Module
x/stable_diffusion_client/src/lib/StableDiffusionApi.ts
stable-diffusion-webui Client writen in Deno
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const createScriptsWithCnUnits = async ( initScripts: {} | undefined, controlNetUnit: ControlNetUnit[],) => { const promises = controlNetUnit.map(async (unit) => await unit.toJson()); const args = await Promise.all(promises); const ControlNet = { args }; const scripts = { ...initScripts, ControlNet }; return scripts;};
/** * @class StableDiffusionApi * @classdesc Stable Diffusion API, a translation layer for [Automatic1111's Stable Diffusion API](https://github.com/AUTOMATIC1111/stable-diffusion-webui) * @param {StableDiffusionApiConfig} config - Configuration object * @property {StableDiffusionApiConfig} config - Configuration object * @property {axios.AxiosInstance} api - Axios instance * @property {ControlNetApi} controlNet - ControlNet API * @example * const api = new StableDiffusionApi() * const result = await api.txt2img({ * prompt: "A computer that has more brain power than a human being", * batch_size: 2, * }) * * // Save the first image * result.image.toFile("result.png") * * // Save all images * result.images.forEach((image, i) => { * image.toFile(`result_${i}.png`) * }) */export default class StableDiffusionApi { private config: StableDiffusionApiConfig; private baseURL: URL; private basicAuth = "";
public ControlNet = new ControlNetApi(this);
public constructor({ host = "127.0.0.1", port = 7860, protocol = "http", timeout = 30000, baseUrl = null, defaultSampler = "Euler a", defaultStepCount = 20, }: StableDiffusionApiConfig = {}) { this.baseURL = new URL( baseUrl || `${protocol}://${host}${port ? `:${port}` : ""}`, ); this.config = { host, port, protocol, timeout, baseUrl, defaultSampler, defaultStepCount, }; }
/** * Set the authentication for the axios API * @param {string} username * @param {string} password * @returns {StableDiffusionApi} this StableDiffusionApi instance */ public setAuth(username: string, password: string): StableDiffusionApi { if (username && password) { this.basicAuth = "Basic" + encodeBase64(username + ":" + password); } return this; }
public async get<T>(uri: string): Promise<T> { const controller = new AbortController(); const headers: HeadersInit = { "Content-Type": "application/json", }; if (this.basicAuth) { headers.Authorization = this.basicAuth; } const options: RequestInit = { method: "GET", signal: controller.signal, headers: { "Content-Type": "application/json", }, }; const promise = fetch(new URL(uri, this.baseURL), options); const timeoutId = setTimeout(() => controller.abort(), this.config.timeout); const response = await promise; clearTimeout(timeoutId); // TODO handle error return response.json() as T; }
public async post<T>(uri: string, body?: unknown): Promise<T> { const controller = new AbortController(); const headers: HeadersInit = { "Content-Type": "application/json", }; if (this.basicAuth) { headers.Authorization = this.basicAuth; } const options: RequestInit = { method: "POST", signal: controller.signal, body: JSON.stringify(body), headers: { "Content-Type": "application/json", }, }; if (body !== undefined) options.body = JSON.stringify(body); const promise = fetch(new URL(uri, this.baseURL), options); const timeoutId = setTimeout(() => controller.abort(), this.config.timeout); const response = await promise; clearTimeout(timeoutId); // TODO handle error return response.json() as T; }
/** * Stable Diffusion txt2img call * @param {Txt2ImgOptions} options * @returns {Promise<StableDiffusionResult>} ApiResult containing the generated image(s) * @memberof StableDiffusionApi * @async * @example * const api = new StableDiffusionApi(); * const result = await api.txt2img({ * prompt: "An angry artist that claims that the Stable Diffusion model contains an exact copy of their artwork", * }); */ public async txt2img( options: Txt2ImgOptions, ): Promise<StableDiffusionResult<Txt2ImgOptions, Txt2ImgInfo>> { let alwayson_scripts = await createScriptsWithCnUnits( options.alwayson_scripts, options.controlnet_units ?? [], ); if (alwayson_scripts.ControlNet.args.length === 0) { alwayson_scripts = {} as any; } const response = await this.post<ApiRawResponse>("/sdapi/v1/txt2img", { enable_hr: options.enable_hr ?? false, hr_scale: options.hr_scale ?? 2, hr_upscaler: options.hr_upscaler ?? "Latent", hr_second_pass_steps: options.hr_second_pass_steps ?? 0, hr_resize_x: options.hr_resize_x ?? 0, hr_resize_y: options.hr_resize_y ?? 0, denoising_strength: options.denoising_strength ?? 0.7, firstphase_width: options.firstphase_width ?? 0, firstphase_height: options.firstphase_height ?? 0, prompt: options.prompt ?? "", styles: options.styles ?? [], seed: options.seed ?? -1, subseed: options.subseed ?? -1, subseed_strength: options.subseed_strength ?? 0.0, seed_resize_from_h: options.seed_resize_from_h ?? 0, seed_resize_from_w: options.seed_resize_from_w ?? 0, batch_size: options.batch_size ?? 1, n_iter: options.n_iter ?? 1, steps: options.steps ?? this.config.defaultStepCount, cfg_scale: options.cfg_scale ?? 7.0, width: options.width ?? 512, height: options.height ?? 512, restore_faces: options.restore_faces ?? false, tiling: options.tiling ?? false, do_not_save_samples: options.do_not_save_samples ?? false, do_not_save_grid: options.do_not_save_grid ?? false, negative_prompt: options.negative_prompt ?? "", eta: options.eta ?? 1.0, s_churn: options.s_churn ?? 0, s_tmax: options.s_tmax ?? 0, s_tmin: options.s_tmin ?? 0, s_noise: options.s_noise ?? 1, override_settings: options.override_settings ?? {}, override_settings_restore_afterwards: options.override_settings_restore_afterwards ?? true, script_args: options.script_args ?? [], script_name: options.script_name ?? null, send_images: options.send_images ?? true, save_images: options.save_images ?? false, alwayson_scripts, sampler_name: options.sampler_name ?? this.config.defaultSampler, use_deprecated_controlnet: options.use_deprecated_controlnet ?? false, }); return new StableDiffusionResult(response); }
/** * Stable Diffusion img2img call * @param {Img2ImgOptions} options Options for the img2img call * @returns {Promise<StableDiffusionResult>} ApiResult containing the generated image(s) * @memberof StableDiffusionApi * @async * @example * const api = new StableDiffusionApi(); * const init_image = sharp("dog.png"); * const result = await api.img2img({ * init_images: [init_image], * prompt: "Just a funky disco dog", * }); */ public async img2img( options: Img2ImgOptions, ): Promise<StableDiffusionResult<Img2ImgOptions, unknown>> { const init_images = await Promise.all( options.init_images.map(async (image) => await toBase64(image)), );
const mask = options.mask_image ? await toBase64(options.mask_image) : null;
const alwayson_scripts = await createScriptsWithCnUnits( options.alwayson_scripts, options.controlnet_units ?? [], );
const response = await this.post<ApiRawResponse>("/sdapi/v1/img2img", { init_images, resize_mode: options.resize_mode ?? 0, denoising_strength: options.denoising_strength ?? 0.75, image_cfg_scale: options.image_cfg_scale ?? 1.5, mask, mask_blur: options.mask_blur ?? 4, inpainting_fill: options.inpainting_fill ?? 0, inpaint_full_res: options.inpaint_full_res ?? true, inpaint_full_res_padding: options.inpaint_full_res_padding ?? 0, inpainting_mask_invert: options.inpainting_mask_invert ?? 0, initial_noise_multiplier: options.initial_noise_multiplier ?? 1, prompt: options.prompt ?? "", styles: options.styles ?? [], seed: options.seed ?? -1, subseed: options.subseed ?? -1, subseed_strength: options.subseed_strength ?? 0, seed_resize_from_h: options.seed_resize_from_h ?? 0, seed_resize_from_w: options.seed_resize_from_w ?? 0, sampler_name: options.sampler_name ?? this.config.defaultSampler, batch_size: options.batch_size ?? 1, n_iter: options.n_iter ?? 1, steps: options.steps ?? this.config.defaultStepCount, cfg_scale: options.cfg_scale ?? 7.0, width: options.width ?? 512, height: options.height ?? 512, restore_faces: options.restore_faces ?? false, tiling: options.tiling ?? false, do_not_save_samples: options.do_not_save_samples ?? false, do_not_save_grid: options.do_not_save_grid ?? false, negative_prompt: options.negative_prompt ?? "", eta: options.eta ?? 1.0, s_churn: options.s_churn ?? 0, s_tmax: options.s_tmax ?? 0, s_tmin: options.s_tmin ?? 0, s_noise: options.s_noise ?? 1, override_settings: options.override_settings ?? {}, override_settings_restore_afterwards: options.override_settings_restore_afterwards ?? true, script_args: options.script_args ?? [], include_init_images: options.include_init_images ?? false, script_name: options.script_name ?? null, send_images: options.send_images ?? true, save_images: options.save_images ?? false, alwayson_scripts, use_deprecated_controlnet: options.use_deprecated_controlnet ?? false, }); return new StableDiffusionResult(response); }
/** * Stable Diffusion extra's call for single images * @param {ExtraSingleOptions} options Options for the extra's call * @returns {Promise<StableDiffusionResult>} ApiResult containing the generated image(s) * @memberof StableDiffusionApi * @async * @example * const api = new StableDiffusionApi(); * const image = sharp("dog.png"); * const result = await api.extraSingle({ * image, * upscaler_1: "Lanczos", * upscaling_resize: 2, * }); */ public async extraSingle( options: ExtraSingleOptions, ): Promise<StableDiffusionResult<ExtraSingleOptions, unknown>> { const image = await toBase64(options.image); const response = await this.post<ApiRawResponse>( "/sdapi/v1/extra-single-image", { image, resize_mode: options.resize_mode ?? 0, show_extras_results: options.show_extras_results ?? true, gfpgan_visibility: options.gfpgan_visibility ?? 0, codeformer_weight: options.codeformer_weight ?? 0, upscaling_resize: options.upscaling_resize ?? 2, upscaling_resize_w: options.upscaling_resize_w ?? 512, upscaling_resize_h: options.upscaling_resize_h ?? 512, upscaling_resize_crop: options.upscaling_resize_crop ?? true, upscaler_1: options.upscaler_1 ?? "None", upscaler_2: options.upscaler_2 ?? "None", extras_upscaler_2_visibility: options.extras_upscaler_2_visibility ?? 0, upscale_first: options.upscale_first ?? false, }, ); return new StableDiffusionResult(response); }
/** * Stable Diffusion extra's call for batch images * @param {ExtraBatchOptions} batchOptions Options for the extra's call * @returns {Promise<StableDiffusionResult>} ApiResult containing the generated image(s) * @memberof StableDiffusionApi * @async * @example * const api = new StableDiffusionApi(); * const image1 = sharp("dog.png"); * const image2 = sharp("cat.png"); * const result = await api.extraBatch({ * images: [image1, image2], * name_list: ["dog", "cat"], * upscaler_1: "Lanczos", * upscaling_resize: 2, * }); */ public async extraBatch( options: ExtraBatchOptions, ): Promise<StableDiffusionResult<ExtraBatchOptions, unknown>> { if (options.images.length !== options.name_list.length) { throw new Error( "The number of images and names must be the same in extraBatch", ); }
const images = await Promise.all( options.images.map(async (image) => await toBase64(image)), );
const image_list = images.map((image, index) => { return { image, name: options.name_list[index], }; });
const response = await this.post<ApiRawResponse>( "/sdapi/v1/extra-batch-images", { image_list, resize_mode: options.resize_mode ?? 0, show_extras_results: options.show_extras_results ?? true, gfpgan_visibility: options.gfpgan_visibility ?? 0, codeformer_weight: options.codeformer_weight ?? 0, upscaling_resize: options.upscaling_resize ?? 2, upscaling_resize_w: options.upscaling_resize_w ?? 512, upscaling_resize_h: options.upscaling_resize_h ?? 512, upscaling_resize_crop: options.upscaling_resize_crop ?? true, upscaler_1: options.upscaler_1 ?? "None", upscaler_2: options.upscaler_2 ?? "None", extras_upscaler_2_visibility: options.extras_upscaler_2_visibility ?? 0, upscale_first: options.upscale_first ?? false, }, ); return new StableDiffusionResult(response); }
/** * Gets the info of a png image * @param {Sharp} image Image to get info from * @returns {Promise<StableDiffusionResult>} ApiResult containing the info */ public async pngInfo(image: Sharp): Promise<StableDiffusionResult<unknown, unknown>> { const image_data = await toBase64(image); const response = await this.post<ApiRawResponse>("/sdapi/v1/png-info", { image: image_data, }); return new StableDiffusionResult(response); }
/** * Interrogates an image with an interrogation model * @param {Sharp} image Image to interrogate * @param model Model to use for interrogation * @returns {Promise<StableDiffusionResult>} The result of the interrogation */ public async interrogate( image: Sharp, model: string, ): Promise<StableDiffusionResult<unknown, unknown>> { const image_data = await toBase64(image); const response = await this.post<ApiRawResponse>( "/sdapi/v1/interrogate", { image: image_data, }, ); return new StableDiffusionResult(response); }
// TODO add other options public async getOptions(): Promise<SDOption> { const response = await this.get<SDOption>("/sdapi/v1/options"); return response; }
public async setOptions(options: Partial<SDOption>) { const response = await this.post("/sdapi/v1/options", options); return response; }
/** * Gets the progress status of the current session * @param {boolean} skipCurrentImage True to skip the current image, false to include it * @returns {Promise<Progress>} The progress status of the current session */ public async getProgress( skipCurrentImage: boolean = false, ): Promise<Progress> { const response = await this.get<Progress>( `/sdapi/v1/progress?skipCurrentImage=${skipCurrentImage}`, ); return response; }
/** * Gets the list of command line flags that are available * @returns {Promise<Record<string, unknown>>} The list of command line flags that are available */ public async getCmdFlags(): Promise<CmdFlags> { const response = await this.get<CmdFlags>( "/sdapi/v1/cmd-flags", ); return response; }
/** * Gets the list of samplers * @returns {Promise<Sampler[]>} The list of samplers */ public async getSamplers(): Promise<Sampler[]> { const response = await this.get<Sampler[]>("/sdapi/v1/samplers"); return response; }
/** * Gets the list of upscalers * @returns {Promise<Upscaler[]>} The list of upscalers */ public async getUpscalers(): Promise<Upscaler[]> { const response = await this.get<Upscaler[]>("/sdapi/v1/upscalers"); return response; }
/** * Gets the list of Stable Diffusion models * @returns {Promise<StableDiffusionModel[]>} The list of Stable Diffusion models */ public async getSdModels(): Promise<StableDiffusionModel[]> { const response = await this.get<StableDiffusionModel[]>( "/sdapi/v1/sd-models", ); return response; }
/** * Gets the list of hypernetworks * @returns {Promise<HyperNetwork[]>} The list of hypernetworks */ public async getHypernetworks(): Promise<HyperNetwork[]> { const response = await this.get<HyperNetwork[]>( "/sdapi/v1/hypernetworks", ); return response; }
/** * Gets the list of face restorers * @returns {Promise<FaceRestorer[]>} The list of face restorers */ public async getFaceRestorers(): Promise<FaceRestorer[]> { const response = await this.get<FaceRestorer[]>( "/sdapi/v1/face-restorers", ); return response; }
/** * Gets the list of Real-ESRGAN models * @returns {Promise<RealESRGanModel[]>} The list of Real-ESRGAN models */ public async getRealesrganModels(): Promise<RealESRGanModel[]> { const response = await this.get<RealESRGanModel[]>( "/sdapi/v1/realesrgan-models", ); return response; }
/** * Gets the list of Stable Diffusion prompt styles * @returns {Promise<PromptStyle[]>} The list of prompt styles */ public async getPromptStyles(): Promise<PromptStyle[]> { const response = await this.get<PromptStyle[]>( "/sdapi/v1/prompt-styles", ); return response; }
/** * Refreshes the list of Stable Diffusion checkpoints * @returns {Promise<void>} */ public async refreshCheckpoints(): Promise<void> { await this.post("/sdapi/v1/refresh-checkpoints"); }
/** * Gets the name of the current Stable Diffusion checkpoint being used * @returns {Promise<string>} The name of the current Stable Diffusion checkpoint being used */ public async getCurrentModel(): Promise<string> { const options = await this.getOptions(); return options.sd_model_checkpoint as string; }
/** * Sets the Stable Diffusion checkpoint to use * @param name Name of the model to set. * @param findClosest If true, will try to find the closest model name if the exact name is not found * @returns {Promise<void>} */ public async setModel( name: string, findClosest: boolean = true, ): Promise<void> { const models = await this.getSdModels(); const modelNames = models.map((model) => model.model_name);
let foundModel = null;
if (modelNames.includes(name)) { foundModel = name; } else if (findClosest) { const fzf = new Fzf(modelNames); const [bestMatch] = fzf.find(name); if (bestMatch && bestMatch.score > 0.5) { foundModel = bestMatch.item; } }
if (foundModel) { const options = { sd_model_checkpoint: foundModel, }; await this.setOptions(options); } else { throw new Error("Model not found"); } }
/** * Waits for the Stable Diffusion server to be ready to accept new requests * @param checkInterval Interval in seconds to check progress * @returns {Promise<boolean>} Only resolves when progress is 0.0 and job_count is 0 */ public waitForReady(checkInterval: number = 5.0): Promise<boolean> { return new Promise((resolve, _reject) => { const interval = setInterval(async () => { const result = await this.getProgress(); const progress = result.progress; const jobCount = result.state.job_count;
if (progress === 0.0 && jobCount === 0) { clearInterval(interval); resolve(true); } else { console.log( `[WAIT]: progress = ${ progress.toFixed(4) }, job_count = ${jobCount}`, ); } }, checkInterval * 1000); }); }}