Install

pip install webuiapi

Usage

create API client

import webuiapi# create API clientapi = webuiapi.WebUIApi()# create API client with custom host, port#api = webuiapi.WebUIApi(host='127.0.0.1', port=7860)# create API client with custom host, port and https#api = webuiapi.WebUIApi(host='webui.example.com', port=443, use_https=True)# create API client with default sampler, steps.#api = webuiapi.WebUIApi(sampler='Euler a', steps=20)# optionally set username, password when --api-auth=username:password is set on webui.# username, password are not protected and can be derived easily if the communication channel is not encrypted.# you can also pass username, password to the WebUIApi constructor.api.set_auth('username', 'password')

txt2img

result1 = api.txt2img(prompt="cute squirrel",                    negative_prompt="ugly, out of frame",                    seed=1003,                    styles=["anime"],                    cfg_scale=7,#                      sampler_index='DDIM',#                      steps=30,#                      enable_hr=True,#                      hr_scale=2,#                      hr_upscaler=webuiapi.HiResUpscaler.Latent,#                      hr_second_pass_steps=20,#                      hr_resize_x=1536,#                      hr_resize_y=1024,#                      denoising_strength=0.4,                    )# images contains the returned images (PIL images)result1.images# image is shorthand for images[0]result1.image# info contains text info about the api callresult1.info# info contains paramteres of the api callresult1.parametersresult1.image

img2img

result2 = api.img2img(images=[result1.image], prompt="cute cat", seed=5555, cfg_scale=6.5, denoising_strength=0.6)result2.image

img2img inpainting

from PIL import Image, ImageDrawmask = Image.new('RGB', result2.image.size, color = 'black')# mask = result2.image.copy()draw = ImageDraw.Draw(mask)draw.ellipse((210,150,310,250), fill='white')draw.ellipse((80,120,160,120+80), fill='white')mask

inpainting_result = api.img2img(images=[result2.image],                                mask_image=mask,                                inpainting_fill=1,                                prompt="cute cat",                                seed=104,                                cfg_scale=5.0,                                denoising_strength=0.7)inpainting_result.image

extra-single-image

result3 = api.extra_single_image(image=result2.image,                                 upscaler_1=webuiapi.Upscaler.ESRGAN_4x,                                 upscaling_resize=1.5)print(result3.image.size)result3.image

extra-batch-images

result4 = api.extra_batch_images(images=[result1.image, inpainting_result.image],                                 upscaler_1=webuiapi.Upscaler.ESRGAN_4x,                                 upscaling_resize=1.5)result4.images[0]

result4.images[1]

Async API support

result = await api.txt2img(prompt="cute kitten",                    seed=1001,                    use_async=True                    )result.image

Scripts support

Example for X/Y/Z Plot script:

(scripts/xyz_grid.py file from AUTOMATIC1111's repo)    def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):    ...

# Available Axis options (Different for txt2img and img2img!)XYZPlotAvailableTxt2ImgScripts = [    "Nothing",    "Seed",    "Var. seed",    "Var. strength",    "Steps",    "Hires steps",    "CFG Scale",    "Prompt S/R",    "Prompt order",    "Sampler",    "Checkpoint name",    "Sigma Churn",    "Sigma min",    "Sigma max",    "Sigma noise",    "Eta",    "Clip skip",    "Denoising",    "Hires upscaler",    "VAE",    "Styles",]XYZPlotAvailableImg2ImgScripts = [    "Nothing",    "Seed",    "Var. seed",    "Var. strength",    "Steps",    "CFG Scale",    "Image CFG Scale",    "Prompt S/R",    "Prompt order",    "Sampler",    "Checkpoint name",    "Sigma Churn",    "Sigma min",    "Sigma max",    "Sigma noise",    "Eta",    "Clip skip",    "Denoising",    "Cond. Image Mask Weight",    "VAE",    "Styles",]# Example callXAxisType = "Steps"XAxisValues = "20,30" YAxisType = "Sampler"YAxisValues = "Euler a, LMS"ZAxisType = "Nothing"ZAxisValues = ""drawLegend = "True"includeLoneImages = "False"includeSubGrids = "False"noFixedSeeds = "False"marginSize = 0# x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_sizeresult = api.txt2img(                    prompt="cute girl with short brown hair in black t-shirt in animation style",                    seed=1003,                    script_name="X/Y/Z Plot",                    script_args=[                        XYZPlotAvailableTxt2ImgScripts.index(XAxisType),                        XAxisValues,                        XYZPlotAvailableTxt2ImgScripts.index(YAxisType),                        YAxisValues,                        XYZPlotAvailableTxt2ImgScripts.index(ZAxisType),                        ZAxisValues,                        drawLegend,                        includeLoneImages,                        includeSubGrids,                        noFixedSeeds,                        marginSize,                        ]                    )result.image

Configuration APIs

# return map of current optionsoptions = api.get_options()# change sd modeloptions = {}options['sd_model_checkpoint'] = 'model.ckpt [7460a6fa]'api.set_options(options)# when calling set_options, do not pass all options returned by get_options().# it makes webui unusable (2022/11/21).# get available sd modelsapi.get_sd_models()# misc get apisapi.get_samplers()api.get_cmd_flags()      api.get_hypernetworks()api.get_face_restorers()api.get_realesrgan_models()api.get_prompt_styles()api.get_artist_categories() # deprecated ?api.get_artists() # deprecated ?api.get_progress()api.get_embeddings()api.get_cmd_flags()api.get_scripts()api.get_memory()# misc apisapi.interrupt()api.skip()

Utility methods

# save current model nameold_model = api.util_get_current_model()# get list of available modelsmodels = api.util_get_model_names()# set model (use exact name)api.util_set_model(models[0])# set model (find closest match)api.util_set_model('robodiffusion')# wait for job completeapi.util_wait_for_ready()

LORA and alwayson_scripts example

r = api.txt2img(prompt='photo of a cute girl with green hair <lora:Moxin_10:0.6> shuimobysim __juice__',                seed=1000,                save_images=True,                alwayson_scripts={"Simple wildcards":[]} # wildcards extension doesn't accept more parameters.               )r.image

Extension support - Model-Keyword

# https://github.com/mix1009/model-keywordmki = webuiapi.ModelKeywordInterface(api)mki.get_keywords()

Extension support - Instruct-Pix2Pix

# Instruct-Pix2Pix extension is now deprecated and is now part of webui.# You can use normal img2img with image_cfg_scale when instruct-pix2pix model is loaded.r = api.img2img(prompt='sunset', images=[pil_img], cfg_scale=7.5, image_cfg_scale=1.5)r.image

Extension support - ControlNet

# https://github.com/Mikubill/sd-webui-controlnetapi.controlnet_model_list()

api.controlnet_version()api.controlnet_module_list()

# normal txt2imgr = api.txt2img(prompt="photo of a beautiful girl with blonde hair", height=512, seed=100)img = r.imageimg

# txt2img with ControlNet (used 1.0 but also supports 1.1)unit1 = webuiapi.ControlNetUnit(input_image=img, module='canny', model='control_canny-fp16 [e3fe7712]')r = api.txt2img(prompt="photo of a beautiful girl", controlnet_units=[unit1])r.image

# img2img with multiple ControlNets (used 1.0 but also supports 1.1)unit1 = webuiapi.ControlNetUnit(input_image=img, module='canny', model='control_canny-fp16 [e3fe7712]')unit2 = webuiapi.ControlNetUnit(input_image=img, module='depth', model='control_depth-fp16 [400750f6]', weight=0.5)r2 = api.img2img(prompt="girl",            images=[img],             width=512,            height=512,            controlnet_units=[unit1, unit2],            sampler_name="Euler a",            cfg_scale=7,           )r2.image

r2.images[1]

r2.images[2]

r = api.controlnet_detect(images=[img], module='canny')r.image