# sdwebuiapi **Repository Path**: tiyee/sdwebuiapi ## Basic Information - **Project Name**: sdwebuiapi - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-05-29 - **Last Updated**: 2024-05-29 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # **[sdwebuiapi](https://github.com/4927525/sdwebuiapi)** **基于gin+gorm+mysql+sd模型的Golang API客户端，可二开** ## 流程图 ![https://cdn.staticaly.com/gh/4927525/images@master/20230628/whiteboard_exported_image.xu44suwx6i8.jpg](https://cdn.staticaly.com/gh/4927525/images@master/20230628/whiteboard_exported_image.xu44suwx6i8.jpg) ## 后端项目架构 ```Bash ├── api (api层) │ └── v1 (v1版本接口) ├── cache (缓存包) ├── config (配置包) ├── dao (数据库操作层) ├── define (全局常量) ├── docs (swagger文档目录) ├── e (错误代码) ├── helper (助手方法包) ├── logs (日志文件) ├── middleware (中间件层) ├── model (模型层) ├── resource (静态资源库) ├── router (路由层) ├── serializer (序列化请求相应参数) ├── server (系统启动) ├── service (service层) ├── sql (sql表结构文件) ├── test (单元测试) └── utils (工具包) ├── jwt (JWT) ├── logger (日志包) └── translate (阿里云机翻翻译) ``` ## 数据库导入 如果是非docker部署的话，需要手动创建ces库，并导入sql/sd.sql文件 ## 项目运行 ### 普通运行 ```Bash go run main.go ``` ### 以二进制文件运行 ```Bash go mod tidy go build -o main ./main ``` ### docker运行 项目根目录内置了 Dockerfile、docker-compose.yml，目的是快速构建项目环境，简易化项目运行难度 ```bash docker-compose up -d ``` ## 主要功能 - 文生图 - 图生图 - 出图进度 - 局部重绘 - 切换pt风格 - 切换服务器模型 - 阿里云机翻 - OSS存储 - 异步队列 - 定时任务 - 失败重试 - 插队功能 - swagger ## 项目规划 - [ ] 加入kafka或是rabbitmq，出图结果异步回调通知服务器 - [ ] 抽离 service 的结构体到 types，引入 sync.Once 模块，重构 service 层 - [x] 加入docker-compose - [ ] 加入 Jaeger 进行链路追踪 - [ ] 加入 Skywalking 监控中间件 - [ ] 加入ELK体系，方便日志查看和管理 - [ ] 加入前端代码 - [x] base642url方法补充 - [x] 绘制图片进度 ## 生命周期 - 初始化redis、mysql、env配置类 - 初始化排队队列 - 初始化路由 - 绑定端口启动服务 ## **主要依赖** | 名称 | 版本 | | ----------------- | ------- | | golang | 1.20v | | gin | v1.9.0 | | gorm | v1.23.9 | | mysql | v1.3.6 | | redis | v6.15.9 | | jwt-go | v3.2.0 | | crypto | v0.8.0 | | logrus | v1.9.0 | | gin-swagger | v1.6.0 | | cron | v1.2.0 | | darabonba-openapi | v2.0.1 | ## 配置文件 ```YAML server: app: dev port: :8009 database: host: 127.0.0.1 port: 3306 user: root dbname: ces pwd: root redis: addr: 127.0.0.1:6379 pwd: root dbname: 0 ``` ## Postman导入 ```bash { "info": { "_postman_id": "13f1f635-8514-4ec7-b68d-64d9cc628951", "name": "sddemo", "schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json", "_exporter_id": "5152396" }, "item": [ { "name": "txt2img", "request": { "method": "POST", "header": [], "body": { "mode": "raw", "raw": "{\r\n \"prompts\": \"((masterpiece)), (((best quality))), ((ultra-detailed)), ((illustration)), dusk, in the cyberpunk city, girl, veil, hair_ornament, artbook, real, realistic, earrings, black choker ,shackles, crop top,\",\r\n \"size\": 1,\r\n \"model_id\":1\r\n}", "options": { "raw": { "language": "json" } } }, "url": { "raw": "http://127.0.0.1:8009/api/v1/sdwebuiapi/txt2img", "protocol": "http", "host": [ "127", "0", "0", "1" ], "port": "8009", "path": [ "api", "v1", "sdwebuiapi", "txt2img" ] } }, "response": [] }, { "name": "img2img", "request": { "method": "POST", "header": [], "body": { "mode": "raw", "raw": "{\r\n \"prompts\": \"color purple\",\r\n \"model_id\": 1,\r\n \"denoising_strength\":0.25,\r\n \"inpainting_fill\":0,\r\n \"size\": 3,\r\n // \"mask_url\":\"xxx\",\r\n \"original_url\":\"xxxx\"\r\n}", "options": { "raw": { "language": "json" } } }, "url": { "raw": "http://127.0.0.1:8009/api/v1/sdwebuiapi/img2img", "protocol": "http", "host": [ "127", "0", "0", "1" ], "port": "8009", "path": [ "api", "v1", "sdwebuiapi", "img2img" ] } }, "response": [] }, { "name": "imgDetail", "request": { "method": "POST", "header": [], "body": { "mode": "raw", "raw": "{\r\n \"id\" : 20\r\n}", "options": { "raw": { "language": "json" } } }, "url": { "raw": "http://127.0.0.1:8009/api/v1/sdwebuiapi/imgDetail", "protocol": "http", "host": [ "127", "0", "0", "1" ], "port": "8009", "path": [ "api", "v1", "sdwebuiapi", "imgDetail" ] } }, "response": [] } ] } ``` ### txt2img ![https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.75mjde8x30w0.jpg](https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.75mjde8x30w0.jpg) ### img2img original_url：原始图片地址 mark_url：遮罩层图片地址，当inpainting_fill参数为1时生效 ![https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.35t3qkzz71c0.jpg](https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.35t3qkzz71c0.jpg) ### imgDetail(查看图片详情 、出图进度) #### 出图中 time字段为剩余出图秒数 ![https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.6jeta9cf9hw0.jpg](https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.6jeta9cf9hw0.jpg) #### 已出图 ![https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.5vfoepa99ak0.jpg](https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.5vfoepa99ak0.jpg) # API > 官网API介绍：https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API ## 原生API ### txt2img > 文生图 #### URI /sdapi/v1/txt2img #### 请求参数(JSON) ```JSON { "enable_hr": false, "denoising_strength": 0, "firstphase_width": 0, "firstphase_height": 0, "hr_scale": 2, "hr_upscaler": "string", "hr_second_pass_steps": 0, "hr_resize_x": 0, "hr_resize_y": 0, "prompt": "", "styles": [ "string" ], "seed": -1, "subseed": -1, "subseed_strength": 0, "seed_resize_from_h": -1, "seed_resize_from_w": -1, "sampler_name": "string", "batch_size": 1, "n_iter": 1, "steps": 50, "cfg_scale": 7, "width": 512, "height": 512, "restore_faces": false, "tiling": false, "negative_prompt": "string", "eta": 0, "s_churn": 0, "s_tmax": 0, "s_tmin": 0, "s_noise": 1, "override_settings": {}, "override_settings_restore_afterwards": true, "script_args": [], "sampler_index": "Euler", "script_name": "string" } ``` #### 请求参数说明 | **参数** | **类型** | **是否必填** | **备注** | | ------------------ | -------- | ------------ | ------------------------------------------------------------ | | prompt | string | Y | 正向提示词 | | negative_prompt | string | Y | 反向提示词 | | denoising_strength | float | Y | 重绘幅度(Denoising strength)，取值范围：0.00-1.00图像模仿自由度，越高越自由发挥，越低和参考图像越接近。 | | seed | int | Y | 种子随机数，默认-1 每次生成的不一样 | | cfg_scale | int | Y | 提示词相关性，默认7一般默认就好，如果提示词比较具体（又长又臭）就调到8、9试试，越低越有创造性 | | steps | int | Y | 降噪步骤数，推荐 50更多的去噪步骤通常会导致更高质量的图像 较慢的推理费用 | | sampler_index | string | Y | 采样器索引 | | sampler_name | string | Y | 采样器名称 | | batch_size | int | Y | 生成批次 | | n_iter | int | Y | 每批数量 | | tiling | boolean | Y | 用于生成一个可以平铺的图像。 | | width | int | Y | 宽度 | | height | int | Y | 高度 | | restore_faces | boolean | Y | 面部修复 | #### 响应参数(JSON) ```Bash { "images":[ "iVBORw0KGgoAAAA..." // 不带base64头的图片字符串 ], "info":{ "all_prompts":null, "all_seeds":null, "all_subseeds":null, "batch_size":0, "cfg_scale":0, "clip_skip":0, "denoising_strength":0, "extra_generation_params":{ }, "face_restoration_model":null, "height":0, "index_of_first_image":0, "infotexts":null, "job_timestamp":"", "negative_prompt":"", "prompt":"", "restore_faces":false, "sampler":"", "sampler_index":0, "sd_model_hash":"", "seed":0, "seed_resize_from_h":0, "seed_resize_from_w":0, "steps":0, "styles":null, "subseed":0, "subseed_strength":0, "width":0 }, "parameters":{ "batch_size":1, "cfg_scale":0, "denoising_strength":0, "enable_hr":false, "eta":0, "firstphase_height":0, "firstphase_width":0, "height":768, "n_iter":1, "negative_prompt":"", "override_settings":{ }, "prompt":"8k,((masterpiece)), (((best quality))), ((ultra-detailed)), ((illustration)), dusk, in the cyberpunk city, girl, veil, hair_ornament, artbook, real, realistic, earrings, black choker ,shackles, crop top,", "restore_faces":false, "s_churn":0, "s_noise":0, "s_tmax":0, "s_tmin":0, "sampler_index":"Euler", "seed":-1, "seed_resize_from_h":-1, "seed_resize_from_w":-1, "steps":45, "styles":null, "subseed":-1, "subseed_strength":0, "tiling":false, "width":1024 } } ``` #### 响应参数说明 ##### images | **参数** | **类型** | **是否必填** | **备注** | | ------------ | -------- | ------------ | ---------------------------------- | | images | list | Y | 生成的图片list | | images.index | string | Y | 生成的图片base64字符串不带base64头 | ##### paramenters | **参数** | **类型** | **是否必填** | **备注** | | ------------------ | -------- | ------------ | ------------------------------------------------------------ | | prompt | string | Y | 正向提示词 | | negative_prompt | string | Y | 反向提示词 | | denoising_strength | float | Y | 重绘幅度(Denoising strength)，取值范围：0.00-1.00图像模仿自由度，越高越自由发挥，越低和参考图像越接近。 | | seed | int | Y | 种子随机数，默认-1 每次生成的不一样 | | cfg_scale | int | Y | 提示词相关性，默认7一般默认就好，如果提示词比较具体（又长又臭）就调到8、9试试，越低越有创造性 | | steps | int | Y | 降噪步骤数，推荐 50更多的去噪步骤通常会导致更高质量的图像 较慢的推理费用 | | sampler_index | string | Y | 采样器索引 | | sampler_name | string | Y | 采样器名称 | | batch_size | int | Y | 生成批次 | | n_iter | int | Y | 每批数量 | | tiling | boolean | Y | 用于生成一个可以平铺的图像。 | | width | int | Y | 宽度 | | height | int | Y | 高度 | | restore_faces | boolean | Y | 面部修复 | ### img2img（inpaint） > 图生图（局部重绘） #### URI /sdapi/v1/img2img #### 请求参数(JSON) ```JSON { "init_images": [ "string" ], "resize_mode": 0, "denoising_strength": 0.75, "image_cfg_scale": 0, "mask": "string", "mask_blur": 4, "inpainting_fill": 0, // 是否重绘 "inpaint_full_res": true, "inpaint_full_res_padding": 0, "inpainting_mask_invert": 0, "initial_noise_multiplier": 0, "prompt": "", "styles": [ "string" ], "seed": -1, "subseed": -1, "subseed_strength": 0, "seed_resize_from_h": -1, "seed_resize_from_w": -1, "sampler_name": "string", "batch_size": 1, "n_iter": 1, "steps": 50, "cfg_scale": 7, "width": 512, "height": 512, "restore_faces": false, "tiling": false, "negative_prompt": "string", "eta": 0, "s_churn": 0, "s_tmax": 0, "s_tmin": 0, "s_noise": 1, "override_settings": {}, "override_settings_restore_afterwards": true, "script_args": [], "sampler_index": "Euler", "include_init_images": false, "script_name": "string" } ``` #### 请求参数说明 | **参数** | **类型** | **是否必填** | **备注** | | ------------------ | -------- | ------------ | ------------------------------------------------------------ | | prompt | string | Y | 正向提示词 | | negative_prompt | string | Y | 反向提示词 | | init_images | list | Y | 底图列表 | | init_images.index | string | Y | 底图base64字符串 | | resize_mode | int | Y | 缩放模式拉伸｜Just Resize简单的将图片缩放至指定比例，不保证原图尺寸比。裁剪｜Crop And Resize将图片按比例进行缩放，多余的直接裁剪掉。填充｜Resize And Fill将图片按比例进行缩放，缺少的部分填充。隐空间直接缩放｜Latent upScale其实与前面三个不同，这个是常用于图像超分辨率的快捷选项，低显存谨慎使用。 | | inpainting_fill | int | Y | 是否局部重绘0：否1：是 | | mask | string | Y | 遮罩层带base64头的base64字符串 | | denoising_strength | float | Y | 重绘幅度(Denoising strength)，取值范围：0.00-1.00图像模仿自由度，越高越自由发挥，越低和参考图像越接近。 | | seed | int | Y | 种子随机数，默认-1 每次生成的不一样 | | cfg_scale | int | Y | 提示词相关性，默认7一般默认就好，如果提示词比较具体（又长又臭）就调到8、9试试，越低越有创造性 | | steps | int | Y | 降噪步骤数，推荐 50更多的去噪步骤通常会导致更高质量的图像 较慢的推理费用 | | sampler_index | string | Y | 采样器索引 | | sampler_name | string | Y | 采样器名称 | | batch_size | int | Y | 生成批次 | | n_iter | int | Y | 每批数量 | | tiling | boolean | Y | 用于生成一个可以平铺的图像。 | | width | int | Y | 宽度 | | height | int | Y | 高度 | | restore_faces | boolean | Y | 面部修复 | #### 响应参数(JSON) ```Bash { "images":[ "iVBORw0KGgoAAAANSUhEUgAAAwAAAAM..." ], "info":"{\"prompt\": \"8k,color purple,\", \"all_prompts\": [\"8k,,color purple,\"], \"negative_prompt\": \"\", \"all_negative_prompts\": [\"\"], \"seed\": 1732878691, \"all_seeds\": [1732878691], \"subseed\": 3147614705, \"all_subseeds\": [3147614705], \"subseed_strength\": 0.0, \"width\": 768, \"height\": 768, \"sampler_name\": \"Euler\", \"cfg_scale\": 10.0, \"steps\": 45, \"batch_size\": 1, \"restore_faces\": false, \"face_restoration_model\": null, \"sd_model_hash\": \"d55fbed80e\", \"seed_resize_from_w\": -1, \"seed_resize_from_h\": -1, \"denoising_strength\": 0.25, \"extra_generation_params\": {}, \"index_of_first_image\": 0, \"infotexts\": [\"8k,,color purple,\\nSteps: 45, Sampler: Euler, CFG scale: 10.0, Seed: 1732878691, Size: 768x768, Model hash: d55fbed80e, Seed resize from: -1x-1, Denoising strength: 0.25, Clip skip: 2, ENSD: 31337\"], \"styles\": [], \"job_timestamp\": \"20230615161236\", \"clip_skip\": 2, \"is_using_inpainting_conditioning\": false}", "parameters":{ "batch_size":1, "cfg_scale":0, "denoising_strength":0.25, "eta":0, "height":768, "include_init_images":false, "init_images":null, "inpaint_full_res":true, "inpaint_full_res_padding":0, "inpainting_fill":0, "inpainting_mask_invert":0, "mask":null, "mask_blur":4, "n_iter":1, "negative_prompt":"", "override_settings":{ }, "prompt":"8k,color purple,", "resize_mode":1, "restore_faces":false, "s_churn":0, "s_noise":0, "s_tmax":0, "s_tmin":0, "sampler_index":"Euler", "seed":-1, "seed_resize_from_h":-1, "seed_resize_from_w":-1, "steps":45, "styles":[ ], "subseed":-1, "subseed_strength":0, "tiling":false, "width":768 } } ``` #### 响应参数说明 ##### images | **参数** | **类型** | **是否必填** | **备注** | | ------------ | -------- | ------------ | ---------------------------------- | | images | list | Y | 生成的图片list | | images.index | string | Y | 生成的图片base64字符串不带base64头 | ##### paramenters | **参数** | **类型** | **是否必填** | **备注** | | ------------------ | -------- | ------------ | ------------------------------------------------------------ | | prompt | string | Y | 正向提示词 | | negative_prompt | string | Y | 反向提示词 | | denoising_strength | float | Y | 重绘幅度(Denoising strength)，取值范围：0.00-1.00图像模仿自由度，越高越自由发挥，越低和参考图像越接近。 | | seed | int | Y | 种子随机数，默认-1 每次生成的不一样 | | cfg_scale | int | Y | 提示词相关性，默认7一般默认就好，如果提示词比较具体（又长又臭）就调到8、9试试，越低越有创造性 | | steps | int | Y | 降噪步骤数，推荐 50更多的去噪步骤通常会导致更高质量的图像 较慢的推理费用 | | sampler_index | string | Y | 采样器索引 | | sampler_name | string | Y | 采样器名称 | | batch_size | int | Y | 生成批次 | | n_iter | int | Y | 每批数量 | | tiling | boolean | Y | 用于生成一个可以平铺的图像。 | | width | int | Y | 宽度 | | height | int | Y | 高度 | | restore_faces | boolean | Y | 面部修复 | | inpainting_fill | int | Y | 是否局部重绘0：否1：是 | ## 应用API http://localhost:8009/swagger/index.html ![https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.1ivawpmyddnk.jpg](https://cdn.staticaly.com/gh/4927525/images@master/20230710/image.1ivawpmyddnk.jpg)