Merge branch 'united' into mkultra
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*.min.lua linguist-vendored
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*documentation.html linguist-vendored
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/static/swagger-ui/* linguist-vendored
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@ -125,20 +125,20 @@
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|||
"# GPU Edition Model Descriptions\n",
|
||||
"| Model | Size | Style | Description |\n",
|
||||
"| --- | --- | --- | --- |\n",
|
||||
"| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
|
||||
"| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
|
||||
"| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
|
||||
"| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
|
||||
"| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
|
||||
"| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
|
||||
"| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
|
||||
"\n",
|
||||
"# [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)\n",
|
||||
"\n",
|
||||
"| Model | Size | Style | Description |\n",
|
||||
"| --- | --- | --- | --- |\n",
|
||||
"| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
|
||||
"| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
|
||||
"| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
|
||||
"| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n",
|
||||
"| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n",
|
||||
|
|
@ -156,6 +156,7 @@
|
|||
"| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |\n",
|
||||
"| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n",
|
||||
"\n",
|
||||
"---\n",
|
||||
"# How to start KoboldAI in 7 simple steps\n",
|
||||
"Using KoboldAI on Google Colab is easy! Simply follow these steps to get started:\n",
|
||||
"1. Mobile phone? Tap the play button below next to \"<--- Tap this if you play on mobile\" to reveal an audio player, play the silent audio to keep the tab alive so Google will not shut you down when your using KoboldAI. If no audio player is revealed your phone browser does not support Google Colab in the mobile view, go to your browser menu and enable Desktop mode before you continue.\n",
|
||||
|
|
|
|||
|
|
@ -148,7 +148,7 @@
|
|||
"\n",
|
||||
"| Model | Size | Style | Description |\n",
|
||||
"| --- | --- | --- | --- |\n",
|
||||
"| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
|
||||
"| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
|
||||
"| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
|
||||
"| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n",
|
||||
"| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n",
|
||||
|
|
@ -163,13 +163,13 @@
|
|||
"\n",
|
||||
"| Model | Size | Style | Description |\n",
|
||||
"| --- | --- | --- | --- |\n",
|
||||
"| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
|
||||
"| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n",
|
||||
"| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n",
|
||||
"| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n",
|
||||
"| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
|
||||
"| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n",
|
||||
"| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n",
|
||||
"| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n",
|
||||
"\n",
|
||||
"| Style | Description |\n",
|
||||
|
|
|
|||
|
|
@ -1,76 +0,0 @@
|
|||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"name": "ColabKobold Code",
|
||||
"provenance": [],
|
||||
"authorship_tag": "ABX9TyOuIHmyxj4U9dipAib4hfIi",
|
||||
"include_colab_link": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
},
|
||||
"accelerator": "TPU"
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "view-in-github",
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"<a href=\"https://colab.research.google.com/github/henk717/KoboldAI/blob/united/colab/vscode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# ColabKobold VSCode Edition\n",
|
||||
"This is a special edition of ColabKobold aimed at developers, it will not start a KoboldAI instance for you to play KoboldAI and instead will launch a fully functional copy of VSCode for easy development.\n",
|
||||
"\n",
|
||||
"Few things of note:\n",
|
||||
"1. Make sure the desired (or no) accelertor is selected on Colab, you do not want a TPU ban for not using it.\n",
|
||||
"1. The Version can be replaced with your github URL and appended with -b for the branch for example \"https://github.com/henk717/koboldai -b united\" dependencies will automatically be installed from requirements.txt or requirements_mtj.txt.\n",
|
||||
"1. With the args you can specify launch options for the KoboldAI Deployment Script, this way you can easily preinstall models to your development instance so you have a model to test with. To install TPU requirements specify the -m TPUMeshTransformerGPTJ argument.\n",
|
||||
"1. You will need an Ngrok auth token which you can obtain here : https://dashboard.ngrok.com/get-started/your-authtoken\n",
|
||||
"1. KoboldAI is installed in /content/koboldai-client opening this folder is enough to automatically get full git history and revision support. Also keep in mind that it mounts your Google Drive, be careful comitting directly from this instance.\n",
|
||||
"1. With Ctrl + Shift + ` you can get a terminal to launch KoboldAI with your own parameters, launching with --colab is recommended.\n",
|
||||
"\n",
|
||||
"# [If you are not a developer and are looking to use KoboldAI click here](https://henk.tech/colabkobold)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "hMRnGz42Xsy3"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "40B1QvI3Xv02"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title VSCode Server\n",
|
||||
"Version = \"United\" #@param [\"Official\", \"United\"] {allow-input: true}\n",
|
||||
"Args = \"-m TPUMeshTransformerGPTJ -a https://api.wandb.ai/files/ve-forbryderne/skein/files/gpt-j-6b-skein-jax/aria2.txt\" #@param {type:\"string\"}\n",
|
||||
"Authtoken = \"\" #@param {type:\"string\"}\n",
|
||||
"\n",
|
||||
"from google.colab import drive\n",
|
||||
"drive.mount('/content/drive/')\n",
|
||||
"\n",
|
||||
"!wget https://henk.tech/ckds -O - | bash /dev/stdin -g $Version -i only $Args\n",
|
||||
"\n",
|
||||
"!pip install colabcode\n",
|
||||
"!pip install 'flask>=2.1.0'\n",
|
||||
"from colabcode import ColabCode\n",
|
||||
"ColabCode(authtoken=Authtoken)"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -16,6 +16,8 @@ dependencies:
|
|||
- bleach=4.1.0
|
||||
- pip
|
||||
- git=2.35.1
|
||||
- marshmallow>=3.13
|
||||
- apispec-webframeworks
|
||||
- pip:
|
||||
- git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
|
||||
- flask-cloudflared
|
||||
|
|
|
|||
|
|
@ -17,9 +17,11 @@ dependencies:
|
|||
- git=2.35.1
|
||||
- sentencepiece
|
||||
- protobuf
|
||||
- marshmallow>=3.13
|
||||
- apispec-webframeworks
|
||||
- pip:
|
||||
- flask-cloudflared
|
||||
- flask-ngrok
|
||||
- lupa==1.10
|
||||
- transformers>=4.20.1
|
||||
- accelerate
|
||||
- accelerate
|
||||
|
|
|
|||
|
|
@ -12,6 +12,8 @@ dependencies:
|
|||
- bleach=4.1.0
|
||||
- pip
|
||||
- git=2.35.1
|
||||
- marshmallow>=3.13
|
||||
- apispec-webframeworks
|
||||
- pip:
|
||||
- --find-links https://download.pytorch.org/whl/rocm4.2/torch_stable.html
|
||||
- torch
|
||||
|
|
|
|||
|
|
@ -14,6 +14,8 @@ dependencies:
|
|||
- git=2.35.1
|
||||
- sentencepiece
|
||||
- protobuf
|
||||
- marshmallow>=3.13
|
||||
- apispec-webframeworks
|
||||
- pip:
|
||||
- --find-links https://download.pytorch.org/whl/rocm4.2/torch_stable.html
|
||||
- torch==1.10.*
|
||||
|
|
|
|||
|
|
@ -263,6 +263,17 @@ gensettingstf = [
|
|||
"default": 0,
|
||||
"tooltip": "Shows outputs to you as they are made. Does not work with more than one gens per action."
|
||||
},
|
||||
{
|
||||
"uitype": "toggle",
|
||||
"unit": "bool",
|
||||
"label": "Probability Viewer",
|
||||
"id": "setshowprobs",
|
||||
"min": 0,
|
||||
"max": 1,
|
||||
"step": 1,
|
||||
"default": 0,
|
||||
"tooltip": "Shows token selection probabilities. Does not work with more than one gens per action."
|
||||
},
|
||||
]
|
||||
|
||||
gensettingsik =[{
|
||||
|
|
|
|||
60
readme.md
60
readme.md
|
|
@ -8,7 +8,7 @@ Stories can be played like a Novel, a text adventure game or used as a chatbot w
|
|||
|
||||
### Adventure mode
|
||||
|
||||
By default KoboldAI will run in a generic mode optimized for writing, but with the right model you can play this like AI Dungeon without any issues. You can enable this in the settings and bring your own prompt, try generating a random prompt or download one of the prompts available at [prompts.aidg.club](https://prompts.aidg.club) .
|
||||
By default KoboldAI will run in a generic mode optimized for writing, but with the right model you can play this like AI Dungeon without any issues. You can enable this in the settings and bring your own prompt, try generating a random prompt or download one of the prompts available at [/aids/ Prompts](https://aetherroom.club/).
|
||||
|
||||
The gameplay will be slightly different than the gameplay in AI Dungeon because we adopted the Type of the Unleashed fork, giving you full control over all the characters because we do not automatically adapt your sentences behind the scenes. This means you can more reliably control characters that are not you.
|
||||
|
||||
|
|
@ -21,7 +21,7 @@ If you want to do this with your friends we advise using the main character as Y
|
|||
|
||||
### Writing assistant
|
||||
|
||||
If you want to use KoboldAI as a writing assistant this is best done in the regular mode with a model optimized for Novels. These models do not make the assumption that there is a You character and focus on Novel like writing. For writing these will often give you better results than Adventure or Generic models. That said, if you give it a good introduction to the story large generic models like 6B can be used if a more specific model is not available for what you wish to write. You can also try to use models that are not specific to what you wish to do, for example a NSFW Novel model for a SFW story if a SFW model is unavailable. This will mean you will have to correct the model more often because of its bias, but can still produce good enough results if it is familiar enough with your topic.
|
||||
If you want to use KoboldAI as a writing assistant this is best done in the regular mode with a model optimized for Novels. These models do not make the assumption that there is a You character and focus on Novel like writing. For writing these will often give you better results than Adventure or Generic models. That said, if you give it a good introduction to the story large generic models like 13B can be used if a more specific model is not available for what you wish to write. You can also try to use models that are not specific to what you wish to do, for example a NSFW Novel model for a SFW story if a SFW model is unavailable. This will mean you will have to correct the model more often because of its bias, but can still produce good enough results if it is familiar enough with your topic.
|
||||
|
||||
### Chatbot Mode
|
||||
|
||||
|
|
@ -48,18 +48,16 @@ If you would like to play KoboldAI online for free on a powerful computer you ca
|
|||
|
||||
Each edition features different models and requires different hardware to run, this means that if you are unable to obtain a TPU or a GPU you might still be able to use the other version. The models you can use are listed underneath the edition. To open a Colab click the big link featuring the editions name.
|
||||
|
||||
### [Click here for the TPU Edition Colab](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)
|
||||
## [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)
|
||||
|
||||
| Model | Size | Style | Description |
|
||||
| --- | --- | --- | --- |
|
||||
| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
|
||||
| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
|
||||
| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
|
||||
| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |
|
||||
| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |
|
||||
| [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). |
|
||||
| [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. |
|
||||
| [Convo](https://huggingface.co/hitomi-team/convo-6B) by Hitomi Team | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). |
|
||||
| [C1](https://huggingface.co/hakurei/c1-6B) by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. |
|
||||
| Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. |
|
||||
| [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. |
|
||||
| [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. |
|
||||
|
|
@ -68,21 +66,23 @@ Each edition features different models and requires different hardware to run, t
|
|||
|
||||
| Model | Size | Style | Description |
|
||||
| --- | --- | --- | --- |
|
||||
| [Fairseq-Dense-2.7B-Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
|
||||
| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
|
||||
| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |
|
||||
| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |
|
||||
| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |
|
||||
| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |
|
||||
| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |
|
||||
| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |
|
||||
| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |
|
||||
| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [Shinen 2.7B ](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |
|
||||
|
||||
| Style | Description |
|
||||
### Styles
|
||||
|
||||
| Type | Description |
|
||||
| --- | --- |
|
||||
| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |
|
||||
| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |
|
||||
| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |
|
||||
| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |
|
||||
| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel Type model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |
|
||||
| Hybrid | Hybrid models are a blend between different Types, for example they are trained on both Novel stories and Adventure stories. These models are great variety models that you can use for multiple different playTypes and modes, but depending on your usage you may need to enable Adventure Mode or the You bias (in userscripts). |
|
||||
| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |
|
||||
|
||||
## Tips to get the most out of Google Colab
|
||||
|
|
@ -94,28 +94,6 @@ Each edition features different models and requires different hardware to run, t
|
|||
* Done with KoboldAI? Go to the Runtime menu, click on Manage Sessions and terminate your open sessions that you no longer need. This trick can help you maintain higher priority towards getting a TPU.
|
||||
* Models stored on Google Drive typically load faster than models we need to download from the internet.
|
||||
|
||||
### [Click here for the GPU Edition Colab](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb)
|
||||
|
||||
| Model | Size | Type | Description |
|
||||
| --- | --- | --- | --- |
|
||||
| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B GPU | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel Type writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B GPU | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |
|
||||
| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B GPU | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |
|
||||
| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B GPU | NSFW | This model is tuned on Literotica to produce a Novel Type model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B GPU | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |
|
||||
| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B GPU | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |
|
||||
|
||||
### Model Types
|
||||
|
||||
| Type | Description |
|
||||
| --- | --- |
|
||||
| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. |
|
||||
| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. |
|
||||
| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel Type model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |
|
||||
| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. |
|
||||
| Hybrid | Hybrid models are a blend between different Types, for example they are trained on both Novel stories and Adventure stories. These models are great variety models that you can use for multiple different playTypes and modes, but depending on your usage you may need to enable Adventure Mode or the You bias (in userscripts). |
|
||||
| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |
|
||||
|
||||
## Install KoboldAI on your own computer
|
||||
|
||||
KoboldAI has a large number of dependencies you will need to install on your computer, unfortunately Python does not make it easy for us to provide instructions that work for everyone. The instructions below will work on most computers, but if you have multiple versions of Python installed conflicts can occur.
|
||||
|
|
@ -195,11 +173,11 @@ If you get these errors you either did not select the correct folder for your cu
|
|||
|
||||
Softprompts (also known as Modules in other products) are addons that can change the output of existing models. For example you may load a softprompt that biases the AI towards a certain subject and style like transcripts from your favorite TV show.
|
||||
|
||||
Since these softprompts are often based on existing franchises we currently do not bundle any of them with KoboldAI due to copyright concerns (We do not want to put the entire project at risk). Instead look at community resources like #softprompts on the [KoboldAI Discord](https://discord.gg/XuQWadgU9k) or the [community hosted mirror](https://storage.henk.tech/KoboldAI/softprompts/) .
|
||||
Since these softprompts are often based on existing franchises we currently do not bundle any of them with KoboldAI due to copyright concerns (We do not want to put the entire project at risk). Instead look at community resources like #softprompts on the [KoboldAI Discord](https://discord.gg/XuQWadgU9k) or the [community hosted mirror](https://storage.henk.tech/KoboldAI/softprompts/).
|
||||
|
||||
That way we are better protected from any DMCA claims as things can be taken down easier than directly on Github. If you have a copyright free softprompt that you made from scratch and is not based on existing IP that you would like to see officially bundled with KoboldAI issue a pull request with your softprompt.
|
||||
|
||||
Training softprompts can be done for free with the [mtj-softtuner colab](https://colab.research.google.com/github/VE-FORBRYDERNE/mtj-softtuner/blob/main/mtj-softtuner.ipynb) , in that case you can leave most of the settings default. Your source data needs to be a folder with text files that are UTF-8 formatted and contain Unix line endings.
|
||||
Training softprompts can be done for free with the [Easy Softprompt Tuner](https://colab.research.google.com/gist/henk717/281fd57ebd2e88d852ef9dcc3f29bebf/easy-softprompt-tuner.ipynb#sandboxMode=true), in that case you can leave most of the settings default. Your source data needs to be a folder with text files that are UTF-8 formatted and contain Unix line endings.
|
||||
|
||||
## Userscripts
|
||||
|
||||
|
|
|
|||
|
|
@ -12,4 +12,6 @@ bleach==4.1.0
|
|||
sentencepiece
|
||||
protobuf
|
||||
accelerate
|
||||
flask-session
|
||||
flask-session
|
||||
marshmallow>=3.13
|
||||
apispec-webframeworks
|
||||
|
|
|
|||
|
|
@ -17,4 +17,6 @@ eventlet
|
|||
lupa==1.10
|
||||
markdown
|
||||
bleach==4.1.0
|
||||
flask-session
|
||||
flask-session
|
||||
marshmallow>=3.13
|
||||
apispec-webframeworks
|
||||
|
|
|
|||
|
|
@ -79,6 +79,7 @@ var rs_close;
|
|||
var seqselmenu;
|
||||
var seqselcontents;
|
||||
var stream_preview;
|
||||
var token_prob_container;
|
||||
|
||||
var storyname = null;
|
||||
var memorymode = false;
|
||||
|
|
@ -511,6 +512,16 @@ function addWiLine(ob) {
|
|||
$(".wisortable-excluded-dynamic").removeClass("wisortable-excluded-dynamic");
|
||||
$(this).parent().css("max-height", "").find(".wicomment").find(".form-control").css("max-height", "");
|
||||
});
|
||||
|
||||
for (const wientry of document.getElementsByClassName("wientry")) {
|
||||
// If we are uninitialized, skip.
|
||||
if ($(wientry).closest(".wilistitem-uninitialized").length) continue;
|
||||
|
||||
// add() will not add if the class is already present
|
||||
wientry.classList.add("tokens-counted");
|
||||
}
|
||||
|
||||
registerTokenCounters();
|
||||
}
|
||||
|
||||
function addWiFolder(uid, ob) {
|
||||
|
|
@ -834,6 +845,7 @@ function exitMemoryMode() {
|
|||
button_actmem.html("Memory");
|
||||
show([button_actback, button_actfwd, button_actretry, button_actwi]);
|
||||
input_text.val("");
|
||||
updateInputBudget(input_text[0]);
|
||||
// Hide Author's Note field
|
||||
anote_menu.slideUp("fast");
|
||||
}
|
||||
|
|
@ -890,7 +902,7 @@ function formatChunkInnerText(chunk) {
|
|||
}
|
||||
|
||||
function dosubmit(disallow_abort) {
|
||||
ignore_stream = false;
|
||||
beginStream();
|
||||
submit_start = Date.now();
|
||||
var txt = input_text.val().replace(/\u00a0/g, " ");
|
||||
if((disallow_abort || gamestate !== "wait") && !memorymode && !gamestarted && ((!adventure || !action_mode) && txt.trim().length == 0)) {
|
||||
|
|
@ -905,7 +917,7 @@ function dosubmit(disallow_abort) {
|
|||
}
|
||||
|
||||
function _dosubmit() {
|
||||
ignore_stream = false;
|
||||
beginStream();
|
||||
var txt = submit_throttle.txt;
|
||||
var disallow_abort = submit_throttle.disallow_abort;
|
||||
submit_throttle = null;
|
||||
|
|
@ -1048,6 +1060,18 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) {
|
|||
if (breadcrumbs.length > 0) {
|
||||
$("#loadmodellistbreadcrumbs").append("<hr size='1'>")
|
||||
}
|
||||
//If we're in the custom load menu (we need to send the path data back in that case)
|
||||
if(['NeoCustom', 'GPT2Custom'].includes(menu)) {
|
||||
$("#loadmodel"+i).off("click").on("click", (function () {
|
||||
return function () {
|
||||
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")});
|
||||
highlightLoadLine($(this));
|
||||
}
|
||||
})(i));
|
||||
$("#custommodelname").removeClass("hidden");
|
||||
$("#custommodelname")[0].setAttribute("menu", menu);
|
||||
}
|
||||
|
||||
for(i=0; i<ar.length; i++) {
|
||||
if (Array.isArray(ar[i][0])) {
|
||||
full_path = ar[i][0][0];
|
||||
|
|
@ -1065,7 +1089,7 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) {
|
|||
html = html + "<span class=\"loadlisticon loadmodellisticon-folder oi oi-folder allowed\" aria-hidden=\"true\"></span>"
|
||||
} else {
|
||||
//this is a model
|
||||
html = html + "<div class=\"loadlistpadding\"></div>"
|
||||
html = html + "<div class=\"loadlisticon oi oi-caret-right allowed\"></div>"
|
||||
}
|
||||
|
||||
//now let's do the delete icon if applicable
|
||||
|
|
@ -1083,6 +1107,7 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) {
|
|||
</div>"
|
||||
loadmodelcontent.append(html);
|
||||
//If this is a menu
|
||||
console.log(ar[i]);
|
||||
if(ar[i][3]) {
|
||||
$("#loadmodel"+i).off("click").on("click", (function () {
|
||||
return function () {
|
||||
|
|
@ -1090,27 +1115,29 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) {
|
|||
disableButtons([load_model_accept]);
|
||||
}
|
||||
})(i));
|
||||
//If we're in the custom load menu (we need to send the path data back in that case)
|
||||
} else if(['NeoCustom', 'GPT2Custom'].includes(menu)) {
|
||||
$("#loadmodel"+i).off("click").on("click", (function () {
|
||||
return function () {
|
||||
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")});
|
||||
highlightLoadLine($(this));
|
||||
}
|
||||
})(i));
|
||||
$("#custommodelname").removeClass("hidden");
|
||||
$("#custommodelname")[0].setAttribute("menu", menu);
|
||||
//Normal load
|
||||
} else {
|
||||
$("#loadmodel"+i).off("click").on("click", (function () {
|
||||
return function () {
|
||||
$("#use_gpu_div").addClass("hidden");
|
||||
$("#modelkey").addClass("hidden");
|
||||
$("#modellayers").addClass("hidden");
|
||||
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name")});
|
||||
highlightLoadLine($(this));
|
||||
}
|
||||
})(i));
|
||||
if (['NeoCustom', 'GPT2Custom'].includes(menu)) {
|
||||
$("#loadmodel"+i).off("click").on("click", (function () {
|
||||
return function () {
|
||||
$("#use_gpu_div").addClass("hidden");
|
||||
$("#modelkey").addClass("hidden");
|
||||
$("#modellayers").addClass("hidden");
|
||||
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")});
|
||||
highlightLoadLine($(this));
|
||||
}
|
||||
})(i));
|
||||
} else {
|
||||
$("#loadmodel"+i).off("click").on("click", (function () {
|
||||
return function () {
|
||||
$("#use_gpu_div").addClass("hidden");
|
||||
$("#modelkey").addClass("hidden");
|
||||
$("#modellayers").addClass("hidden");
|
||||
socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name")});
|
||||
highlightLoadLine($(this));
|
||||
}
|
||||
})(i));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2086,6 +2113,11 @@ function unbindGametext() {
|
|||
gametext_bound = false;
|
||||
}
|
||||
|
||||
function beginStream() {
|
||||
ignore_stream = false;
|
||||
token_prob_container[0].innerHTML = "";
|
||||
}
|
||||
|
||||
function endStream() {
|
||||
// Clear stream, the real text is about to be displayed.
|
||||
ignore_stream = true;
|
||||
|
|
@ -2123,6 +2155,45 @@ function RemoveAllButFirstOption(selectElement) {
|
|||
}
|
||||
}
|
||||
|
||||
function interpolateRGB(color0, color1, t) {
|
||||
return [
|
||||
color0[0] + ((color1[0] - color0[0]) * t),
|
||||
color0[1] + ((color1[1] - color0[1]) * t),
|
||||
color0[2] + ((color1[2] - color0[2]) * t),
|
||||
]
|
||||
}
|
||||
|
||||
function updateInputBudget(inputElement) {
|
||||
let data = {"unencoded": inputElement.value, "field": inputElement.id};
|
||||
|
||||
if (inputElement.id === "anoteinput") {
|
||||
data["anotetemplate"] = $("#anotetemplate").val();
|
||||
}
|
||||
|
||||
socket.send({"cmd": "getfieldbudget", "data": data});
|
||||
}
|
||||
|
||||
function registerTokenCounters() {
|
||||
// Add token counters to all input containers with the class of "tokens-counted",
|
||||
// if a token counter is not already a child of said container.
|
||||
for (const el of document.getElementsByClassName("tokens-counted")) {
|
||||
if (el.getElementsByClassName("input-token-usage").length) continue;
|
||||
|
||||
let span = document.createElement("span");
|
||||
span.classList.add("input-token-usage");
|
||||
span.innerText = "?/? Tokens";
|
||||
el.appendChild(span);
|
||||
|
||||
let inputElement = el.querySelector("input, textarea");
|
||||
|
||||
inputElement.addEventListener("input", function() {
|
||||
updateInputBudget(this);
|
||||
});
|
||||
|
||||
updateInputBudget(inputElement);
|
||||
}
|
||||
}
|
||||
|
||||
//=================================================================//
|
||||
// READY/RUNTIME
|
||||
//=================================================================//
|
||||
|
|
@ -2214,6 +2285,8 @@ $(document).ready(function(){
|
|||
rs_close = $("#btn_rsclose");
|
||||
seqselmenu = $("#seqselmenu");
|
||||
seqselcontents = $("#seqselcontents");
|
||||
token_prob_container = $("#token_prob_container");
|
||||
token_prob_menu = $("#token_prob_menu");
|
||||
|
||||
// Connect to SocketIO server
|
||||
socket = io.connect(window.document.origin, {transports: ['polling', 'websocket'], closeOnBeforeunload: false});
|
||||
|
|
@ -2277,14 +2350,68 @@ $(document).ready(function(){
|
|||
// appearing after the output. To combat this, we only allow tokens
|
||||
// to be displayed after requesting and before recieving text.
|
||||
if (ignore_stream) return;
|
||||
if (!$("#setoutputstreaming")[0].checked) return;
|
||||
|
||||
if (!stream_preview) {
|
||||
let streamingEnabled = $("#setoutputstreaming")[0].checked;
|
||||
let probabilitiesEnabled = $("#setshowprobs")[0].checked;
|
||||
|
||||
if (!streamingEnabled && !probabilitiesEnabled) return;
|
||||
|
||||
if (!stream_preview && streamingEnabled) {
|
||||
stream_preview = document.createElement("span");
|
||||
game_text.append(stream_preview);
|
||||
}
|
||||
|
||||
stream_preview.innerText += msg.data.join("");
|
||||
for (const token of msg.data) {
|
||||
if (streamingEnabled) stream_preview.innerText += token.decoded;
|
||||
|
||||
if (probabilitiesEnabled) {
|
||||
// Probability display
|
||||
let probDiv = document.createElement("div");
|
||||
probDiv.classList.add("token-probs");
|
||||
|
||||
let probTokenSpan = document.createElement("span");
|
||||
probTokenSpan.classList.add("token-probs-header");
|
||||
probTokenSpan.innerText = token.decoded.replaceAll("\n", "\\n");
|
||||
probDiv.appendChild(probTokenSpan);
|
||||
|
||||
let probTable = document.createElement("table");
|
||||
let probTBody = document.createElement("tbody");
|
||||
probTable.appendChild(probTBody);
|
||||
|
||||
for (const probToken of token.probabilities) {
|
||||
let tr = document.createElement("tr");
|
||||
let rgb = interpolateRGB(
|
||||
[255, 255, 255],
|
||||
[0, 255, 0],
|
||||
probToken.score
|
||||
).map(Math.round);
|
||||
let color = `rgb(${rgb.join(", ")})`;
|
||||
|
||||
if (probToken.decoded === token.decoded) {
|
||||
tr.classList.add("token-probs-final-token");
|
||||
}
|
||||
|
||||
let tds = {};
|
||||
|
||||
for (const property of ["tokenId", "decoded", "score"]) {
|
||||
let td = document.createElement("td");
|
||||
td.style.color = color;
|
||||
tds[property] = td;
|
||||
tr.appendChild(td);
|
||||
}
|
||||
|
||||
tds.tokenId.innerText = probToken.tokenId;
|
||||
tds.decoded.innerText = probToken.decoded.toString().replaceAll("\n", "\\n");
|
||||
tds.score.innerText = (probToken.score * 100).toFixed(2) + "%";
|
||||
|
||||
probTBody.appendChild(tr);
|
||||
}
|
||||
|
||||
probDiv.appendChild(probTable);
|
||||
token_prob_container.append(probDiv);
|
||||
}
|
||||
}
|
||||
|
||||
scrollToBottom();
|
||||
} else if(msg.cmd == "updatescreen") {
|
||||
var _gamestarted = gamestarted;
|
||||
|
|
@ -2316,6 +2443,7 @@ $(document).ready(function(){
|
|||
scrollToBottom();
|
||||
} else if(msg.cmd == "updatechunk") {
|
||||
hideMessage();
|
||||
game_text.attr('contenteditable', allowedit);
|
||||
if (typeof submit_start !== 'undefined') {
|
||||
$("#runtime")[0].innerHTML = `Generation time: ${Math.round((Date.now() - submit_start)/1000)} sec`;
|
||||
delete submit_start;
|
||||
|
|
@ -2409,6 +2537,7 @@ $(document).ready(function(){
|
|||
memorytext = msg.data;
|
||||
input_text.val(msg.data);
|
||||
}
|
||||
updateInputBudget(input_text[0]);
|
||||
} else if(msg.cmd == "setmemory") {
|
||||
memorytext = msg.data;
|
||||
if(memorymode) {
|
||||
|
|
@ -2530,6 +2659,7 @@ $(document).ready(function(){
|
|||
} else if(msg.cmd == "setanote") {
|
||||
// Set contents of Author's Note field
|
||||
anote_input.val(msg.data);
|
||||
updateInputBudget(anote_input[0]);
|
||||
} else if(msg.cmd == "setanotetemplate") {
|
||||
// Set contents of Author's Note Template field
|
||||
$("#anotetemplate").val(msg.data);
|
||||
|
|
@ -2559,6 +2689,14 @@ $(document).ready(function(){
|
|||
} else if(msg.cmd == "updateoutputstreaming") {
|
||||
// Update toggle state
|
||||
$("#setoutputstreaming").prop('checked', msg.data).change();
|
||||
} else if(msg.cmd == "updateshowprobs") {
|
||||
$("#setshowprobs").prop('checked', msg.data).change();
|
||||
|
||||
if(msg.data) {
|
||||
token_prob_menu.removeClass("hidden");
|
||||
} else {
|
||||
token_prob_menu.addClass("hidden");
|
||||
}
|
||||
} else if(msg.cmd == "allowtoggle") {
|
||||
// Allow toggle change states to propagate
|
||||
allowtoggle = msg.data;
|
||||
|
|
@ -2761,6 +2899,8 @@ $(document).ready(function(){
|
|||
if (msg.key) {
|
||||
$("#modelkey").removeClass("hidden");
|
||||
$("#modelkey")[0].value = msg.key_value;
|
||||
//if we're in the API list, disable to load button until the model is selected (after the API Key is entered)
|
||||
disableButtons([load_model_accept]);
|
||||
} else {
|
||||
$("#modelkey").addClass("hidden");
|
||||
|
||||
|
|
@ -2798,6 +2938,7 @@ $(document).ready(function(){
|
|||
}
|
||||
} else if(msg.cmd == 'oai_engines') {
|
||||
$("#oaimodel").removeClass("hidden")
|
||||
enableButtons([load_model_accept]);
|
||||
selected_item = 0;
|
||||
length = $("#oaimodel")[0].options.length;
|
||||
for (let i = 0; i < length; i++) {
|
||||
|
|
@ -2820,6 +2961,7 @@ $(document).ready(function(){
|
|||
$("#showmodelnamecontainer").removeClass("hidden");
|
||||
} else if(msg.cmd == 'hide_model_name') {
|
||||
$("#showmodelnamecontainer").addClass("hidden");
|
||||
location.reload();
|
||||
//console.log("Closing window");
|
||||
} else if(msg.cmd == 'model_load_status') {
|
||||
$("#showmodelnamecontent").html("<div class=\"flex\"><div class=\"loadlistpadding\"></div><div class=\"loadlistitem\" style='align: left'>" + msg.data + "</div></div>");
|
||||
|
|
@ -2833,7 +2975,18 @@ $(document).ready(function(){
|
|||
opt.innerHTML = engine[1];
|
||||
$("#oaimodel")[0].appendChild(opt);
|
||||
}
|
||||
} else if(msg.cmd == 'showfieldbudget') {
|
||||
let inputElement = document.getElementById(msg.data.field);
|
||||
let tokenBudgetElement = inputElement.parentNode.getElementsByClassName("input-token-usage")[0];
|
||||
if (msg.data.max === null) {
|
||||
tokenBudgetElement.innerText = "";
|
||||
} else {
|
||||
let tokenLength = msg.data.length ?? "?";
|
||||
let tokenMax = msg.data.max ?? "?";
|
||||
tokenBudgetElement.innerText = `${tokenLength}/${tokenMax} Tokens`;
|
||||
}
|
||||
}
|
||||
enableButtons([load_model_accept]);
|
||||
});
|
||||
|
||||
socket.on('disconnect', function() {
|
||||
|
|
@ -2954,7 +3107,7 @@ $(document).ready(function(){
|
|||
});
|
||||
|
||||
button_actretry.on("click", function(ev) {
|
||||
ignore_stream = false;
|
||||
beginStream();
|
||||
hideMessage();
|
||||
socket.send({'cmd': 'retry', 'chatname': chatmode ? chat_name.val() : undefined, 'data': ''});
|
||||
hidegenseqs();
|
||||
|
|
@ -3201,7 +3354,7 @@ $(document).ready(function(){
|
|||
});
|
||||
|
||||
rs_accept.on("click", function(ev) {
|
||||
ignore_stream = false;
|
||||
beginStream();
|
||||
hideMessage();
|
||||
socket.send({'cmd': 'rndgame', 'memory': $("#rngmemory").val(), 'data': topic.val()});
|
||||
hideRandomStoryPopup();
|
||||
|
|
@ -3301,6 +3454,15 @@ $(document).ready(function(){
|
|||
|
||||
if (handled) ev.preventDefault();
|
||||
});
|
||||
|
||||
$("#anotetemplate").on("input", function() {
|
||||
updateInputBudget(anote_input[0]);
|
||||
})
|
||||
|
||||
registerTokenCounters();
|
||||
|
||||
updateInputBudget(input_text[0]);
|
||||
|
||||
});
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1647,4 +1647,78 @@ body.connected .popupfooter, .popupfooter.always-available {
|
|||
.breadcrumbitem:hover {
|
||||
cursor: pointer;
|
||||
background-color: #688f1f;
|
||||
}
|
||||
|
||||
#token_prob_menu {
|
||||
color: white;
|
||||
background-color: #262626;
|
||||
}
|
||||
|
||||
.token-probs {
|
||||
display: inline-block;
|
||||
text-align: center;
|
||||
margin-right: 5px;
|
||||
}
|
||||
|
||||
.token-probs > table {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
.token-probs > table > tbody > tr > td {
|
||||
border: 1px solid #262626;
|
||||
border-collapse: collapse;
|
||||
padding: 2px 15px;
|
||||
}
|
||||
|
||||
.token-probs > table > tbody > tr {
|
||||
background-color: #3e3e3e;
|
||||
}
|
||||
|
||||
.token-probs > table > tbody > tr:nth-child(2n) {
|
||||
background-color: #575757;
|
||||
}
|
||||
|
||||
.token-probs-final-token {
|
||||
font-weight: bold;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
.token-probs-final-token > td {
|
||||
background: #5c8a5a;
|
||||
}
|
||||
|
||||
.token-probs-header {
|
||||
display: block;
|
||||
}
|
||||
|
||||
#token_prob_container {
|
||||
overflow-x: auto;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.tokens-counted {
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.input-token-usage {
|
||||
color: white;
|
||||
position: absolute;
|
||||
font-size: 10px;
|
||||
bottom: 2px;
|
||||
right: 5px;
|
||||
|
||||
-webkit-user-select: none;
|
||||
-moz-user-select: none;
|
||||
-ms-user-select: none;
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
/* Override needed here due to the 10px right padding on inputrowleft; add 10 px. */
|
||||
#inputrowleft > .input-token-usage {
|
||||
right: 15px;
|
||||
bottom: 1px;
|
||||
}
|
||||
|
||||
.wientry > .input-token-usage {
|
||||
bottom: 8px;
|
||||
}
|
||||
|
|
@ -0,0 +1,202 @@
|
|||
|
||||
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|
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|
||||
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/*!
|
||||
* MIT License
|
||||
*
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* Copyright (c) 2020 Romans Pokrovskis
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a { color: #8c8cfa; }
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::-webkit-scrollbar-track-piece { background-color: rgba(255, 255, 255, .2) !important; }
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::-webkit-scrollbar-track { background-color: rgba(255, 255, 255, .3) !important; }
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||||
overflow-y: scroll;
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|
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body {
|
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background: #1f1f1f;
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background-color: #1f1f1f;
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background-image: none !important;
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}
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button, input, select, textarea {
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background-color: #1f1f1f;
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color: #bfbfbf;
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}
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font, html { color: #bfbfbf; }
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.swagger-ui, .swagger-ui section h3 { color: #b5bac9; }
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.swagger-ui a { background-color: transparent; }
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.swagger-ui mark {
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background-color: #664b00;
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color: #bfbfbf;
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}
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.swagger-ui .debug-grid-16 { background: url(data:image/png;base64,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) 0 0; }
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|
||||
.swagger-ui .debug-grid-8-solid { background: url(data:image/jpeg;base64,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) 0 0 #1c1c21; }
|
||||
|
||||
.swagger-ui .debug-grid-16-solid { background: url(data:image/png;base64,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) 0 0 #1c1c21; }
|
||||
|
||||
.swagger-ui .b--black { border-color: #000; }
|
||||
|
||||
.swagger-ui .b--near-black { border-color: #121212; }
|
||||
|
||||
.swagger-ui .b--dark-gray { border-color: #333; }
|
||||
|
||||
.swagger-ui .b--mid-gray { border-color: #545454; }
|
||||
|
||||
.swagger-ui .b--gray { border-color: #787878; }
|
||||
|
||||
.swagger-ui .b--silver { border-color: #999; }
|
||||
|
||||
.swagger-ui .b--light-silver { border-color: #6e6e6e; }
|
||||
|
||||
.swagger-ui .b--moon-gray { border-color: #4d4d4d; }
|
||||
|
||||
.swagger-ui .b--light-gray { border-color: #2b2b2b; }
|
||||
|
||||
.swagger-ui .b--near-white { border-color: #242424; }
|
||||
|
||||
.swagger-ui .b--white { border-color: #1c1c21; }
|
||||
|
||||
.swagger-ui .b--white-90 { border-color: rgba(28, 28, 33, .9); }
|
||||
|
||||
.swagger-ui .b--white-80 { border-color: rgba(28, 28, 33, .8); }
|
||||
|
||||
.swagger-ui .b--white-70 { border-color: rgba(28, 28, 33, .7); }
|
||||
|
||||
.swagger-ui .b--white-60 { border-color: rgba(28, 28, 33, .6); }
|
||||
|
||||
.swagger-ui .b--white-50 { border-color: rgba(28, 28, 33, .5); }
|
||||
|
||||
.swagger-ui .b--white-40 { border-color: rgba(28, 28, 33, .4); }
|
||||
|
||||
.swagger-ui .b--white-30 { border-color: rgba(28, 28, 33, .3); }
|
||||
|
||||
.swagger-ui .b--white-20 { border-color: rgba(28, 28, 33, .2); }
|
||||
|
||||
.swagger-ui .b--white-10 { border-color: rgba(28, 28, 33, .1); }
|
||||
|
||||
.swagger-ui .b--white-05 { border-color: rgba(28, 28, 33, .05); }
|
||||
|
||||
.swagger-ui .b--white-025 { border-color: rgba(28, 28, 33, .024); }
|
||||
|
||||
.swagger-ui .b--white-0125 { border-color: rgba(28, 28, 33, .01); }
|
||||
|
||||
.swagger-ui .b--black-90 { border-color: rgba(0, 0, 0, .9); }
|
||||
|
||||
.swagger-ui .b--black-80 { border-color: rgba(0, 0, 0, .8); }
|
||||
|
||||
.swagger-ui .b--black-70 { border-color: rgba(0, 0, 0, .7); }
|
||||
|
||||
.swagger-ui .b--black-60 { border-color: rgba(0, 0, 0, .6); }
|
||||
|
||||
.swagger-ui .b--black-50 { border-color: rgba(0, 0, 0, .5); }
|
||||
|
||||
.swagger-ui .b--black-40 { border-color: rgba(0, 0, 0, .4); }
|
||||
|
||||
.swagger-ui .b--black-30 { border-color: rgba(0, 0, 0, .3); }
|
||||
|
||||
.swagger-ui .b--black-20 { border-color: rgba(0, 0, 0, .2); }
|
||||
|
||||
.swagger-ui .b--black-10 { border-color: rgba(0, 0, 0, .1); }
|
||||
|
||||
.swagger-ui .b--black-05 { border-color: rgba(0, 0, 0, .05); }
|
||||
|
||||
.swagger-ui .b--black-025 { border-color: rgba(0, 0, 0, .024); }
|
||||
|
||||
.swagger-ui .b--black-0125 { border-color: rgba(0, 0, 0, .01); }
|
||||
|
||||
.swagger-ui .b--dark-red { border-color: #bc2f36; }
|
||||
|
||||
.swagger-ui .b--red { border-color: #c83932; }
|
||||
|
||||
.swagger-ui .b--light-red { border-color: #ab3c2b; }
|
||||
|
||||
.swagger-ui .b--orange { border-color: #cc6e33; }
|
||||
|
||||
.swagger-ui .b--purple { border-color: #5e2ca5; }
|
||||
|
||||
.swagger-ui .b--light-purple { border-color: #672caf; }
|
||||
|
||||
.swagger-ui .b--dark-pink { border-color: #ab2b81; }
|
||||
|
||||
.swagger-ui .b--hot-pink { border-color: #c03086; }
|
||||
|
||||
.swagger-ui .b--pink { border-color: #8f2464; }
|
||||
|
||||
.swagger-ui .b--light-pink { border-color: #721d4d; }
|
||||
|
||||
.swagger-ui .b--dark-green { border-color: #1c6e50; }
|
||||
|
||||
.swagger-ui .b--green { border-color: #279b70; }
|
||||
|
||||
.swagger-ui .b--light-green { border-color: #228762; }
|
||||
|
||||
.swagger-ui .b--navy { border-color: #0d1d35; }
|
||||
|
||||
.swagger-ui .b--dark-blue { border-color: #20497e; }
|
||||
|
||||
.swagger-ui .b--blue { border-color: #4380d0; }
|
||||
|
||||
.swagger-ui .b--light-blue { border-color: #20517e; }
|
||||
|
||||
.swagger-ui .b--lightest-blue { border-color: #143a52; }
|
||||
|
||||
.swagger-ui .b--washed-blue { border-color: #0c312d; }
|
||||
|
||||
.swagger-ui .b--washed-green { border-color: #0f3d2c; }
|
||||
|
||||
.swagger-ui .b--washed-red { border-color: #411010; }
|
||||
|
||||
.swagger-ui .b--transparent { border-color: transparent; }
|
||||
|
||||
.swagger-ui .b--gold, .swagger-ui .b--light-yellow, .swagger-ui .b--washed-yellow, .swagger-ui .b--yellow { border-color: #664b00; }
|
||||
|
||||
.swagger-ui .shadow-1 { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
|
||||
|
||||
.swagger-ui .shadow-2 { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
|
||||
|
||||
.swagger-ui .shadow-3 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
|
||||
|
||||
.swagger-ui .shadow-4 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
|
||||
|
||||
.swagger-ui .shadow-5 { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
|
||||
|
||||
@media screen and (min-width: 30em) {
|
||||
.swagger-ui .shadow-1-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
|
||||
|
||||
.swagger-ui .shadow-2-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
|
||||
|
||||
.swagger-ui .shadow-3-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
|
||||
|
||||
.swagger-ui .shadow-4-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
|
||||
|
||||
.swagger-ui .shadow-5-ns { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
|
||||
}
|
||||
|
||||
@media screen and (max-width: 60em) and (min-width: 30em) {
|
||||
.swagger-ui .shadow-1-m { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
|
||||
|
||||
.swagger-ui .shadow-2-m { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
|
||||
|
||||
.swagger-ui .shadow-3-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
|
||||
|
||||
.swagger-ui .shadow-4-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
|
||||
|
||||
.swagger-ui .shadow-5-m { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
|
||||
}
|
||||
|
||||
@media screen and (min-width: 60em) {
|
||||
.swagger-ui .shadow-1-l { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; }
|
||||
|
||||
.swagger-ui .shadow-2-l { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; }
|
||||
|
||||
.swagger-ui .shadow-3-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; }
|
||||
|
||||
.swagger-ui .shadow-4-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; }
|
||||
|
||||
.swagger-ui .shadow-5-l { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; }
|
||||
}
|
||||
|
||||
.swagger-ui .black-05 { color: rgba(191, 191, 191, .05); }
|
||||
|
||||
.swagger-ui .bg-black-05 { background-color: rgba(0, 0, 0, .05); }
|
||||
|
||||
.swagger-ui .black-90, .swagger-ui .hover-black-90:focus, .swagger-ui .hover-black-90:hover { color: rgba(191, 191, 191, .9); }
|
||||
|
||||
.swagger-ui .black-80, .swagger-ui .hover-black-80:focus, .swagger-ui .hover-black-80:hover { color: rgba(191, 191, 191, .8); }
|
||||
|
||||
.swagger-ui .black-70, .swagger-ui .hover-black-70:focus, .swagger-ui .hover-black-70:hover { color: rgba(191, 191, 191, .7); }
|
||||
|
||||
.swagger-ui .black-60, .swagger-ui .hover-black-60:focus, .swagger-ui .hover-black-60:hover { color: rgba(191, 191, 191, .6); }
|
||||
|
||||
.swagger-ui .black-50, .swagger-ui .hover-black-50:focus, .swagger-ui .hover-black-50:hover { color: rgba(191, 191, 191, .5); }
|
||||
|
||||
.swagger-ui .black-40, .swagger-ui .hover-black-40:focus, .swagger-ui .hover-black-40:hover { color: rgba(191, 191, 191, .4); }
|
||||
|
||||
.swagger-ui .black-30, .swagger-ui .hover-black-30:focus, .swagger-ui .hover-black-30:hover { color: rgba(191, 191, 191, .3); }
|
||||
|
||||
.swagger-ui .black-20, .swagger-ui .hover-black-20:focus, .swagger-ui .hover-black-20:hover { color: rgba(191, 191, 191, .2); }
|
||||
|
||||
.swagger-ui .black-10, .swagger-ui .hover-black-10:focus, .swagger-ui .hover-black-10:hover { color: rgba(191, 191, 191, .1); }
|
||||
|
||||
.swagger-ui .hover-white-90:focus, .swagger-ui .hover-white-90:hover, .swagger-ui .white-90 { color: rgba(255, 255, 255, .9); }
|
||||
|
||||
.swagger-ui .hover-white-80:focus, .swagger-ui .hover-white-80:hover, .swagger-ui .white-80 { color: rgba(255, 255, 255, .8); }
|
||||
|
||||
.swagger-ui .hover-white-70:focus, .swagger-ui .hover-white-70:hover, .swagger-ui .white-70 { color: rgba(255, 255, 255, .7); }
|
||||
|
||||
.swagger-ui .hover-white-60:focus, .swagger-ui .hover-white-60:hover, .swagger-ui .white-60 { color: rgba(255, 255, 255, .6); }
|
||||
|
||||
.swagger-ui .hover-white-50:focus, .swagger-ui .hover-white-50:hover, .swagger-ui .white-50 { color: rgba(255, 255, 255, .5); }
|
||||
|
||||
.swagger-ui .hover-white-40:focus, .swagger-ui .hover-white-40:hover, .swagger-ui .white-40 { color: rgba(255, 255, 255, .4); }
|
||||
|
||||
.swagger-ui .hover-white-30:focus, .swagger-ui .hover-white-30:hover, .swagger-ui .white-30 { color: rgba(255, 255, 255, .3); }
|
||||
|
||||
.swagger-ui .hover-white-20:focus, .swagger-ui .hover-white-20:hover, .swagger-ui .white-20 { color: rgba(255, 255, 255, .2); }
|
||||
|
||||
.swagger-ui .hover-white-10:focus, .swagger-ui .hover-white-10:hover, .swagger-ui .white-10 { color: rgba(255, 255, 255, .1); }
|
||||
|
||||
.swagger-ui .hover-moon-gray:focus, .swagger-ui .hover-moon-gray:hover, .swagger-ui .moon-gray { color: #ccc; }
|
||||
|
||||
.swagger-ui .hover-light-gray:focus, .swagger-ui .hover-light-gray:hover, .swagger-ui .light-gray { color: #ededed; }
|
||||
|
||||
.swagger-ui .hover-near-white:focus, .swagger-ui .hover-near-white:hover, .swagger-ui .near-white { color: #f5f5f5; }
|
||||
|
||||
.swagger-ui .dark-red, .swagger-ui .hover-dark-red:focus, .swagger-ui .hover-dark-red:hover { color: #e6999d; }
|
||||
|
||||
.swagger-ui .hover-red:focus, .swagger-ui .hover-red:hover, .swagger-ui .red { color: #e69d99; }
|
||||
|
||||
.swagger-ui .hover-light-red:focus, .swagger-ui .hover-light-red:hover, .swagger-ui .light-red { color: #e6a399; }
|
||||
|
||||
.swagger-ui .hover-orange:focus, .swagger-ui .hover-orange:hover, .swagger-ui .orange { color: #e6b699; }
|
||||
|
||||
.swagger-ui .gold, .swagger-ui .hover-gold:focus, .swagger-ui .hover-gold:hover { color: #e6d099; }
|
||||
|
||||
.swagger-ui .hover-yellow:focus, .swagger-ui .hover-yellow:hover, .swagger-ui .yellow { color: #e6da99; }
|
||||
|
||||
.swagger-ui .hover-light-yellow:focus, .swagger-ui .hover-light-yellow:hover, .swagger-ui .light-yellow { color: #ede6b6; }
|
||||
|
||||
.swagger-ui .hover-purple:focus, .swagger-ui .hover-purple:hover, .swagger-ui .purple { color: #b99ae4; }
|
||||
|
||||
.swagger-ui .hover-light-purple:focus, .swagger-ui .hover-light-purple:hover, .swagger-ui .light-purple { color: #bb99e6; }
|
||||
|
||||
.swagger-ui .dark-pink, .swagger-ui .hover-dark-pink:focus, .swagger-ui .hover-dark-pink:hover { color: #e699cc; }
|
||||
|
||||
.swagger-ui .hot-pink, .swagger-ui .hover-hot-pink:focus, .swagger-ui .hover-hot-pink:hover, .swagger-ui .hover-pink:focus, .swagger-ui .hover-pink:hover, .swagger-ui .pink { color: #e699c7; }
|
||||
|
||||
.swagger-ui .hover-light-pink:focus, .swagger-ui .hover-light-pink:hover, .swagger-ui .light-pink { color: #edb6d5; }
|
||||
|
||||
.swagger-ui .dark-green, .swagger-ui .green, .swagger-ui .hover-dark-green:focus, .swagger-ui .hover-dark-green:hover, .swagger-ui .hover-green:focus, .swagger-ui .hover-green:hover { color: #99e6c9; }
|
||||
|
||||
.swagger-ui .hover-light-green:focus, .swagger-ui .hover-light-green:hover, .swagger-ui .light-green { color: #a1e8ce; }
|
||||
|
||||
.swagger-ui .hover-navy:focus, .swagger-ui .hover-navy:hover, .swagger-ui .navy { color: #99b8e6; }
|
||||
|
||||
.swagger-ui .blue, .swagger-ui .dark-blue, .swagger-ui .hover-blue:focus, .swagger-ui .hover-blue:hover, .swagger-ui .hover-dark-blue:focus, .swagger-ui .hover-dark-blue:hover { color: #99bae6; }
|
||||
|
||||
.swagger-ui .hover-light-blue:focus, .swagger-ui .hover-light-blue:hover, .swagger-ui .light-blue { color: #a9cbea; }
|
||||
|
||||
.swagger-ui .hover-lightest-blue:focus, .swagger-ui .hover-lightest-blue:hover, .swagger-ui .lightest-blue { color: #d6e9f5; }
|
||||
|
||||
.swagger-ui .hover-washed-blue:focus, .swagger-ui .hover-washed-blue:hover, .swagger-ui .washed-blue { color: #f7fdfc; }
|
||||
|
||||
.swagger-ui .hover-washed-green:focus, .swagger-ui .hover-washed-green:hover, .swagger-ui .washed-green { color: #ebfaf4; }
|
||||
|
||||
.swagger-ui .hover-washed-yellow:focus, .swagger-ui .hover-washed-yellow:hover, .swagger-ui .washed-yellow { color: #fbf9ef; }
|
||||
|
||||
.swagger-ui .hover-washed-red:focus, .swagger-ui .hover-washed-red:hover, .swagger-ui .washed-red { color: #f9e7e7; }
|
||||
|
||||
.swagger-ui .color-inherit, .swagger-ui .hover-inherit:focus, .swagger-ui .hover-inherit:hover { color: inherit; }
|
||||
|
||||
.swagger-ui .bg-black-90, .swagger-ui .hover-bg-black-90:focus, .swagger-ui .hover-bg-black-90:hover { background-color: rgba(0, 0, 0, .9); }
|
||||
|
||||
.swagger-ui .bg-black-80, .swagger-ui .hover-bg-black-80:focus, .swagger-ui .hover-bg-black-80:hover { background-color: rgba(0, 0, 0, .8); }
|
||||
|
||||
.swagger-ui .bg-black-70, .swagger-ui .hover-bg-black-70:focus, .swagger-ui .hover-bg-black-70:hover { background-color: rgba(0, 0, 0, .7); }
|
||||
|
||||
.swagger-ui .bg-black-60, .swagger-ui .hover-bg-black-60:focus, .swagger-ui .hover-bg-black-60:hover { background-color: rgba(0, 0, 0, .6); }
|
||||
|
||||
.swagger-ui .bg-black-50, .swagger-ui .hover-bg-black-50:focus, .swagger-ui .hover-bg-black-50:hover { background-color: rgba(0, 0, 0, .5); }
|
||||
|
||||
.swagger-ui .bg-black-40, .swagger-ui .hover-bg-black-40:focus, .swagger-ui .hover-bg-black-40:hover { background-color: rgba(0, 0, 0, .4); }
|
||||
|
||||
.swagger-ui .bg-black-30, .swagger-ui .hover-bg-black-30:focus, .swagger-ui .hover-bg-black-30:hover { background-color: rgba(0, 0, 0, .3); }
|
||||
|
||||
.swagger-ui .bg-black-20, .swagger-ui .hover-bg-black-20:focus, .swagger-ui .hover-bg-black-20:hover { background-color: rgba(0, 0, 0, .2); }
|
||||
|
||||
.swagger-ui .bg-white-90, .swagger-ui .hover-bg-white-90:focus, .swagger-ui .hover-bg-white-90:hover { background-color: rgba(28, 28, 33, .9); }
|
||||
|
||||
.swagger-ui .bg-white-80, .swagger-ui .hover-bg-white-80:focus, .swagger-ui .hover-bg-white-80:hover { background-color: rgba(28, 28, 33, .8); }
|
||||
|
||||
.swagger-ui .bg-white-70, .swagger-ui .hover-bg-white-70:focus, .swagger-ui .hover-bg-white-70:hover { background-color: rgba(28, 28, 33, .7); }
|
||||
|
||||
.swagger-ui .bg-white-60, .swagger-ui .hover-bg-white-60:focus, .swagger-ui .hover-bg-white-60:hover { background-color: rgba(28, 28, 33, .6); }
|
||||
|
||||
.swagger-ui .bg-white-50, .swagger-ui .hover-bg-white-50:focus, .swagger-ui .hover-bg-white-50:hover { background-color: rgba(28, 28, 33, .5); }
|
||||
|
||||
.swagger-ui .bg-white-40, .swagger-ui .hover-bg-white-40:focus, .swagger-ui .hover-bg-white-40:hover { background-color: rgba(28, 28, 33, .4); }
|
||||
|
||||
.swagger-ui .bg-white-30, .swagger-ui .hover-bg-white-30:focus, .swagger-ui .hover-bg-white-30:hover { background-color: rgba(28, 28, 33, .3); }
|
||||
|
||||
.swagger-ui .bg-white-20, .swagger-ui .hover-bg-white-20:focus, .swagger-ui .hover-bg-white-20:hover { background-color: rgba(28, 28, 33, .2); }
|
||||
|
||||
.swagger-ui .bg-black, .swagger-ui .hover-bg-black:focus, .swagger-ui .hover-bg-black:hover { background-color: #000; }
|
||||
|
||||
.swagger-ui .bg-near-black, .swagger-ui .hover-bg-near-black:focus, .swagger-ui .hover-bg-near-black:hover { background-color: #121212; }
|
||||
|
||||
.swagger-ui .bg-dark-gray, .swagger-ui .hover-bg-dark-gray:focus, .swagger-ui .hover-bg-dark-gray:hover { background-color: #333; }
|
||||
|
||||
.swagger-ui .bg-mid-gray, .swagger-ui .hover-bg-mid-gray:focus, .swagger-ui .hover-bg-mid-gray:hover { background-color: #545454; }
|
||||
|
||||
.swagger-ui .bg-gray, .swagger-ui .hover-bg-gray:focus, .swagger-ui .hover-bg-gray:hover { background-color: #787878; }
|
||||
|
||||
.swagger-ui .bg-silver, .swagger-ui .hover-bg-silver:focus, .swagger-ui .hover-bg-silver:hover { background-color: #999; }
|
||||
|
||||
.swagger-ui .bg-white, .swagger-ui .hover-bg-white:focus, .swagger-ui .hover-bg-white:hover { background-color: #1c1c21; }
|
||||
|
||||
.swagger-ui .bg-transparent, .swagger-ui .hover-bg-transparent:focus, .swagger-ui .hover-bg-transparent:hover { background-color: transparent; }
|
||||
|
||||
.swagger-ui .bg-dark-red, .swagger-ui .hover-bg-dark-red:focus, .swagger-ui .hover-bg-dark-red:hover { background-color: #bc2f36; }
|
||||
|
||||
.swagger-ui .bg-red, .swagger-ui .hover-bg-red:focus, .swagger-ui .hover-bg-red:hover { background-color: #c83932; }
|
||||
|
||||
.swagger-ui .bg-light-red, .swagger-ui .hover-bg-light-red:focus, .swagger-ui .hover-bg-light-red:hover { background-color: #ab3c2b; }
|
||||
|
||||
.swagger-ui .bg-orange, .swagger-ui .hover-bg-orange:focus, .swagger-ui .hover-bg-orange:hover { background-color: #cc6e33; }
|
||||
|
||||
.swagger-ui .bg-gold, .swagger-ui .bg-light-yellow, .swagger-ui .bg-washed-yellow, .swagger-ui .bg-yellow, .swagger-ui .hover-bg-gold:focus, .swagger-ui .hover-bg-gold:hover, .swagger-ui .hover-bg-light-yellow:focus, .swagger-ui .hover-bg-light-yellow:hover, .swagger-ui .hover-bg-washed-yellow:focus, .swagger-ui .hover-bg-washed-yellow:hover, .swagger-ui .hover-bg-yellow:focus, .swagger-ui .hover-bg-yellow:hover { background-color: #664b00; }
|
||||
|
||||
.swagger-ui .bg-purple, .swagger-ui .hover-bg-purple:focus, .swagger-ui .hover-bg-purple:hover { background-color: #5e2ca5; }
|
||||
|
||||
.swagger-ui .bg-light-purple, .swagger-ui .hover-bg-light-purple:focus, .swagger-ui .hover-bg-light-purple:hover { background-color: #672caf; }
|
||||
|
||||
.swagger-ui .bg-dark-pink, .swagger-ui .hover-bg-dark-pink:focus, .swagger-ui .hover-bg-dark-pink:hover { background-color: #ab2b81; }
|
||||
|
||||
.swagger-ui .bg-hot-pink, .swagger-ui .hover-bg-hot-pink:focus, .swagger-ui .hover-bg-hot-pink:hover { background-color: #c03086; }
|
||||
|
||||
.swagger-ui .bg-pink, .swagger-ui .hover-bg-pink:focus, .swagger-ui .hover-bg-pink:hover { background-color: #8f2464; }
|
||||
|
||||
.swagger-ui .bg-light-pink, .swagger-ui .hover-bg-light-pink:focus, .swagger-ui .hover-bg-light-pink:hover { background-color: #721d4d; }
|
||||
|
||||
.swagger-ui .bg-dark-green, .swagger-ui .hover-bg-dark-green:focus, .swagger-ui .hover-bg-dark-green:hover { background-color: #1c6e50; }
|
||||
|
||||
.swagger-ui .bg-green, .swagger-ui .hover-bg-green:focus, .swagger-ui .hover-bg-green:hover { background-color: #279b70; }
|
||||
|
||||
.swagger-ui .bg-light-green, .swagger-ui .hover-bg-light-green:focus, .swagger-ui .hover-bg-light-green:hover { background-color: #228762; }
|
||||
|
||||
.swagger-ui .bg-navy, .swagger-ui .hover-bg-navy:focus, .swagger-ui .hover-bg-navy:hover { background-color: #0d1d35; }
|
||||
|
||||
.swagger-ui .bg-dark-blue, .swagger-ui .hover-bg-dark-blue:focus, .swagger-ui .hover-bg-dark-blue:hover { background-color: #20497e; }
|
||||
|
||||
.swagger-ui .bg-blue, .swagger-ui .hover-bg-blue:focus, .swagger-ui .hover-bg-blue:hover { background-color: #4380d0; }
|
||||
|
||||
.swagger-ui .bg-light-blue, .swagger-ui .hover-bg-light-blue:focus, .swagger-ui .hover-bg-light-blue:hover { background-color: #20517e; }
|
||||
|
||||
.swagger-ui .bg-lightest-blue, .swagger-ui .hover-bg-lightest-blue:focus, .swagger-ui .hover-bg-lightest-blue:hover { background-color: #143a52; }
|
||||
|
||||
.swagger-ui .bg-washed-blue, .swagger-ui .hover-bg-washed-blue:focus, .swagger-ui .hover-bg-washed-blue:hover { background-color: #0c312d; }
|
||||
|
||||
.swagger-ui .bg-washed-green, .swagger-ui .hover-bg-washed-green:focus, .swagger-ui .hover-bg-washed-green:hover { background-color: #0f3d2c; }
|
||||
|
||||
.swagger-ui .bg-washed-red, .swagger-ui .hover-bg-washed-red:focus, .swagger-ui .hover-bg-washed-red:hover { background-color: #411010; }
|
||||
|
||||
.swagger-ui .bg-inherit, .swagger-ui .hover-bg-inherit:focus, .swagger-ui .hover-bg-inherit:hover { background-color: inherit; }
|
||||
|
||||
.swagger-ui .shadow-hover { transition: all .5s cubic-bezier(.165, .84, .44, 1) 0s; }
|
||||
|
||||
.swagger-ui .shadow-hover::after {
|
||||
border-radius: inherit;
|
||||
box-shadow: rgba(0, 0, 0, .2) 0 0 16px 2px;
|
||||
content: "";
|
||||
height: 100%;
|
||||
left: 0;
|
||||
opacity: 0;
|
||||
position: absolute;
|
||||
top: 0;
|
||||
transition: opacity .5s cubic-bezier(.165, .84, .44, 1) 0s;
|
||||
width: 100%;
|
||||
z-index: -1;
|
||||
}
|
||||
|
||||
.swagger-ui .bg-animate, .swagger-ui .bg-animate:focus, .swagger-ui .bg-animate:hover { transition: background-color .15s ease-in-out 0s; }
|
||||
|
||||
.swagger-ui .nested-links a {
|
||||
color: #99bae6;
|
||||
transition: color .15s ease-in 0s;
|
||||
}
|
||||
|
||||
.swagger-ui .nested-links a:focus, .swagger-ui .nested-links a:hover {
|
||||
color: #a9cbea;
|
||||
transition: color .15s ease-in 0s;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock-tag {
|
||||
border-bottom: 1px solid rgba(58, 64, 80, .3);
|
||||
color: #b5bac9;
|
||||
transition: all .2s ease 0s;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock-tag svg, .swagger-ui section.models h4 svg { transition: all .4s ease 0s; }
|
||||
|
||||
.swagger-ui .opblock {
|
||||
border: 1px solid #000;
|
||||
border-radius: 4px;
|
||||
box-shadow: rgba(0, 0, 0, .19) 0 0 3px;
|
||||
margin: 0 0 15px;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock .tab-header .tab-item.active h4 span::after { background: gray; }
|
||||
|
||||
.swagger-ui .opblock.is-open .opblock-summary { border-bottom: 1px solid #000; }
|
||||
|
||||
.swagger-ui .opblock .opblock-section-header {
|
||||
background: rgba(28, 28, 33, .8);
|
||||
box-shadow: rgba(0, 0, 0, .1) 0 1px 2px;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock .opblock-section-header > label > span { padding: 0 10px 0 0; }
|
||||
|
||||
.swagger-ui .opblock .opblock-summary-method {
|
||||
background: #000;
|
||||
color: #fff;
|
||||
text-shadow: rgba(0, 0, 0, .1) 0 1px 0;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-post {
|
||||
background: rgba(72, 203, 144, .1);
|
||||
border-color: #48cb90;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-post .opblock-summary-method, .swagger-ui .opblock.opblock-post .tab-header .tab-item.active h4 span::after { background: #48cb90; }
|
||||
|
||||
.swagger-ui .opblock.opblock-post .opblock-summary { border-color: #48cb90; }
|
||||
|
||||
.swagger-ui .opblock.opblock-put {
|
||||
background: rgba(213, 157, 88, .1);
|
||||
border-color: #d59d58;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-put .opblock-summary-method, .swagger-ui .opblock.opblock-put .tab-header .tab-item.active h4 span::after { background: #d59d58; }
|
||||
|
||||
.swagger-ui .opblock.opblock-put .opblock-summary { border-color: #d59d58; }
|
||||
|
||||
.swagger-ui .opblock.opblock-delete {
|
||||
background: rgba(200, 50, 50, .1);
|
||||
border-color: #c83232;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-delete .opblock-summary-method, .swagger-ui .opblock.opblock-delete .tab-header .tab-item.active h4 span::after { background: #c83232; }
|
||||
|
||||
.swagger-ui .opblock.opblock-delete .opblock-summary { border-color: #c83232; }
|
||||
|
||||
.swagger-ui .opblock.opblock-get {
|
||||
background: rgba(42, 105, 167, .1);
|
||||
border-color: #2a69a7;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-get .opblock-summary-method, .swagger-ui .opblock.opblock-get .tab-header .tab-item.active h4 span::after { background: #2a69a7; }
|
||||
|
||||
.swagger-ui .opblock.opblock-get .opblock-summary { border-color: #2a69a7; }
|
||||
|
||||
.swagger-ui .opblock.opblock-patch {
|
||||
background: rgba(92, 214, 188, .1);
|
||||
border-color: #5cd6bc;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-patch .opblock-summary-method, .swagger-ui .opblock.opblock-patch .tab-header .tab-item.active h4 span::after { background: #5cd6bc; }
|
||||
|
||||
.swagger-ui .opblock.opblock-patch .opblock-summary { border-color: #5cd6bc; }
|
||||
|
||||
.swagger-ui .opblock.opblock-head {
|
||||
background: rgba(140, 63, 207, .1);
|
||||
border-color: #8c3fcf;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-head .opblock-summary-method, .swagger-ui .opblock.opblock-head .tab-header .tab-item.active h4 span::after { background: #8c3fcf; }
|
||||
|
||||
.swagger-ui .opblock.opblock-head .opblock-summary { border-color: #8c3fcf; }
|
||||
|
||||
.swagger-ui .opblock.opblock-options {
|
||||
background: rgba(36, 89, 143, .1);
|
||||
border-color: #24598f;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-options .opblock-summary-method, .swagger-ui .opblock.opblock-options .tab-header .tab-item.active h4 span::after { background: #24598f; }
|
||||
|
||||
.swagger-ui .opblock.opblock-options .opblock-summary { border-color: #24598f; }
|
||||
|
||||
.swagger-ui .opblock.opblock-deprecated {
|
||||
background: rgba(46, 46, 46, .1);
|
||||
border-color: #2e2e2e;
|
||||
opacity: .6;
|
||||
}
|
||||
|
||||
.swagger-ui .opblock.opblock-deprecated .opblock-summary-method, .swagger-ui .opblock.opblock-deprecated .tab-header .tab-item.active h4 span::after { background: #2e2e2e; }
|
||||
|
||||
.swagger-ui .opblock.opblock-deprecated .opblock-summary { border-color: #2e2e2e; }
|
||||
|
||||
.swagger-ui .filter .operation-filter-input { border: 2px solid #2b3446; }
|
||||
|
||||
.swagger-ui .tab li:first-of-type::after { background: rgba(0, 0, 0, .2); }
|
||||
|
||||
.swagger-ui .download-contents {
|
||||
background: #7c8192;
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.swagger-ui .scheme-container {
|
||||
background: #1c1c21;
|
||||
box-shadow: rgba(0, 0, 0, .15) 0 1px 2px 0;
|
||||
}
|
||||
|
||||
.swagger-ui .loading-container .loading::before {
|
||||
animation: 1s linear 0s infinite normal none running rotation, .5s ease 0s 1 normal none running opacity;
|
||||
border-color: rgba(0, 0, 0, .6) rgba(84, 84, 84, .1) rgba(84, 84, 84, .1);
|
||||
}
|
||||
|
||||
.swagger-ui .response-control-media-type--accept-controller select { border-color: #196619; }
|
||||
|
||||
.swagger-ui .response-control-media-type__accept-message { color: #99e699; }
|
||||
|
||||
.swagger-ui .version-pragma__message code { background-color: #3b3b3b; }
|
||||
|
||||
.swagger-ui .btn {
|
||||
background: 0 0;
|
||||
border: 2px solid gray;
|
||||
box-shadow: rgba(0, 0, 0, .1) 0 1px 2px;
|
||||
color: #b5bac9;
|
||||
}
|
||||
|
||||
.swagger-ui .btn:hover { box-shadow: rgba(0, 0, 0, .3) 0 0 5px; }
|
||||
|
||||
.swagger-ui .btn.authorize, .swagger-ui .btn.cancel {
|
||||
background-color: transparent;
|
||||
border-color: #a72a2a;
|
||||
color: #e69999;
|
||||
}
|
||||
|
||||
.swagger-ui .btn.authorize {
|
||||
border-color: #48cb90;
|
||||
color: #9ce3c3;
|
||||
}
|
||||
|
||||
.swagger-ui .btn.authorize svg { fill: #9ce3c3; }
|
||||
|
||||
.swagger-ui .btn.execute {
|
||||
background-color: #5892d5;
|
||||
border-color: #5892d5;
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.swagger-ui .copy-to-clipboard { background: #7c8192; }
|
||||
|
||||
.swagger-ui .copy-to-clipboard button { background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"16\" height=\"16\" aria-hidden=\"true\"><path fill=\"%23fff\" fill-rule=\"evenodd\" d=\"M2 13h4v1H2v-1zm5-6H2v1h5V7zm2 3V8l-3 3 3 3v-2h5v-2H9zM4.5 9H2v1h2.5V9zM2 12h2.5v-1H2v1zm9 1h1v2c-.02.28-.11.52-.3.7-.19.18-.42.28-.7.3H1c-.55 0-1-.45-1-1V4c0-.55.45-1 1-1h3c0-1.11.89-2 2-2 1.11 0 2 .89 2 2h3c.55 0 1 .45 1 1v5h-1V6H1v9h10v-2zM2 5h8c0-.55-.45-1-1-1H8c-.55 0-1-.45-1-1s-.45-1-1-1-1 .45-1 1-.45 1-1 1H3c-.55 0-1 .45-1 1z\"/></svg>") 50% center no-repeat; }
|
||||
|
||||
.swagger-ui select {
|
||||
background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 20 20\"><path d=\"M13.418 7.859a.695.695 0 01.978 0 .68.68 0 010 .969l-3.908 3.83a.697.697 0 01-.979 0l-3.908-3.83a.68.68 0 010-.969.695.695 0 01.978 0L10 11l3.418-3.141z\"/></svg>") right 10px center/20px no-repeat #212121;
|
||||
background: url(data:image/svg+xml;base64,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) right 10px center/20px no-repeat #1c1c21;
|
||||
border: 2px solid #41444e;
|
||||
}
|
||||
|
||||
.swagger-ui select[multiple] { background: #212121; }
|
||||
|
||||
.swagger-ui button.invalid, .swagger-ui input[type=email].invalid, .swagger-ui input[type=file].invalid, .swagger-ui input[type=password].invalid, .swagger-ui input[type=search].invalid, .swagger-ui input[type=text].invalid, .swagger-ui select.invalid, .swagger-ui textarea.invalid {
|
||||
background: #390e0e;
|
||||
border-color: #c83232;
|
||||
}
|
||||
|
||||
.swagger-ui input[type=email], .swagger-ui input[type=file], .swagger-ui input[type=password], .swagger-ui input[type=search], .swagger-ui input[type=text], .swagger-ui textarea {
|
||||
background: #1c1c21;
|
||||
border: 1px solid #404040;
|
||||
}
|
||||
|
||||
.swagger-ui textarea {
|
||||
background: rgba(28, 28, 33, .8);
|
||||
color: #b5bac9;
|
||||
}
|
||||
|
||||
.swagger-ui input[disabled], .swagger-ui select[disabled] {
|
||||
background-color: #1f1f1f;
|
||||
color: #bfbfbf;
|
||||
}
|
||||
|
||||
.swagger-ui textarea[disabled] {
|
||||
background-color: #41444e;
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.swagger-ui select[disabled] { border-color: #878787; }
|
||||
|
||||
.swagger-ui textarea:focus { border: 2px solid #2a69a7; }
|
||||
|
||||
.swagger-ui .checkbox input[type=checkbox] + label > .item {
|
||||
background: #303030;
|
||||
box-shadow: #303030 0 0 0 2px;
|
||||
}
|
||||
|
||||
.swagger-ui .checkbox input[type=checkbox]:checked + label > .item { background: url("data:image/svg+xml;charset=utf-8,<svg width=\"10\" height=\"8\" viewBox=\"3 7 10 8\" xmlns=\"http://www.w3.org/2000/svg\"><path fill=\"%2341474E\" fill-rule=\"evenodd\" d=\"M6.333 15L3 11.667l1.333-1.334 2 2L11.667 7 13 8.333z\"/></svg>") 50% center no-repeat #303030; }
|
||||
|
||||
.swagger-ui .dialog-ux .backdrop-ux { background: rgba(0, 0, 0, .8); }
|
||||
|
||||
.swagger-ui .dialog-ux .modal-ux {
|
||||
background: #1c1c21;
|
||||
border: 1px solid #2e2e2e;
|
||||
box-shadow: rgba(0, 0, 0, .2) 0 10px 30px 0;
|
||||
}
|
||||
|
||||
.swagger-ui .dialog-ux .modal-ux-header .close-modal { background: 0 0; }
|
||||
|
||||
.swagger-ui .model .deprecated span, .swagger-ui .model .deprecated td { color: #bfbfbf !important; }
|
||||
|
||||
.swagger-ui .model-toggle::after { background: url("data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"24\" height=\"24\"><path d=\"M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z\"/></svg>") 50% center/100% no-repeat; }
|
||||
|
||||
.swagger-ui .model-hint {
|
||||
background: rgba(0, 0, 0, .7);
|
||||
color: #ebebeb;
|
||||
}
|
||||
|
||||
.swagger-ui section.models { border: 1px solid rgba(58, 64, 80, .3); }
|
||||
|
||||
.swagger-ui section.models.is-open h4 { border-bottom: 1px solid rgba(58, 64, 80, .3); }
|
||||
|
||||
.swagger-ui section.models .model-container { background: rgba(0, 0, 0, .05); }
|
||||
|
||||
.swagger-ui section.models .model-container:hover { background: rgba(0, 0, 0, .07); }
|
||||
|
||||
.swagger-ui .model-box { background: rgba(0, 0, 0, .1); }
|
||||
|
||||
.swagger-ui .prop-type { color: #aaaad4; }
|
||||
|
||||
.swagger-ui table thead tr td, .swagger-ui table thead tr th {
|
||||
border-bottom: 1px solid rgba(58, 64, 80, .2);
|
||||
color: #b5bac9;
|
||||
}
|
||||
|
||||
.swagger-ui .parameter__name.required::after { color: rgba(230, 153, 153, .6); }
|
||||
|
||||
.swagger-ui .topbar .download-url-wrapper .select-label { color: #f0f0f0; }
|
||||
|
||||
.swagger-ui .topbar .download-url-wrapper .download-url-button {
|
||||
background: #63a040;
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.swagger-ui .info .title small { background: #7c8492; }
|
||||
|
||||
.swagger-ui .info .title small.version-stamp { background-color: #7a9b27; }
|
||||
|
||||
.swagger-ui .auth-container .errors {
|
||||
background-color: #350d0d;
|
||||
color: #b5bac9;
|
||||
}
|
||||
|
||||
.swagger-ui .errors-wrapper {
|
||||
background: rgba(200, 50, 50, .1);
|
||||
border: 2px solid #c83232;
|
||||
}
|
||||
|
||||
.swagger-ui .markdown code, .swagger-ui .renderedmarkdown code {
|
||||
background: rgba(0, 0, 0, .05);
|
||||
color: #c299e6;
|
||||
}
|
||||
|
||||
.swagger-ui .model-toggle:after { background: url(data:image/svg+xml;base64,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) 50% no-repeat; }
|
||||
|
||||
.swagger-ui .expand-operation svg, .swagger-ui section.models h4 svg { fill: #fff; }
|
||||
|
||||
::-webkit-scrollbar-track { background-color: #646464 !important; }
|
||||
|
||||
::-webkit-scrollbar-thumb {
|
||||
background-color: #242424 !important;
|
||||
border: 2px solid #3e4346 !important;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button:vertical:start:decrement {
|
||||
background: linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%), linear-gradient(230deg, #696969 40%, transparent 41%), linear-gradient(0deg, #696969 40%, transparent 31%);
|
||||
background-color: #b6b6b6;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button:vertical:end:increment {
|
||||
background: linear-gradient(310deg, #696969 40%, transparent 41%), linear-gradient(50deg, #696969 40%, transparent 41%), linear-gradient(180deg, #696969 40%, transparent 31%);
|
||||
background-color: #b6b6b6;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button:horizontal:end:increment {
|
||||
background: linear-gradient(210deg, #696969 40%, transparent 41%), linear-gradient(330deg, #696969 40%, transparent 41%), linear-gradient(90deg, #696969 30%, transparent 31%);
|
||||
background-color: #b6b6b6;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button:horizontal:start:decrement {
|
||||
background: linear-gradient(30deg, #696969 40%, transparent 41%), linear-gradient(150deg, #696969 40%, transparent 41%), linear-gradient(270deg, #696969 30%, transparent 31%);
|
||||
background-color: #b6b6b6;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button, ::-webkit-scrollbar-track-piece { background-color: #3e4346 !important; }
|
||||
|
||||
.swagger-ui .black, .swagger-ui .checkbox, .swagger-ui .dark-gray, .swagger-ui .download-url-wrapper .loading, .swagger-ui .errors-wrapper .errors small, .swagger-ui .fallback, .swagger-ui .filter .loading, .swagger-ui .gray, .swagger-ui .hover-black:focus, .swagger-ui .hover-black:hover, .swagger-ui .hover-dark-gray:focus, .swagger-ui .hover-dark-gray:hover, .swagger-ui .hover-gray:focus, .swagger-ui .hover-gray:hover, .swagger-ui .hover-light-silver:focus, .swagger-ui .hover-light-silver:hover, .swagger-ui .hover-mid-gray:focus, .swagger-ui .hover-mid-gray:hover, .swagger-ui .hover-near-black:focus, .swagger-ui .hover-near-black:hover, .swagger-ui .hover-silver:focus, .swagger-ui .hover-silver:hover, .swagger-ui .light-silver, .swagger-ui .markdown pre, .swagger-ui .mid-gray, .swagger-ui .model .property, .swagger-ui .model .property.primitive, .swagger-ui .model-title, .swagger-ui .near-black, .swagger-ui .parameter__extension, .swagger-ui .parameter__in, .swagger-ui .prop-format, .swagger-ui .renderedmarkdown pre, .swagger-ui .response-col_links .response-undocumented, .swagger-ui .response-col_status .response-undocumented, .swagger-ui .silver, .swagger-ui section.models h4, .swagger-ui section.models h5, .swagger-ui span.token-not-formatted, .swagger-ui span.token-string, .swagger-ui table.headers .header-example, .swagger-ui table.model tr.description, .swagger-ui table.model tr.extension { color: #bfbfbf; }
|
||||
|
||||
.swagger-ui .hover-white:focus, .swagger-ui .hover-white:hover, .swagger-ui .info .title small pre, .swagger-ui .topbar a, .swagger-ui .white { color: #fff; }
|
||||
|
||||
.swagger-ui .bg-black-10, .swagger-ui .hover-bg-black-10:focus, .swagger-ui .hover-bg-black-10:hover, .swagger-ui .stripe-dark:nth-child(2n + 1) { background-color: rgba(0, 0, 0, .1); }
|
||||
|
||||
.swagger-ui .bg-white-10, .swagger-ui .hover-bg-white-10:focus, .swagger-ui .hover-bg-white-10:hover, .swagger-ui .stripe-light:nth-child(2n + 1) { background-color: rgba(28, 28, 33, .1); }
|
||||
|
||||
.swagger-ui .bg-light-silver, .swagger-ui .hover-bg-light-silver:focus, .swagger-ui .hover-bg-light-silver:hover, .swagger-ui .striped--light-silver:nth-child(2n + 1) { background-color: #6e6e6e; }
|
||||
|
||||
.swagger-ui .bg-moon-gray, .swagger-ui .hover-bg-moon-gray:focus, .swagger-ui .hover-bg-moon-gray:hover, .swagger-ui .striped--moon-gray:nth-child(2n + 1) { background-color: #4d4d4d; }
|
||||
|
||||
.swagger-ui .bg-light-gray, .swagger-ui .hover-bg-light-gray:focus, .swagger-ui .hover-bg-light-gray:hover, .swagger-ui .striped--light-gray:nth-child(2n + 1) { background-color: #2b2b2b; }
|
||||
|
||||
.swagger-ui .bg-near-white, .swagger-ui .hover-bg-near-white:focus, .swagger-ui .hover-bg-near-white:hover, .swagger-ui .striped--near-white:nth-child(2n + 1) { background-color: #242424; }
|
||||
|
||||
.swagger-ui .opblock-tag:hover, .swagger-ui section.models h4:hover { background: rgba(0, 0, 0, .02); }
|
||||
|
||||
.swagger-ui .checkbox p, .swagger-ui .dialog-ux .modal-ux-content h4, .swagger-ui .dialog-ux .modal-ux-content p, .swagger-ui .dialog-ux .modal-ux-header h3, .swagger-ui .errors-wrapper .errors h4, .swagger-ui .errors-wrapper hgroup h4, .swagger-ui .info .base-url, .swagger-ui .info .title, .swagger-ui .info h1, .swagger-ui .info h2, .swagger-ui .info h3, .swagger-ui .info h4, .swagger-ui .info h5, .swagger-ui .info li, .swagger-ui .info p, .swagger-ui .info table, .swagger-ui .loading-container .loading::after, .swagger-ui .model, .swagger-ui .opblock .opblock-section-header h4, .swagger-ui .opblock .opblock-section-header > label, .swagger-ui .opblock .opblock-summary-description, .swagger-ui .opblock .opblock-summary-operation-id, .swagger-ui .opblock .opblock-summary-path, .swagger-ui .opblock .opblock-summary-path__deprecated, .swagger-ui .opblock-description-wrapper, .swagger-ui .opblock-description-wrapper h4, .swagger-ui .opblock-description-wrapper p, .swagger-ui .opblock-external-docs-wrapper, .swagger-ui .opblock-external-docs-wrapper h4, .swagger-ui .opblock-external-docs-wrapper p, .swagger-ui .opblock-tag small, .swagger-ui .opblock-title_normal, .swagger-ui .opblock-title_normal h4, .swagger-ui .opblock-title_normal p, .swagger-ui .parameter__name, .swagger-ui .parameter__type, .swagger-ui .response-col_links, .swagger-ui .response-col_status, .swagger-ui .responses-inner h4, .swagger-ui .responses-inner h5, .swagger-ui .scheme-container .schemes > label, .swagger-ui .scopes h2, .swagger-ui .servers > label, .swagger-ui .tab li, .swagger-ui label, .swagger-ui select, .swagger-ui table.headers td { color: #b5bac9; }
|
||||
|
||||
.swagger-ui .download-url-wrapper .failed, .swagger-ui .filter .failed, .swagger-ui .model-deprecated-warning, .swagger-ui .parameter__deprecated, .swagger-ui .parameter__name.required span, .swagger-ui table.model tr.property-row .star { color: #e69999; }
|
||||
|
||||
.swagger-ui .opblock-body pre.microlight, .swagger-ui textarea.curl {
|
||||
background: #41444e;
|
||||
border-radius: 4px;
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.swagger-ui .expand-methods svg, .swagger-ui .expand-methods:hover svg { fill: #bfbfbf; }
|
||||
|
||||
.swagger-ui .auth-container, .swagger-ui .dialog-ux .modal-ux-header { border-bottom: 1px solid #2e2e2e; }
|
||||
|
||||
.swagger-ui .topbar .download-url-wrapper .select-label select, .swagger-ui .topbar .download-url-wrapper input[type=text] { border: 2px solid #63a040; }
|
||||
|
||||
.swagger-ui .info a, .swagger-ui .info a:hover, .swagger-ui .scopes h2 a { color: #99bde6; }
|
||||
|
||||
/* Dark Scrollbar */
|
||||
::-webkit-scrollbar {
|
||||
width: 14px;
|
||||
height: 14px;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button {
|
||||
background-color: #3e4346 !important;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-track {
|
||||
background-color: #646464 !important;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-track-piece {
|
||||
background-color: #3e4346 !important;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-thumb {
|
||||
height: 50px;
|
||||
background-color: #242424 !important;
|
||||
border: 2px solid #3e4346 !important;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-corner {}
|
||||
|
||||
::-webkit-resizer {}
|
||||
|
||||
::-webkit-scrollbar-button:vertical:start:decrement {
|
||||
background:
|
||||
linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%),
|
||||
linear-gradient(230deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
|
||||
linear-gradient(0deg, #696969 40%, rgba(0, 0, 0, 0) 31%);
|
||||
background-color: #b6b6b6;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button:vertical:end:increment {
|
||||
background:
|
||||
linear-gradient(310deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
|
||||
linear-gradient(50deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
|
||||
linear-gradient(180deg, #696969 40%, rgba(0, 0, 0, 0) 31%);
|
||||
background-color: #b6b6b6;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button:horizontal:end:increment {
|
||||
background:
|
||||
linear-gradient(210deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
|
||||
linear-gradient(330deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
|
||||
linear-gradient(90deg, #696969 30%, rgba(0, 0, 0, 0) 31%);
|
||||
background-color: #b6b6b6;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-button:horizontal:start:decrement {
|
||||
background:
|
||||
linear-gradient(30deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
|
||||
linear-gradient(150deg, #696969 40%, rgba(0, 0, 0, 0) 41%),
|
||||
linear-gradient(270deg, #696969 30%, rgba(0, 0, 0, 0) 31%);
|
||||
background-color: #b6b6b6;
|
||||
}
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
/*! Swagger UI 4.13.2 | https://swagger.io/tools/swagger-ui/ | Apache License 2.0 (license file can be found at ./LICENSE) */
|
||||
html {
|
||||
box-sizing: border-box;
|
||||
overflow: -moz-scrollbars-vertical;
|
||||
overflow-y: scroll;
|
||||
}
|
||||
|
||||
*,
|
||||
*:before,
|
||||
*:after {
|
||||
box-sizing: inherit;
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0;
|
||||
background: #fafafa;
|
||||
}
|
||||
|
|
@ -0,0 +1,79 @@
|
|||
<!doctype html>
|
||||
<html lang="en-US">
|
||||
<head>
|
||||
<title>Swagger UI: OAuth2 Redirect</title>
|
||||
</head>
|
||||
<body>
|
||||
<script>
|
||||
'use strict';
|
||||
function run () {
|
||||
var oauth2 = window.opener.swaggerUIRedirectOauth2;
|
||||
var sentState = oauth2.state;
|
||||
var redirectUrl = oauth2.redirectUrl;
|
||||
var isValid, qp, arr;
|
||||
|
||||
if (/code|token|error/.test(window.location.hash)) {
|
||||
qp = window.location.hash.substring(1);
|
||||
} else {
|
||||
qp = location.search.substring(1);
|
||||
}
|
||||
|
||||
arr = qp.split("&");
|
||||
arr.forEach(function (v,i,_arr) { _arr[i] = '"' + v.replace('=', '":"') + '"';});
|
||||
qp = qp ? JSON.parse('{' + arr.join() + '}',
|
||||
function (key, value) {
|
||||
return key === "" ? value : decodeURIComponent(value);
|
||||
}
|
||||
) : {};
|
||||
|
||||
isValid = qp.state === sentState;
|
||||
|
||||
if ((
|
||||
oauth2.auth.schema.get("flow") === "accessCode" ||
|
||||
oauth2.auth.schema.get("flow") === "authorizationCode" ||
|
||||
oauth2.auth.schema.get("flow") === "authorization_code"
|
||||
) && !oauth2.auth.code) {
|
||||
if (!isValid) {
|
||||
oauth2.errCb({
|
||||
authId: oauth2.auth.name,
|
||||
source: "auth",
|
||||
level: "warning",
|
||||
message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
|
||||
});
|
||||
}
|
||||
|
||||
if (qp.code) {
|
||||
delete oauth2.state;
|
||||
oauth2.auth.code = qp.code;
|
||||
oauth2.callback({auth: oauth2.auth, redirectUrl: redirectUrl});
|
||||
} else {
|
||||
let oauthErrorMsg;
|
||||
if (qp.error) {
|
||||
oauthErrorMsg = "["+qp.error+"]: " +
|
||||
(qp.error_description ? qp.error_description+ ". " : "no accessCode received from the server. ") +
|
||||
(qp.error_uri ? "More info: "+qp.error_uri : "");
|
||||
}
|
||||
|
||||
oauth2.errCb({
|
||||
authId: oauth2.auth.name,
|
||||
source: "auth",
|
||||
level: "error",
|
||||
message: oauthErrorMsg || "[Authorization failed]: no accessCode received from the server."
|
||||
});
|
||||
}
|
||||
} else {
|
||||
oauth2.callback({auth: oauth2.auth, token: qp, isValid: isValid, redirectUrl: redirectUrl});
|
||||
}
|
||||
window.close();
|
||||
}
|
||||
|
||||
if (document.readyState !== 'loading') {
|
||||
run();
|
||||
} else {
|
||||
document.addEventListener('DOMContentLoaded', function () {
|
||||
run();
|
||||
});
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -39,12 +39,8 @@
|
|||
<div class="collapse navbar-collapse" id="navbarNavDropdown">
|
||||
<ul class="nav navbar-nav">
|
||||
{% if not hide_ai_menu %}
|
||||
<li class="nav-item dropdown">
|
||||
<a class="nav-link dropdown-toggle" href="#" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">AI</a>
|
||||
<div class="dropdown-menu">
|
||||
<a class="dropdown-item" href="#" id="btn_loadmodel">Load Model</a>
|
||||
<a class="dropdown-item" href="#" id="btn_showmodel">Model Info</a>
|
||||
</div>
|
||||
<li class="nav-item">
|
||||
<a class="nav-link" href="#" id="btn_loadmodel">AI</a>
|
||||
</li>
|
||||
{% endif %}
|
||||
<li class="nav-item dropdown">
|
||||
|
|
@ -123,6 +119,11 @@
|
|||
</div>
|
||||
<div class="row" id="formatmenu">
|
||||
</div>
|
||||
|
||||
<div id="token_prob_menu" class="row hidden">
|
||||
<div id="token_prob_container"></div>
|
||||
</div>
|
||||
|
||||
<div class="layer-container">
|
||||
<div class="layer-bottom row" id="gamescreen">
|
||||
<span id="gametext" contenteditable="true"><p>...</p></span>
|
||||
|
|
@ -156,7 +157,7 @@
|
|||
<div id="inputrowmode">
|
||||
<button type="button" class="btn btn-secondary hidden" id="btnmode">Mode:<br/><b id="btnmode_label">Story</b></button>
|
||||
</div>
|
||||
<div id="inputrowleft">
|
||||
<div id="inputrowleft" class="tokens-counted">
|
||||
<textarea class="form-control" id="input_text" placeholder="Enter text here"></textarea>
|
||||
</div>
|
||||
<div id="inputrowright">
|
||||
|
|
@ -169,7 +170,7 @@
|
|||
<div class="anotelabel no-padding">
|
||||
Author's Note
|
||||
</div>
|
||||
<div class="anotefield">
|
||||
<div class="anotefield tokens-counted">
|
||||
<textarea class="form-control" placeholder="Author's Note" id="anoteinput"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
|
|
@ -291,7 +292,7 @@
|
|||
<div id="loadmodellistbreadcrumbs">
|
||||
|
||||
</div>
|
||||
<div id="loadmodellistcontent" style="overflow: scroll; height: 300px;">
|
||||
<div id="loadmodellistcontent" style="overflow: auto; height: 300px;">
|
||||
</div>
|
||||
<div class="popupfooter">
|
||||
<input class="form-control hidden" type="text" placeholder="key" id="modelkey" onblur="socket.send({'cmd': 'OAI_Key_Update', 'key': $('#modelkey')[0].value});">
|
||||
|
|
@ -443,10 +444,10 @@
|
|||
<div class="popuptitletext">Model Info</div>
|
||||
</div>
|
||||
<div id=showmodelnamecontent style="width:50%;">
|
||||
Read Only
|
||||
Model Info Missing
|
||||
</div>
|
||||
<div class="popupfooter" style="width:50% center;">
|
||||
<button type="button" class="btn btn-primary" onclick='$("#showmodelnamecontainer").addClass("hidden");'>OK</button>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -0,0 +1,35 @@
|
|||
{# This is the HTML template for Swagger UI (the GUI for the API documentation at /api/latest/docs) #}
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<title>KoboldAI API</title>
|
||||
<meta charset="UTF-8">
|
||||
<link rel="stylesheet" type="text/css" href="/static/swagger-ui/swagger-ui.css" />
|
||||
<link rel="stylesheet" type="text/css" href="/static/swagger-ui/index.css" />
|
||||
<script>
|
||||
if (window.matchMedia && window.matchMedia("(prefers-color-scheme: dark)").matches) document.write('<link rel="stylesheet" type="text/css" href="/static/swagger-ui/SwaggerDark.css" />');
|
||||
</script>
|
||||
</head>
|
||||
<body>
|
||||
<div id="swagger-ui"></div>
|
||||
<script src="/static/swagger-ui/swagger-ui-bundle.js" charset="UTF-8"></script>
|
||||
<script>
|
||||
window.onload = function() {
|
||||
window.ui = SwaggerUIBundle({
|
||||
url: "{{ url }}",
|
||||
oauth2RedirectUrl: "/static/swagger-ui/oauth2-redirect.html",
|
||||
dom_id: "#swagger-ui",
|
||||
deepLinking: true,
|
||||
defaultModelsExpandDepth: 0, // Causes the "Schemas" section at the bottom to be collapsed by default
|
||||
presets: [
|
||||
SwaggerUIBundle.presets.apis
|
||||
],
|
||||
plugins: [
|
||||
SwaggerUIBundle.plugins.DownloadUrl
|
||||
],
|
||||
layout: "BaseLayout"
|
||||
});
|
||||
};
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Loading…
Reference in New Issue