artdwn/stable-diffusion-backup

# Stable Diffusion web UI A browser interface based on Gradio library for Stable Diffusion.  ## Features [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): - Original txt2img and img2img modes - One click install and run script (but you still must install python and git) - Outpainting - Inpainting - Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - a man in a `((tuxedo))` - will pay more attention to tuxedo - a man in a `(tuxedo:1.21)` - alternative syntax - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection - Adjust sampler eta values (noise multiplier) - More advanced noise setting options - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches - Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings - drag and drop an image/text-parameters to promptbox - Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page - Running arbitrary python code from UI (must run with `--allow-code` to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Can use a separate neural network to produce previews with almost none VRAM or compute requirement - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences - Seed resizing, a way to generate same image but at slightly different resolution - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img - Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args) - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI - Generate forever option - Training tab - hypernetworks and embeddings options - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) - Clip skip - Hypernetworks - Loras (same as Hypernetworks but more pretty) - A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt - Can select to load a different VAE from settings screen - Estimated completion time in progress bar - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions - Now without any bad letters! - Load checkpoints in safetensors format - Eased resolution restriction: generated image's dimension must be a multiple of 8 rather than 64 - Now with a license! - Reorder elements in the UI from settings screen ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for: - [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) - [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. - [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page) Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) ### Installation on Windows 10/11 with NVidia-GPUs using release package 1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents. 2. Run `update.bat`. 3. Run `run.bat`. > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) ### Automatic Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH". 2. Install [git](https://git-scm.com/download/win). 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. ### Automatic Installation on Linux 1. Install the dependencies: ```bash # Debian-based: sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0 # Red Hat-based: sudo dnf install wget git python3 # Arch-based: sudo pacman -S wget git python3 ``` 2. Navigate to the directory you would like the webui to be installed and execute the following command: ```bash wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh ``` 3. Run `webui.sh`. 4. Check `webui-user.sh` for options. ### Installation on Apple Silicon Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). ## Contributing Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). ## Credits Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers - k-diffusion - https://github.com/crowsonkb/k-diffusion.git - GFPGAN - https://github.com/TencentARC/GFPGAN.git - CodeFormer - https://github.com/sczhou/CodeFormer - ESRGAN - https://github.com/xinntao/ESRGAN - SwinIR - https://github.com/JingyunLiang/SwinIR - Swin2SR - https://github.com/mv-lab/swin2sr - LDSR - https://github.com/Hafiidz/latent-diffusion - MiDaS - https://github.com/isl-org/MiDaS - Ideas for optimizations - https://github.com/basujindal/stable-diffusion - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) - Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - xformers - https://github.com/facebookresearch/xformers - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru - Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix - Security advice - RyotaK - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd - LyCORIS - KohakuBlueleaf - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You)

原始信息汇总

Stable Diffusion web UI 数据集概述

功能特点

• 原始模式：txt2img 和 img2img 模式
• 一键安装和运行脚本：需自行安装 Python 和 Git
• 图像处理：
  • 外绘（Outpainting）
  • 内绘（Inpainting）
  • 颜色草图（Color Sketch）
  • 提示矩阵（Prompt Matrix）
  • 稳定扩散放大（Stable Diffusion Upscale）
• 注意力控制：
  • 指定模型应更多关注的文本部分
  • 通过快捷键调整注意力
• 循环回传：多次运行 img2img 处理
• X/Y/Z 绘图：绘制三维参数图像
• 文本反转：
  • 自定义嵌入名称和数量
  • 支持半精度浮点数
  • 可在 8GB 或 6GB 内存上训练
• 额外选项卡：
  • GFPGAN：修复人脸的神经网络
  • CodeFormer：人脸修复工具
  • RealESRGAN 和 ESRGAN：神经网络放大器
  • SwinIR 和 Swin2SR：神经网络放大器
  • LDSR：潜在扩散超分辨率放大
• 调整选项：
  • 调整长宽比
  • 选择采样方法
  • 中断处理
  • 支持 4GB 或 2GB 显卡
• 生成参数：
  • 保存生成图像的参数
  • 拖放图像恢复参数
• 设置页面：
  • 运行任意 Python 代码
  • 鼠标悬停提示
  • 调整 UI 元素默认值
• 进度条和实时预览：
  • 使用单独的神经网络生成预览
• 负提示：指定不希望出现的图像特征
• 样式：保存并应用提示样式
• 变体：生成相似但略有不同的图像
• 种子调整：在不同分辨率下生成相似图像
• CLIP 审讯器：根据图像猜测提示
• 提示编辑：中途更改提示
• 批处理：使用 img2img 处理一组文件
• 高分辨率修复：一键生成高分辨率图像
• 动态加载检查点：合并最多三个检查点
• 自定义脚本：社区提供的扩展功能
• 组合扩散：同时使用多个提示
• 无令牌限制：支持超过 75 个令牌的提示
• DeepDanbooru 集成：为动漫提示生成标签
• xformers：提高特定显卡的速度
• 历史记录选项卡：在 UI 内查看和管理图像
• 生成永久选项：持续生成图像
• 训练选项卡：
  • 超网络和嵌入选项
  • 图像预处理
• Clip 跳过：超网络和 Loras
• 独立 UI：选择并预览嵌入、超网络或 Loras
• 不同 VAE 选择：从设置屏幕加载
• 完成时间估计：在进度条中显示
• API 支持：支持 RunwayML 的 inpainting 模型
• 美学梯度：通过 clip 图像嵌入生成特定美学图像
• Stable Diffusion 2.0 支持：详见维基
• Alt-Diffusion 支持：详见维基
• 加载 safetensors 格式的检查点：放宽分辨率限制
• 重新排序 UI 元素：从设置屏幕调整

安装和运行

• 依赖项：确保满足所有依赖项
• 平台支持：
  • NVidia（推荐）
  • AMD
  • Intel CPU 和 GPU
• 在线服务：使用 Google Colab 等在线服务

Windows 安装步骤

1. 下载并解压 sd.webui.zip
2. 运行 update.bat
3. 运行 run.bat

自动安装（Windows）

1. 安装 Python 3.10.6 并添加到 PATH
2. 安装 Git
3. 下载稳定扩散 web UI 仓库
4. 运行 webui-user.bat

自动安装（Linux）

1. 安装依赖项
2. 下载并运行 webui.sh
3. 检查 webui-user.sh 中的选项

Apple Silicon 安装

• 详见维基页面

搜集汇总

数据集介绍

构建方式

artdwn/stable-diffusion-backup 数据集并非传统意义上的标注数据集合，而是基于 Stable Diffusion Web UI 这一开源图形化界面工具的镜像备份。该工具由 Gradio 库构建，旨在为 Stable Diffusion 模型提供直观的浏览器交互界面。数据集的构建方式主要围绕对原始 Web UI 项目代码、配置、依赖及运行脚本的完整存档，确保用户能够复现原始环境。其核心在于整合了从文本到图像（txt2img）、图像到图像（img2img）等核心生成模式，并集成了多种图像修复、放大、面部优化等扩展功能模块，形成一个即开即用的综合生成平台。

特点

该数据集最显著的特点在于其高度的集成性与扩展性。它不仅支持基础的扩散模型推理，更囊括了诸如 GFPGAN、CodeFormer 等面部修复网络，以及 RealESRGAN、SwinIR 等多种超分辨率算法。此外，它提供了丰富的提示词控制机制，包括注意力调节、负向提示、提示矩阵与组合式扩散，允许用户精细调控生成内容。数据集还支持 Textual Inversion、Hypernetworks、LoRA 等轻量级微调技术，以及实时中断、种子控制、参数保存与恢复等实用功能，极大地降低了高级图像生成技术的使用门槛。

使用方法

使用该数据集时，用户需首先根据操作系统（Windows、Linux 或 macOS）安装 Python 与 Git 等基础依赖。随后，通过执行项目提供的自动安装脚本（如 webui-user.bat 或 webui.sh）即可完成环境搭建。启动后，用户通过浏览器访问本地 Gradio 界面，在文本框中输入正向与负向提示词，选择采样方法、分辨率等参数，点击生成按钮即可获得图像。数据集亦支持通过 API 进行程序化调用，便于集成到更复杂的自动化工作流中。对于高级用户，可通过修改配置文件或加载社区扩展脚本进一步定制功能。

背景与挑战

背景概述

Stable Diffusion web UI是由匿名开发者与社区共同构建的开源项目，诞生于2022年，旨在为Stable Diffusion模型提供直观的图形化交互界面。该工具基于Gradio库开发，集成了文本生成图像、图像修复、超分辨率重建等丰富功能，并支持Textual Inversion、LoRA等先进微调技术，大幅降低了扩散模型的使用门槛。其核心研究问题在于如何将复杂的生成式AI模型封装为易于操作的工具，同时保留灵活的定制能力。自发布以来，该界面迅速成为Stable Diffusion生态中最重要的基础设施之一，推动了生成式AI在艺术创作、设计等领域的普及，吸引了全球数以万计的研究者与爱好者参与贡献，对相关领域产生了深远影响。

当前挑战

该数据集及工具面临的核心挑战包括：首先，在领域问题层面，如何平衡生成图像的质量与计算资源消耗是一大难题，尤其是在低显存设备上实现高效推理；其次，多模态生成中语义对齐的精度不足，导致复杂提示词下的输出常出现逻辑矛盾；此外，构建过程中需解决跨平台兼容性问题，确保在Windows、Linux及Apple Silicon等异构环境下的稳定运行；同时，社区贡献的扩展模块质量参差不齐，维护统一接口与安全标准成为持续性挑战；最后，生成内容的伦理与版权风险监管机制尚不完善，为实际部署带来法律与道德层面的不确定性。

常用场景

经典使用场景

Stable Diffusion web UI 数据集最经典的使用场景在于为文本到图像（txt2img）和图像到图像（img2img）的生成任务提供直观的浏览器化交互界面。研究者与创作者可通过该平台便捷地探索扩散模型的生成能力，利用提示矩阵、注意力机制调节以及多种采样方法，对生成过程进行精细控制。该界面集成了图像修复、超分辨率重建、风格迁移等核心功能，使得复杂模型的操作门槛大幅降低，成为视觉内容生成领域广泛采用的实验与创作工具。

解决学术问题

该数据集有效解决了扩散模型在学术研究中面临的工程化与可复现性难题。它提供了统一的参数记录与图像元数据存储机制，使得每一次生成实验的配置和结果都能被完整保存与回溯，极大促进了研究过程的透明性与可验证性。此外，通过支持Textual Inversion、Hypernetworks、LoRA等轻量化微调技术，该平台为概念学习、风格迁移等前沿课题提供了便捷的实验平台，推动了生成模型在少样本学习与个性化生成方向的理论探索。

衍生相关工作

围绕该数据集衍生了一系列具有影响力的经典工作。其中，基于其插件架构开发的Aesthetic Gradients实现了图像美学的可控引导，Composable Diffusion探索了多提示联合生成的可能性，而Prompt Editing技术则开创了生成过程中语义动态切换的新范式。此外，社区贡献的xformers优化和多种自定义脚本，进一步催生了高效推理与功能扩展的研究方向，这些衍生工作共同构成了一个蓬勃发展的生态，持续推动着生成式AI技术的边界拓展。