere safe. \n \
+                                                                                      If you do not want to register, you can use `0000000000` as api_key to connect anonymously.\
+                                                                                      However anonymous accounts have the lowest priority when there's too many concurrent requests! \
+                                                                                      To increase your priority you will need a unique API key and then to increase your Kudos \
+                                                                                      read more about them at https://dbzer0.com/blog/the-kudos-based-economy-for-the-koboldai-horde/.")
+
         with txt2img_tab:
             col1, col2, col3, col4, col5 = st.columns(5, gap='medium')
 
@@ -229,72 +291,64 @@ def layout():
                 st.title("Slider Parameters")
 
                 # Width
-				st.session_state["defaults"].txt2img.width.value = int(st.text_input("Default Image Width", value=st.session_state['defaults'].txt2img.width.value, 
+                st.session_state["defaults"].txt2img.width.value = st.number_input("Default Image Width", value=st.session_state['defaults'].txt2img.width.value,
-																					 help="Set the default width for the generated image. Default is: 512"))
+                                                                                   help="Set the default width for the generated image. Default is: 512")
 
-				st.session_state["defaults"].txt2img.width.min_value = int(st.text_input("Minimum Image Width", value=st.session_state['defaults'].txt2img.width.min_value,
+                st.session_state["defaults"].txt2img.width.min_value = st.number_input("Minimum Image Width", value=st.session_state['defaults'].txt2img.width.min_value,
-																						 help="Set the default minimum value for the width slider. Default is: 64"))
+                                                                                       help="Set the default minimum value for the width slider. Default is: 64")
 
-				st.session_state["defaults"].txt2img.width.max_value = int(st.text_input("Maximum Image Width", value=st.session_state['defaults'].txt2img.width.max_value,
+                st.session_state["defaults"].txt2img.width.max_value = st.number_input("Maximum Image Width", value=st.session_state['defaults'].txt2img.width.max_value,
-																						 help="Set the default maximum value for the width slider. Default is: 2048"))
+                                                                                       help="Set the default maximum value for the width slider. Default is: 2048")
 
                 # Height
-				st.session_state["defaults"].txt2img.height.value = int(st.text_input("Default Image Height", value=st.session_state['defaults'].txt2img.height.value, 
+                st.session_state["defaults"].txt2img.height.value = st.number_input("Default Image Height", value=st.session_state['defaults'].txt2img.height.value,
-																					  help="Set the default height for the generated image. Default is: 512"))
+                                                                                    help="Set the default height for the generated image. Default is: 512")
 
-				st.session_state["defaults"].txt2img.height.min_value = int(st.text_input("Minimum Image Height", value=st.session_state['defaults'].txt2img.height.min_value,
+                st.session_state["defaults"].txt2img.height.min_value = st.number_input("Minimum Image Height", value=st.session_state['defaults'].txt2img.height.min_value,
-																						  help="Set the default minimum value for the height slider. Default is: 64"))
+                                                                                        help="Set the default minimum value for the height slider. Default is: 64")
 
-				st.session_state["defaults"].txt2img.height.max_value = int(st.text_input("Maximum Image Height", value=st.session_state['defaults'].txt2img.height.max_value,
+                st.session_state["defaults"].txt2img.height.max_value = st.number_input("Maximum Image Height", value=st.session_state['defaults'].txt2img.height.max_value,
-																						  help="Set the default maximum value for the height slider. Default is: 2048"))
+                                                                                        help="Set the default maximum value for the height slider. Default is: 2048")
 
                 with col2:
                     # CFG
-					st.session_state["defaults"].txt2img.cfg_scale.value = float(st.text_input("Default CFG Scale", value=st.session_state['defaults'].txt2img.cfg_scale.value,
+                    st.session_state["defaults"].txt2img.cfg_scale.value = st.number_input("Default CFG Scale", value=st.session_state['defaults'].txt2img.cfg_scale.value,
-																							   help="Set the default value for the CFG Scale. Default is: 7.5"))
+                                                                                           help="Set the default value for the CFG Scale. Default is: 7.5")
 
-					st.session_state["defaults"].txt2img.cfg_scale.min_value = float(st.text_input("Minimum CFG Scale Value", value=st.session_state['defaults'].txt2img.cfg_scale.min_value,
+                    st.session_state["defaults"].txt2img.cfg_scale.min_value = st.number_input("Minimum CFG Scale Value", value=st.session_state['defaults'].txt2img.cfg_scale.min_value,
-																								   help="Set the default minimum value for the CFG scale slider. Default is: 1"))
+                                                                                               help="Set the default minimum value for the CFG scale slider. Default is: 1")
-		
-					st.session_state["defaults"].txt2img.cfg_scale.max_value = float(st.text_input("Maximum CFG Scale Value", 
-																								   value=st.session_state['defaults'].txt2img.cfg_scale.max_value,
-																								   help="Set the default maximum value for the CFG scale slider. Default is: 30"))
-		
-					st.session_state["defaults"].txt2img.cfg_scale.step = float(st.text_input("CFG Slider Steps", value=st.session_state['defaults'].txt2img.cfg_scale.step,
-																							  help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5"))
 
+                    st.session_state["defaults"].txt2img.cfg_scale.step = st.number_input("CFG Slider Steps", value=st.session_state['defaults'].txt2img.cfg_scale.step,
+                                                                                          help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5")
                     # Sampling Steps
-					st.session_state["defaults"].txt2img.sampling_steps.value = int(st.text_input("Default Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps.value, 
+                    st.session_state["defaults"].txt2img.sampling_steps.value = st.number_input("Default Sampling Steps", value=st.session_state['defaults'].txt2img.sampling_steps.value,
-																								  help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)"))
+                                                                                                help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")
 
-					st.session_state["defaults"].txt2img.sampling_steps.min_value = int(st.text_input("Minimum Sampling Steps", 
+                    st.session_state["defaults"].txt2img.sampling_steps.min_value = st.number_input("Minimum Sampling Steps",
                                                                                                     value=st.session_state['defaults'].txt2img.sampling_steps.min_value,
-																									  help="Set the default minimum value for the sampling steps slider. Default is: 1"))
+                                                                                                    help="Set the default minimum value for the sampling steps slider. Default is: 1")
 
-					st.session_state["defaults"].txt2img.sampling_steps.max_value = int(st.text_input("Maximum Sampling Steps", 
+                    st.session_state["defaults"].txt2img.sampling_steps.step = st.number_input("Sampling Slider Steps",
-																									  value=st.session_state['defaults'].txt2img.sampling_steps.max_value,
-																									  help="Set the default maximum value for the sampling steps slider. Default is: 250"))
-		
-					st.session_state["defaults"].txt2img.sampling_steps.step = int(st.text_input("Sampling Slider Steps", 
                                                                                                value=st.session_state['defaults'].txt2img.sampling_steps.step,
-																								 help="Set the default value for the number of steps on the sampling steps slider. Default is: 10"))
+                                                                                               help="Set the default value for the number of steps on the sampling steps slider. Default is: 10")
 
             with col3:
                 st.title("General Parameters")
 
                 # Batch Count
-				st.session_state["defaults"].txt2img.batch_count.value = int(st.text_input("Batch count", value=st.session_state['defaults'].txt2img.batch_count.value,
+                st.session_state["defaults"].txt2img.batch_count.value = st.number_input("Batch count", value=st.session_state['defaults'].txt2img.batch_count.value,
-																					 help="How many iterations or batches of images to generate in total."))
+                                                                                         help="How many iterations or batches of images to generate in total.")
 
-				st.session_state["defaults"].txt2img.batch_size.value = int(st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
+                st.session_state["defaults"].txt2img.batch_size.value = st.number_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
                                                                                         help="How many images are at once in a batch.\
                                                                                         It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                         takes to finish generation as more images are generated at once.\
-																					 Default: 1"))				
+                                                                                        Default: 1")
 
                 default_sampler_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
                 st.session_state["defaults"].txt2img.default_sampler = st.selectbox("Default Sampler",
-																					default_sampler_list, index=default_sampler_list.index(st.session_state['defaults'].txt2img.default_sampler),
+                                                                                    default_sampler_list, index=default_sampler_list.index(
+                                                                                        st.session_state['defaults'].txt2img.default_sampler),
                                                                                     help="Defaut sampler to use for txt2img. Default: k_euler")
 
                 st.session_state['defaults'].txt2img.seed = st.text_input("Default Seed", value=st.session_state['defaults'].txt2img.seed, help="Default seed.")
@@ -324,34 +378,36 @@ def layout():
                 st.session_state["defaults"].txt2img.write_info_files = st.checkbox("Write Info Files For Images", value=st.session_state['defaults'].txt2img.write_info_files,
                                                                                     help="Choose to write the info files along with the generated images. Default: True")
 
-				st.session_state["defaults"].txt2img.use_GFPGAN = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2img.use_GFPGAN, help="Choose to use GFPGAN. Default: False")
+                st.session_state["defaults"].txt2img.use_GFPGAN = st.checkbox(
+                    "Use GFPGAN", value=st.session_state['defaults'].txt2img.use_GFPGAN, help="Choose to use GFPGAN. Default: False")
 
                 st.session_state["defaults"].txt2img.use_upscaling = st.checkbox("Use Upscaling", value=st.session_state['defaults'].txt2img.use_upscaling,
                                                                                  help="Choose to turn on upscaling by default. Default: False")
 
                 st.session_state["defaults"].txt2img.update_preview = True
-				st.session_state["defaults"].txt2img.update_preview_frequency = int(st.text_input("Preview Image Update Frequency",
+                st.session_state["defaults"].txt2img.update_preview_frequency = st.number_input("Preview Image Update Frequency",
+                                                                                                min_value=0,
                                                                                                 value=st.session_state['defaults'].txt2img.update_preview_frequency,
-																								  help="Set the default value for the frrquency of the preview image updates. Default is: 10"))
+                                                                                                help="Set the default value for the frrquency of the preview image updates. Default is: 10")
 
             with col5:
                 st.title("Variation Parameters")
 
-				st.session_state["defaults"].txt2img.variant_amount.value = float(st.text_input("Default Variation Amount", 
+                st.session_state["defaults"].txt2img.variant_amount.value = st.number_input("Default Variation Amount",
                                                                                             value=st.session_state['defaults'].txt2img.variant_amount.value,
-																								help="Set the default variation to use. Default is: 0.0"))
+                                                                                            help="Set the default variation to use. Default is: 0.0")
 
-				st.session_state["defaults"].txt2img.variant_amount.min_value = float(st.text_input("Minimum Variation Amount",
+                st.session_state["defaults"].txt2img.variant_amount.min_value = st.number_input("Minimum Variation Amount",
                                                                                                 value=st.session_state['defaults'].txt2img.variant_amount.min_value,
-																									help="Set the default minimum value for the variation slider. Default is: 0.0"))
+                                                                                                help="Set the default minimum value for the variation slider. Default is: 0.0")
 
-				st.session_state["defaults"].txt2img.variant_amount.max_value = float(st.text_input("Maximum Variation Amount",
+                st.session_state["defaults"].txt2img.variant_amount.max_value = st.number_input("Maximum Variation Amount",
                                                                                                 value=st.session_state['defaults'].txt2img.variant_amount.max_value,
-																									help="Set the default maximum value for the variation slider. Default is: 1.0"))
+                                                                                                help="Set the default maximum value for the variation slider. Default is: 1.0")
 
-				st.session_state["defaults"].txt2img.variant_amount.step = float(st.text_input("Variation Slider Steps",
+                st.session_state["defaults"].txt2img.variant_amount.step = st.number_input("Variation Slider Steps",
                                                                                            value=st.session_state['defaults'].txt2img.variant_amount.step,
-																							   help="Set the default value for the number of steps on the variation slider. Default is: 1"))
+                                                                                           help="Set the default value for the number of steps on the variation slider. Default is: 1")
 
                 st.session_state['defaults'].txt2img.variant_seed = st.text_input("Default Variation Seed", value=st.session_state['defaults'].txt2img.variant_seed,
                                                                                   help="Default variation seed.")
@@ -363,134 +419,122 @@ def layout():
                 st.title("Image Editing")
 
                 # Denoising
-				st.session_state["defaults"].img2img.denoising_strength.value = float(st.text_input("Default Denoising Amount",
+                st.session_state["defaults"].img2img.denoising_strength.value = st.number_input("Default Denoising Amount",
                                                                                                 value=st.session_state['defaults'].img2img.denoising_strength.value,
-																									help="Set the default denoising to use. Default is: 0.75"))
+                                                                                                help="Set the default denoising to use. Default is: 0.75")
 
-				st.session_state["defaults"].img2img.denoising_strength.min_value = float(st.text_input("Minimum Denoising Amount", 
+                st.session_state["defaults"].img2img.denoising_strength.min_value = st.number_input("Minimum Denoising Amount",
                                                                                                     value=st.session_state['defaults'].img2img.denoising_strength.min_value,
-																										help="Set the default minimum value for the denoising slider. Default is: 0.0"))
+                                                                                                    help="Set the default minimum value for the denoising slider. Default is: 0.0")
 
-				st.session_state["defaults"].img2img.denoising_strength.max_value = float(st.text_input("Maximum Denoising Amount",
+                st.session_state["defaults"].img2img.denoising_strength.max_value = st.number_input("Maximum Denoising Amount",
                                                                                                     value=st.session_state['defaults'].img2img.denoising_strength.max_value,
-																										help="Set the default maximum value for the denoising slider. Default is: 1.0"))
+                                                                                                    help="Set the default maximum value for the denoising slider. Default is: 1.0")
 
-				st.session_state["defaults"].img2img.denoising_strength.step = float(st.text_input("Denoising Slider Steps",
+                st.session_state["defaults"].img2img.denoising_strength.step = st.number_input("Denoising Slider Steps",
                                                                                                value=st.session_state['defaults'].img2img.denoising_strength.step,
-																								   help="Set the default value for the number of steps on the denoising slider. Default is: 0.01"))
+                                                                                               help="Set the default value for the number of steps on the denoising slider. Default is: 0.01")
 
                 # Masking
-				st.session_state["defaults"].img2img.mask_mode = int(st.text_input("Default Mask Mode", value=st.session_state['defaults'].img2img.mask_mode,
+                st.session_state["defaults"].img2img.mask_mode = st.number_input("Default Mask Mode", value=st.session_state['defaults'].img2img.mask_mode,
-																				   help="Set the default mask mode to use. 0 = Keep Masked Area, 1 = Regenerate Masked Area. Default is: 0"))
+                                                                                 help="Set the default mask mode to use. 0 = Keep Masked Area, 1 = Regenerate Masked Area. Default is: 0")
 
                 st.session_state["defaults"].img2img.mask_restore = st.checkbox("Default Mask Restore", value=st.session_state['defaults'].img2img.mask_restore,
                                                                                 help="Mask Restore. Default: False")
 
-				st.session_state["defaults"].img2img.resize_mode = int(st.text_input("Default Resize Mode", value=st.session_state['defaults'].img2img.resize_mode, 
+                st.session_state["defaults"].img2img.resize_mode = st.number_input("Default Resize Mode", value=st.session_state['defaults'].img2img.resize_mode,
-																					 help="Set the default resizing mode. 0 = Just Resize, 1 = Crop and Resize, 3 = Resize and Fill. Default is: 0"))
+                                                                                   help="Set the default resizing mode. 0 = Just Resize, 1 = Crop and Resize, 3 = Resize and Fill. Default is: 0")
 
             with col2:
                 st.title("Slider Parameters")
 
                 # Width
-				st.session_state["defaults"].img2img.width.value = int(st.text_input("Default Outputted Image Width", value=st.session_state['defaults'].img2img.width.value,
+                st.session_state["defaults"].img2img.width.value = st.number_input("Default Outputted Image Width", value=st.session_state['defaults'].img2img.width.value,
-																					 help="Set the default width for the generated image. Default is: 512"))
+                                                                                   help="Set the default width for the generated image. Default is: 512")
 
-				st.session_state["defaults"].img2img.width.min_value = int(st.text_input("Minimum Outputted Image Width", value=st.session_state['defaults'].img2img.width.min_value,
+                st.session_state["defaults"].img2img.width.min_value = st.number_input("Minimum Outputted Image Width", value=st.session_state['defaults'].img2img.width.min_value,
-																						 help="Set the default minimum value for the width slider. Default is: 64"))
+                                                                                       help="Set the default minimum value for the width slider. Default is: 64")
 
-				st.session_state["defaults"].img2img.width.max_value = int(st.text_input("Maximum Outputted Image Width", value=st.session_state['defaults'].img2img.width.max_value,
+                st.session_state["defaults"].img2img.width.max_value = st.number_input("Maximum Outputted Image Width", value=st.session_state['defaults'].img2img.width.max_value,
-																						 help="Set the default maximum value for the width slider. Default is: 2048"))
+                                                                                       help="Set the default maximum value for the width slider. Default is: 2048")
 
                 # Height
-				st.session_state["defaults"].img2img.height.value = int(st.text_input("Default Outputted Image Height", value=st.session_state['defaults'].img2img.height.value, 
+                st.session_state["defaults"].img2img.height.value = st.number_input("Default Outputted Image Height", value=st.session_state['defaults'].img2img.height.value,
-																					  help="Set the default height for the generated image. Default is: 512"))
+                                                                                    help="Set the default height for the generated image. Default is: 512")
 
-				st.session_state["defaults"].img2img.height.min_value = int(st.text_input("Minimum Outputted Image Height", value=st.session_state['defaults'].img2img.height.min_value,
+                st.session_state["defaults"].img2img.height.min_value = st.number_input("Minimum Outputted Image Height", value=st.session_state['defaults'].img2img.height.min_value,
-																						  help="Set the default minimum value for the height slider. Default is: 64"))
+                                                                                        help="Set the default minimum value for the height slider. Default is: 64")
 
-				st.session_state["defaults"].img2img.height.max_value = int(st.text_input("Maximum Outputted Image Height", value=st.session_state['defaults'].img2img.height.max_value, 
+                st.session_state["defaults"].img2img.height.max_value = st.number_input("Maximum Outputted Image Height", value=st.session_state['defaults'].img2img.height.max_value,
-																						  help="Set the default maximum value for the height slider. Default is: 2048"))
+                                                                                        help="Set the default maximum value for the height slider. Default is: 2048")
 
                 # CFG
-				st.session_state["defaults"].img2img.cfg_scale.value = float(st.text_input("Default Img2Img CFG Scale", value=st.session_state['defaults'].img2img.cfg_scale.value,
+                st.session_state["defaults"].img2img.cfg_scale.value = st.number_input("Default Img2Img CFG Scale", value=st.session_state['defaults'].img2img.cfg_scale.value,
-																						   help="Set the default value for the CFG Scale. Default is: 7.5"))
+                                                                                       help="Set the default value for the CFG Scale. Default is: 7.5")
 
-				st.session_state["defaults"].img2img.cfg_scale.min_value = float(st.text_input("Minimum Img2Img CFG Scale Value", 
+                st.session_state["defaults"].img2img.cfg_scale.min_value = st.number_input("Minimum Img2Img CFG Scale Value",
                                                                                            value=st.session_state['defaults'].img2img.cfg_scale.min_value,
-																							   help="Set the default minimum value for the CFG scale slider. Default is: 1"))
+                                                                                           help="Set the default minimum value for the CFG scale slider. Default is: 1")
-    
-				st.session_state["defaults"].img2img.cfg_scale.max_value = float(st.text_input("Maximum Img2Img CFG Scale Value",
-																							   value=st.session_state['defaults'].img2img.cfg_scale.max_value, 
-																							   help="Set the default maximum value for the CFG scale slider. Default is: 30"))
 
                 with col3:
-					st.session_state["defaults"].img2img.cfg_scale.step = float(st.text_input("Img2Img CFG Slider Steps",
+                    st.session_state["defaults"].img2img.cfg_scale.step = st.number_input("Img2Img CFG Slider Steps",
                                                                                           value=st.session_state['defaults'].img2img.cfg_scale.step,
-																							  help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5"))
+                                                                                          help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5")
 
                     # Sampling Steps
-					st.session_state["defaults"].img2img.sampling_steps.value = int(st.text_input("Default Img2Img Sampling Steps",
+                    st.session_state["defaults"].img2img.sampling_steps.value = st.number_input("Default Img2Img Sampling Steps",
                                                                                                 value=st.session_state['defaults'].img2img.sampling_steps.value,
-																								  help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)"))
+                                                                                                help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")
 
-					st.session_state["defaults"].img2img.sampling_steps.min_value = int(st.text_input("Minimum Img2Img Sampling Steps", 
+                    st.session_state["defaults"].img2img.sampling_steps.min_value = st.number_input("Minimum Img2Img Sampling Steps",
                                                                                                     value=st.session_state['defaults'].img2img.sampling_steps.min_value,
-																									  help="Set the default minimum value for the sampling steps slider. Default is: 1"))
+                                                                                                    help="Set the default minimum value for the sampling steps slider. Default is: 1")
 
-					st.session_state["defaults"].img2img.sampling_steps.max_value = int(st.text_input("Maximum Img2Img Sampling Steps",
+                    st.session_state["defaults"].img2img.sampling_steps.step = st.number_input("Img2Img Sampling Slider Steps",
-																									  value=st.session_state['defaults'].img2img.sampling_steps.max_value, 
-																									  help="Set the default maximum value for the sampling steps slider. Default is: 250"))
-		
-					st.session_state["defaults"].img2img.sampling_steps.step = int(st.text_input("Img2Img Sampling Slider Steps",
                                                                                                value=st.session_state['defaults'].img2img.sampling_steps.step,
-																								 help="Set the default value for the number of steps on the sampling steps slider. Default is: 10"))
+                                                                                               help="Set the default value for the number of steps on the sampling steps slider. Default is: 10")
 
                     # Batch Count
-					st.session_state["defaults"].img2img.batch_count.value = int(st.text_input("Img2img Batch count", value=st.session_state["defaults"].img2img.batch_count.value,
+                    st.session_state["defaults"].img2img.batch_count.value = st.number_input("Img2img Batch count", value=st.session_state["defaults"].img2img.batch_count.value,
-																							   help="How many iterations or batches of images to generate in total."))
+                                                                                             help="How many iterations or batches of images to generate in total.")
 
-					st.session_state["defaults"].img2img.batch_size.value = int(st.text_input("Img2img Batch size", value=st.session_state["defaults"].img2img.batch_size.value,
+                    st.session_state["defaults"].img2img.batch_size.value = st.number_input("Img2img Batch size", value=st.session_state["defaults"].img2img.batch_size.value,
                                                                                             help="How many images are at once in a batch.\
                                                                                             It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                             takes to finish generation as more images are generated at once.\
-																							  Default: 1")) 							
+                                                                                            Default: 1")
                     with col4:
                         # Inference Steps
-						st.session_state["defaults"].img2img.num_inference_steps.value = int(st.text_input("Default Inference Steps", 
+                        st.session_state["defaults"].img2img.num_inference_steps.value = st.number_input("Default Inference Steps",
                                                                                                          value=st.session_state['defaults'].img2img.num_inference_steps.value,
-																										   help="Set the default number of inference steps to use. Default is: 200"))
+                                                                                                         help="Set the default number of inference steps to use. Default is: 200")
 
-						st.session_state["defaults"].img2img.num_inference_steps.min_value = int(st.text_input("Minimum Sampling Steps", 
+                        st.session_state["defaults"].img2img.num_inference_steps.min_value = st.number_input("Minimum Sampling Steps",
                                                                                                              value=st.session_state['defaults'].img2img.num_inference_steps.min_value,
-																											   help="Set the default minimum value for the inference steps slider. Default is: 10"))
+                                                                                                             help="Set the default minimum value for the inference steps slider. Default is: 10")
 
-						st.session_state["defaults"].img2img.num_inference_steps.max_value = int(st.text_input("Maximum Sampling Steps", 
+                        st.session_state["defaults"].img2img.num_inference_steps.max_value = st.number_input("Maximum Sampling Steps",
                                                                                                              value=st.session_state['defaults'].img2img.num_inference_steps.max_value,
-																											   help="Set the default maximum value for the inference steps slider. Default is: 500"))
+                                                                                                             help="Set the default maximum value for the inference steps slider. Default is: 500")
 
-						st.session_state["defaults"].img2img.num_inference_steps.step = int(st.text_input("Inference Slider Steps",
+                        st.session_state["defaults"].img2img.num_inference_steps.step = st.number_input("Inference Slider Steps",
                                                                                                         value=st.session_state['defaults'].img2img.num_inference_steps.step,
                                                                                                         help="Set the default value for the number of steps on the inference steps slider.\
-																										  Default is: 10"))
+                                                                                                        Default is: 10")
 
                         # Find Noise Steps
-						st.session_state["defaults"].img2img.find_noise_steps.value = int(st.text_input("Default Find Noise Steps",
+                        st.session_state["defaults"].img2img.find_noise_steps.value = st.number_input("Default Find Noise Steps",
                                                                                                       value=st.session_state['defaults'].img2img.find_noise_steps.value,
-																										help="Set the default number of find noise steps to use. Default is: 100"))
+                                                                                                      help="Set the default number of find noise steps to use. Default is: 100")
 
-						st.session_state["defaults"].img2img.find_noise_steps.min_value = int(st.text_input("Minimum Find Noise Steps",
+                        st.session_state["defaults"].img2img.find_noise_steps.min_value = st.number_input("Minimum Find Noise Steps",
                                                                                                           value=st.session_state['defaults'].img2img.find_noise_steps.min_value,
-																											help="Set the default minimum value for the find noise steps slider. Default is: 0"))
+                                                                                                          help="Set the default minimum value for the find noise steps slider. Default is: 0")
 
-						st.session_state["defaults"].img2img.find_noise_steps.max_value = int(st.text_input("Maximum Find Noise Steps",
+                        st.session_state["defaults"].img2img.find_noise_steps.step = st.number_input("Find Noise Slider Steps",
-																											value=st.session_state['defaults'].img2img.find_noise_steps.max_value,
-																											help="Set the default maximum value for the find noise steps slider. Default is: 500"))
-			
-						st.session_state["defaults"].img2img.find_noise_steps.step = int(st.text_input("Find Noise Slider Steps",
                                                                                                      value=st.session_state['defaults'].img2img.find_noise_steps.step,
                                                                                                      help="Set the default value for the number of steps on the find noise steps slider. \
-																									   Default is: 10"))
+                                                                                                     Default is: 100")
 
             with col5:
                 st.title("General Parameters")
@@ -527,30 +571,32 @@ def layout():
                                                                                     value=st.session_state['defaults'].img2img.write_info_files,
                                                                                     help="Choose to write the info files along with the generated images. Default: True")
 
-				st.session_state["defaults"].img2img.use_GFPGAN = st.checkbox("Img2Img Use GFPGAN", value=st.session_state['defaults'].img2img.use_GFPGAN, help="Choose to use GFPGAN. Default: False")
+                st.session_state["defaults"].img2img.use_GFPGAN = st.checkbox(
+                    "Img2Img Use GFPGAN", value=st.session_state['defaults'].img2img.use_GFPGAN, help="Choose to use GFPGAN. Default: False")
 
                 st.session_state["defaults"].img2img.use_RealESRGAN = st.checkbox("Img2Img Use RealESRGAN", value=st.session_state['defaults'].img2img.use_RealESRGAN,
                                                                                   help="Choose to use RealESRGAN. Default: False")
 
                 st.session_state["defaults"].img2img.update_preview = True
-				st.session_state["defaults"].img2img.update_preview_frequency = int(st.text_input("Img2Img Preview Image Update Frequency",
+                st.session_state["defaults"].img2img.update_preview_frequency = st.number_input("Img2Img Preview Image Update Frequency",
+                                                                                                min_value=0,
                                                                                                 value=st.session_state['defaults'].img2img.update_preview_frequency,
-																								  help="Set the default value for the frrquency of the preview image updates. Default is: 10"))
+                                                                                                help="Set the default value for the frrquency of the preview image updates. Default is: 10")
 
                 st.title("Variation Parameters")
 
-				st.session_state["defaults"].img2img.variant_amount = float(st.text_input("Default Img2Img Variation Amount", 
+                st.session_state["defaults"].img2img.variant_amount = st.number_input("Default Img2Img Variation Amount",
                                                                                       value=st.session_state['defaults'].img2img.variant_amount,
-																						  help="Set the default variation to use. Default is: 0.0"))
+                                                                                      help="Set the default variation to use. Default is: 0.0")
 
                 # I THINK THESE ARE MISSING FROM THE CONFIG FILE
-				# st.session_state["defaults"].img2img.variant_amount.min_value = float(st.text_input("Minimum Img2Img Variation Amount", 
+                # st.session_state["defaults"].img2img.variant_amount.min_value = st.number_input("Minimum Img2Img Variation Amount",
                 # value=st.session_state['defaults'].img2img.variant_amount.min_value, help="Set the default minimum value for the variation slider. Default is: 0.0"))
 
-				# st.session_state["defaults"].img2img.variant_amount.max_value = float(st.text_input("Maximum Img2Img Variation Amount",
+                # st.session_state["defaults"].img2img.variant_amount.max_value = st.number_input("Maximum Img2Img Variation Amount",
                 # value=st.session_state['defaults'].img2img.variant_amount.max_value, help="Set the default maximum value for the variation slider. Default is: 1.0"))
 
-				# st.session_state["defaults"].img2img.variant_amount.step = float(st.text_input("Img2Img Variation Slider Steps", 
+                # st.session_state["defaults"].img2img.variant_amount.step = st.number_input("Img2Img Variation Slider Steps",
                 # value=st.session_state['defaults'].img2img.variant_amount.step, help="Set the default value for the number of steps on the variation slider. Default is: 1"))
 
                 st.session_state['defaults'].img2img.variant_seed = st.text_input("Default Img2Img Variation Seed",
@@ -561,12 +607,12 @@ def layout():
 
             st.title("Image-To-Text")
 
-			st.session_state["defaults"].img2txt.batch_size = int(st.text_input("Default Img2Txt Batch Size", value=st.session_state['defaults'].img2txt.batch_size, 
+            st.session_state["defaults"].img2txt.batch_size = st.number_input("Default Img2Txt Batch Size", value=st.session_state['defaults'].img2txt.batch_size,
-																				help="Set the default batch size for Img2Txt. Default is: 420?"))
+                                                                              help="Set the default batch size for Img2Txt. Default is: 420?")
 
-			st.session_state["defaults"].img2txt.blip_image_eval_size = int(st.text_input("Default Blip Image Size Evaluation", 
+            st.session_state["defaults"].img2txt.blip_image_eval_size = st.number_input("Default Blip Image Size Evaluation",
                                                                                         value=st.session_state['defaults'].img2txt.blip_image_eval_size,
-																						  help="Set the default value for the blip image evaluation size. Default is: 512"))
+                                                                                        help="Set the default value for the blip image evaluation size. Default is: 512")
 
         with txt2vid_tab:
             col1, col2, col3, col4, col5 = st.columns(5, gap="medium")
@@ -575,92 +621,83 @@ def layout():
                 st.title("Slider Parameters")
 
                 # Width
-				st.session_state["defaults"].txt2vid.width.value = int(st.text_input("Default txt2vid Image Width",
+                st.session_state["defaults"].txt2vid.width.value = st.number_input("Default txt2vid Image Width",
                                                                                    value=st.session_state['defaults'].txt2vid.width.value,
-																					 help="Set the default width for the generated image. Default is: 512"))
+                                                                                   help="Set the default width for the generated image. Default is: 512")
 
-				st.session_state["defaults"].txt2vid.width.min_value = int(st.text_input("Minimum txt2vid Image Width",
+                st.session_state["defaults"].txt2vid.width.min_value = st.number_input("Minimum txt2vid Image Width",
                                                                                        value=st.session_state['defaults'].txt2vid.width.min_value,
-																						 help="Set the default minimum value for the width slider. Default is: 64"))
+                                                                                       help="Set the default minimum value for the width slider. Default is: 64")
 
-				st.session_state["defaults"].txt2vid.width.max_value = int(st.text_input("Maximum txt2vid Image Width", 
+                st.session_state["defaults"].txt2vid.width.max_value = st.number_input("Maximum txt2vid Image Width",
                                                                                        value=st.session_state['defaults'].txt2vid.width.max_value,
-																						 help="Set the default maximum value for the width slider. Default is: 2048"))
+                                                                                       help="Set the default maximum value for the width slider. Default is: 2048")
 
                 # Height
-				st.session_state["defaults"].txt2vid.height.value = int(st.text_input("Default txt2vid Image Height",
+                st.session_state["defaults"].txt2vid.height.value = st.number_input("Default txt2vid Image Height",
                                                                                     value=st.session_state['defaults'].txt2vid.height.value,
-																					  help="Set the default height for the generated image. Default is: 512"))
+                                                                                    help="Set the default height for the generated image. Default is: 512")
 
-				st.session_state["defaults"].txt2vid.height.min_value = int(st.text_input("Minimum txt2vid Image Height", 
+                st.session_state["defaults"].txt2vid.height.min_value = st.number_input("Minimum txt2vid Image Height",
                                                                                         value=st.session_state['defaults'].txt2vid.height.min_value,
-																						  help="Set the default minimum value for the height slider. Default is: 64"))
+                                                                                        help="Set the default minimum value for the height slider. Default is: 64")
 
-				st.session_state["defaults"].txt2vid.height.max_value = int(st.text_input("Maximum txt2vid Image Height",
+                st.session_state["defaults"].txt2vid.height.max_value = st.number_input("Maximum txt2vid Image Height",
                                                                                         value=st.session_state['defaults'].txt2vid.height.max_value,
-																						  help="Set the default maximum value for the height slider. Default is: 2048"))
+                                                                                        help="Set the default maximum value for the height slider. Default is: 2048")
 
                 # CFG
-				st.session_state["defaults"].txt2vid.cfg_scale.value = float(st.text_input("Default txt2vid CFG Scale",
+                st.session_state["defaults"].txt2vid.cfg_scale.value = st.number_input("Default txt2vid CFG Scale",
                                                                                        value=st.session_state['defaults'].txt2vid.cfg_scale.value,
-																						   help="Set the default value for the CFG Scale. Default is: 7.5"))
+                                                                                       help="Set the default value for the CFG Scale. Default is: 7.5")
 
-				st.session_state["defaults"].txt2vid.cfg_scale.min_value = float(st.text_input("Minimum txt2vid CFG Scale Value",
+                st.session_state["defaults"].txt2vid.cfg_scale.min_value = st.number_input("Minimum txt2vid CFG Scale Value",
                                                                                            value=st.session_state['defaults'].txt2vid.cfg_scale.min_value,
-																							   help="Set the default minimum value for the CFG scale slider. Default is: 1"))
+                                                                                           help="Set the default minimum value for the CFG scale slider. Default is: 1")
 
-				st.session_state["defaults"].txt2vid.cfg_scale.max_value = float(st.text_input("Maximum txt2vid CFG Scale Value", 
+                st.session_state["defaults"].txt2vid.cfg_scale.step = st.number_input("txt2vid CFG Slider Steps",
-																							   value=st.session_state['defaults'].txt2vid.cfg_scale.max_value, 
-																							   help="Set the default maximum value for the CFG scale slider. Default is: 30"))
-    
-				st.session_state["defaults"].txt2vid.cfg_scale.step = float(st.text_input("txt2vid CFG Slider Steps", 
                                                                                       value=st.session_state['defaults'].txt2vid.cfg_scale.step,
-																						  help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5"))
+                                                                                      help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5")
 
                 with col2:
                     # Sampling Steps
-					st.session_state["defaults"].txt2vid.sampling_steps.value = int(st.text_input("Default txt2vid Sampling Steps",
+                    st.session_state["defaults"].txt2vid.sampling_steps.value = st.number_input("Default txt2vid Sampling Steps",
                                                                                                 value=st.session_state['defaults'].txt2vid.sampling_steps.value,
-																								  help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)"))
+                                                                                                help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")
 
-					st.session_state["defaults"].txt2vid.sampling_steps.min_value = int(st.text_input("Minimum txt2vid Sampling Steps",
+                    st.session_state["defaults"].txt2vid.sampling_steps.min_value = st.number_input("Minimum txt2vid Sampling Steps",
                                                                                                     value=st.session_state['defaults'].txt2vid.sampling_steps.min_value,
-																									  help="Set the default minimum value for the sampling steps slider. Default is: 1"))
+                                                                                                    help="Set the default minimum value for the sampling steps slider. Default is: 1")
 
-					st.session_state["defaults"].txt2vid.sampling_steps.max_value = int(st.text_input("Maximum txt2vid Sampling Steps", 
+                    st.session_state["defaults"].txt2vid.sampling_steps.step = st.number_input("txt2vid Sampling Slider Steps",
-																									  value=st.session_state['defaults'].txt2vid.sampling_steps.max_value,
-																									  help="Set the default maximum value for the sampling steps slider. Default is: 250"))
-		
-					st.session_state["defaults"].txt2vid.sampling_steps.step = int(st.text_input("txt2vid Sampling Slider Steps", 
                                                                                                value=st.session_state['defaults'].txt2vid.sampling_steps.step,
-																								 help="Set the default value for the number of steps on the sampling steps slider. Default is: 10"))
+                                                                                               help="Set the default value for the number of steps on the sampling steps slider. Default is: 10")
 
                     # Batch Count
-					st.session_state["defaults"].txt2vid.batch_count.value = int(st.text_input("txt2vid Batch count", value=st.session_state['defaults'].txt2vid.batch_count.value,
+                    st.session_state["defaults"].txt2vid.batch_count.value = st.number_input("txt2vid Batch count", value=st.session_state['defaults'].txt2vid.batch_count.value,
-																							  help="How many iterations or batches of images to generate in total."))
+                                                                                             help="How many iterations or batches of images to generate in total.")
 
-					st.session_state["defaults"].txt2vid.batch_size.value = int(st.text_input("txt2vid Batch size", value=st.session_state.defaults.txt2vid.batch_size.value,
+                    st.session_state["defaults"].txt2vid.batch_size.value = st.number_input("txt2vid Batch size", value=st.session_state.defaults.txt2vid.batch_size.value,
                                                                                             help="How many images are at once in a batch.\
                                                                                             It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                             takes to finish generation as more images are generated at once.\
-																							  Default: 1") )
+                                                                                            Default: 1")
 
                     # Inference Steps
-					st.session_state["defaults"].txt2vid.num_inference_steps.value = int(st.text_input("Default Txt2Vid Inference Steps",
+                    st.session_state["defaults"].txt2vid.num_inference_steps.value = st.number_input("Default Txt2Vid Inference Steps",
                                                                                                      value=st.session_state['defaults'].txt2vid.num_inference_steps.value,
-																									   help="Set the default number of inference steps to use. Default is: 200"))
+                                                                                                     help="Set the default number of inference steps to use. Default is: 200")
 
-					st.session_state["defaults"].txt2vid.num_inference_steps.min_value = int(st.text_input("Minimum Txt2Vid Sampling Steps", 
+                    st.session_state["defaults"].txt2vid.num_inference_steps.min_value = st.number_input("Minimum Txt2Vid Sampling Steps",
                                                                                                          value=st.session_state['defaults'].txt2vid.num_inference_steps.min_value,
-																										   help="Set the default minimum value for the inference steps slider. Default is: 10"))
+                                                                                                         help="Set the default minimum value for the inference steps slider. Default is: 10")
 
-					st.session_state["defaults"].txt2vid.num_inference_steps.max_value = int(st.text_input("Maximum Txt2Vid Sampling Steps",
+                    st.session_state["defaults"].txt2vid.num_inference_steps.max_value = st.number_input("Maximum Txt2Vid Sampling Steps",
                                                                                                          value=st.session_state['defaults'].txt2vid.num_inference_steps.max_value,
-																										   help="Set the default maximum value for the inference steps slider. Default is: 500"))
+                                                                                                         help="Set the default maximum value for the inference steps slider. Default is: 500")
-		
+                    st.session_state["defaults"].txt2vid.num_inference_steps.step = st.number_input("Txt2Vid Inference Slider Steps",
-					st.session_state["defaults"].txt2vid.num_inference_steps.step = int(st.text_input("Txt2Vid Inference Slider Steps",
                                                                                                     value=st.session_state['defaults'].txt2vid.num_inference_steps.step,
-																									  help="Set the default value for the number of steps on the inference steps slider. Default is: 10"))
+                                                                                                    help="Set the default value for the number of steps on the inference steps slider. Default is: 10")
 
             with col3:
                 st.title("General Parameters")
@@ -694,6 +731,10 @@ def layout():
                 st.session_state["defaults"].txt2vid.save_video = st.checkbox("Save Txt2Vid Video", value=st.session_state['defaults'].txt2vid.save_video,
                                                                               help="Choose to save the Txt2Vid video. Default: True")
 
+                st.session_state["defaults"].txt2vid.save_video_on_stop = st.checkbox("Save video on Stop", value=st.session_state['defaults'].txt2vid.save_video_on_stop,
+                                                                                      help="Save a video with all the images generated as frames when we hit the stop button \
+																					  during a generation.")
+
                 st.session_state["defaults"].txt2vid.group_by_prompt = st.checkbox("Group By txt2vid Prompt", value=st.session_state['defaults'].txt2vid.group_by_prompt,
                                                                                    help="Choose to save images grouped by their prompt. Default: False")
 
@@ -704,8 +745,8 @@ def layout():
                 st.session_state["defaults"].txt2vid.do_loop = st.checkbox("Loop Generations", value=st.session_state['defaults'].txt2vid.do_loop,
                                                                            help="Choose to loop or something, IDK.... Default: False")
 
-				st.session_state["defaults"].txt2vid.max_frames = int(st.text_input("Txt2Vid Max Video Frames", value=st.session_state['defaults'].txt2vid.max_frames,
+                st.session_state["defaults"].txt2vid.max_duration_in_seconds = st.number_input("Txt2Vid Max Duration in Seconds", value=st.session_state['defaults'].txt2vid.max_duration_in_seconds,
-																					help="Set the default value for the number of video frames generated. Default is: 100"))
+                                                                                  help="Set the default value for the max duration in seconds for the video generated. Default is: 30")
 
                 st.session_state["defaults"].txt2vid.write_info_files = st.checkbox("Write Info Files For txt2vid Images", value=st.session_state['defaults'].txt2vid.write_info_files,
                                                                                     help="Choose to write the info files along with the generated images. Default: True")
@@ -717,67 +758,66 @@ def layout():
                                                                                   help="Choose to use RealESRGAN. Default: False")
 
                 st.session_state["defaults"].txt2vid.update_preview = True
-				st.session_state["defaults"].txt2vid.update_preview_frequency = int(st.text_input("txt2vid Preview Image Update Frequency", 
+                st.session_state["defaults"].txt2vid.update_preview_frequency = st.number_input("txt2vid Preview Image Update Frequency",
                                                                                                 value=st.session_state['defaults'].txt2vid.update_preview_frequency,
-																								  help="Set the default value for the frrquency of the preview image updates. Default is: 10"))
+                                                                                                help="Set the default value for the frrquency of the preview image updates. Default is: 10")
 
             with col4:
                 st.title("Variation Parameters")
 
-				st.session_state["defaults"].txt2vid.variant_amount.value = float(st.text_input("Default txt2vid Variation Amount", 
+                st.session_state["defaults"].txt2vid.variant_amount.value = st.number_input("Default txt2vid Variation Amount",
                                                                                             value=st.session_state['defaults'].txt2vid.variant_amount.value,
-																								help="Set the default variation to use. Default is: 0.0"))
+                                                                                            help="Set the default variation to use. Default is: 0.0")
 
-				st.session_state["defaults"].txt2vid.variant_amount.min_value = float(st.text_input("Minimum txt2vid Variation Amount", 
+                st.session_state["defaults"].txt2vid.variant_amount.min_value = st.number_input("Minimum txt2vid Variation Amount",
                                                                                                 value=st.session_state['defaults'].txt2vid.variant_amount.min_value,
-																									help="Set the default minimum value for the variation slider. Default is: 0.0"))
+                                                                                                help="Set the default minimum value for the variation slider. Default is: 0.0")
 
-				st.session_state["defaults"].txt2vid.variant_amount.max_value = float(st.text_input("Maximum txt2vid Variation Amount", 
+                st.session_state["defaults"].txt2vid.variant_amount.max_value = st.number_input("Maximum txt2vid Variation Amount",
                                                                                                 value=st.session_state['defaults'].txt2vid.variant_amount.max_value,
-																									help="Set the default maximum value for the variation slider. Default is: 1.0"))
+                                                                                                help="Set the default maximum value for the variation slider. Default is: 1.0")
 
-				st.session_state["defaults"].txt2vid.variant_amount.step = float(st.text_input("txt2vid Variation Slider Steps", 
+                st.session_state["defaults"].txt2vid.variant_amount.step = st.number_input("txt2vid Variation Slider Steps",
                                                                                            value=st.session_state['defaults'].txt2vid.variant_amount.step,
-																							   help="Set the default value for the number of steps on the variation slider. Default is: 1"))
+                                                                                           help="Set the default value for the number of steps on the variation slider. Default is: 1")
 
                 st.session_state['defaults'].txt2vid.variant_seed = st.text_input("Default txt2vid Variation Seed",
                                                                                   value=st.session_state['defaults'].txt2vid.variant_seed, help="Default variation seed.")
 
-    
             with col5:
                 st.title("Beta Parameters")
 
                 # Beta Start
-				st.session_state["defaults"].txt2vid.beta_start.value = float(st.text_input("Default txt2vid Beta Start Value",
+                st.session_state["defaults"].txt2vid.beta_start.value = st.number_input("Default txt2vid Beta Start Value",
                                                                                         value=st.session_state['defaults'].txt2vid.beta_start.value,
-																							help="Set the default variation to use. Default is: 0.0"))
+                                                                                        help="Set the default variation to use. Default is: 0.0")
 
-				st.session_state["defaults"].txt2vid.beta_start.min_value = float(st.text_input("Minimum txt2vid Beta Start Amount",
+                st.session_state["defaults"].txt2vid.beta_start.min_value = st.number_input("Minimum txt2vid Beta Start Amount",
                                                                                             value=st.session_state['defaults'].txt2vid.beta_start.min_value,
-																								help="Set the default minimum value for the variation slider. Default is: 0.0"))
+                                                                                            help="Set the default minimum value for the variation slider. Default is: 0.0")
 
-				st.session_state["defaults"].txt2vid.beta_start.max_value = float(st.text_input("Maximum txt2vid Beta Start Amount", 
+                st.session_state["defaults"].txt2vid.beta_start.max_value = st.number_input("Maximum txt2vid Beta Start Amount",
                                                                                             value=st.session_state['defaults'].txt2vid.beta_start.max_value,
-																								help="Set the default maximum value for the variation slider. Default is: 1.0"))
+                                                                                            help="Set the default maximum value for the variation slider. Default is: 1.0")
 
-				st.session_state["defaults"].txt2vid.beta_start.step = float(st.text_input("txt2vid Beta Start Slider Steps", value=st.session_state['defaults'].txt2vid.beta_start.step, 
+                st.session_state["defaults"].txt2vid.beta_start.step = st.number_input("txt2vid Beta Start Slider Steps", value=st.session_state['defaults'].txt2vid.beta_start.step,
-																						   help="Set the default value for the number of steps on the variation slider. Default is: 1"))
+                                                                                       help="Set the default value for the number of steps on the variation slider. Default is: 1")
 
                 st.session_state["defaults"].txt2vid.beta_start.format = st.text_input("Default txt2vid Beta Start Format", value=st.session_state['defaults'].txt2vid.beta_start.format,
                                                                                        help="Set the default Beta Start Format. Default is: %.5\f")
 
                 # Beta End
-				st.session_state["defaults"].txt2vid.beta_end.value = float(st.text_input("Default txt2vid Beta End Value", value=st.session_state['defaults'].txt2vid.beta_end.value,
+                st.session_state["defaults"].txt2vid.beta_end.value = st.number_input("Default txt2vid Beta End Value", value=st.session_state['defaults'].txt2vid.beta_end.value,
-																						  help="Set the default variation to use. Default is: 0.0"))
+                                                                                      help="Set the default variation to use. Default is: 0.0")
 
-				st.session_state["defaults"].txt2vid.beta_end.min_value = float(st.text_input("Minimum txt2vid Beta End Amount", value=st.session_state['defaults'].txt2vid.beta_end.min_value,
+                st.session_state["defaults"].txt2vid.beta_end.min_value = st.number_input("Minimum txt2vid Beta End Amount", value=st.session_state['defaults'].txt2vid.beta_end.min_value,
-																							  help="Set the default minimum value for the variation slider. Default is: 0.0"))
+                                                                                          help="Set the default minimum value for the variation slider. Default is: 0.0")
 
-				st.session_state["defaults"].txt2vid.beta_end.max_value = float(st.text_input("Maximum txt2vid Beta End Amount", value=st.session_state['defaults'].txt2vid.beta_end.max_value, 
+                st.session_state["defaults"].txt2vid.beta_end.max_value = st.number_input("Maximum txt2vid Beta End Amount", value=st.session_state['defaults'].txt2vid.beta_end.max_value,
-																							  help="Set the default maximum value for the variation slider. Default is: 1.0"))
+                                                                                          help="Set the default maximum value for the variation slider. Default is: 1.0")
 
-				st.session_state["defaults"].txt2vid.beta_end.step = float(st.text_input("txt2vid Beta End Slider Steps", value=st.session_state['defaults'].txt2vid.beta_end.step,
+                st.session_state["defaults"].txt2vid.beta_end.step = st.number_input("txt2vid Beta End Slider Steps", value=st.session_state['defaults'].txt2vid.beta_end.step,
-																						 help="Set the default value for the number of steps on the variation slider. Default is: 1"))
+                                                                                     help="Set the default value for the number of steps on the variation slider. Default is: 1")
 
                 st.session_state["defaults"].txt2vid.beta_end.format = st.text_input("Default txt2vid Beta End Format", value=st.session_state['defaults'].txt2vid.beta_start.format,
                                                                                      help="Set the default Beta Start Format. Default is: %.5\f")
@@ -788,8 +828,8 @@ def layout():
             with col1:
                 st.title("GFPGAN")
 
-				st.session_state["defaults"].gfpgan.strength = int(st.text_input("Default Img2Txt Batch Size", value=st.session_state['defaults'].gfpgan.strength, 
+                st.session_state["defaults"].gfpgan.strength = st.number_input("Default Img2Txt Batch Size", value=st.session_state['defaults'].gfpgan.strength,
-																				 help="Set the default global strength for GFPGAN. Default is: 100"))
+                                                                               help="Set the default global strength for GFPGAN. Default is: 100")
             with col2:
                 st.title("GoBig")
             with col3:
@@ -814,8 +854,8 @@ def layout():
             #st.info("Under Construction. :construction_worker:")
             col1, col2, col3, col4, col5 = st.columns(5, gap='large')
             with col1:
-				st.session_state["defaults"].concepts_library.concepts_per_page = int(st.text_input("Concepts Per Page", value=st.session_state['defaults'].concepts_library.concepts_per_page,
+                st.session_state["defaults"].concepts_library.concepts_per_page = st.number_input("Concepts Per Page", value=st.session_state['defaults'].concepts_library.concepts_per_page,
-																									help="Number of concepts per page to show on the Concepts Library. Default: '12'"))
+                                                                                                  help="Number of concepts per page to show on the Concepts Library. Default: '12'")
 
         # add space for the buttons at the bottom
         st.markdown("---")
@@ -841,3 +881,4 @@ def layout():
 
         if reset_button:
             st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml")
+            st.experimental_rerun()

scripts/__init__.py

@@ -0,0 +1 @@
+from logger import set_logger_verbosity, quiesce_logger

scripts/barfi_baklavajs.py

@@ -0,0 +1,97 @@
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sandbox-webui/).
+
+# Copyright 2022 Sygil-Dev team.
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+# base webui import and utils.
+#from sd_utils import *
+from sd_utils import *
+# streamlit imports
+
+#streamlit components section
+
+#other imports
+import os, time, requests
+import sys
+
+from barfi import st_barfi, barfi_schemas, Block
+
+# Temp imports
+
+# end of imports
+#---------------------------------------------------------------------------------------------------------------
+
+
+def layout():
+    #st.info("Under Construction. :construction_worker:")
+
+    #from barfi import st_barfi, Block
+
+    #add = Block(name='Addition')
+    #sub = Block(name='Subtraction')
+    #mul = Block(name='Multiplication')
+    #div = Block(name='Division')
+
+    #barfi_result = st_barfi(base_blocks= [add, sub, mul, div])
+    # or if you want to use a category to organise them in the frontend sub-menu
+    #barfi_result = st_barfi(base_blocks= {'Op 1': [add, sub], 'Op 2': [mul, div]})
+
+    col1, col2, col3 = st.columns([1, 8, 1])
+
+    from barfi import st_barfi, barfi_schemas, Block
+
+
+    with col2:
+        feed = Block(name='Feed')
+        feed.add_output()
+        def feed_func(self):
+            self.set_interface(name='Output 1', value=4)
+        feed.add_compute(feed_func)
+
+        splitter = Block(name='Splitter')
+        splitter.add_input()
+        splitter.add_output()
+        splitter.add_output()
+        def splitter_func(self):
+            in_1 = self.get_interface(name='Input 1')
+            value = (in_1/2)
+            self.set_interface(name='Output 1', value=value)
+            self.set_interface(name='Output 2', value=value)
+        splitter.add_compute(splitter_func)
+
+        mixer = Block(name='Mixer')
+        mixer.add_input()
+        mixer.add_input()
+        mixer.add_output()
+        def mixer_func(self):
+            in_1 = self.get_interface(name='Input 1')
+            in_2 = self.get_interface(name='Input 2')
+            value = (in_1 + in_2)
+            self.set_interface(name='Output 1', value=value)
+        mixer.add_compute(mixer_func)
+
+        result = Block(name='Result')
+        result.add_input()
+        def result_func(self):
+            in_1 = self.get_interface(name='Input 1')
+        result.add_compute(result_func)
+
+        load_schema = st.selectbox('Select a saved schema:', barfi_schemas())
+
+        compute_engine = st.checkbox('Activate barfi compute engine', value=False)
+
+        barfi_result = st_barfi(base_blocks=[feed, result, mixer, splitter],
+                            compute_engine=compute_engine, load_schema=load_schema)
+
+        if barfi_result:
+            st.write(barfi_result)

scripts/custom_components/draggable_number_input/__init__.py

@@ -0,0 +1,11 @@
+import os
+import streamlit.components.v1 as components
+
+def load(pixel_per_step = 50):
+	parent_dir = os.path.dirname(os.path.abspath(__file__))
+	file = os.path.join(parent_dir, "main.js")
+
+	with open(file) as f:
+		javascript_main = f.read()
+		javascript_main = javascript_main.replace("%%pixelPerStep%%",str(pixel_per_step))
+		components.html(f"<script>{javascript_main}</script>")

scripts/custom_components/draggable_number_input/main.js

@@ -0,0 +1,192 @@
+// iframe parent
+var parentDoc = window.parent.document
+
+// check for mouse pointer locking support, not a requirement but improves the overall experience
+var havePointerLock = 'pointerLockElement' in parentDoc ||
+  'mozPointerLockElement' in parentDoc ||
+  'webkitPointerLockElement' in parentDoc;
+  
+// the pointer locking exit function
+parentDoc.exitPointerLock = parentDoc.exitPointerLock || parentDoc.mozExitPointerLock || parentDoc.webkitExitPointerLock;
+
+// how far should the mouse travel for a step in pixel
+var pixelPerStep = %%pixelPerStep%%;
+// how many steps did the mouse move in as float
+var movementDelta = 0.0;
+// value when drag started
+var lockedValue = 0.0;
+// minimum value from field
+var lockedMin = 0.0;
+// maximum value from field
+var lockedMax = 0.0;
+// how big should the field steps be
+var lockedStep = 0.0;
+// the currently locked in field
+var lockedField = null;
+
+// lock box to just request pointer lock for one element
+var lockBox = document.createElement("div");
+lockBox.classList.add("lockbox");
+parentDoc.body.appendChild(lockBox);
+lockBox.requestPointerLock = lockBox.requestPointerLock || lockBox.mozRequestPointerLock || lockBox.webkitRequestPointerLock;
+
+function Lock(field)
+{
+	var rect = field.getBoundingClientRect();
+	lockBox.style.left = (rect.left-2.5)+"px";
+	lockBox.style.top = (rect.top-2.5)+"px";
+	
+	lockBox.style.width = (rect.width+2.5)+"px";
+	lockBox.style.height = (rect.height+5)+"px";
+	
+	lockBox.requestPointerLock();
+}
+
+function Unlock()
+{
+	parentDoc.exitPointerLock();
+	lockBox.style.left = "0px";
+	lockBox.style.top = "0px";
+	
+	lockBox.style.width = "0px";
+	lockBox.style.height = "0px";
+	lockedField.focus();
+}
+
+parentDoc.addEventListener('mousedown', (e) => {
+	// if middle is down
+	if(e.button === 1)
+	{
+		if(e.target.tagName === 'INPUT' && e.target.type === 'number')
+		{
+			e.preventDefault();
+			var field = e.target;
+			if(havePointerLock)
+				Lock(field);
+
+			// save current field
+			lockedField = e.target;
+			// add class for styling
+			lockedField.classList.add("value-dragging");
+			// reset movement delta
+			movementDelta = 0.0;
+			// set to 0 if field is empty
+			if(lockedField.value === '')
+				lockedField.value = 0.0;
+				
+			// save current field value
+			lockedValue = parseFloat(lockedField.value);
+			
+			if(lockedField.min === '' || lockedField.min === '-Infinity')
+				lockedMin = -99999999.0;
+			else
+				lockedMin = parseFloat(lockedField.min);
+			
+			if(lockedField.max === '' || lockedField.max === 'Infinity')
+				lockedMax = 99999999.0;
+			else
+				lockedMax = parseFloat(lockedField.max);
+			
+			if(lockedField.step === '' || lockedField.step === 'Infinity')
+				lockedStep = 1.0;
+			else
+				lockedStep = parseFloat(lockedField.step);
+			
+			// lock pointer if available
+			if(havePointerLock)
+				Lock(lockedField);
+			
+			// add drag event
+			parentDoc.addEventListener("mousemove", onDrag, false);
+		}
+	}
+});
+
+function onDrag(e)
+{
+	if(lockedField !== null)
+	{
+		// add movement to delta
+		movementDelta += e.movementX / pixelPerStep;
+		if(lockedField === NaN)
+			return;
+		// set new value
+		let value = lockedValue + Math.floor(Math.abs(movementDelta)) * lockedStep * Math.sign(movementDelta);
+		lockedField.focus();
+		lockedField.select();
+		parentDoc.execCommand('insertText', false /*no UI*/, Math.min(Math.max(value, lockedMin), lockedMax));
+	}
+}
+
+parentDoc.addEventListener('mouseup', (e) => {
+	// if mouse is up
+	if(e.button === 1)
+	{
+		// release pointer lock if available
+		if(havePointerLock)
+			Unlock();
+		
+		if(lockedField !== null && lockedField !== NaN)
+		{
+			// stop drag event
+			parentDoc.removeEventListener("mousemove", onDrag, false);
+			// remove class for styling
+			lockedField.classList.remove("value-dragging");
+			// remove reference
+			lockedField = null;
+		}
+	}
+});
+
+// only execute once (even though multiple iframes exist)
+if(!parentDoc.hasOwnProperty("dragableInitialized"))
+{
+	var parentCSS =
+`
+/* Make input-instruction not block mouse events */
+.input-instructions,.input-instructions > *{
+	pointer-events: none;
+	user-select: none;
+	-moz-user-select: none;
+	-khtml-user-select: none;
+	-webkit-user-select: none;
+	-o-user-select: none;
+}
+
+.lockbox {
+	background-color: transparent;
+	position: absolute;
+	pointer-events: none;
+	user-select: none;
+	-moz-user-select: none;
+	-khtml-user-select: none;
+	-webkit-user-select: none;
+	-o-user-select: none;
+	border-left: dotted 2px rgb(255,75,75);
+	border-top: dotted 2px rgb(255,75,75);
+	border-bottom: dotted 2px rgb(255,75,75);
+	border-right: dotted 1px rgba(255,75,75,0.2);
+	border-top-left-radius: 0.25rem;
+	border-bottom-left-radius: 0.25rem;
+	z-index: 1000;
+}
+`;
+	
+	// get parent document head
+	var head = parentDoc.getElementsByTagName('head')[0];
+	// add style tag
+	var s = document.createElement('style');
+    // set type attribute
+	s.setAttribute('type', 'text/css');
+    // add css forwarded from python
+	if (s.styleSheet) {   // IE
+        s.styleSheet.cssText = parentCSS;
+    } else {                // the world
+        s.appendChild(document.createTextNode(parentCSS));
+    }
+	// add style to head
+    head.appendChild(s);
+	// set flag so this only runs once
+	parentDoc["dragableInitialized"] = true;
+}
+

scripts/custom_components/sygil_suggestions/__init__.py

@@ -0,0 +1,46 @@
+import os
+from collections import defaultdict
+import streamlit.components.v1 as components
+
+# where to save the downloaded key_phrases
+key_phrases_file = "data/tags/key_phrases.json"
+# the loaded key phrase json as text
+key_phrases_json = ""
+# where to save the downloaded key_phrases
+thumbnails_file = "data/tags/thumbnails.json"
+# the loaded key phrase json as text
+thumbnails_json = ""
+
+def init():
+	global key_phrases_json, thumbnails_json
+	with open(key_phrases_file) as f:
+		key_phrases_json = f.read()
+	with open(thumbnails_file) as f:
+		thumbnails_json = f.read()
+
+def suggestion_area(placeholder):
+	# get component path
+	parent_dir = os.path.dirname(os.path.abspath(__file__))
+	# get file paths
+	javascript_file = os.path.join(parent_dir, "main.js")
+	stylesheet_file = os.path.join(parent_dir, "main.css")
+	parent_stylesheet_file = os.path.join(parent_dir, "parent.css")
+
+	# load file texts
+	with open(javascript_file) as f:
+		javascript_main = f.read()
+	with open(stylesheet_file) as f:
+		stylesheet_main = f.read()
+	with open(parent_stylesheet_file) as f:
+		parent_stylesheet = f.read()
+
+	# add suggestion area div box
+	html = "<div id='scroll_area' class='st-bg'><div id='suggestion_area'>javascript failed</div></div>"
+	# add loaded style
+	html += f"<style>{stylesheet_main}</style>"
+	# set default variables
+	html += f"<script>var thumbnails = {thumbnails_json};\nvar keyPhrases = {key_phrases_json};\nvar parentCSS = `{parent_stylesheet}`;\nvar placeholder='{placeholder}';</script>"
+	# add main java script
+	html += f"\n<script>{javascript_main}</script>"
+	# add component to site
+	components.html(html, width=None, height=None, scrolling=True)

scripts/custom_components/sygil_suggestions/main.css

@@ -0,0 +1,81 @@
+*
+{
+	padding: 0px;
+	margin: 0px;
+	user-select: none;
+	-moz-user-select: none;
+	-khtml-user-select: none;
+	-webkit-user-select: none;
+	-o-user-select: none;
+}
+
+body
+{
+	width: 100%;
+	height: 100%;
+	padding-left: calc( 1em - 1px );
+	padding-top: calc( 1em - 1px );
+	overflow: hidden;
+}
+
+/* width */
+::-webkit-scrollbar {
+  width: 7px;
+}
+
+/* Track */
+::-webkit-scrollbar-track {
+  background: rgb(10, 13, 19); 
+}
+ 
+/* Handle */
+::-webkit-scrollbar-thumb {
+  background: #6c6e72; 
+  border-radius: 3px;
+}
+
+/* Handle on hover */
+::-webkit-scrollbar-thumb:hover {
+  background: #6c6e72; 
+}
+
+#scroll_area
+{
+	display: flex;
+	overflow-x: hidden;
+	overflow-y: auto;
+}
+
+#suggestion_area
+{
+	overflow-x: hidden;
+	width: calc( 100% - 2em - 2px );
+	margin-bottom: calc( 1em + 13px );
+	min-height: 50px;
+}
+
+span
+{
+	border: 1px solid rgba(250, 250, 250, 0.2);
+	border-radius: 0.25rem;
+	font-size: 1rem;
+	font-family: "Source Sans Pro", sans-serif;
+	
+	background-color: rgb(38, 39, 48);
+	color: white;
+	display: inline-block;
+	padding: 0.5rem;
+	margin-right: 3px;
+	cursor: pointer;
+	user-select: none;
+	-moz-user-select: none;
+	-khtml-user-select: none;
+	-webkit-user-select: none;
+	-o-user-select: none;
+}
+
+span:hover
+{
+	color: rgb(255,75,75);
+	border-color: rgb(255,75,75);
+}

scripts/custom_components/sygil_suggestions/parent.css

@@ -0,0 +1,84 @@
+.suggestion-frame
+{
+	position: absolute;
+	
+	/* make as small as possible */
+	margin: 0px;
+	padding: 0px;
+	min-height: 0px;
+	line-height: 0;
+	
+	/* animate transitions of the height property */
+	-webkit-transition: height 1s;
+	-moz-transition: height 1s;
+	-ms-transition: height 1s;
+	-o-transition: height 1s;
+	transition: height 1s, border-bottom-width 1s;
+	
+	/* block selection */
+	user-select: none;
+	-moz-user-select: none;
+	-khtml-user-select: none;
+	-webkit-user-select: none;
+	-o-user-select: none;
+	
+	z-index: 700;
+	
+	outline: 1px solid rgba(250, 250, 250, 0.2);
+	outline-offset: 0px;
+	border-radius: 0.25rem;
+	background: rgb(14, 17, 23);
+	
+	box-sizing: border-box;
+    -moz-box-sizing: border-box;
+    -webkit-box-sizing: border-box;
+	border-bottom: solid 13px rgb(14, 17, 23) !important;
+	border-left: solid 13px rgb(14, 17, 23) !important;
+}
+
+#phrase-tooltip
+{
+	display: none;
+	pointer-events: none;
+	position: absolute;
+	border-bottom-left-radius: 0.5rem;
+	border-top-right-radius: 0.5rem;
+	border-bottom-right-radius: 0.5rem;
+	border: solid rgb(255,75,75) 2px;
+	background-color: rgb(38, 39, 48);
+	color: rgb(255,75,75);
+	font-size: 1rem;
+	font-family: "Source Sans Pro", sans-serif;
+	padding: 0.5rem;
+	
+	cursor: default;
+	user-select: none;
+	-moz-user-select: none;
+	-khtml-user-select: none;
+	-webkit-user-select: none;
+	-o-user-select: none;
+	z-index: 1000;
+}
+
+#phrase-tooltip:has(img)
+{
+	transform: scale(1.25, 1.25);
+	-ms-transform: scale(1.25, 1.25);
+	-webkit-transform: scale(1.25, 1.25);
+}
+
+#phrase-tooltip>img
+{
+	pointer-events: none;
+	border-bottom-left-radius: 0.5rem;
+	border-top-right-radius: 0.5rem;
+	border-bottom-right-radius: 0.5rem;
+	
+	cursor: default;
+	user-select: none;
+	-moz-user-select: none;
+	-khtml-user-select: none;
+	-webkit-user-select: none;
+	-o-user-select: none;
+	z-index: 1500;
+}

scripts/diffusers_textual_inversion_2.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

scripts/home.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

scripts/hydrus_api/__init__.py

@@ -0,0 +1,766 @@
+# Copyright (C) 2021 cryzed
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+import enum
+import json
+import os
+import typing as T
+from collections import abc
+
+import requests
+
+__version__ = "4.0.0"
+
+DEFAULT_API_URL = "http://127.0.0.1:45869/"
+HYDRUS_METADATA_ENCODING = "utf-8"
+AUTHENTICATION_TIMEOUT_CODE = 419
+
+
+class HydrusAPIException(Exception):
+    pass
+
+
+class ConnectionError(HydrusAPIException, requests.ConnectTimeout):
+    pass
+
+
+class APIError(HydrusAPIException):
+    def __init__(self, response: requests.Response):
+        super().__init__(response.text)
+        self.response = response
+
+
+class MissingParameter(APIError):
+    pass
+
+
+class InsufficientAccess(APIError):
+    pass
+
+
+class DatabaseLocked(APIError):
+    pass
+
+
+class ServerError(APIError):
+    pass
+
+
+# Customize IntEnum, so we can just do str(Enum.member) to get the string representation of its value unmodified,
+# without users having to access .value explicitly
+class StringableIntEnum(enum.IntEnum):
+    def __str__(self):
+        return str(self.value)
+
+
+@enum.unique
+class Permission(StringableIntEnum):
+    IMPORT_URLS = 0
+    IMPORT_FILES = 1
+    ADD_TAGS = 2
+    SEARCH_FILES = 3
+    MANAGE_PAGES = 4
+    MANAGE_COOKIES = 5
+    MANAGE_DATABASE = 6
+    ADD_NOTES = 7
+
+
+@enum.unique
+class URLType(StringableIntEnum):
+    POST_URL = 0
+    FILE_URL = 2
+    GALLERY_URL = 3
+    WATCHABLE_URL = 4
+    UNKNOWN_URL = 5
+
+
+@enum.unique
+class ImportStatus(StringableIntEnum):
+    IMPORTABLE = 0
+    SUCCESS = 1
+    EXISTS = 2
+    PREVIOUSLY_DELETED = 3
+    FAILED = 4
+    VETOED = 7
+
+
+@enum.unique
+class TagAction(StringableIntEnum):
+    ADD = 0
+    DELETE = 1
+    PEND = 2
+    RESCIND_PENDING = 3
+    PETITION = 4
+    RESCIND_PETITION = 5
+
+
+@enum.unique
+class TagStatus(StringableIntEnum):
+    CURRENT = 0
+    PENDING = 1
+    DELETED = 2
+    PETITIONED = 3
+
+
+@enum.unique
+class PageType(StringableIntEnum):
+    GALLERY_DOWNLOADER = 1
+    SIMPLE_DOWNLOADER = 2
+    HARD_DRIVE_IMPORT = 3
+    PETITIONS = 5
+    FILE_SEARCH = 6
+    URL_DOWNLOADER = 7
+    DUPLICATES = 8
+    THREAD_WATCHER = 9
+    PAGE_OF_PAGES = 10
+
+
+@enum.unique
+class FileSortType(StringableIntEnum):
+    FILE_SIZE = 0
+    DURATION = 1
+    IMPORT_TIME = 2
+    FILE_TYPE = 3
+    RANDOM = 4
+    WIDTH = 5
+    HEIGHT = 6
+    RATIO = 7
+    NUMBER_OF_PIXELS = 8
+    NUMBER_OF_TAGS = 9
+    NUMBER_OF_MEDIA_VIEWS = 10
+    TOTAL_MEDIA_VIEWTIME = 11
+    APPROXIMATE_BITRATE = 12
+    HAS_AUDIO = 13
+    MODIFIED_TIME = 14
+    FRAMERATE = 15
+    NUMBER_OF_FRAMES = 16
+
+
+class BinaryFileLike(T.Protocol):
+    def read(self):
+        ...
+
+
+# The client should accept all objects that either support the iterable or mapping protocol. We must ensure that objects
+# are either lists or dicts, so Python's json module can handle them
+class JSONEncoder(json.JSONEncoder):
+    def default(self, object_: T.Any):
+        if isinstance(object_, abc.Mapping):
+            return dict(object_)
+        if isinstance(object_, abc.Iterable):
+            return list(object_)
+        return super().default(object_)
+
+
+class Client:
+    VERSION = 31
+
+    # Access Management
+    _GET_API_VERSION_PATH = "/api_version"
+    _REQUEST_NEW_PERMISSIONS_PATH = "/request_new_permissions"
+    _GET_SESSION_KEY_PATH = "/session_key"
+    _VERIFY_ACCESS_KEY_PATH = "/verify_access_key"
+    _GET_SERVICES_PATH = "/get_services"
+
+    # Adding Files
+    _ADD_FILE_PATH = "/add_files/add_file"
+    _DELETE_FILES_PATH = "/add_files/delete_files"
+    _UNDELETE_FILES_PATH = "/add_files/undelete_files"
+    _ARCHIVE_FILES_PATH = "/add_files/archive_files"
+    _UNARCHIVE_FILES_PATH = "/add_files/unarchive_files"
+
+    # Adding Tags
+    _CLEAN_TAGS_PATH = "/add_tags/clean_tags"
+    _SEARCH_TAGS_PATH = "/add_tags/search_tags"
+    _ADD_TAGS_PATH = "/add_tags/add_tags"
+
+    # Adding URLs
+    _GET_URL_FILES_PATH = "/add_urls/get_url_files"
+    _GET_URL_INFO_PATH = "/add_urls/get_url_info"
+    _ADD_URL_PATH = "/add_urls/add_url"
+    _ASSOCIATE_URL_PATH = "/add_urls/associate_url"
+
+    # Adding Notes
+    _SET_NOTES_PATH = "/add_notes/set_notes"
+    _DELETE_NOTES_PATH = "/add_notes/delete_notes"
+
+    # Managing Cookies and HTTP Headers
+    _GET_COOKIES_PATH = "/manage_cookies/get_cookies"
+    _SET_COOKIES_PATH = "/manage_cookies/set_cookies"
+    _SET_USER_AGENT_PATH = "/manage_headers/set_user_agent"
+
+    # Managing Pages
+    _GET_PAGES_PATH = "/manage_pages/get_pages"
+    _GET_PAGE_INFO_PATH = "/manage_pages/get_page_info"
+    _ADD_FILES_TO_PAGE_PATH = "/manage_pages/add_files"
+    _FOCUS_PAGE_PATH = "/manage_pages/focus_page"
+
+    # Searching and Fetching Files
+    _SEARCH_FILES_PATH = "/get_files/search_files"
+    _GET_FILE_METADATA_PATH = "/get_files/file_metadata"
+    _GET_FILE_PATH = "/get_files/file"
+    _GET_THUMBNAIL_PATH = "/get_files/thumbnail"
+
+    # Managing the Database
+    _LOCK_DATABASE_PATH = "/manage_database/lock_on"
+    _UNLOCK_DATABASE_PATH = "/manage_database/lock_off"
+    _MR_BONES_PATH = "/manage_database/mr_bones"
+
+    def __init__(
+        self,
+        access_key = None,
+        api_url: str = DEFAULT_API_URL,
+        session = None,
+    ):
+        """
+        See https://hydrusnetwork.github.io/hydrus/help/client_api.html for documentation.
+        """
+
+        self.access_key = access_key
+        self.api_url = api_url.rstrip("/")
+        self.session = session or requests.Session()
+
+    def _api_request(self, method: str, path: str, **kwargs: T.Any):
+        if self.access_key is not None:
+            kwargs.setdefault("headers", {}).update({"Hydrus-Client-API-Access-Key": self.access_key})
+
+        # Make sure we use our custom JSONEncoder that can serialize all objects that implement the iterable or mapping
+        # protocol
+        json_data = kwargs.pop("json", None)
+        if json_data is not None:
+            kwargs["data"] = json.dumps(json_data, cls=JSONEncoder)
+            # Since we aren't using the json keyword-argument, we have to set the Content-Type manually
+            kwargs["headers"]["Content-Type"] = "application/json"
+
+        try:
+            response = self.session.request(method, self.api_url + path, **kwargs)
+        except requests.RequestException as error:
+            # Re-raise connection and timeout errors as hydrus.ConnectionErrors so these are more easy to handle for
+            # client applications
+            raise ConnectionError(*error.args)
+
+        try:
+            response.raise_for_status()
+        except requests.HTTPError:
+            if response.status_code == requests.codes.bad_request:
+                raise MissingParameter(response)
+            elif response.status_code in {
+                requests.codes.unauthorized,
+                requests.codes.forbidden,
+                AUTHENTICATION_TIMEOUT_CODE,
+            }:
+                raise InsufficientAccess(response)
+            elif response.status_code == requests.codes.service_unavailable:
+                raise DatabaseLocked(response)
+            elif response.status_code == requests.codes.server_error:
+                raise ServerError(response)
+            raise APIError(response)
+
+        return response
+
+    def get_api_version(self):
+        response = self._api_request("GET", self._GET_API_VERSION_PATH)
+        return response.json()
+
+    def request_new_permissions(self, name, permissions):
+        response = self._api_request(
+            "GET",
+            self._REQUEST_NEW_PERMISSIONS_PATH,
+            params={"name": name, "basic_permissions": json.dumps(permissions, cls=JSONEncoder)},
+        )
+        return response.json()["access_key"]
+
+    def get_session_key(self):
+        response = self._api_request("GET", self._GET_SESSION_KEY_PATH)
+        return response.json()["session_key"]
+
+    def verify_access_key(self):
+        response = self._api_request("GET", self._VERIFY_ACCESS_KEY_PATH)
+        return response.json()
+
+    def get_services(self):
+        response = self._api_request("GET", self._GET_SERVICES_PATH)
+        return response.json()
+
+    def add_file(self, path_or_file: T.Union[str, os.PathLike, BinaryFileLike]):
+        if isinstance(path_or_file, (str, os.PathLike)):
+            response = self._api_request("POST", self._ADD_FILE_PATH, json={"path": os.fspath(path_or_file)})
+        else:
+            response = self._api_request(
+                "POST",
+                self._ADD_FILE_PATH,
+                data=path_or_file.read(),
+                headers={"Content-Type": "application/octet-stream"},
+            )
+
+        return response.json()
+
+    def delete_files(
+        self,
+        hashes = None,
+        file_ids  = None,
+        file_service_name = None,
+        file_service_key = None,
+        reason = None
+    ):
+        if hashes is None and file_ids is None:
+            raise ValueError("At least one of hashes, file_ids is required")
+        if file_service_name is not None and file_service_key is not None:
+            raise ValueError("Exactly one of file_service_name, file_service_key is required")
+
+        payload: dict[str, T.Any] = {}
+        if hashes is not None:
+            payload["hashes"] = hashes
+        if file_ids is not None:
+            payload["file_ids"] = file_ids
+        if file_service_name is not None:
+            payload["file_service_name"] = file_service_name
+        if file_service_key is not None:
+            payload["file_service_key"] = file_service_key
+        if reason is not None:
+            payload["reason"] = reason
+
+        self._api_request("POST", self._DELETE_FILES_PATH, json=payload)
+
+    def undelete_files(
+        self,
+        hashes = None,
+        file_ids = None,
+        file_service_name = None,
+        file_service_key = None,
+    ):
+        if hashes is None and file_ids is None:
+            raise ValueError("At least one of hashes, file_ids is required")
+        if file_service_name is not None and file_service_key is not None:
+            raise ValueError("Exactly one of file_service_name, file_service_key is required")
+
+        payload: dict[str, T.Any] = {}
+        if hashes is not None:
+            payload["hashes"] = hashes
+        if file_ids is not None:
+            payload["file_ids"] = file_ids
+        if file_service_name is not None:
+            payload["file_service_name"] = file_service_name
+        if file_service_key is not None:
+            payload["file_service_key"] = file_service_key
+
+        self._api_request("POST", self._UNDELETE_FILES_PATH, json=payload)
+
+    def archive_files(
+        self,
+        hashes = None,
+        file_ids = None
+    ):
+        if hashes is None and file_ids is None:
+            raise ValueError("At least one of hashes, file_ids is required")
+
+        payload: dict[str, T.Any] = {}
+        if hashes is not None:
+            payload["hashes"] = hashes
+        if file_ids is not None:
+            payload["file_ids"] = file_ids
+
+        self._api_request("POST", self._ARCHIVE_FILES_PATH, json=payload)
+
+    def unarchive_files(
+        self,
+        hashes = None,
+        file_ids = None
+    ):
+        if hashes is None and file_ids is None:
+            raise ValueError("At least one of hashes, file_ids is required")
+
+        payload: dict[str, T.Any] = {}
+        if hashes is not None:
+            payload["hashes"] = hashes
+        if file_ids is not None:
+            payload["file_ids"] = file_ids
+
+        self._api_request("POST", self._UNARCHIVE_FILES_PATH, json=payload)
+
+    def clean_tags(self, tags ):
+        response = self._api_request("GET", self._CLEAN_TAGS_PATH, params={"tags": json.dumps(tags, cls=JSONEncoder)})
+        return response.json()["tags"]
+
+    def search_tags(
+        self,
+        search: str,
+        tag_service_key = None,
+        tag_service_name = None
+    ):
+        if tag_service_name is not None and tag_service_key is not None:
+            raise ValueError("Exactly one of tag_service_name, tag_service_key is required")
+
+        payload: dict[str, T.Any] = {"search": search}
+        if tag_service_key is not None:
+            payload["tag_service_key"] = tag_service_key
+        if tag_service_name is not None:
+            payload["tag_service_name"] = tag_service_name
+
+        response = self._api_request("GET", self._SEARCH_TAGS_PATH, params=payload)
+        return response.json()["tags"]
+
+    def add_tags(
+        self,
+        hashes = None,
+        file_ids = None,
+        service_names_to_tags = None,
+        service_keys_to_tags = None,
+        service_names_to_actions_to_tags = None,
+        service_keys_to_actions_to_tags = None,
+    ):
+        if hashes is None and file_ids is None:
+            raise ValueError("At least one of hashes, file_ids is required")
+        if (
+            service_names_to_tags is None
+            and service_keys_to_tags is None
+            and service_names_to_actions_to_tags is None
+            and service_keys_to_actions_to_tags is None
+        ):
+            raise ValueError(
+                "At least one of service_names_to_tags, service_keys_to_tags, service_names_to_actions_to_tags or "
+                "service_keys_to_actions_to_tags is required"
+            )
+
+        payload: dict[str, T.Any] = {}
+        if hashes is not None:
+            payload["hashes"] = hashes
+        if file_ids is not None:
+            payload["file_ids"] = file_ids
+        if service_names_to_tags is not None:
+            payload["service_names_to_tags"] = service_names_to_tags
+        if service_keys_to_tags is not None:
+            payload["service_keys_to_tags"] = service_keys_to_tags
+        if service_names_to_actions_to_tags is not None:
+            payload["service_names_to_actions_to_tags"] = service_names_to_actions_to_tags
+        if service_keys_to_actions_to_tags is not None:
+            payload["service_keys_to_actions_to_tags"] = service_keys_to_actions_to_tags
+
+        self._api_request("POST", self._ADD_TAGS_PATH, json=payload)
+
+    def get_url_files(self, url: str):
+        response = self._api_request("GET", self._GET_URL_FILES_PATH, params={"url": url})
+        return response.json()
+
+    def get_url_info(self, url: str):
+        response = self._api_request("GET", self._GET_URL_INFO_PATH, params={"url": url})
+        return response.json()
+
+    def add_url(
+        self,
+        url: str,
+        destination_page_key = None,
+        destination_page_name = None,
+        show_destination_page = None,
+        service_names_to_additional_tags = None,
+        service_keys_to_additional_tags = None,
+        filterable_tags = None,
+    ):
+        if destination_page_key is not None and destination_page_name is not None:
+            raise ValueError("Exactly one of destination_page_key, destination_page_name is required")
+
+        payload: dict[str, T.Any] = {"url": url}
+        if destination_page_key is not None:
+            payload["destination_page_key"] = destination_page_key
+        if destination_page_name is not None:
+            payload["destination_page_name"] = destination_page_name
+        if show_destination_page is not None:
+            payload["show_destination_page"] = show_destination_page
+        if service_names_to_additional_tags is not None:
+            payload["service_names_to_additional_tags"] = service_names_to_additional_tags
+        if service_keys_to_additional_tags is not None:
+            payload["service_keys_to_additional_tags"] = service_keys_to_additional_tags
+        if filterable_tags is not None:
+            payload["filterable_tags"] = filterable_tags
+
+        response = self._api_request("POST", self._ADD_URL_PATH, json=payload)
+        return response.json()
+
+    def associate_url(
+        self,
+        hashes = None,
+        file_ids = None,
+        urls_to_add = None,
+        urls_to_delete = None,
+    ):
+        if hashes is None and file_ids is None:
+            raise ValueError("At least one of hashes, file_ids is required")
+        if urls_to_add is None and urls_to_delete is None:
+            raise ValueError("At least one of urls_to_add, urls_to_delete is required")
+
+        payload: dict[str, T.Any] = {}
+        if hashes is not None:
+            payload["hashes"] = hashes
+        if file_ids is not None:
+            payload["file_ids"] = file_ids
+        if urls_to_add is not None:
+            urls_to_add = urls_to_add
+            payload["urls_to_add"] = urls_to_add
+        if urls_to_delete is not None:
+            urls_to_delete = urls_to_delete
+            payload["urls_to_delete"] = urls_to_delete
+
+        self._api_request("POST", self._ASSOCIATE_URL_PATH, json=payload)
+
+    def set_notes(self, notes , hash_= None, file_id = None):
+        if (hash_ is None and file_id is None) or (hash_ is not None and file_id is not None):
+            raise ValueError("Exactly one of hash_, file_id is required")
+
+        payload: dict[str, T.Any] = {"notes": notes}
+        if hash_ is not None:
+            payload["hash"] = hash_
+        if file_id is not None:
+            payload["file_id"] = file_id
+
+        self._api_request("POST", self._SET_NOTES_PATH, json=payload)
+
+    def delete_notes(
+        self,
+        note_names ,
+        hash_ = None,
+        file_id = None
+    ):
+        if (hash_ is None and file_id is None) or (hash_ is not None and file_id is not None):
+            raise ValueError("Exactly one of hash_, file_id is required")
+
+        payload: dict[str, T.Any] = {"note_names": note_names}
+        if hash_ is not None:
+            payload["hash"] = hash_
+        if file_id is not None:
+            payload["file_id"] = file_id
+
+        self._api_request("POST", self._DELETE_NOTES_PATH, json=payload)
+
+    def get_cookies(self, domain: str):
+        response = self._api_request("GET", self._GET_COOKIES_PATH, params={"domain": domain})
+        return response.json()["cookies"]
+
+    def set_cookies(self, cookies ):
+        self._api_request("POST", self._SET_COOKIES_PATH, json={"cookies": cookies})
+
+    def set_user_agent(self, user_agent: str):
+        self._api_request("POST", self._SET_USER_AGENT_PATH, json={"user-agent": user_agent})
+
+    def get_pages(self):
+        response = self._api_request("GET", self._GET_PAGES_PATH)
+        return response.json()["pages"]
+
+    def get_page_info(self, page_key: str, simple = None):
+        parameters = {"page_key": page_key}
+        if simple is not None:
+            parameters["simple"] = json.dumps(simple, cls=JSONEncoder)
+
+        response = self._api_request("GET", self._GET_PAGE_INFO_PATH, params=parameters)
+        return response.json()["page_info"]
+
+    def add_files_to_page(
+        self,
+        page_key: str,
+        file_ids = None,
+        hashes = None
+    ):
+        if file_ids is None and hashes is None:
+            raise ValueError("At least one of file_ids, hashes is required")
+
+        payload: dict[str, T.Any] = {"page_key": page_key}
+        if file_ids is not None:
+            payload["file_ids"] = file_ids
+        if hashes is not None:
+            payload["hashes"] = hashes
+
+        self._api_request("POST", self._ADD_FILES_TO_PAGE_PATH, json=payload)
+
+    def focus_page(self, page_key: str):
+        self._api_request("POST", self._FOCUS_PAGE_PATH, json={"page_key": page_key})
+
+    def search_files(
+        self,
+        tags,
+        file_service_name = None,
+        file_service_key = None,
+        tag_service_name = None,
+        tag_service_key = None,
+        file_sort_type = None,
+        file_sort_asc = None,
+        return_hashes = None,
+    ):
+        if file_service_name is not None and file_service_key is not None:
+            raise ValueError("Exactly one of file_service_name, file_service_key is required")
+        if tag_service_name is not None and tag_service_key is not None:
+            raise ValueError("Exactly one of tag_service_name, tag_service_key is required")
+
+        parameters: dict[str, T.Union[str, int]] = {"tags": json.dumps(tags, cls=JSONEncoder)}
+        if file_service_name is not None:
+            parameters["file_service_name"] = file_service_name
+        if file_service_key is not None:
+            parameters["file_service_key"] = file_service_key
+
+        if tag_service_name is not None:
+            parameters["tag_service_name"] = tag_service_name
+        if tag_service_key is not None:
+            parameters["tag_service_key"] = tag_service_key
+
+        if file_sort_type is not None:
+            parameters["file_sort_type"] = file_sort_type
+        if file_sort_asc is not None:
+            parameters["file_sort_asc"] = json.dumps(file_sort_asc, cls=JSONEncoder)
+        if return_hashes is not None:
+            parameters["return_hashes"] = json.dumps(return_hashes, cls=JSONEncoder)
+
+        response = self._api_request("GET", self._SEARCH_FILES_PATH, params=parameters)
+        return response.json()["hashes" if return_hashes else "file_ids"]
+
+    def get_file_metadata(
+        self,
+        hashes = None,
+        file_ids = None,
+        create_new_file_ids = None,
+        only_return_identifiers = None,
+        only_return_basic_information = None,
+        detailed_url_information = None,
+        hide_service_name_tags = None,
+        include_notes = None
+    ):
+        if hashes is None and file_ids is None:
+            raise ValueError("At least one of hashes, file_ids is required")
+
+        parameters = {}
+        if hashes is not None:
+            parameters["hashes"] = json.dumps(hashes, cls=JSONEncoder)
+        if file_ids is not None:
+            parameters["file_ids"] = json.dumps(file_ids, cls=JSONEncoder)
+
+        if create_new_file_ids is not None:
+            parameters["create_new_file_ids"] = json.dumps(create_new_file_ids, cls=JSONEncoder)
+        if only_return_identifiers is not None:
+            parameters["only_return_identifiers"] = json.dumps(only_return_identifiers, cls=JSONEncoder)
+        if only_return_basic_information is not None:
+            parameters["only_return_basic_information"] = json.dumps(only_return_basic_information, cls=JSONEncoder)
+        if detailed_url_information is not None:
+            parameters["detailed_url_information"] = json.dumps(detailed_url_information, cls=JSONEncoder)
+        if hide_service_name_tags is not None:
+            parameters["hide_service_name_tags"] = json.dumps(hide_service_name_tags, cls=JSONEncoder)
+        if include_notes is not None:
+            parameters["include_notes"] = json.dumps(include_notes, cls=JSONEncoder)
+
+        response = self._api_request("GET", self._GET_FILE_METADATA_PATH, params=parameters)
+        return response.json()["metadata"]
+
+    def get_file(self, hash_ = None, file_id = None):
+        if (hash_ is None and file_id is None) or (hash_ is not None and file_id is not None):
+            raise ValueError("Exactly one of hash_, file_id is required")
+
+        parameters: dict[str, T.Union[str, int]] = {}
+        if hash_ is not None:
+            parameters["hash"] = hash_
+        if file_id is not None:
+            parameters["file_id"] = file_id
+
+        return self._api_request("GET", self._GET_FILE_PATH, params=parameters, stream=True)
+
+    def get_thumbnail(self, hash_ = None, file_id = None):
+        if (hash_ is None and file_id is None) or (hash_ is not None and file_id is not None):
+            raise ValueError("Exactly one of hash_, file_id is required")
+
+        parameters: dict[str, T.Union[str, int]] = {}
+        if hash_ is not None:
+            parameters["hash"] = hash_
+        if file_id is not None:
+            parameters["file_id"] = file_id
+
+        return self._api_request("GET", self._GET_THUMBNAIL_PATH, params=parameters, stream=True)
+
+    def lock_database(self):
+        self._api_request("POST", self._LOCK_DATABASE_PATH)
+
+    def unlock_database(self):
+        self._api_request("POST", self._UNLOCK_DATABASE_PATH)
+
+    def get_mr_bones(self):
+        return self._api_request("GET", self._MR_BONES_PATH).json()["boned_stats"]
+
+    def add_and_tag_files(
+        self,
+        paths_or_files,
+        tags ,
+        service_names = None,
+        service_keys = None,
+    ):
+        """Convenience method to add and tag multiple files at the same time.
+
+        If service_names and service_keys aren't specified, the default service name "my tags" will be used. If a file
+        already exists in Hydrus, it will also be tagged.
+
+        Returns:
+            list[dict[str, T.Any]]: Returns results of all `Client.add_file()` calls, matching the order of the
+            paths_or_files iterable
+        """
+        if service_names is None and service_keys is None:
+            service_names = ("my tags",)
+
+        results = []
+        hashes = set()
+        for path_or_file in paths_or_files:
+            result = self.add_file(path_or_file)
+            results.append(result)
+            if result["status"] != ImportStatus.FAILED:
+                hashes.add(result["hash"])
+
+        service_names_to_tags = {name: tags for name in service_names} if service_names is not None else None
+        service_keys_to_tags = {key: tags for key in service_keys} if service_keys is not None else None
+        # Ignore type, we know that hashes only contains strings
+        self.add_tags(hashes, service_names_to_tags=service_names_to_tags, service_keys_to_tags=service_keys_to_tags)  # type: ignore
+        return results
+
+    def get_page_list(self):
+        """Convenience method that returns a flattened version of the page tree from `Client.get_pages()`.
+
+        Returns:
+            list[dict[str, T.Any]]: A list of every "pages" value in the page tree in pre-order (NLR)
+        """
+        tree = self.get_pages()
+        pages = []
+
+        def walk_tree(page: dict[str, T.Any]):
+            pages.append(page)
+            # Ignore type, we know that pages is always a list
+            for sub_page in page.get("pages", ()):  # type: ignore
+                # Ignore type, we know that sub_page is always a dict
+                walk_tree(sub_page)  # type: ignore
+
+        walk_tree(tree)
+        return pages
+
+
+__all__ = [
+    "__version__",
+    "DEFAULT_API_URL",
+    "HYDRUS_METADATA_ENCODING",
+    "HydrusAPIException",
+    "ConnectionError",
+    "APIError",
+    "MissingParameter",
+    "InsufficientAccess",
+    "DatabaseLocked",
+    "ServerError",
+    "Permission",
+    "URLType",
+    "ImportStatus",
+    "TagAction",
+    "TagStatus",
+    "PageType",
+    "FileSortType",
+    "Client",
+]

scripts/hydrus_api/utils.py

@@ -0,0 +1,102 @@
+# Copyright (C) 2021 cryzed
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+import collections
+import os
+import typing as T
+from collections import abc
+
+from hydrus_api import DEFAULT_API_URL, HYDRUS_METADATA_ENCODING, Client, Permission
+
+X = T.TypeVar("X")
+
+
+class TextFileLike(T.Protocol):
+    def read(self) -> str:
+        pass
+
+
+def verify_permissions(
+    client: Client, permissions: abc.Iterable[T.Union[int, Permission]], exact: bool = False
+) -> bool:
+    granted_permissions = set(client.verify_access_key()["basic_permissions"])
+    return granted_permissions == set(permissions) if exact else granted_permissions.issuperset(permissions)
+
+
+def cli_request_api_key(
+    name: str,
+    permissions: abc.Iterable[T.Union[int, Permission]],
+    verify: bool = True,
+    exact: bool = False,
+    api_url: str = DEFAULT_API_URL,
+) -> str:
+    while True:
+        input(
+            'Navigate to "services->review services->local->client api" in the Hydrus client and click "add->from api '
+            'request". Then press enter to continue...'
+        )
+        access_key = Client(api_url=api_url).request_new_permissions(name, permissions)
+        input("Press OK and then apply in the Hydrus client dialog. Then press enter to continue...")
+
+        client = Client(access_key, api_url)
+        if verify and not verify_permissions(client, permissions, exact):
+            granted = client.verify_access_key()["basic_permissions"]
+            print(
+                f"The granted permissions ({granted}) differ from the requested permissions ({permissions}), please "
+                "grant all requested permissions."
+            )
+            continue
+
+        return access_key
+
+
+def parse_hydrus_metadata(text: str) -> collections.defaultdict[T.Optional[str], set[str]]:
+    namespaces = collections.defaultdict(set)
+    for line in (line.strip() for line in text.splitlines()):
+        if not line:
+            continue
+
+        parts = line.split(":", 1)
+        namespace, tag = (None, line) if len(parts) == 1 else parts
+        namespaces[namespace].add(tag)
+
+    # Ignore type, mypy has trouble figuring out that tag isn't optional
+    return namespaces  # type: ignore
+
+
+def parse_hydrus_metadata_file(
+    path_or_file: T.Union[str, os.PathLike, TextFileLike]
+) -> collections.defaultdict[T.Optional[str], set[str]]:
+    if isinstance(path_or_file, (str, os.PathLike)):
+        with open(path_or_file, encoding=HYDRUS_METADATA_ENCODING) as file:
+            return parse_hydrus_metadata(file.read())
+
+    return parse_hydrus_metadata(path_or_file.read())
+
+
+# Useful for splitting up requests to get_file_metadata()
+def yield_chunks(sequence: T.Sequence[X], chunk_size: int, offset: int = 0) -> T.Generator[T.Sequence[X], None, None]:
+    while offset < len(sequence):
+        yield sequence[offset : offset + chunk_size]
+        offset += chunk_size
+
+
+__all__ = [
+    "verify_permissions",
+    "cli_request_api_key",
+    "parse_hydrus_metadata",
+    "parse_hydrus_metadata_file",
+    "yield_chunks",
+]

scripts/img2img.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -30,12 +30,18 @@ import torch
 import skimage
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
+
+# streamlit components
+from custom_components import sygil_suggestions
+from streamlit_drawable_canvas import st_canvas
+
 # Temp imports
 
 
 # end of imports
 #---------------------------------------------------------------------------------------------------------------
 
+sygil_suggestions.init()
 
 try:
 	# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -49,7 +55,7 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
 	    mask_restore: bool = False, ddim_steps: int = 50, sampler_name: str = 'DDIM',
 	    n_iter: int = 1,  cfg_scale: float = 7.5, denoising_strength: float = 0.8,
 	    seed: int = -1, noise_mode: int = 0, find_noise_steps: str = "", height: int = 512, width: int = 512, resize_mode: int = 0, fp = None,
-	    variant_amount: float = None, variant_seed: int = None, ddim_eta:float = 0.0,
+	    variant_amount: float = 0.0, variant_seed: int = None, ddim_eta:float = 0.0,
 	    write_info_files:bool = True, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
 	    save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
 	    save_as_jpg: bool = True, use_GFPGAN: bool = True, GFPGAN_model: str = 'GFPGANv1.4',
@@ -202,7 +208,7 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
 			samples_ddim = K.sampling.__dict__[f'sample_{sampler.get_sampler_name()}'](model_wrap_cfg, xi, sigma_sched,
 												   extra_args={'cond': conditioning, 'uncond': unconditional_conditioning,
 													       'cond_scale': cfg_scale, 'mask': z_mask, 'x0': x0, 'xi': xi}, disable=False,
-												   callback=generation_callback)
+												   callback=generation_callback if not server_state["bridge"] else None)
 		else:
 
 			x0, z_mask = init_data
@@ -234,7 +240,7 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
 			from skimage import exposure
 			do_color_correction = True
 		except:
-			print("Install scikit-image to perform color correction on loopback")		
+			logger.error("Install scikit-image to perform color correction on loopback")
 
 		for i in range(n_iter):
 			if do_color_correction and i == 0:
@@ -365,7 +371,9 @@ def layout():
 		img2img_input_col, img2img_generate_col = st.columns([10,1])
 		with img2img_input_col:
 			#prompt = st.text_area("Input Text","")
-			prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
+			placeholder = "A corgi wearing a top hat as an oil painting."
+			prompt = st.text_area("Input Text","", placeholder=placeholder, height=54)
+			sygil_suggestions.suggestion_area(placeholder)
 
 		# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
 		img2img_generate_col.write("")
@@ -374,7 +382,7 @@ def layout():
 
 
 		# creating the page layout using columns
-		col1_img2img_layout, col2_img2img_layout, col3_img2img_layout = st.columns([1,2,2], gap="small")    
+		col1_img2img_layout, col2_img2img_layout, col3_img2img_layout = st.columns([2,4,4], gap="medium")
 
 		with col1_img2img_layout:
 			# If we have custom models available on the "models/custom"
@@ -386,14 +394,13 @@ def layout():
 							    help="Select the model you want to use. This option is only available if you have custom models \
 							    on your 'models/custom' folder. The model name that will be shown here is the same as the name\
 							    the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
-							    will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4") 	
+							    will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5")
 			else:
-				st.session_state["custom_model"] = "Stable Diffusion v1.4"
+				st.session_state["custom_model"] = "Stable Diffusion v1.5"
 
 
-			st.session_state["sampling_steps"] = st.slider("Sampling Steps", value=st.session_state['defaults'].img2img.sampling_steps.value,
+			st.session_state["sampling_steps"] = st.number_input("Sampling Steps", value=st.session_state['defaults'].img2img.sampling_steps.value,
 																 min_value=st.session_state['defaults'].img2img.sampling_steps.min_value,
-														   max_value=st.session_state['defaults'].img2img.sampling_steps.max_value,
 																 step=st.session_state['defaults'].img2img.sampling_steps.step)
 
 			sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a",  "k_heun", "PLMS", "DDIM"]
@@ -406,9 +413,10 @@ def layout():
 									   value=st.session_state['defaults'].img2img.height.value, step=st.session_state['defaults'].img2img.height.step)
 			seed = st.text_input("Seed:", value=st.session_state['defaults'].img2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
 
-			cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].img2img.cfg_scale.min_value,
+			cfg_scale = st.number_input("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].img2img.cfg_scale.min_value,
-											  max_value=st.session_state['defaults'].img2img.cfg_scale.max_value, value=st.session_state['defaults'].img2img.cfg_scale.value,
+						  value=st.session_state['defaults'].img2img.cfg_scale.value,
-											  step=st.session_state['defaults'].img2img.cfg_scale.step, help="How strongly the image should follow the prompt.")
+					      step=st.session_state['defaults'].img2img.cfg_scale.step,
+					      help="How strongly the image should follow the prompt.")
 
 			st.session_state["denoising_strength"] = st.slider("Denoising Strength:", value=st.session_state['defaults'].img2img.denoising_strength.value,
 									   min_value=st.session_state['defaults'].img2img.denoising_strength.min_value,
@@ -419,7 +427,7 @@ def layout():
 			mask_expander = st.empty()
 			with mask_expander.expander("Mask"):
 				mask_mode_list = ["Mask", "Inverted mask", "Image alpha"]
-				mask_mode = st.selectbox("Mask Mode", mask_mode_list,
+				mask_mode = st.selectbox("Mask Mode", mask_mode_list, index=st.session_state["defaults"].img2img.mask_mode,
 							 help="Select how you want your image to be masked.\"Mask\" modifies the image where the mask is white.\n\
 							 \"Inverted mask\" modifies the image where the mask is black. \"Image alpha\" modifies the image where the image is transparent."
 							 )
@@ -427,27 +435,46 @@ def layout():
 
 
 				noise_mode_list = ["Seed", "Find Noise", "Matched Noise", "Find+Matched Noise"]
-				noise_mode = st.selectbox(
+				noise_mode = st.selectbox("Noise Mode", noise_mode_list, index=noise_mode_list.index(st.session_state['defaults'].img2img.noise_mode), help="")
-							"Noise Mode", noise_mode_list,
+				#noise_mode = noise_mode_list.index(noise_mode)
-							help=""
+				find_noise_steps = st.number_input("Find Noise Steps", value=st.session_state['defaults'].img2img.find_noise_steps.value,
-						)
+											 min_value=st.session_state['defaults'].img2img.find_noise_steps.min_value,
-				noise_mode = noise_mode_list.index(noise_mode)
-				find_noise_steps = st.slider("Find Noise Steps", value=st.session_state['defaults'].img2img.find_noise_steps.value,
-											 min_value=st.session_state['defaults'].img2img.find_noise_steps.min_value, max_value=st.session_state['defaults'].img2img.find_noise_steps.max_value,
 											 step=st.session_state['defaults'].img2img.find_noise_steps.step)
 
-			with st.expander("Batch Options"):
+				# Specify canvas parameters in application
-				st.session_state["batch_count"] = int(st.text_input("Batch count.", value=st.session_state['defaults'].img2img.batch_count.value,
+				drawing_mode = st.selectbox(
-																help="How many iterations or batches of images to generate in total."))
+					"Drawing tool:",
+					(
+						"freedraw",
+						"transform",
+						#"line",
+						"rect",
+						"circle",
+						#"polygon",
+						),
+				)
 
-				st.session_state["batch_size"] = int(st.text_input("Batch size", value=st.session_state.defaults.img2img.batch_size.value,
+				stroke_width = st.slider("Stroke width: ", 1, 100, 50)
+				stroke_color = st.color_picker("Stroke color hex: ", value="#EEEEEE")
+				bg_color = st.color_picker("Background color hex: ", "#7B6E6E")
+
+				display_toolbar = st.checkbox("Display toolbar", True)
+				#realtime_update = st.checkbox("Update in realtime", True)
+
+			with st.expander("Batch Options"):
+				st.session_state["batch_count"] = st.number_input("Batch count.", value=st.session_state['defaults'].img2img.batch_count.value,
+										  help="How many iterations or batches of images to generate in total.")
+
+				st.session_state["batch_size"] = st.number_input("Batch size", value=st.session_state.defaults.img2img.batch_size.value,
 										 help="How many images are at once in a batch.\
 										 It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
-                                            Default: 1")) 	
+										 Default: 1")
 
 			with st.expander("Preview Settings"):
 				st.session_state["update_preview"] = st.session_state["defaults"].general.update_preview
-				st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].img2img.update_preview_frequency,
+				st.session_state["update_preview_frequency"] = st.number_input("Update Image Preview Frequency",
+											       min_value=0,
+											       value=st.session_state['defaults'].img2img.update_preview_frequency,
 											       help="Frequency in steps at which the the preview image is updated. By default the frequency \
 											       is set to 1 step.")
 			#
@@ -518,7 +545,9 @@ def layout():
 									upscaling_method_list.append("LDSR")
 
 								st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list,
-																								index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method))
+                                                                                               index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method)
+                                                                                                    if st.session_state['defaults'].general.upscaling_method in upscaling_method_list
+                                                                                                    else 0)
 
 								if st.session_state["RealESRGAN_available"]:
 									with st.expander("RealESRGAN"):
@@ -545,14 +574,14 @@ def layout():
 										st.session_state["LDSR_model"] = st.selectbox("LDSR model", st.session_state["LDSR_models"],
 																					  index=st.session_state["LDSR_models"].index(st.session_state['defaults'].general.LDSR_model))
 
-										st.session_state["ldsr_sampling_steps"] = int(st.text_input("Sampling Steps", value=st.session_state['defaults'].img2img.LDSR_config.sampling_steps,
+										st.session_state["ldsr_sampling_steps"] = st.number_input("Sampling Steps", value=st.session_state['defaults'].img2img.LDSR_config.sampling_steps,
-																									help=""))
+																									help="")
 
-										st.session_state["preDownScale"] = int(st.text_input("PreDownScale", value=st.session_state['defaults'].img2img.LDSR_config.preDownScale,
+										st.session_state["preDownScale"] = st.number_input("PreDownScale", value=st.session_state['defaults'].img2img.LDSR_config.preDownScale,
-																							 help=""))
+																							 help="")
 
-										st.session_state["postDownScale"] = int(st.text_input("postDownScale", value=st.session_state['defaults'].img2img.LDSR_config.postDownScale,
+										st.session_state["postDownScale"] = st.number_input("postDownScale", value=st.session_state['defaults'].img2img.LDSR_config.postDownScale,
-																							  help=""))
+																							  help="")
 
 										downsample_method_list = ['Nearest', 'Lanczos']
 										st.session_state["downsample_method"] = st.selectbox("Downsample Method", downsample_method_list,
@@ -574,55 +603,63 @@ def layout():
 			editor_image = st.empty()
 			st.session_state["editor_image"] = editor_image
 
+			st.form_submit_button("Refresh")
+
+			#if "canvas" not in st.session_state:
+			st.session_state["canvas"] = st.empty()
+
 			masked_image_holder = st.empty()
 			image_holder = st.empty()
 
-			st.form_submit_button("Refresh")
-			
 			uploaded_images = st.file_uploader(
-						"Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"],
+						"Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp", 'jfif'],
 						help="Upload an image which will be used for the image to image generation.",
 					)
 			if uploaded_images:
-				image = Image.open(uploaded_images).convert('RGBA')
+				image = Image.open(uploaded_images).convert('RGB')
 				new_img = image.resize((width, height))
-				image_holder.image(new_img)
+				#image_holder.image(new_img)
 
-			mask_holder = st.empty()
+			#mask_holder = st.empty()
 
-			uploaded_masks = st.file_uploader(
+			#uploaded_masks = st.file_uploader(
-						"Upload Mask", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"],
+						#"Upload Mask", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp", 'jfif'],
-						help="Upload an mask image which will be used for masking the image to image generation.",
+						#help="Upload an mask image which will be used for masking the image to image generation.",
+					#)
+
+			#
+			# Create a canvas component
+			with st.session_state["canvas"]:
+				st.session_state["uploaded_masks"] = st_canvas(
+					fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
+					stroke_width=stroke_width,
+					stroke_color=stroke_color,
+					background_color=bg_color,
+					background_image=image if uploaded_images else None,
+					update_streamlit=True,
+					width=width,
+					height=height,
+					drawing_mode=drawing_mode,
+					initial_drawing=st.session_state["uploaded_masks"].json_data if "uploaded_masks" in st.session_state else None,
+					display_toolbar= display_toolbar,
+					key="full_app",
 				)
-			if uploaded_masks:
-				mask_expander.expander("Mask", expanded=True)
-				mask = Image.open(uploaded_masks)
-				if mask.mode == "RGBA":
-					mask = mask.convert('RGBA')
-					background = Image.new('RGBA', mask.size, (0, 0, 0))
-					mask = Image.alpha_composite(background, mask)
-				mask = mask.resize((width, height))
-				mask_holder.image(mask)
 
-			if uploaded_images and uploaded_masks:
+				#try:
-				if mask_mode != 2:
+					##print (type(st.session_state["uploaded_masks"]))
-					final_img = new_img.copy()
+					#if st.session_state["uploaded_masks"] != None:
-					alpha_layer = mask.convert('L')
+						#mask_expander.expander("Mask", expanded=True)
-					strength = st.session_state["denoising_strength"]
+						#mask = Image.fromarray(st.session_state["uploaded_masks"].image_data)
-					if mask_mode == 0:
-						alpha_layer = ImageOps.invert(alpha_layer)
-						alpha_layer = alpha_layer.point(lambda a: a * strength)
-						alpha_layer = ImageOps.invert(alpha_layer)
-					elif mask_mode == 1:
-						alpha_layer = alpha_layer.point(lambda a: a * strength)
-						alpha_layer = ImageOps.invert(alpha_layer)
 
-					final_img.putalpha(alpha_layer)
+					#st.image(mask)
-	
-					with masked_image_holder.container():
-						st.text("Masked Image Preview")
-						st.image(final_img)
 
+					#if mask.mode == "RGBA":
+						#mask = mask.convert('RGBA')
+						#background = Image.new('RGBA', mask.size, (0, 0, 0))
+						#mask = Image.alpha_composite(background, mask)
+						#mask = mask.resize((width, height))
+				#except AttributeError:
+					#pass
 
 			with col3_img2img_layout:
 				result_tab = st.tabs(["Result"])
@@ -636,7 +673,6 @@ def layout():
 				st.session_state["progress_bar_text"] = st.empty()
 				st.session_state["progress_bar"] = st.empty()
 
-	
 				message = st.empty()
 
 				#if uploaded_images:
@@ -657,14 +693,17 @@ def layout():
 								CustomModel_available=server_state["CustomModel_available"], custom_model=st.session_state["custom_model"])
 
 			if uploaded_images:
-				image = Image.open(uploaded_images).convert('RGBA')
+				#image = Image.fromarray(image).convert('RGBA')
-				new_img = image.resize((width, height))
+				#new_img = image.resize((width, height))
-				#img_array = np.array(image) # if you want to pass it to OpenCV
+				###img_array = np.array(image) # if you want to pass it to OpenCV
+				#image_holder.image(new_img)
 				new_mask = None
-				if uploaded_masks:
+
-					mask = Image.open(uploaded_masks).convert('RGBA')
+				if st.session_state["uploaded_masks"]:
+					mask = Image.fromarray(st.session_state["uploaded_masks"].image_data)
 					new_mask = mask.resize((width, height))
 
+					#masked_image_holder.image(new_mask)
 				try:
 					output_images, seed, info, stats = img2img(prompt=prompt, init_info=new_img, init_info_mask=new_mask, mask_mode=mask_mode,
 										   mask_restore=img2img_mask_restore, ddim_steps=st.session_state["sampling_steps"],
@@ -686,7 +725,7 @@ def layout():
 					message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
 
 				except (StopException, KeyError):
-					print(f"Received Streamlit StopException")
+					logger.info(f"Received Streamlit StopException")
 
 				# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
 				# use the current col2 first tab to show the preview_img and update it as its generated.

scripts/img2txt.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -54,19 +54,23 @@ from PIL import Image
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 from ldm.models.blip import blip_decoder
+#import hashlib
 
 # end of imports
 # ---------------------------------------------------------------------------------------------------------------
 
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 blip_image_eval_size = 512
-#blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'
+
-server_state["clip_models"] = {}
+st.session_state["log"] = []
-server_state["preprocesses"] = {}
 
 def load_blip_model():
-    print("Loading BLIP Model")
+    logger.info("Loading BLIP Model")
-    st.session_state["log_message"].code("Loading BLIP Model", language='')
+    if "log" not in st.session_state:
+        st.session_state["log"] = []
+
+    st.session_state["log"].append("Loading BLIP Model")
+    st.session_state["log_message"].code('\n'.join(st.session_state["log"]), language='')
 
     if "blip_model" not in server_state:
         with server_state_lock['blip_model']:
@@ -75,66 +79,16 @@ def load_blip_model():
 
             server_state["blip_model"] = server_state["blip_model"].eval()
 
-            #if not st.session_state["defaults"].general.optimized:
             server_state["blip_model"] = server_state["blip_model"].to(device).half()
 
-            print("BLIP Model Loaded")
+            logger.info("BLIP Model Loaded")
-            st.session_state["log_message"].code("BLIP Model Loaded", language='')
+            st.session_state["log"].append("BLIP Model Loaded")
+            st.session_state["log_message"].code('\n'.join(st.session_state["log"]), language='')
     else:
-        print("BLIP Model already loaded")
+        logger.info("BLIP Model already loaded")
-        st.session_state["log_message"].code("BLIP Model Already Loaded", language='')
+        st.session_state["log"].append("BLIP Model already loaded")
+        st.session_state["log_message"].code('\n'.join(st.session_state["log"]), language='')
 
-    #return server_state["blip_model"]
-
-
-#
-def artstation_links():
-    """Find and save every artstation link for the first 500 pages of the explore page."""
-    # collecting links to the list()
-    links = []
-    
-    with open('data/img2txt/artstation_links.txt', 'w') as f:
-        for page_num in range(1,500):
-            response = requests.get(f'https://www.artstation.com/api/v2/community/explore/projects/trending.json?page={page_num}&dimension=all&per_page=100').text
-            # open json response
-            data = json.loads(response)
-            
-            # loopinh through json response
-            for result in data['data']:
-                # still looping and grabbing url's
-                url = result['url']
-                links.append(url)
-                # writing each link on the new line (\n)
-                f.write(f'{url}\n')  
-    return links
-#
-def artstation_users():
-    """Get all the usernames and full name of the users on the first 500 pages of artstation explore page."""
-    # collect username and full name
-    artists = []
-    
-    # opening a .txt file
-    with open('data/img2txt/artstation_artists.txt', 'w') as f:
-        for page_num in range(1,500):
-            response = requests.get(f'https://www.artstation.com/api/v2/community/explore/projects/trending.json?page={page_num}&dimension=all&per_page=100').text
-            # open json response
-            data = json.loads(response)
-    
-    
-            # loopinh through json response
-            for item in data['data']:
-                #print (item['user'])
-                username = item['user']['username']
-                full_name = item['user']['full_name']
-    
-                # still looping and grabbing url's
-                artists.append(username)
-                artists.append(full_name)
-                # writing each link on the new line (\n)
-                f.write(f'{slugify(username)}\n')
-                f.write(f'{slugify(full_name)}\n')
-    
-    return artists
 
 def generate_caption(pil_image):
 
@@ -149,7 +103,6 @@ def generate_caption(pil_image):
     with torch.no_grad():
         caption = server_state["blip_model"].generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5)
 
-    #print (caption)
     return caption[0]
 
 def load_list(filename):
@@ -188,39 +141,37 @@ def batch_rank(model, image_features, text_array, batch_size=st.session_state["d
     return ranks
 
 def interrogate(image, models):
-
-    #server_state["blip_model"] = 
     load_blip_model()
 
-    print("Generating Caption")
+    logger.info("Generating Caption")
-    st.session_state["log_message"].code("Generating Caption", language='')
+    st.session_state["log"].append("Generating Caption")
+    st.session_state["log_message"].code('\n'.join(st.session_state["log"]), language='')
     caption = generate_caption(image)
 
     if st.session_state["defaults"].general.optimized:
         del server_state["blip_model"]
         clear_cuda()
 
-    print("Caption Generated")
+    logger.info("Caption Generated")
-    st.session_state["log_message"].code("Caption Generated", language='')
+    st.session_state["log"].append("Caption Generated")
+    st.session_state["log_message"].code('\n'.join(st.session_state["log"]), language='')
 
     if len(models) == 0:
-        print(f"\n\n{caption}")
+        logger.info(f"\n\n{caption}")
         return
 
     table = []
-    bests = [[('', 0)]]*5
+    bests = [[('', 0)]]*7
 
-    print("Ranking Text")
+    logger.info("Ranking Text")
-    
+    st.session_state["log"].append("Ranking Text")
-    #if "clip_model" in server_state:
+    st.session_state["log_message"].code('\n'.join(st.session_state["log"]), language='')
-        #print (server_state["clip_model"])
-        
-    #print (st.session_state["log_message"])
 
     for model_name in models:
         with torch.no_grad(), torch.autocast('cuda', dtype=torch.float16):
-            print(f"Interrogating with {model_name}...")
+            logger.info(f"Interrogating with {model_name}...")
-            st.session_state["log_message"].code(f"Interrogating with {model_name}...", language='')
+            st.session_state["log"].append(f"Interrogating with {model_name}...")
+            st.session_state["log_message"].code('\n'.join(st.session_state["log"]), language='')
 
             if model_name not in server_state["clip_models"]:
                 if not st.session_state["defaults"].img2txt.keep_all_models_loaded:
@@ -233,11 +184,14 @@ def interrogate(image, models):
                         del server_state["preprocesses"][model]
                         clear_cuda()
                 if model_name == 'ViT-H-14':
-                    server_state["clip_models"][model_name], _, server_state["preprocesses"][model_name] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s32b_b79k', cache_dir='models/clip')
+                    server_state["clip_models"][model_name], _, server_state["preprocesses"][model_name] = \
+                    open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s32b_b79k', cache_dir='models/clip')
                 elif model_name == 'ViT-g-14':
-                    server_state["clip_models"][model_name], _, server_state["preprocesses"][model_name] = open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s12b_b42k', cache_dir='models/clip')
+                    server_state["clip_models"][model_name], _, server_state["preprocesses"][model_name] = \
+                    open_clip.create_model_and_transforms(model_name, pretrained='laion2b_s12b_b42k', cache_dir='models/clip')
                 else:
-                    server_state["clip_models"][model_name], server_state["preprocesses"][model_name] = clip.load(model_name, device=device, download_root='models/clip')
+                    server_state["clip_models"][model_name], server_state["preprocesses"][model_name] = \
+                    clip.load(model_name, device=device, download_root='models/clip')
                 server_state["clip_models"][model_name] = server_state["clip_models"][model_name].cuda().eval()
 
             images = server_state["preprocesses"][model_name](image).unsqueeze(0).cuda()
@@ -256,15 +210,22 @@ def interrogate(image, models):
             ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["trending_list"]))
             ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["movements"]))
             ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["flavors"]))
+            #ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["domains"]))
+            #ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["subreddits"]))
+            ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["techniques"]))
+            ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["tags"]))
+
             # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["genres"]))
             # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["styles"]))
-            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["techniques"]))
             # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["subjects"]))
             # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["colors"]))
             # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["moods"]))
             # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["themes"]))
             # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["keywords"]))
 
+            #print (bests)
+            #print (ranks)
+
             for i in range(len(ranks)):
                 confidence_sum = 0
                 for ci in range(len(ranks[i])):
@@ -272,55 +233,53 @@ def interrogate(image, models):
                 if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):
                     bests[i] = ranks[i]
 
+            for best in bests:
+                best.sort(key=lambda x: x[1], reverse=True)
+                # prune to 3
+                best = best[:3]
+
             row = [model_name]
+
             for r in ranks:
                 row.append(', '.join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))
 
+            #for rank in ranks:
+            #    rank.sort(key=lambda x: x[1], reverse=True)
+            #    row.append(f'{rank[0][0]} {rank[0][1]:.2f}%')
+
             table.append(row)
 
             if st.session_state["defaults"].general.optimized:
                 del server_state["clip_models"][model_name]
                 gc.collect()
 
-    # for i in range(len(st.session_state["uploaded_image"])):
     st.session_state["prediction_table"][st.session_state["processed_image_count"]].dataframe(pd.DataFrame(
-        table, columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors"]))
+        table, columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors", "Techniques", "Tags"]))
 
-    flaves = ', '.join([f"{x[0]}" for x in bests[4]])
     medium = bests[0][0][0]
+    artist = bests[1][0][0]
+    trending = bests[2][0][0]
+    movement = bests[3][0][0]
+    flavors = bests[4][0][0]
+    #domains = bests[5][0][0]
+    #subreddits = bests[6][0][0]
+    techniques = bests[5][0][0]
+    tags = bests[6][0][0]
+
+
     if caption.startswith(medium):
         st.session_state["text_result"][st.session_state["processed_image_count"]].code(
-            f"\n\n{caption} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}", language="")
+            f"\n\n{caption} {artist}, {trending}, {movement}, {techniques}, {flavors}, {tags}", language="")
     else:
         st.session_state["text_result"][st.session_state["processed_image_count"]].code(
-            f"\n\n{caption}, {medium} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}", language="")
+            f"\n\n{caption}, {medium} {artist}, {trending}, {movement}, {techniques}, {flavors}, {tags}", language="")
 
-    #
+    logger.info("Finished Interrogating.")
-    print("Finished Interrogating.")
+    st.session_state["log"].append("Finished Interrogating.")
-    st.session_state["log_message"].code("Finished Interrogating.", language="")
+    st.session_state["log_message"].code('\n'.join(st.session_state["log"]), language='')
-#
 
 
 def img2txt():
-    data_path = "data/"
-
-    server_state["artists"] = load_list(os.path.join(data_path, 'img2txt', 'artists.txt'))
-    server_state["flavors"] = load_list(os.path.join(data_path, 'img2txt', 'flavors.txt'))
-    server_state["mediums"] = load_list(os.path.join(data_path, 'img2txt', 'mediums.txt'))
-    server_state["movements"] = load_list(os.path.join(data_path, 'img2txt', 'movements.txt'))
-    server_state["sites"] = load_list(os.path.join(data_path, 'img2txt', 'sites.txt'))
-    # server_state["genres"] = load_list(os.path.join(data_path, 'img2txt', 'genres.txt'))
-    # server_state["styles"] = load_list(os.path.join(data_path, 'img2txt', 'styles.txt'))
-    # server_state["techniques"] = load_list(os.path.join(data_path, 'img2txt', 'techniques.txt'))
-    # server_state["subjects"] = load_list(os.path.join(data_path, 'img2txt', 'subjects.txt'))
-
-    server_state["trending_list"] = [site for site in server_state["sites"]]
-    server_state["trending_list"].extend(["trending on "+site for site in server_state["sites"]])
-    server_state["trending_list"].extend(["featured on "+site for site in server_state["sites"]])
-    server_state["trending_list"].extend([site+" contest winner" for site in server_state["sites"]])
-
-    #image_path_or_url = "https://i.redd.it/e2e8gimigjq91.jpg"
-
     models = []
 
     if st.session_state["ViT-L/14"]:
@@ -330,6 +289,24 @@ def img2txt():
     if st.session_state["ViT-g-14"]:
         models.append('ViT-g-14')
 
+    if st.session_state["ViTB32"]:
+        models.append('ViT-B/32')
+    if st.session_state['ViTB16']:
+        models.append('ViT-B/16')
+
+    if st.session_state["ViTL14_336px"]:
+        models.append('ViT-L/14@336px')
+    if st.session_state["RN101"]:
+        models.append('RN101')
+    if st.session_state["RN50"]:
+        models.append('RN50')
+    if st.session_state["RN50x4"]:
+        models.append('RN50x4')
+    if st.session_state["RN50x16"]:
+        models.append('RN50x16')
+    if st.session_state["RN50x64"]:
+        models.append('RN50x64')
+
     # if str(image_path_or_url).startswith('http://') or str(image_path_or_url).startswith('https://'):
         #image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert('RGB')
     # else:
@@ -352,7 +329,36 @@ def img2txt():
 def layout():
     #set_page_title("Image-to-Text - Stable Diffusion WebUI")
     #st.info("Under Construction. :construction_worker:")
-
+    #
+    if "clip_models" not in server_state:
+        server_state["clip_models"] = {}
+    if "preprocesses" not in server_state:
+        server_state["preprocesses"] = {}
+    data_path = "data/"
+    if "artists" not in server_state:
+        server_state["artists"] = load_list(os.path.join(data_path, 'img2txt', 'artists.txt'))
+    if "flavors" not in server_state:
+        server_state["flavors"] = random.choices(load_list(os.path.join(data_path, 'img2txt', 'flavors.txt')), k=2000)
+    if "mediums" not in server_state:
+        server_state["mediums"] = load_list(os.path.join(data_path, 'img2txt', 'mediums.txt'))
+    if "movements" not in server_state:
+        server_state["movements"] = load_list(os.path.join(data_path, 'img2txt', 'movements.txt'))
+    if "sites" not in server_state:
+        server_state["sites"] = load_list(os.path.join(data_path, 'img2txt', 'sites.txt'))
+    #server_state["domains"] = load_list(os.path.join(data_path, 'img2txt', 'domains.txt'))
+    #server_state["subreddits"] = load_list(os.path.join(data_path, 'img2txt', 'subreddits.txt'))
+    if "techniques" not in server_state:
+        server_state["techniques"] = load_list(os.path.join(data_path, 'img2txt', 'techniques.txt'))
+    if "tags" not in server_state:
+        server_state["tags"] = load_list(os.path.join(data_path, 'img2txt', 'tags.txt'))
+    #server_state["genres"] = load_list(os.path.join(data_path, 'img2txt', 'genres.txt'))
+    # server_state["styles"] = load_list(os.path.join(data_path, 'img2txt', 'styles.txt'))
+    # server_state["subjects"] = load_list(os.path.join(data_path, 'img2txt', 'subjects.txt'))
+    if "trending_list" not in server_state:
+        server_state["trending_list"] = [site for site in server_state["sites"]]
+        server_state["trending_list"].extend(["trending on "+site for site in server_state["sites"]])
+        server_state["trending_list"].extend(["featured on "+site for site in server_state["sites"]])
+        server_state["trending_list"].extend([site+" contest winner" for site in server_state["sites"]])
     with st.form("img2txt-inputs"):
         st.session_state["generation_mode"] = "img2txt"
 
@@ -361,16 +367,27 @@ def layout():
         col1, col2 = st.columns([1, 4], gap="large")
 
         with col1:
-            #url = st.text_area("Input Text","")
+            st.session_state["uploaded_image"] = st.file_uploader('Input Image', type=['png', 'jpg', 'jpeg', 'jfif', 'webp'], accept_multiple_files=True)
-            #url = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
-            #st.subheader("Input Image")
-            st.session_state["uploaded_image"] = st.file_uploader('Input Image', type=['png', 'jpg', 'jpeg'], accept_multiple_files=True)
 
             with st.expander("CLIP models", expanded=True):
                 st.session_state["ViT-L/14"] = st.checkbox("ViT-L/14", value=True, help="ViT-L/14 model.")
                 st.session_state["ViT-H-14"] = st.checkbox("ViT-H-14", value=False, help="ViT-H-14 model.")
                 st.session_state["ViT-g-14"] = st.checkbox("ViT-g-14", value=False, help="ViT-g-14 model.")
 
+
+
+            with st.expander("Others"):
+                st.info("For DiscoDiffusion and JAX enable all the same models here as you intend to use when generating your images.")
+
+                st.session_state["ViTL14_336px"] = st.checkbox("ViTL14_336px", value=False, help="ViTL14_336px model.")
+                st.session_state["ViTB16"] = st.checkbox("ViTB16", value=False, help="ViTB16 model.")
+                st.session_state["ViTB32"] = st.checkbox("ViTB32", value=False, help="ViTB32 model.")
+                st.session_state["RN50"] = st.checkbox("RN50", value=False, help="RN50 model.")
+                st.session_state["RN50x4"] = st.checkbox("RN50x4", value=False, help="RN50x4 model.")
+                st.session_state["RN50x16"] = st.checkbox("RN50x16", value=False, help="RN50x16 model.")
+                st.session_state["RN50x64"] = st.checkbox("RN50x64", value=False, help="RN50x64 model.")
+                st.session_state["RN101"] = st.checkbox("RN101", value=False, help="RN101 model.")
+
             #
             # st.subheader("Logs:")
 
@@ -380,7 +397,9 @@ def layout():
         with col2:
             st.subheader("Image")
 
-            refresh = st.form_submit_button("Refresh", help='Refresh the image preview to show your uploaded image instead of the default placeholder.')
+            image_col1, image_col2 = st.columns([10,25])
+            with image_col1:
+                refresh = st.form_submit_button("Update Preview Image", help='Refresh the image preview to show your uploaded image instead of the default placeholder.')
 
             if st.session_state["uploaded_image"]:
                 #print (type(st.session_state["uploaded_image"]))
@@ -419,11 +438,12 @@ def layout():
                 #st.session_state["input_image_preview"].code('', language="")
                 st.image("images/streamlit/img2txt_placeholder.png", clamp=True)
 
+        with image_col2:
             #
             # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
             # generate_col1.title("")
             # generate_col1.title("")
-        generate_button = st.form_submit_button("Generate!")
+            generate_button = st.form_submit_button("Generate!", help="Start interrogating the images to generate a prompt from each of the selected images")
 
     if generate_button:
         # if model, pipe, RealESRGAN or GFPGAN is in st.session_state remove the model and pipe form session_state so that they are reloaded.

scripts/imglab.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

scripts/logger.py

@@ -70,6 +70,7 @@ genfmt = "<level>{level: <10}</level> @ <green>{time:YYYY-MM-DD HH:mm:ss}</green
 initfmt = "<magenta>INIT      </magenta> | <level>{extra[status]: <10}</level> | <magenta>{message}</magenta>"
 msgfmt = "<level>{level: <10}</level> | <level>{message}</level>"
 
+try:
     logger.level("GENERATION", no=24, color="<cyan>")
     logger.level("PROMPT", no=23, color="<yellow>")
     logger.level("INIT", no=31, color="<white>")
@@ -79,6 +80,9 @@ logger.level("INIT_ERR", no=31, color="<red>")
     # Messages contain important information without which this application might not be able to be used
     # As such, they have the highest priority
     logger.level("MESSAGE", no=61, color="<green>")
+except TypeError:
+    pass
+
 
 logger.__class__.generation = partialmethod(logger.__class__.log, "GENERATION")
 logger.__class__.prompt = partialmethod(logger.__class__.log, "PROMPT")
@@ -97,3 +101,5 @@ config = {
     ],
 }
 logger.configure(**config)
+
+logger.add("logs/log_{time:MM-DD-YYYY!UTC}.log", rotation="8 MB", compression="zip", level='INFO')    # Once the file is too old, it's rotated

scripts/modeldownload.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

scripts/post_processing.py

@@ -0,0 +1,34 @@
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sandbox-webui/).
+
+# Copyright 2022 Sygil-Dev team.
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+# base webui import and utils.
+#from sd_utils import *
+from sd_utils import *
+# streamlit imports
+
+#streamlit components section
+
+#other imports
+import os, time, requests
+import sys
+
+# Temp imports
+
+# end of imports
+#---------------------------------------------------------------------------------------------------------------
+
+
+def layout():
+    st.info("Under Construction. :construction_worker:")

scripts/relauncher.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

scripts/sd_concept_library.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -238,7 +238,7 @@ def layout():
 			with st.container():
 				if downloaded_concepts_count == 0:
 					st.write("You don't have any concepts in your library ")
-					st.markdown("To add concepts to your library, download some from the [sd-concepts-library](https://github.com/sd-webui/sd-concepts-library) \
+					st.markdown("To add concepts to your library, download some from the [sd-concepts-library](https://github.com/Sygil-Dev/sd-concepts-library) \
 						repository and save the content of `sd-concepts-library` into ```./models/custom/sd-concepts-library``` or just create your own concepts :wink:.", unsafe_allow_html=False)
 				else:
 					if len(st.session_state["results"]) == 0:

scripts/sd_concepts_library_downloader.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or

scripts/stable_diffusion_pipeline.py

@@ -1,26 +1,144 @@
-import gc
 import inspect
-import warnings
+from typing import Callable, List, Optional, Union
-from typing import List, Optional, Union
+from pathlib import Path
+from torchvision.transforms.functional import pil_to_tensor
+import librosa
+from PIL import Image
+from torchvision.io import write_video
+import numpy as np
+import time
+import json
 
 import torch
-
+from diffusers.configuration_utils import FrozenDict
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.utils import deprecate, logging
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from diffusers import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-#from diffusers.safety_checker import StableDiffusionSafetyChecker
+
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from torch import nn
+
+from sd_utils import RealESRGANModel
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class StableDiffusionPipeline(DiffusionPipeline):
+def get_timesteps_arr(audio_filepath, offset, duration, fps=30, margin=1.0, smooth=0.0):
+    y, sr = librosa.load(audio_filepath, offset=offset, duration=duration)
+
+    # librosa.stft hardcoded defaults...
+    # n_fft defaults to 2048
+    # hop length is win_length // 4
+    # win_length defaults to n_fft
+    D = librosa.stft(y, n_fft=2048, hop_length=2048 // 4, win_length=2048)
+
+    # Extract percussive elements
+    D_harmonic, D_percussive = librosa.decompose.hpss(D, margin=margin)
+    y_percussive = librosa.istft(D_percussive, length=len(y))
+
+    # Get normalized melspectrogram
+    spec_raw = librosa.feature.melspectrogram(y=y_percussive, sr=sr)
+    spec_max = np.amax(spec_raw, axis=0)
+    spec_norm = (spec_max - np.min(spec_max)) / np.ptp(spec_max)
+
+    # Resize cumsum of spec norm to our desired number of interpolation frames
+    x_norm = np.linspace(0, spec_norm.shape[-1], spec_norm.shape[-1])
+    y_norm = np.cumsum(spec_norm)
+    y_norm /= y_norm[-1]
+    x_resize = np.linspace(0, y_norm.shape[-1], int(duration*fps))
+
+    T = np.interp(x_resize, x_norm, y_norm)
+
+    # Apply smoothing
+    return T * (1 - smooth) + np.linspace(0.0, 1.0, T.shape[0]) * smooth
+
+
+def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
+    """helper function to spherically interpolate two arrays v1 v2"""
+
+    if not isinstance(v0, np.ndarray):
+        inputs_are_torch = True
+        input_device = v0.device
+        v0 = v0.cpu().numpy()
+        v1 = v1.cpu().numpy()
+
+    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+    if np.abs(dot) > DOT_THRESHOLD:
+        v2 = (1 - t) * v0 + t * v1
+    else:
+        theta_0 = np.arccos(dot)
+        sin_theta_0 = np.sin(theta_0)
+        theta_t = theta_0 * t
+        sin_theta_t = np.sin(theta_t)
+        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+        s1 = sin_theta_t / sin_theta_0
+        v2 = s0 * v0 + s1 * v1
+
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(input_device)
+
+    return v2
+
+
+def make_video_pyav(
+    frames_or_frame_dir: Union[str, Path, torch.Tensor],
+    audio_filepath: Union[str, Path] = None,
+    fps: int = 30,
+    audio_offset: int = 0,
+    audio_duration: int = 2,
+    sr: int = 22050,
+    output_filepath: Union[str, Path] = "output.mp4",
+    glob_pattern: str = "*.png",
+):
+    """
+    TODO - docstring here
+
+    frames_or_frame_dir: (Union[str, Path, torch.Tensor]):
+        Either a directory of images, or a tensor of shape (T, C, H, W) in range [0, 255].
+    """
+
+    # Torchvision write_video doesn't support pathlib paths
+    output_filepath = str(output_filepath)
+
+    if isinstance(frames_or_frame_dir, (str, Path)):
+        frames = None
+        for img in sorted(Path(frames_or_frame_dir).glob(glob_pattern)):
+            frame = pil_to_tensor(Image.open(img)).unsqueeze(0)
+            frames = frame if frames is None else torch.cat([frames, frame])
+    else:
+        frames = frames_or_frame_dir
+
+    # TCHW -> THWC
+    frames = frames.permute(0, 2, 3, 1)
+
+    if audio_filepath:
+        # Read audio, convert to tensor
+        audio, sr = librosa.load(audio_filepath, sr=sr, mono=True, offset=audio_offset, duration=audio_duration)
+        audio_tensor = torch.tensor(audio).unsqueeze(0)
+
+        write_video(
+            output_filepath,
+            frames,
+            fps=fps,
+            audio_array=audio_tensor,
+            audio_fps=sr,
+            audio_codec="aac",
+            options={"crf": "10", "pix_fmt": "yuv420p"},
+        )
+    else:
+        write_video(output_filepath, frames, fps=fps, options={"crf": "10", "pix_fmt": "yuv420p"})
+
+    return output_filepath
+
+
+class StableDiffusionWalkPipeline(DiffusionPipeline):
     r"""
-    Pipeline for text-to-image generation using Stable Diffusion.
+    Pipeline for generating videos by interpolating  Stable Diffusion's latent space.
-
     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
     Args:
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
@@ -35,6 +153,11 @@ class StableDiffusionPipeline(DiffusionPipeline):
         scheduler ([`SchedulerMixin`]):
             A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
     def __init__(
@@ -43,10 +166,36 @@ class StableDiffusionPipeline(DiffusionPipeline):
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
     ):
         super().__init__()
-        scheduler = scheduler.set_format("pt")
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warn(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
         self.register_modules(
             vae=vae,
             text_encoder=text_encoder,
@@ -60,10 +209,8 @@ class StableDiffusionPipeline(DiffusionPipeline):
     def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
         r"""
         Enable sliced attention computation.
-
         When this option is enabled, the attention module will split the input tensor in slices, to compute attention
         in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
         Args:
             slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                 When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
@@ -84,35 +231,31 @@ class StableDiffusionPipeline(DiffusionPipeline):
         # set slice_size = `None` to disable `attention slicing`
         self.enable_attention_slicing(None)
 
-    def enable_minimal_memory_usage(self):
-        """Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs"""
-        self.unet.to(torch.float16).to(torch.device("cuda"))
-        self.enable_attention_slicing(1)
-
-        torch.cuda.empty_cache()
-        gc.collect()
-
     @torch.no_grad()
     def __call__(
         self,
-        prompt: Union[str, List[str]],
+        prompt: Optional[Union[str, List[str]]] = None,
-        height: Optional[int] = 512,
+        height: int = 512,
-        width: Optional[int] = 512,
+        width: int = 512,
-        num_inference_steps: Optional[int] = 50,
+        num_inference_steps: int = 50,
-        guidance_scale: Optional[float] = 7.5,
+        guidance_scale: float = 7.5,
-        eta: Optional[float] = 0.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
         generator: Optional[torch.Generator] = None,
         latents: Optional[torch.FloatTensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        text_embeddings: Optional[torch.FloatTensor] = None,
         **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
-
         Args:
-            prompt (`str` or `List[str]`):
+            prompt (`str` or `List[str]`, *optional*, defaults to `None`):
-                The prompt or prompts to guide the image generation.
+                The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
             height (`int`, *optional*, defaults to 512):
                 The height in pixels of the generated image.
             width (`int`, *optional*, defaults to 512):
@@ -126,6 +269,11 @@ class StableDiffusionPipeline(DiffusionPipeline):
                 Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                 usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
             eta (`float`, *optional*, defaults to 0.0):
                 Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                 [`schedulers.DDIMScheduler`], will be ignored for others.
@@ -138,11 +286,20 @@ class StableDiffusionPipeline(DiffusionPipeline):
                 tensor will ge generated by sampling using the supplied random `generator`.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                 plain tuple.
-
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
+                Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
+                `prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
+                the supplied `prompt`.
         Returns:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
@@ -151,18 +308,18 @@ class StableDiffusionPipeline(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
 
-        if "torch_device" in kwargs:
+        if height % 8 != 0 or width % 8 != 0:
-            # device = kwargs.pop("torch_device")
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-            warnings.warn(
+
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+        if (callback_steps is None) or (
-                " Consider using `pipe.to(torch_device)` instead."
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
             )
 
-            # Set device as before (to be removed in 0.3.0)
+        if text_embeddings is None:
-            # if device is None:
-            #    device = "cuda" if torch.cuda.is_available() else "cpu"
-            # self.to(device)
-
             if isinstance(prompt, str):
                 batch_size = 1
             elif isinstance(prompt, list):
@@ -170,18 +327,30 @@ class StableDiffusionPipeline(DiffusionPipeline):
             else:
                 raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
 
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
             # get prompt text embeddings
-        text_input = self.tokenizer(
+            text_inputs = self.tokenizer(
                 prompt,
                 padding="max_length",
                 max_length=self.tokenizer.model_max_length,
-            truncation=True,
                 return_tensors="pt",
             )
-        text_embeddings = self.text_encoder(text_input.input_ids.to(self.text_encoder.device))[0].to(self.unet.device)
+            text_input_ids = text_inputs.input_ids
+
+            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+                removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+                print(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+        else:
+            batch_size = text_embeddings.shape[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
 
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
@@ -189,13 +358,39 @@ class StableDiffusionPipeline(DiffusionPipeline):
         do_classifier_free_guidance = guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
-            max_length = text_input.input_ids.shape[-1]
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""]
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = self.tokenizer.model_max_length
             uncond_input = self.tokenizer(
-                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+                uncond_tokens,
-            )
+                padding="max_length",
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.text_encoder.device))[0].to(
+                max_length=max_length,
-                self.unet.device
+                truncation=True,
+                return_tensors="pt",
             )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
 
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
@@ -207,30 +402,30 @@ class StableDiffusionPipeline(DiffusionPipeline):
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_device = "cpu" if self.device.type == "mps" else self.device
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
-        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
         if latents is None:
-            latents = torch.randn(
+            if self.device.type == "mps":
-                latents_shape,
+                # randn does not exist on mps
-                generator=generator,
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
-                device=latents_device,
+                    self.device
                 )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
         else:
             if latents.shape != latents_shape:
                 raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
             latents = latents.to(self.device)
 
         # set timesteps
-        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
+        self.scheduler.set_timesteps(num_inference_steps)
-        extra_set_kwargs = {}
-        if accepts_offset:
-            extra_set_kwargs["offset"] = 1
 
-        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
 
-        # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
+        # scale the initial noise by the standard deviation required by the scheduler
-        if isinstance(self.scheduler, LMSDiscreteScheduler):
+        latents = latents * self.scheduler.init_noise_sigma
-            latents = latents * self.scheduler.sigmas[0]
 
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
@@ -241,18 +436,13 @@ class StableDiffusionPipeline(DiffusionPipeline):
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                sigma = self.scheduler.sigmas[i]
-                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
-                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
 
             # predict the noise residual
-            noise_pred = self.unet(
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-                latent_model_input.to(self.unet.device), t.to(self.unet.device), encoder_hidden_states=text_embeddings
-            ).sample
 
             # perform guidance
             if do_classifier_free_guidance:
@@ -260,29 +450,29 @@ class StableDiffusionPipeline(DiffusionPipeline):
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-                latents = self.scheduler.step(
+
-                    noise_pred, i, latents.to(self.unet.device), **extra_step_kwargs
+            # call the callback, if provided
-                ).prev_sample
+            if callback is not None and i % callback_steps == 0:
-            else:
+                callback(i, t, latents)
-                latents = self.scheduler.step(
-                    noise_pred, t.to(self.unet.device), latents.to(self.unet.device), **extra_step_kwargs
-                ).prev_sample
 
-        # scale and decode the image latents with vae
         latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents.to(self.vae.device)).sample
+        image = self.vae.decode(latents).sample
 
         image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.to(self.vae.device).to(self.vae.device).cpu().permute(0, 2, 3, 1).numpy()
 
-        # run safety checker
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        safety_cheker_input = (
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-            self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt")
+
-            .to(self.vae.device)
+        if self.safety_checker is not None:
-            .to(self.vae.dtype)
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
             )
-        image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
 
         if output_type == "pil":
             image = self.numpy_to_pil(image)
@@ -291,3 +481,377 @@ class StableDiffusionPipeline(DiffusionPipeline):
             return (image, has_nsfw_concept)
 
         return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    def generate_inputs(self, prompt_a, prompt_b, seed_a, seed_b, noise_shape, T, batch_size):
+        embeds_a = self.embed_text(prompt_a)
+        embeds_b = self.embed_text(prompt_b)
+
+        latents_a = self.init_noise(seed_a, noise_shape)
+        latents_b = self.init_noise(seed_b, noise_shape)
+
+        batch_idx = 0
+        embeds_batch, noise_batch = None, None
+        for i, t in enumerate(T):
+            embeds = torch.lerp(embeds_a, embeds_b, t)
+            noise = slerp(float(t), latents_a, latents_b)
+
+            embeds_batch = embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
+            noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise])
+            batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
+            if not batch_is_ready:
+                continue
+            yield batch_idx, embeds_batch, noise_batch
+            batch_idx += 1
+            del embeds_batch, noise_batch
+            torch.cuda.empty_cache()
+            embeds_batch, noise_batch = None, None
+
+    def make_clip_frames(
+        self,
+        prompt_a: str,
+        prompt_b: str,
+        seed_a: int,
+        seed_b: int,
+        num_interpolation_steps: int = 5,
+        save_path: Union[str, Path] = "outputs/",
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        eta: float = 0.0,
+        height: int = 512,
+        width: int = 512,
+        upsample: bool = False,
+        batch_size: int = 1,
+        image_file_ext: str = ".png",
+        T: np.ndarray = None,
+        skip: int = 0,
+    ):
+        save_path = Path(save_path)
+        save_path.mkdir(parents=True, exist_ok=True)
+
+        T = T if T is not None else np.linspace(0.0, 1.0, num_interpolation_steps)
+        if T.shape[0] != num_interpolation_steps:
+            raise ValueError(f"Unexpected T shape, got {T.shape}, expected dim 0 to be {num_interpolation_steps}")
+
+        if upsample:
+            if getattr(self, "upsampler", None) is None:
+                self.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
+            self.upsampler.to(self.device)
+
+        batch_generator = self.generate_inputs(
+            prompt_a,
+            prompt_b,
+            seed_a,
+            seed_b,
+            (1, self.unet.in_channels, height // 8, width // 8),
+            T[skip:],
+            batch_size,
+        )
+
+        frame_index = skip
+        for _, embeds_batch, noise_batch in batch_generator:
+            with torch.autocast("cuda"):
+                outputs = self(
+                    latents=noise_batch,
+                    text_embeddings=embeds_batch,
+                    height=height,
+                    width=width,
+                    guidance_scale=guidance_scale,
+                    eta=eta,
+                    num_inference_steps=num_inference_steps,
+                    output_type="pil" if not upsample else "numpy",
+                )["images"]
+
+                for image in outputs:
+                    frame_filepath = save_path / (f"frame%06d{image_file_ext}" % frame_index)
+                    image = image if not upsample else self.upsampler(image)
+                    image.save(frame_filepath)
+                    frame_index += 1
+
+    def walk(
+        self,
+        prompts: Optional[List[str]] = None,
+        seeds: Optional[List[int]] = None,
+        num_interpolation_steps: Optional[Union[int, List[int]]] = 5,  # int or list of int
+        output_dir: Optional[str] = "./dreams",
+        name: Optional[str] = None,
+        image_file_ext: Optional[str] = ".png",
+        fps: Optional[int] = 30,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        eta: Optional[float] = 0.0,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        upsample: Optional[bool] = False,
+        batch_size: Optional[int] = 1,
+        resume: Optional[bool] = False,
+        audio_filepath: str = None,
+        audio_start_sec: Optional[Union[int, float]] = None,
+        margin: Optional[float] = 1.0,
+        smooth: Optional[float] = 0.0,
+    ):
+        """Generate a video from a sequence of prompts and seeds. Optionally, add audio to the
+        video to interpolate to the intensity of the audio.
+
+        Args:
+            prompts (Optional[List[str]], optional):
+                list of text prompts. Defaults to None.
+            seeds (Optional[List[int]], optional):
+                list of random seeds corresponding to prompts. Defaults to None.
+            num_interpolation_steps (Union[int, List[int]], *optional*):
+                How many interpolation steps between each prompt. Defaults to None.
+            output_dir (Optional[str], optional):
+                Where to save the video. Defaults to './dreams'.
+            name (Optional[str], optional):
+                Name of the subdirectory of output_dir. Defaults to None.
+            image_file_ext (Optional[str], *optional*, defaults to '.png'):
+                The extension to use when writing video frames.
+            fps (Optional[int], *optional*, defaults to 30):
+                The frames per second in the resulting output videos.
+            num_inference_steps (Optional[int], *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (Optional[float], *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            eta (Optional[float], *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            height (Optional[int], *optional*, defaults to 512):
+                height of the images to generate.
+            width (Optional[int], *optional*, defaults to 512):
+                width of the images to generate.
+            upsample (Optional[bool], *optional*, defaults to False):
+                When True, upsamples images with realesrgan.
+            batch_size (Optional[int], *optional*, defaults to 1):
+                Number of images to generate at once.
+            resume (Optional[bool], *optional*, defaults to False):
+                When True, resumes from the last frame in the output directory based
+                on available prompt config. Requires you to provide the `name` argument.
+            audio_filepath (str, *optional*, defaults to None):
+                Optional path to an audio file to influence the interpolation rate.
+            audio_start_sec (Optional[Union[int, float]], *optional*, defaults to 0):
+                Global start time of the provided audio_filepath.
+            margin (Optional[float], *optional*, defaults to 1.0):
+                Margin from librosa hpss to use for audio interpolation.
+            smooth (Optional[float], *optional*, defaults to 0.0):
+                Smoothness of the audio interpolation. 1.0 means linear interpolation.
+
+        This function will create sub directories for each prompt and seed pair.
+
+        For example, if you provide the following prompts and seeds:
+
+        ```
+        prompts = ['a dog', 'a cat', 'a bird']
+        seeds = [1, 2, 3]
+        num_interpolation_steps = 5
+        output_dir = 'output_dir'
+        name = 'name'
+        fps = 5
+        ```
+
+        Then the following directories will be created:
+
+        ```
+        output_dir
+        ├── name
+        │   ├── name_000000
+        │   │   ├── frame000000.png
+        │   │   ├── ...
+        │   │   ├── frame000004.png
+        │   │   ├── name_000000.mp4
+        │   ├── name_000001
+        │   │   ├── frame000000.png
+        │   │   ├── ...
+        │   │   ├── frame000004.png
+        │   │   ├── name_000001.mp4
+        │   ├── ...
+        │   ├── name.mp4
+        |   |── prompt_config.json
+        ```
+
+        Returns:
+            str: The resulting video filepath. This video includes all sub directories' video clips.
+        """
+
+        output_path = Path(output_dir)
+
+        name = name or time.strftime("%Y%m%d-%H%M%S")
+        save_path_root = output_path / name
+        save_path_root.mkdir(parents=True, exist_ok=True)
+
+        # Where the final video of all the clips combined will be saved
+        output_filepath = save_path_root / f"{name}.mp4"
+
+        # If using same number of interpolation steps between, we turn into list
+        if not resume and isinstance(num_interpolation_steps, int):
+            num_interpolation_steps = [num_interpolation_steps] * (len(prompts) - 1)
+
+        if not resume:
+            audio_start_sec = audio_start_sec or 0
+
+        # Save/reload prompt config
+        prompt_config_path = save_path_root / "prompt_config.json"
+        if not resume:
+            prompt_config_path.write_text(
+                json.dumps(
+                    dict(
+                        prompts=prompts,
+                        seeds=seeds,
+                        num_interpolation_steps=num_interpolation_steps,
+                        fps=fps,
+                        num_inference_steps=num_inference_steps,
+                        guidance_scale=guidance_scale,
+                        eta=eta,
+                        upsample=upsample,
+                        height=height,
+                        width=width,
+                        audio_filepath=audio_filepath,
+                        audio_start_sec=audio_start_sec,
+                    ),
+                    indent=2,
+                    sort_keys=False,
+                )
+            )
+        else:
+            data = json.load(open(prompt_config_path))
+            prompts = data["prompts"]
+            seeds = data["seeds"]
+            num_interpolation_steps = data["num_interpolation_steps"]
+            fps = data["fps"]
+            num_inference_steps = data["num_inference_steps"]
+            guidance_scale = data["guidance_scale"]
+            eta = data["eta"]
+            upsample = data["upsample"]
+            height = data["height"]
+            width = data["width"]
+            audio_filepath = data["audio_filepath"]
+            audio_start_sec = data["audio_start_sec"]
+
+        for i, (prompt_a, prompt_b, seed_a, seed_b, num_step) in enumerate(
+            zip(prompts, prompts[1:], seeds, seeds[1:], num_interpolation_steps)
+        ):
+            # {name}_000000 / {name}_000001 / ...
+            save_path = save_path_root / f"{name}_{i:06d}"
+
+            # Where the individual clips will be saved
+            step_output_filepath = save_path / f"{name}_{i:06d}.mp4"
+
+            # Determine if we need to resume from a previous run
+            skip = 0
+            if resume:
+                if step_output_filepath.exists():
+                    print(f"Skipping {save_path} because frames already exist")
+                    continue
+
+                existing_frames = sorted(save_path.glob(f"*{image_file_ext}"))
+                if existing_frames:
+                    skip = int(existing_frames[-1].stem[-6:]) + 1
+                    if skip + 1 >= num_step:
+                        print(f"Skipping {save_path} because frames already exist")
+                        continue
+                    print(f"Resuming {save_path.name} from frame {skip}")
+
+            audio_offset = audio_start_sec + sum(num_interpolation_steps[:i]) / fps
+            audio_duration = num_step / fps
+
+            self.make_clip_frames(
+                prompt_a,
+                prompt_b,
+                seed_a,
+                seed_b,
+                num_interpolation_steps=num_step,
+                save_path=save_path,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale,
+                eta=eta,
+                height=height,
+                width=width,
+                upsample=upsample,
+                batch_size=batch_size,
+                skip=skip,
+                T=get_timesteps_arr(
+                    audio_filepath,
+                    offset=audio_offset,
+                    duration=audio_duration,
+                    fps=fps,
+                    margin=margin,
+                    smooth=smooth,
+                )
+                if audio_filepath
+                else None,
+            )
+            make_video_pyav(
+                save_path,
+                audio_filepath=audio_filepath,
+                fps=fps,
+                output_filepath=step_output_filepath,
+                glob_pattern=f"*{image_file_ext}",
+                audio_offset=audio_offset,
+                audio_duration=audio_duration,
+                sr=44100,
+            )
+
+        return make_video_pyav(
+            save_path_root,
+            audio_filepath=audio_filepath,
+            fps=fps,
+            audio_offset=audio_start_sec,
+            audio_duration=sum(num_interpolation_steps) / fps,
+            output_filepath=output_filepath,
+            glob_pattern=f"**/*{image_file_ext}",
+            sr=44100,
+        )
+
+    def embed_text(self, text):
+        """Helper to embed some text"""
+        with torch.autocast("cuda"):
+            text_input = self.tokenizer(
+                text,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            with torch.no_grad():
+                embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
+        return embed
+
+    def init_noise(self, seed, noise_shape):
+        """Helper to initialize noise"""
+        # randn does not exist on mps, so we create noise on CPU here and move it to the device after initialization
+        if self.device.type == "mps":
+            noise = torch.randn(
+                noise_shape,
+                device='cpu',
+                generator=torch.Generator(device='cpu').manual_seed(seed),
+            ).to(self.device)
+        else:
+            noise = torch.randn(
+                noise_shape,
+                device=self.device,
+                generator=torch.Generator(device=self.device).manual_seed(seed),
+            )
+        return noise
+
+    @classmethod
+    def from_pretrained(cls, *args, tiled=False, **kwargs):
+        """Same as diffusers `from_pretrained` but with tiled option, which makes images tilable"""
+        if tiled:
+
+            def patch_conv(**patch):
+                cls = nn.Conv2d
+                init = cls.__init__
+
+                def __init__(self, *args, **kwargs):
+                    return init(self, *args, **kwargs, **patch)
+
+                cls.__init__ = __init__
+
+            patch_conv(padding_mode="circular")
+
+        pipeline = super().from_pretrained(*args, **kwargs)
+        pipeline.tiled = tiled
+        return pipeline

scripts/textual_inversion.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -28,7 +28,7 @@ from transformers import CLIPTextModel, CLIPTokenizer
 import argparse
 import itertools
 import math
-import os
+import os, sys
 import random
 #import datetime
 #from pathlib import Path
@@ -937,4 +937,3 @@ def layout():
 			# Start TensorBoard
 			st_tensorboard(logdir=os.path.join("outputs", "textual_inversion"), port=8888)
 
-		

scripts/txt2img.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -25,16 +25,19 @@ from streamlit.elements.image import image_to_url
 
 #other imports
 import uuid
-from typing import Union
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
 
+# streamlit components
+from custom_components import sygil_suggestions
+
 # Temp imports
 
 
 # end of imports
 #---------------------------------------------------------------------------------------------------------------
 
+sygil_suggestions.init()
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -90,19 +93,203 @@ class plugin_info():
     isTab = True
     displayPriority = 1
 
+@logger.catch(reraise=True)
+def stable_horde(outpath, prompt, seed, sampler_name, save_grid, batch_size,
+                 n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, GFPGAN_model,
+                 use_RealESRGAN, realesrgan_model_name, use_LDSR,
+                 LDSR_model_name, ddim_eta, normalize_prompt_weights,
+                 save_individual_images, sort_samples, write_info_files,
+                 jpg_sample, variant_amount, variant_seed, api_key,
+                 nsfw=True, censor_nsfw=False):
+
+    log = []
+
+    log.append("Generating image with Stable Horde.")
+
+    st.session_state["progress_bar_text"].code('\n'.join(log), language='')
+
+    # start time after garbage collection (or before?)
+    start_time = time.time()
+
+    # We will use this date here later for the folder name, need to start_time if not need
+    run_start_dt = datetime.datetime.now()
+
+    mem_mon = MemUsageMonitor('MemMon')
+    mem_mon.start()
+
+    os.makedirs(outpath, exist_ok=True)
+
+    sample_path = os.path.join(outpath, "samples")
+    os.makedirs(sample_path, exist_ok=True)
+
+    params = {
+        "sampler_name": "k_euler",
+        "toggles": [1,4],
+        "cfg_scale": cfg_scale,
+        "seed": str(seed),
+        "width": width,
+        "height": height,
+        "seed_variation": variant_seed if variant_seed else 1,
+        "steps": int(steps),
+        "n": int(n_iter)
+        # You can put extra params here if you wish
+    }
+
+    final_submit_dict = {
+        "prompt": prompt,
+        "params": params,
+        "nsfw": nsfw,
+        "censor_nsfw": censor_nsfw,
+        "trusted_workers": True,
+        "workers": []
+    }
+    log.append(final_submit_dict)
+
+    headers = {"apikey": api_key}
+    logger.debug(final_submit_dict)
+    st.session_state["progress_bar_text"].code('\n'.join(str(log)), language='')
+
+    horde_url = "https://stablehorde.net"
+
+    submit_req = requests.post(f'{horde_url}/api/v2/generate/async', json = final_submit_dict, headers = headers)
+    if submit_req.ok:
+        submit_results = submit_req.json()
+        logger.debug(submit_results)
+
+        log.append(submit_results)
+        st.session_state["progress_bar_text"].code(''.join(str(log)), language='')
+
+        req_id = submit_results['id']
+        is_done = False
+        while not is_done:
+            chk_req = requests.get(f'{horde_url}/api/v2/generate/check/{req_id}')
+            if not chk_req.ok:
+                logger.error(chk_req.text)
+                return
+            chk_results = chk_req.json()
+            logger.info(chk_results)
+            is_done = chk_results['done']
+            time.sleep(1)
+        retrieve_req = requests.get(f'{horde_url}/api/v2/generate/status/{req_id}')
+        if not retrieve_req.ok:
+            logger.error(retrieve_req.text)
+            return
+        results_json = retrieve_req.json()
+        # logger.debug(results_json)
+        results = results_json['generations']
+
+        output_images = []
+        comments = []
+        prompt_matrix_parts = []
+
+        if not st.session_state['defaults'].general.no_verify_input:
+            try:
+                check_prompt_length(prompt, comments)
+            except:
+                import traceback
+                logger.info("Error verifying input:", file=sys.stderr)
+                logger.info(traceback.format_exc(), file=sys.stderr)
+
+        all_prompts = batch_size * n_iter * [prompt]
+        all_seeds = [seed + x for x in range(len(all_prompts))]
+
+        for iter in range(len(results)):
+            b64img = results[iter]["img"]
+            base64_bytes = b64img.encode('utf-8')
+            img_bytes = base64.b64decode(base64_bytes)
+            img = Image.open(BytesIO(img_bytes))
+
+            sanitized_prompt = slugify(prompt)
+
+            prompts = all_prompts[iter * batch_size:(iter + 1) * batch_size]
+            #captions = prompt_matrix_parts[n * batch_size:(n + 1) * batch_size]
+            seeds = all_seeds[iter * batch_size:(iter + 1) * batch_size]
+
+            if sort_samples:
+                full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)
+
+
+                sanitized_prompt = sanitized_prompt[:200-len(full_path)]
+                sample_path_i = os.path.join(sample_path, sanitized_prompt)
+
+                #print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
+                #print(os.path.join(os.getcwd(), sample_path_i))
+
+                os.makedirs(sample_path_i, exist_ok=True)
+                base_count = get_next_sequence_number(sample_path_i)
+                filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[iter]}"
+            else:
+                full_path = os.path.join(os.getcwd(), sample_path)
+                sample_path_i = sample_path
+                base_count = get_next_sequence_number(sample_path_i)
+                filename = f"{base_count:05}-{steps}_{sampler_name}_{seed}_{sanitized_prompt}"[:200-len(full_path)] #same as before
+
+            save_sample(img, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
+                                            normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img=None,
+                                            denoising_strength=0.75, resize_mode=None, uses_loopback=False, uses_random_seed_loopback=False,
+                                            save_grid=save_grid,
+                                            sort_samples=sampler_name, sampler_name=sampler_name, ddim_eta=ddim_eta, n_iter=n_iter,
+                                            batch_size=batch_size, i=iter, save_individual_images=save_individual_images,
+                                            model_name="Stable Diffusion v1.5")
+
+            output_images.append(img)
+
+            # update image on the UI so we can see the progress
+            if "preview_image" in st.session_state:
+                st.session_state["preview_image"].image(img)
+
+            if "progress_bar_text" in st.session_state:
+                st.session_state["progress_bar_text"].empty()
+
+            #if len(results) > 1:
+                #final_filename = f"{iter}_{filename}"
+            #img.save(final_filename)
+            #logger.info(f"Saved {final_filename}")
+    else:
+        if "progress_bar_text" in st.session_state:
+            st.session_state["progress_bar_text"].error(submit_req.text)
+
+        logger.error(submit_req.text)
+
+    mem_max_used, mem_total = mem_mon.read_and_stop()
+    time_diff = time.time()-start_time
+
+    info = f"""
+            {prompt}
+            Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN else ''}{', '+realesrgan_model_name if use_RealESRGAN else ''}
+            {', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
+
+    stats = f'''
+            Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
+            Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%'''
+
+    for comment in comments:
+        info += "\n\n" + comment
+
+    #mem_mon.stop()
+    #del mem_mon
+    torch_gc()
+
+    return output_images, seed, info, stats
+
+
 #
+@logger.catch(reraise=True)
 def txt2img(prompt: str, ddim_steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
             height: int, width: int, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
             save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
             save_as_jpg: bool = True, use_GFPGAN: bool = True, GFPGAN_model: str = 'GFPGANv1.3', use_RealESRGAN: bool = False,
             RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B", use_LDSR: bool = True, LDSR_model: str = "model",
-            fp = None, variant_amount: float = None,
+            fp = None, variant_amount: float = 0.0,
-            variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True):
+            variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True,
+            use_stable_horde: bool = False, stable_horde_key:str = "0000000000"):
 
     outpath = st.session_state['defaults'].general.outdir_txt2img
 
     seed = seed_to_int(seed)
 
+    if not use_stable_horde:
+
         if sampler_name == 'PLMS':
             sampler = PLMSSampler(server_state["model"])
         elif sampler_name == 'DDIM':
@@ -127,11 +314,45 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, n_iter: int, batch_
 
         def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
             samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale,
-                                         unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x, img_callback=generation_callback,
+                                             unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x,
-                                                 log_every_t=int(st.session_state.update_preview_frequency))
+                                             img_callback=generation_callback if not server_state["bridge"] else None,
+                                                     log_every_t=int(st.session_state.update_preview_frequency if not server_state["bridge"] else 100))
 
             return samples_ddim
 
+
+    if use_stable_horde:
+        output_images, seed, info, stats = stable_horde(
+            prompt=prompt,
+            seed=seed,
+            outpath=outpath,
+            sampler_name=sampler_name,
+            save_grid=save_grid,
+            batch_size=batch_size,
+            n_iter=n_iter,
+            steps=ddim_steps,
+            cfg_scale=cfg_scale,
+            width=width,
+            height=height,
+            prompt_matrix=separate_prompts,
+            use_GFPGAN=use_GFPGAN,
+            GFPGAN_model=GFPGAN_model,
+            use_RealESRGAN=use_RealESRGAN,
+            realesrgan_model_name=RealESRGAN_model,
+            use_LDSR=use_LDSR,
+            LDSR_model_name=LDSR_model,
+            ddim_eta=ddim_eta,
+            normalize_prompt_weights=normalize_prompt_weights,
+            save_individual_images=save_individual_images,
+            sort_samples=group_by_prompt,
+            write_info_files=write_info_files,
+            jpg_sample=save_as_jpg,
+            variant_amount=variant_amount,
+            variant_seed=variant_seed,
+            api_key=stable_horde_key
+        )
+    else:
+
         #try:
         output_images, seed, info, stats = process_images(
             outpath=outpath,
@@ -148,11 +369,11 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, n_iter: int, batch_
             width=width,
             height=height,
             prompt_matrix=separate_prompts,
-                use_GFPGAN=st.session_state["use_GFPGAN"],
+            use_GFPGAN=use_GFPGAN,
-                GFPGAN_model=st.session_state["GFPGAN_model"],
+            GFPGAN_model=GFPGAN_model,
-                use_RealESRGAN=st.session_state["use_RealESRGAN"],
+            use_RealESRGAN=use_RealESRGAN,
             realesrgan_model_name=RealESRGAN_model,
-                use_LDSR=st.session_state["use_LDSR"],
+            use_LDSR=use_LDSR,
             LDSR_model_name=LDSR_model,
             ddim_eta=ddim_eta,
             normalize_prompt_weights=normalize_prompt_weights,
@@ -175,6 +396,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, n_iter: int, batch_
         #return [], seed, 'err', stats
 
 #
+@logger.catch(reraise=True)
 def layout():
     with st.form("txt2img-inputs"):
         st.session_state["generation_mode"] = "txt2img"
@@ -183,20 +405,22 @@ def layout():
 
         with input_col1:
             #prompt = st.text_area("Input Text","")
-            prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
+            placeholder = "A corgi wearing a top hat as an oil painting."
+            prompt = st.text_area("Input Text","", placeholder=placeholder, height=54)
+            sygil_suggestions.suggestion_area(placeholder)
 
         # creating the page layout using columns
-        col1, col2, col3 = st.columns([1,2,1], gap="large")
+        col1, col2, col3 = st.columns([2,5,2], gap="large")
 
         with col1:
             width = st.slider("Width:", min_value=st.session_state['defaults'].txt2img.width.min_value, max_value=st.session_state['defaults'].txt2img.width.max_value,
                               value=st.session_state['defaults'].txt2img.width.value, step=st.session_state['defaults'].txt2img.width.step)
             height = st.slider("Height:", min_value=st.session_state['defaults'].txt2img.height.min_value, max_value=st.session_state['defaults'].txt2img.height.max_value,
                                value=st.session_state['defaults'].txt2img.height.value, step=st.session_state['defaults'].txt2img.height.step)
-            cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].txt2img.cfg_scale.min_value,
+            cfg_scale = st.number_input("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].txt2img.cfg_scale.min_value,
-                                  max_value=st.session_state['defaults'].txt2img.cfg_scale.max_value,
                                   value=st.session_state['defaults'].txt2img.cfg_scale.value, step=st.session_state['defaults'].txt2img.cfg_scale.step,
                                   help="How strongly the image should follow the prompt.")
+
             seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
 
             with st.expander("Batch Options"):
@@ -210,19 +434,21 @@ def layout():
                                        #It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
                                        #Default: 1")
 
-                st.session_state["batch_count"] = int(st.text_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
+                st.session_state["batch_count"] = st.number_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
-                                                                help="How many iterations or batches of images to generate in total."))
+                                                                help="How many iterations or batches of images to generate in total.")
 
-                st.session_state["batch_size"] = int(st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
+                st.session_state["batch_size"] = st.number_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
                                                                    help="How many images are at once in a batch.\
                                                                    It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes \
                                                                    to finish generation as more images are generated at once.\
-                                                                   Default: 1") )
+                                                                   Default: 1")
 
             with st.expander("Preview Settings"):
 
                 st.session_state["update_preview"] = st.session_state["defaults"].general.update_preview
-                st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
+                st.session_state["update_preview_frequency"] = st.number_input("Update Image Preview Frequency",
+                                                                               min_value=0,
+                                                                               value=st.session_state['defaults'].txt2img.update_preview_frequency,
                                                                                help="Frequency in steps at which the the preview image is updated. By default the frequency \
                                                                                is set to 10 step.")
 
@@ -249,7 +475,7 @@ def layout():
 
             with gallery_tab:
                 st.session_state["gallery"] = st.empty()
-                st.session_state["gallery"].info("Nothing but crickets here, try generating something first.")
+                #st.session_state["gallery"].info("Nothing but crickets here, try generating something first.")
 
         with col3:
             # If we have custom models available on the "models/custom"
@@ -262,18 +488,23 @@ def layout():
                                                                 help="Select the model you want to use. This option is only available if you have custom models \
                                                                 on your 'models/custom' folder. The model name that will be shown here is the same as the name\
                                                                 the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
-                                                                will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
+                                                                will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5")
 
-            st.session_state.sampling_steps = st.slider("Sampling Steps", value=st.session_state.defaults.txt2img.sampling_steps.value,
+            st.session_state.sampling_steps = st.number_input("Sampling Steps", value=st.session_state.defaults.txt2img.sampling_steps.value,
                                                               min_value=st.session_state.defaults.txt2img.sampling_steps.min_value,
-                                                        max_value=st.session_state['defaults'].txt2img.sampling_steps.max_value,
+                                                              step=st.session_state['defaults'].txt2img.sampling_steps.step,
-                                                        step=st.session_state['defaults'].txt2img.sampling_steps.step)
+                                                              help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")
 
             sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a",  "k_heun", "PLMS", "DDIM"]
             sampler_name = st.selectbox("Sampling method", sampler_name_list,
                                         index=sampler_name_list.index(st.session_state['defaults'].txt2img.default_sampler), help="Sampling method to use. Default: k_euler")
 
             with st.expander("Advanced"):
+                with st.expander("Stable Horde"):
+                    use_stable_horde = st.checkbox("Use Stable Horde", value=False, help="Use the Stable Horde to generate images. More info can be found at https://stablehorde.net/")
+                    stable_horde_key = st.text_input("Stable Horde Api Key", value=st.session_state['defaults'].general.stable_horde_api, type="password",
+                                                     help="Optional Api Key used for the Stable Horde Bridge, if no api key is added the horde will be used anonymously.")
+
                 with st.expander("Output Settings"):
                     separate_prompts = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].txt2img.separate_prompts,
                                                    help="Separate multiple prompts using the `|` character, and get all combinations of them.")
@@ -294,13 +525,13 @@ def layout():
                     save_as_jpg = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].txt2img.save_as_jpg, help="Saves the images as jpg instead of png.")
 
                 # check if GFPGAN, RealESRGAN and LDSR are available.
-                if "GFPGAN_available" not in st.session_state:
+                #if "GFPGAN_available" not in st.session_state:
                 GFPGAN_available()
 
-                if "RealESRGAN_available" not in st.session_state:
+                #if "RealESRGAN_available" not in st.session_state:
                 RealESRGAN_available()
 
-                if "LDSR_available" not in st.session_state:
+                #if "LDSR_available" not in st.session_state:
                 LDSR_available()
 
                 if st.session_state["GFPGAN_available"] or st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
@@ -339,7 +570,9 @@ def layout():
 
                                 #print (st.session_state["RealESRGAN_available"])
                                 st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list,
-                                                                                    index=upscaling_method_list.index(str(st.session_state['defaults'].general.upscaling_method)))
+                                                                                    index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method)
+                                                                                        if st.session_state['defaults'].general.upscaling_method in upscaling_method_list
+                                                                                        else 0)
 
                                 if st.session_state["RealESRGAN_available"]:
                                     with st.expander("RealESRGAN"):
@@ -366,14 +599,14 @@ def layout():
                                         st.session_state["LDSR_model"] = st.selectbox("LDSR model", st.session_state["LDSR_models"],
                                                                                       index=st.session_state["LDSR_models"].index(st.session_state['defaults'].general.LDSR_model))
 
-                                        st.session_state["ldsr_sampling_steps"] = int(st.text_input("Sampling Steps", value=st.session_state['defaults'].txt2img.LDSR_config.sampling_steps,
+                                        st.session_state["ldsr_sampling_steps"] = st.number_input("Sampling Steps", value=st.session_state['defaults'].txt2img.LDSR_config.sampling_steps,
-                                                                                      help=""))
+                                                                                      help="")
 
-                                        st.session_state["preDownScale"] = int(st.text_input("PreDownScale", value=st.session_state['defaults'].txt2img.LDSR_config.preDownScale,
+                                        st.session_state["preDownScale"] = st.number_input("PreDownScale", value=st.session_state['defaults'].txt2img.LDSR_config.preDownScale,
-                                                                               help=""))
+                                                                               help="")
 
-                                        st.session_state["postDownScale"] = int(st.text_input("postDownScale", value=st.session_state['defaults'].txt2img.LDSR_config.postDownScale,
+                                        st.session_state["postDownScale"] = st.number_input("postDownScale", value=st.session_state['defaults'].txt2img.LDSR_config.postDownScale,
-                                                                               help=""))
+                                                                               help="")
 
                                         downsample_method_list = ['Nearest', 'Lanczos']
                                         st.session_state["downsample_method"] = st.selectbox("Downsample Method", downsample_method_list,
@@ -401,13 +634,13 @@ def layout():
         if generate_button:
 
             with col2:
+                if not use_stable_horde:
                     with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
                         load_models(use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
                                     use_GFPGAN=st.session_state["use_GFPGAN"], GFPGAN_model=st.session_state["GFPGAN_model"] ,
                                     use_RealESRGAN=st.session_state["use_RealESRGAN"], RealESRGAN_model=st.session_state["RealESRGAN_model"],
                                     CustomModel_available=server_state["CustomModel_available"], custom_model=st.session_state["custom_model"])
 
-
                 #print(st.session_state['use_RealESRGAN'])
                 #print(st.session_state['use_LDSR'])
                 #try:
@@ -418,46 +651,14 @@ def layout():
                                                             save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state['GFPGAN_model'],
                                                             use_RealESRGAN=st.session_state["use_RealESRGAN"], RealESRGAN_model=st.session_state["RealESRGAN_model"],
                                                             use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
-                                                            variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
+                                                            variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files,
+                                                            use_stable_horde=use_stable_horde, stable_horde_key=stable_horde_key)
 
                 message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
 
-            #history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
-
-            #if 'latestImages' in st.session_state:
-                #for i in output_images:
-                    ##push the new image to the list of latest images and remove the oldest one
-                    ##remove the last index from the list\
-                    #st.session_state['latestImages'].pop()
-                    ##add the new image to the start of the list
-                    #st.session_state['latestImages'].insert(0, i)
-                #PlaceHolder.empty()
-                #with PlaceHolder.container():
-                    #col1, col2, col3 = st.columns(3)
-                    #col1_cont = st.container()
-                    #col2_cont = st.container()
-                    #col3_cont = st.container()
-                    #images = st.session_state['latestImages']
-                    #with col1_cont:
-                        #with col1:
-                            #[st.image(images[index]) for index in [0, 3, 6] if index < len(images)]
-                    #with col2_cont:
-                        #with col2:
-                            #[st.image(images[index]) for index in [1, 4, 7] if index < len(images)]
-                    #with col3_cont:
-                        #with col3:
-                            #[st.image(images[index]) for index in [2, 5, 8] if index < len(images)]
-                    #historyGallery = st.empty()
-
-                ## check if output_images length is the same as seeds length
-                #with gallery_tab:
-                    #st.markdown(createHTMLGallery(output_images,seeds), unsafe_allow_html=True)
-
-
-                    #st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
-                    
             with gallery_tab:
-                print(seeds)
+                logger.info(seeds)
+                st.session_state["gallery"].text = ""
                 sdGallery(output_images)
 
 

scripts/webui.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -2783,8 +2783,19 @@ if __name__ == '__main__':
     if opt.bridge:
         try:
             import bridgeData as cd
-        except:
+        except ModuleNotFoundError as e:
+            logger.warning("No bridgeData found. Falling back to default where no CLI args are set.")
+            logger.warning(str(e))
+        except SyntaxError as e:
+            logger.warning("bridgeData found, but is malformed. Falling back to default where no CLI args are set.")
+            logger.warning(str(e))
+        except Exception as e:
             logger.warning("No bridgeData found, use default where no CLI args are set")
+            logger.warning(str(e))
+        finally:
+            try: # check if cd exists (i.e. bridgeData loaded properly)
+                cd
+            except: # if not, create defaults
                 class temp(object):
                     def __init__(self):
                         random.seed()

scripts/webui_streamlit.py

@@ -1,6 +1,6 @@
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
 
-# Copyright 2022 sd-webui team.
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -20,13 +20,14 @@
 # We import hydralit like this to replace the previous stuff
 # we had with native streamlit as it lets ur replace things 1:1
 #import hydralit as st
+import collections.abc
 from sd_utils import *
 
 # streamlit imports
 import streamlit_nested_layout
 
 #streamlit components section
-from st_on_hover_tabs import on_hover_tabs
+#from st_on_hover_tabs import on_hover_tabs
 from streamlit_server_state import server_state, server_state_lock
 
 #other imports
@@ -35,26 +36,43 @@ import warnings
 import os, toml
 import k_diffusion as K
 from omegaconf import OmegaConf
+import argparse
 
-if not "defaults" in st.session_state:
+# import custom components
-    st.session_state["defaults"] = {}
+from custom_components import draggable_number_input
-    
-st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml")
-
-if (os.path.exists("configs/webui/userconfig_streamlit.yaml")):
-    user_defaults = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
-    st.session_state["defaults"] = OmegaConf.merge(st.session_state["defaults"], user_defaults)
-else:
-	OmegaConf.save(config=st.session_state.defaults, f="configs/webui/userconfig_streamlit.yaml")
-	loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
-	assert st.session_state.defaults == loaded		
-
-if (os.path.exists(".streamlit/config.toml")):
-	st.session_state["streamlit_config"] = toml.load(".streamlit/config.toml")
 
 # end of imports
 #---------------------------------------------------------------------------------------------------------------
 
+load_configs()
+
+help = """
+A double dash (`--`) is used to separate streamlit arguments from app arguments.
+As a result using "streamlit run webui_streamlit.py --headless"
+will show the help for streamlit itself and not pass any argument to our app,
+we need to use "streamlit run webui_streamlit.py -- --headless"
+in order to pass a command argument to this app."""
+parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+parser.add_argument("--headless", action='store_true', help="Don't launch web server, util if you just want to run the stable horde bridge.", default=False)
+
+parser.add_argument("--bridge", action='store_true', help="don't launch web server, but make this instance into a Horde bridge.", default=False)
+parser.add_argument('--horde_api_key', action="store", required=False, type=str, help="The API key corresponding to the owner of this Horde instance")
+parser.add_argument('--horde_name', action="store", required=False, type=str, help="The server name for the Horde. It will be shown to the world and there can be only one.")
+parser.add_argument('--horde_url', action="store", required=False, type=str, help="The SH Horde URL. Where the bridge will pickup prompts and send the finished generations.")
+parser.add_argument('--horde_priority_usernames',type=str, action='append', required=False, help="Usernames which get priority use in this horde instance. The owner's username is always in this list.")
+parser.add_argument('--horde_max_power',type=int, required=False, help="How much power this instance has to generate pictures. Min: 2")
+parser.add_argument('--horde_sfw', action='store_true', required=False, help="Set to true if you do not want this worker generating NSFW images.")
+parser.add_argument('--horde_blacklist', nargs='+', required=False, help="List the words that you want to blacklist.")
+parser.add_argument('--horde_censorlist', nargs='+', required=False, help="List the words that you want to censor.")
+parser.add_argument('--horde_censor_nsfw', action='store_true', required=False, help="Set to true if you want this bridge worker to censor NSFW images.")
+parser.add_argument('--horde_model', action='store', required=False, help="Which model to run on this horde.")
+parser.add_argument('-v', '--verbosity', action='count', default=0, help="The default logging level is ERROR or higher. This value increases the amount of logging seen in your screen")
+parser.add_argument('-q', '--quiet', action='count', default=0, help="The default logging level is ERROR or higher. This value decreases the amount of logging seen in your screen")
+opt = parser.parse_args()
+
+with server_state_lock["bridge"]:
+    server_state["bridge"] = opt.bridge
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -88,16 +106,55 @@ def load_css(isLocal, nameOrURL):
     else:
         remote_css(nameOrURL)
 
+@logger.catch(reraise=True)
 def layout():
         """Layout functions to define all the streamlit layout here."""
-	st.set_page_config(page_title="Stable Diffusion Playground", layout="wide")
+        if not st.session_state["defaults"].debug.enable_hydralit:
-	#app = st.HydraApp(title='Stable Diffusion WebUI', favicon="", sidebar_state="expanded",
+            st.set_page_config(page_title="Stable Diffusion Playground", layout="wide", initial_sidebar_state="collapsed")
+
+        #app = st.HydraApp(title='Stable Diffusion WebUI', favicon="", sidebar_state="expanded", layout="wide",
                                         #hide_streamlit_markers=False, allow_url_nav=True , clear_cross_app_sessions=False)
 
         with st.empty():
             # load css as an external file, function has an option to local or remote url. Potential use when running from cloud infra that might not have access to local path.
             load_css(True, 'frontend/css/streamlit.main.css')
 
+        #
+        # specify the primary menu definition
+        menu_data = [
+            {'id': 'Stable Diffusion', 'label': 'Stable Diffusion', 'icon': 'bi bi-grid-1x2-fill'},
+            {'id': 'Train','label':"Train", 'icon': "bi bi-lightbulb-fill", 'submenu':[
+                {'id': 'Textual Inversion', 'label': 'Textual Inversion', 'icon': 'bi bi-lightbulb-fill'},
+                {'id': 'Fine Tunning', 'label': 'Fine Tunning', 'icon': 'bi bi-lightbulb-fill'},
+                ]},
+            {'id': 'Model Manager', 'label': 'Model Manager', 'icon': 'bi bi-cloud-arrow-down-fill'},
+            {'id': 'Tools','label':"Tools", 'icon': "bi bi-tools", 'submenu':[
+                {'id': 'API Server', 'label': 'API Server', 'icon': 'bi bi-server'},
+                {'id': 'Barfi/BaklavaJS', 'label': 'Barfi/BaklavaJS', 'icon': 'bi bi-diagram-3-fill'},
+                #{'id': 'API Server', 'label': 'API Server', 'icon': 'bi bi-server'},
+                ]},
+            {'id': 'Settings', 'label': 'Settings', 'icon': 'bi bi-gear-fill'},
+            #{'icon': "fa-solid fa-radar",'label':"Dropdown1", 'submenu':[
+            #    {'id':' subid11','icon': "fa fa-paperclip", 'label':"Sub-item 1"},{'id':'subid12','icon': "💀", 'label':"Sub-item 2"},{'id':'subid13','icon': "fa fa-database", 'label':"Sub-item 3"}]},
+            #{'icon': "far fa-chart-bar", 'label':"Chart"},#no tooltip message
+            #{'id':' Crazy return value 💀','icon': "💀", 'label':"Calendar"},
+            #{'icon': "fas fa-tachometer-alt", 'label':"Dashboard",'ttip':"I'm the Dashboard tooltip!"}, #can add a tooltip message
+            #{'icon': "far fa-copy", 'label':"Right End"},
+            #{'icon': "fa-solid fa-radar",'label':"Dropdown2", 'submenu':[{'label':"Sub-item 1", 'icon': "fa fa-meh"},{'label':"Sub-item 2"},{'icon':'🙉','label':"Sub-item 3",}]},
+        ]
+
+        over_theme = {'txc_inactive': '#FFFFFF', "menu_background":'#000000'}
+
+        menu_id = hc.nav_bar(
+            menu_definition=menu_data,
+            #home_name='Home',
+            #login_name='Logout',
+            hide_streamlit_markers=False,
+            override_theme=over_theme,
+            sticky_nav=True,
+            sticky_mode='pinned',
+        )
+
         # check if the models exist on their respective folders
         with server_state_lock["GFPGAN_available"]:
             if os.path.exists(os.path.join(st.session_state["defaults"].general.GFPGAN_dir, f"{st.session_state['defaults'].general.GFPGAN_model}.pth")):
@@ -111,27 +168,32 @@ def layout():
             else:
                 server_state["RealESRGAN_available"] = False
 
-	with st.sidebar:		
+        #with st.sidebar:
-		tabs = on_hover_tabs(tabName=['Stable Diffusion', "Textual Inversion","Model Manager","Settings"], 
+            #page = on_hover_tabs(tabName=['Stable Diffusion', "Textual Inversion","Model Manager","Settings"],
-                        iconName=['dashboard','model_training' ,'cloud_download', 'settings'], default_choice=0)
+                                 #iconName=['dashboard','model_training' ,'cloud_download', 'settings'], default_choice=0)
 
             # need to see how to get the icons to show for the hydralit option_bar
-		#tabs = hc.option_bar([{'icon':'grid-outline','label':'Stable Diffusion'}, {'label':"Textual Inversion"},
+            #page = hc.option_bar([{'icon':'grid-outline','label':'Stable Diffusion'}, {'label':"Textual Inversion"},
                                                         #{'label':"Model Manager"},{'label':"Settings"}],
                                                         #horizontal_orientation=False,
                                                         #override_theme={'txc_inactive': 'white','menu_background':'#111', 'stVerticalBlock': '#111','txc_active':'yellow','option_active':'blue'})
 
-	if tabs =='Stable Diffusion':
+        #
+        #if menu_id == "Home":
+            #st.info("Under Construction. :construction_worker:")
+
+        if menu_id == "Stable Diffusion":
             # set the page url and title
-		st.experimental_set_query_params(page='stable-diffusion')
+            #st.experimental_set_query_params(page='stable-diffusion')
             try:
                 set_page_title("Stable Diffusion Playground")
             except NameError:
                 st.experimental_rerun()
 
-		txt2img_tab, img2img_tab, txt2vid_tab, img2txt_tab, concept_library_tab = st.tabs(["Text-to-Image", "Image-to-Image", 
+            txt2img_tab, img2img_tab, txt2vid_tab, img2txt_tab, post_processing_tab, concept_library_tab = st.tabs(["Text-to-Image", "Image-to-Image",
+                                                                                                                    #"Inpainting",
                                                                                                                     "Text-to-Video", "Image-To-Text",
-		                                                                      "Concept Library"])
+                                                                                                                    "Post-Processing","Concept Library"])
             #with home_tab:
                     #from home import layout
                     #layout()
@@ -144,6 +206,10 @@ def layout():
                 from img2img import layout
                 layout()
 
+            #with inpainting_tab:
+                #from inpainting import layout
+                #layout()
+
             with txt2vid_tab:
                 from txt2vid import layout
                 layout()
@@ -152,26 +218,127 @@ def layout():
                 from img2txt import layout
                 layout()
 
+            with post_processing_tab:
+                from post_processing import layout
+                layout()
+
             with concept_library_tab:
                 from sd_concept_library import layout
                 layout()
 
         #
-	elif tabs == 'Model Manager':
+        elif menu_id == 'Model Manager':
             set_page_title("Model Manager - Stable Diffusion Playground")
 
             from ModelManager import layout
             layout()
 
-	elif tabs == 'Textual Inversion':		
+        elif menu_id == 'Textual Inversion':
             from textual_inversion import layout
             layout()
 
-	elif tabs == 'Settings':
+        elif menu_id == 'Fine Tunning':
+            #from textual_inversion import layout
+            #layout()
+            st.info("Under Construction. :construction_worker:")
+
+        elif menu_id == 'API Server':
+            set_page_title("API Server - Stable Diffusion Playground")
+            from APIServer import layout
+            layout()
+
+        elif menu_id == 'Barfi/BaklavaJS':
+            set_page_title("Barfi/BaklavaJS - Stable Diffusion Playground")
+            from barfi_baklavajs import layout
+            layout()
+
+        elif menu_id == 'Settings':
             set_page_title("Settings - Stable Diffusion Playground")
 
             from Settings import layout
             layout()
 
+        # calling dragable input component module at the end, so it works on all pages
+        draggable_number_input.load()
+
+
 if __name__ == '__main__':
+    set_logger_verbosity(opt.verbosity)
+    quiesce_logger(opt.quiet)
+
+    if not opt.headless:
         layout()
+
+    with server_state_lock["bridge"]:
+        if server_state["bridge"]:
+            try:
+                import bridgeData as cd
+            except ModuleNotFoundError as e:
+                logger.warning("No bridgeData found. Falling back to default where no CLI args are set.")
+                logger.debug(str(e))
+            except SyntaxError as e:
+                logger.warning("bridgeData found, but is malformed. Falling back to default where no CLI args are set.")
+                logger.debug(str(e))
+            except Exception as e:
+                logger.warning("No bridgeData found, use default where no CLI args are set")
+                logger.debug(str(e))
+            finally:
+                try: # check if cd exists (i.e. bridgeData loaded properly)
+                    cd
+                except: # if not, create defaults
+                    class temp(object):
+                        def __init__(self):
+                            random.seed()
+                            self.horde_url = "https://stablehorde.net"
+                            # Give a cool name to your instance
+                            self.horde_name = f"Automated Instance #{random.randint(-100000000, 100000000)}"
+                            # The api_key identifies a unique user in the horde
+                            self.horde_api_key = "0000000000"
+                            # Put other users whose prompts you want to prioritize.
+                            # The owner's username is always included so you don't need to add it here, unless you want it to have lower priority than another user
+                            self.horde_priority_usernames = []
+                            self.horde_max_power = 8
+                            self.nsfw = True
+                            self.censor_nsfw = False
+                            self.blacklist = []
+                            self.censorlist = []
+                            self.models_to_load = ["stable_diffusion"]
+                    cd = temp()
+            horde_api_key = opt.horde_api_key if opt.horde_api_key else cd.horde_api_key
+            horde_name = opt.horde_name if opt.horde_name else cd.horde_name
+            horde_url = opt.horde_url if opt.horde_url else cd.horde_url
+            horde_priority_usernames = opt.horde_priority_usernames if opt.horde_priority_usernames else cd.horde_priority_usernames
+            horde_max_power = opt.horde_max_power if opt.horde_max_power else cd.horde_max_power
+            # Not used yet
+            horde_models = [opt.horde_model] if opt.horde_model else cd.models_to_load
+            try:
+                horde_nsfw = not opt.horde_sfw if opt.horde_sfw else cd.horde_nsfw
+            except AttributeError:
+                horde_nsfw = True
+            try:
+                horde_censor_nsfw = opt.horde_censor_nsfw if opt.horde_censor_nsfw else cd.horde_censor_nsfw
+            except AttributeError:
+                horde_censor_nsfw = False
+            try:
+                horde_blacklist = opt.horde_blacklist if opt.horde_blacklist else cd.horde_blacklist
+            except AttributeError:
+                horde_blacklist = []
+            try:
+                horde_censorlist = opt.horde_censorlist if opt.horde_censorlist else cd.horde_censorlist
+            except AttributeError:
+                horde_censorlist = []
+            if horde_max_power < 2:
+                horde_max_power = 2
+            horde_max_pixels = 64*64*8*horde_max_power
+            logger.info(f"Joining Horde with parameters: Server Name '{horde_name}'. Horde URL '{horde_url}'. Max Pixels {horde_max_pixels}")
+
+            try:
+                thread = threading.Thread(target=run_bridge(1, horde_api_key, horde_name, horde_url,
+                                                            horde_priority_usernames, horde_max_pixels,
+                                                            horde_nsfw, horde_censor_nsfw, horde_blacklist,
+                                                            horde_censorlist), args=())
+                thread.daemon = True
+                thread.start()
+                #run_bridge(1, horde_api_key, horde_name, horde_url, horde_priority_usernames, horde_max_pixels, horde_nsfw, horde_censor_nsfw, horde_blacklist, horde_censorlist)
+            except KeyboardInterrupt:
+                print(f"Keyboard Interrupt Received. Ending Bridge")

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 setup(
-    name='sd-webui',
+    name='sygil-webui',
     version='0.0.1',
     description='',
     packages=find_packages(),

streamlit_webview.py

@@ -0,0 +1,15 @@
+import os, webview
+from streamlit.web import bootstrap
+from streamlit import config as _config
+
+webview.create_window('Sygil', 'http://localhost:8501', width=1000, height=800, min_size=(500, 500))
+webview.start()
+
+dirname = os.path.dirname(__file__)
+filename = os.path.join(dirname, 'scripts/webui_streamlit.py')
+
+_config.set_option("server.headless", True)
+args = []
+
+#streamlit.cli.main_run(filename, args)
+bootstrap.run(filename,'',args, flag_options={})

webui.cmd

@@ -1,17 +1,17 @@
 @echo off
-:: This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+:: This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
-
+:: 
-:: Copyright 2022 sd-webui team.
+:: Copyright 2022 Sygil-Dev team.
 :: This program is free software: you can redistribute it and/or modify
 :: it under the terms of the GNU Affero General Public License as published by
 :: the Free Software Foundation, either version 3 of the License, or
 :: (at your option) any later version.
-
+:: 
 :: This program is distributed in the hope that it will be useful,
 :: but WITHOUT ANY WARRANTY; without even the implied warranty of
 :: MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 :: GNU Affero General Public License for more details.
-
+:: 
 :: You should have received a copy of the GNU Affero General Public License
 :: along with this program.  If not, see <http://www.gnu.org/licenses/>. 
 :: Run all commands using this script's directory as the working directory
@@ -31,7 +31,11 @@ IF EXIST custom-conda-path.txt (
   FOR /F %%i IN (custom-conda-path.txt) DO set v_custom_path=%%i
 )
 
-set v_paths=%ProgramData%\miniconda3
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
+
+set v_paths=%INSTALL_ENV_DIR%
+set v_paths=%v_paths%;%ProgramData%\miniconda3
 set v_paths=%v_paths%;%USERPROFILE%\miniconda3
 set v_paths=%v_paths%;%ProgramData%\anaconda3
 set v_paths=%v_paths%;%USERPROFILE%\anaconda3
@@ -58,20 +62,23 @@ IF "%v_conda_path%"=="" (
 
 :CONDA_FOUND
 echo Stashing local changes and pulling latest update...
+git status --porcelain=1 -uno | findstr . && set "HasChanges=1" || set "HasChanges=0"
 call git stash
 call git pull
+IF "%HasChanges%" == "0" GOTO SKIP_RESTORE
+
 set /P restore="Do you want to restore changes you made before updating? (Y/N): "
 IF /I "%restore%" == "N" (
-  echo Removing changes please wait...
+  echo Removing changes...
   call git stash drop
-  echo Changes removed, press any key to continue...
+  echo Changes removed
-  pause >nul
 ) ELSE IF /I "%restore%" == "Y" (
-  echo Restoring changes, please wait...
+  echo Restoring changes...
   call git stash pop --quiet
-  echo Changes restored, press any key to continue...
+  echo Changes restored
-  pause >nul
 )
+
+:SKIP_RESTORE
 call "%v_conda_path%\Scripts\activate.bat"
 
 for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml')  DO set v_cur_hash=%%a
@@ -95,12 +102,11 @@ call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%"
 
 :PROMPT
 set SETUPTOOLS_USE_DISTUTILS=stdlib
-IF EXIST "models\ldm\stable-diffusion-v1\model.ckpt" (
+IF EXIST "models\ldm\stable-diffusion-v1\Stable Diffusion v1.5.ckpt" (
-  set "PYTHONPATH=%~dp0"
+  python -m streamlit run scripts\webui_streamlit.py --theme.base dark
-  python scripts\relauncher.py %*
 ) ELSE (
-  echo Your model file does not exist! Place it in 'models\ldm\stable-diffusion-v1' with the name 'model.ckpt'.
+  echo Your model file does not exist! Once the WebUI launches please visit the Model Manager page and download the models by using the Download button for each model.
-  pause
+  python -m streamlit run scripts\webui_streamlit.py --theme.base dark
 )
 
 ::cmd /k

webui.sh

@@ -1,7 +1,8 @@
 #!/bin/bash -i
-# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
 
-# Copyright 2022 sd-webui team.
+# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
+
+# Copyright 2022 Sygil-Dev team.
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU Affero General Public License as published by
 # the Free Software Foundation, either version 3 of the License, or
@@ -23,6 +24,9 @@ ENV_MODIFIED=$(date -r $ENV_FILE "+%s")
 ENV_MODIFED_FILE=".env_updated"
 ENV_UPDATED=0
 
+INSTALL_ENV_DIR="$(pwd)/../installer_files/env" # since linux-sd.sh clones the repo into a subfolder
+if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
+
 # Models used for upscaling
 GFPGAN_MODEL="https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth"
 LATENT_DIFFUSION_REPO="https://github.com/devilismyfriend/latent-diffusion.git"
@@ -30,7 +34,7 @@ LSDR_CONFIG="https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
 LSDR_MODEL="https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
 REALESRGAN_MODEL="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
 REALESRGAN_ANIME_MODEL="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
-SD_CONCEPT_REPO="https://github.com/sd-webui/sd-concepts-library/archive/refs/heads/main.zip"
+SD_CONCEPT_REPO="https://github.com/Sygil-Dev/sd-concepts-library/archive/refs/heads/main.zip"
 
 
 if [[ -f $ENV_MODIFED_FILE ]]; then 
@@ -49,6 +53,11 @@ conda_env_setup () {
         CUSTOM_CONDA_PATH=$(cat custom-conda-path.txt)
     fi
 
+    # If a custom conda isn't specified, and the installer downloaded conda for the user, then use that
+    if [ -f "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ "$CUSTOM_CONDA_PATH" == "" ]; then
+        . "$INSTALL_ENV_DIR/etc/profile.d/conda.sh"
+    fi
+
     # If custom path is set above, try to setup conda environment
     if [ -f "${CUSTOM_CONDA_PATH}/etc/profile.d/conda.sh" ]; then
         . "${CUSTOM_CONDA_PATH}/etc/profile.d/conda.sh"
@@ -85,22 +94,6 @@ conda_env_activation () {
     conda info | grep active
 }
 
-# Check to see if the SD model already exists, if not then it creates it and prompts the user to add the SD AI models to the repo directory
-sd_model_loading () {
-    if [ -f "$DIRECTORY/models/ldm/stable-diffusion-v1/model.ckpt" ]; then
-        printf "AI Model already in place. Continuing...\n\n"
-    else
-        printf "\n\n########## MOVE MODEL FILE ##########\n\n"
-        printf "Please download the 1.4 AI Model from Huggingface (or another source) and place it inside of the stable-diffusion-webui folder\n\n"
-        read -p "Once you have sd-v1-4.ckpt in the project root, Press Enter...\n\n"
-        
-        # Check to make sure checksum of models is the original one from HuggingFace and not a fake model set
-        printf "fe4efff1e174c627256e44ec2991ba279b3816e364b49f9be2abc0b3ff3f8556 sd-v1-4.ckpt" | sha256sum --check || exit 1
-        mv sd-v1-4.ckpt $DIRECTORY/models/ldm/stable-diffusion-v1/model.ckpt
-        rm -r ./Models
-    fi
-}
-
 # Checks to see if the upscaling models exist in their correct locations. If they do not they will be downloaded as required
 post_processor_model_loading () {
     # Check to see if GFPGAN has been added yet, if not it will download it and place it in the proper directory
@@ -154,9 +147,16 @@ post_processor_model_loading () {
 
 # Show the user a prompt asking them which version of the WebUI they wish to use, Streamlit or Gradio
 launch_webui () {
+    # skip the prompt if --bridge command-line argument is detected
+    for arg in "$@"; do
+        if [ "$arg" == "--bridge" ]; then
+           python -u scripts/relauncher.py "$@"
+           return
+        fi
+    done
     printf "\n\n########## LAUNCH USING STREAMLIT OR GRADIO? ##########\n\n"
     printf "Do you wish to run the WebUI using the Gradio or StreamLit Interface?\n\n"
-    printf "Streamlit: \nHas A More Modern UI \nMore Features Planned \nWill Be The Main UI Going Forward \nCurrently In Active Development \nMissing Some Gradio Features\n\n"
+    printf "Streamlit: \nHas A More Modern UI \nMore Features Planned \nWill Be The Main UI Going Forward \nCurrently In Active Development \n\n"
     printf "Gradio: \nCurrently Feature Complete \nUses An Older Interface Style \nWill Not Receive Major Updates\n\n"
     printf "Which Version of the WebUI Interface do you wish to use?\n"
     select yn in "Streamlit" "Gradio"; do
@@ -173,9 +173,9 @@ start_initialization () {
     sd_model_loading
     post_processor_model_loading
     conda_env_activation
-    if [ ! -e "models/ldm/stable-diffusion-v1/model.ckpt" ]; then
+    if [ ! -e "models/ldm/stable-diffusion-v1/*.ckpt" ]; then
-        echo "Your model file does not exist! Place it in 'models/ldm/stable-diffusion-v1' with the name 'model.ckpt'."
+        echo "Your model file does not exist! Streamlit will handle this automatically, however Gradio still requires this file be placed manually. If you intend to use the Gradio interface, place it in 'models/ldm/stable-diffusion-v1' with the name 'model.ckpt'."
-        exit 1
+        read -p "Once you have sd-v1-4.ckpt in the project root, if you are going to use Gradio, Press Enter...\n\n"
     fi
     launch_webui "$@"
 

webui_legacy.cmd

@@ -1,17 +1,17 @@
 @echo off
-:: This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
+:: This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
-:: 
+
-:: Copyright 2022 sd-webui team.
+:: Copyright 2022 Sygil-Dev team.
 :: This program is free software: you can redistribute it and/or modify
 :: it under the terms of the GNU Affero General Public License as published by
 :: the Free Software Foundation, either version 3 of the License, or
 :: (at your option) any later version.
-:: 
+
 :: This program is distributed in the hope that it will be useful,
 :: but WITHOUT ANY WARRANTY; without even the implied warranty of
 :: MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 :: GNU Affero General Public License for more details.
-:: 
+
 :: You should have received a copy of the GNU Affero General Public License
 :: along with this program.  If not, see <http://www.gnu.org/licenses/>. 
 :: Run all commands using this script's directory as the working directory
@@ -58,20 +58,23 @@ IF "%v_conda_path%"=="" (
 
 :CONDA_FOUND
 echo Stashing local changes and pulling latest update...
+git status --porcelain=1 -uno | findstr . && set "HasChanges=1" || set "HasChanges=0"
 call git stash
 call git pull
+IF "%HasChanges%" == "0" GOTO SKIP_RESTORE
+
 set /P restore="Do you want to restore changes you made before updating? (Y/N): "
 IF /I "%restore%" == "N" (
-  echo Removing changes please wait...
+  echo Removing changes...
   call git stash drop
-  echo Changes removed, press any key to continue...
+  echo Changes removed
-  pause >nul
 ) ELSE IF /I "%restore%" == "Y" (
-  echo Restoring changes, please wait...
+  echo Restoring changes...
   call git stash pop --quiet
-  echo Changes restored, press any key to continue...
+  echo Changes restored
-  pause >nul
 )
+
+:SKIP_RESTORE
 call "%v_conda_path%\Scripts\activate.bat"
 
 for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml')  DO set v_cur_hash=%%a
@@ -96,7 +99,8 @@ call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%"
 :PROMPT
 set SETUPTOOLS_USE_DISTUTILS=stdlib
 IF EXIST "models\ldm\stable-diffusion-v1\model.ckpt" (
-  python -m streamlit run scripts\webui_streamlit.py --theme.base dark
+  set "PYTHONPATH=%~dp0"
+  python scripts\relauncher.py %*
 ) ELSE (
   echo Your model file does not exist! Place it in 'models\ldm\stable-diffusion-v1' with the name 'model.ckpt'.
   pause