webuiapi.py

import json
import requests
import io
import base64
from PIL import Image
from dataclasses import dataclass
from enum import Enum
from typing import List, Dict, Any

class Upscaler(str, Enum):
    none = 'None'
    Lanczos = 'Lanczos'
    Nearest = 'Nearest'
    LDSR = 'LDSR'
    BSRGAN = 'BSRGAN'
    ESRGAN_4x = 'ESRGAN_4x'
    R_ESRGAN_General_4xV3 = 'R-ESRGAN General 4xV3'
    ScuNET_GAN = 'ScuNET GAN'
    ScuNET_PSNR = 'ScuNET PSNR'
    SwinIR_4x = 'SwinIR 4x'

class HiResUpscaler(str, Enum):
    none = 'None'
    Latent = 'Latent'
    LatentAntialiased = 'Latent (antialiased)'
    LatentBicubic = 'Latent (bicubic)'
    LatentBicubicAntialiased = 'Latent (bicubic antialiased)'
    LatentNearest = 'Latent (nearist)'
    LatentNearestExact = 'Latent (nearist-exact)'
    Lanczos = 'Lanczos'
    Nearest = 'Nearest'
    ESRGAN_4x = 'ESRGAN_4x'
    LDSR = 'LDSR'
    ScuNET_GAN = 'ScuNET GAN'
    ScuNET_PSNR = 'ScuNET PSNR'
    SwinIR_4x = 'SwinIR 4x'


@dataclass
class WebUIApiResult:
    images: list
    parameters: dict
    info: dict

    @property
    def image(self):
        return self.images[0]
    
class ControlNetUnit:
    def __init__(self,
        input_image: Image = None,
        mask: Image = None,
        module: str = "none",
        model: str = "None",
        weight: float = 1.0,
        resize_mode: str = "Scale to Fit (Inner Fit)",
        lowvram: bool = False,
        processor_res: int = 64,
        threshold_a: float = 64,
        threshold_b: float = 64,
        guidance: float = 1.0,
        guidance_start: float = 0.0,
        guidance_end: float = 1.0,
        guessmode: bool = False):
        
        self.input_image = input_image
        self.mask = mask
        self.module = module
        self.model = model
        self.weight = weight
        self.resize_mode = resize_mode
        self.lowvram = lowvram
        self.processor_res = processor_res
        self.threshold_a = threshold_a
        self.threshold_b = threshold_b
        self.guidance = guidance
        self.guidance_start = guidance_start
        self.guidance_end = guidance_end
        self.guessmode = guessmode

    def to_dict(self):
        return {
            "input_image": raw_b64_img(self.input_image) if self.input_image else "",
            "mask": raw_b64_img(self.mask) if self.mask else "",
            "module": self.module,
            "model": self.model,
            "weight": self.weight,
            "resize_mode": self.resize_mode,
            "lowvram": self.lowvram,
            "processor_res": self.processor_res,
            "threshold_a": self.threshold_a,
            "threshold_b": self.threshold_b,
            "guidance": self.guidance,
            "guidance_start": self.guidance_start,
            "guidance_end": self.guidance_end,
            "guessmode": self.guessmode,
        }


def b64_img(image: Image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_base64 = 'data:image/png;base64,' + str(base64.b64encode(buffered.getvalue()), 'utf-8')
    return img_base64

def raw_b64_img(image: Image):
    # XXX controlnet only accepts RAW base64 without headers
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_base64 = str(base64.b64encode(buffered.getvalue()), 'utf-8')
    return img_base64

class WebUIApi:
    def __init__(self,
                 host='127.0.0.1',
                 port=7860,
                 baseurl=None,
                 sampler='Euler a',
                 steps=20,
                 use_https=False):
        if baseurl is None:
            if use_https:
                baseurl = f'https://{host}:{port}/sdapi/v1'
            else:
                baseurl = f'http://{host}:{port}/sdapi/v1'

        self.baseurl = baseurl
        self.default_sampler = sampler
        self.default_steps = steps

        self.session = requests.Session()

    def set_auth(self, username, password):
        self.session.auth = (username, password)

    def _to_api_result(self, response):

        if response.status_code != 200:
            raise RuntimeError(response.status_code, response.text)

        r = response.json()
        images = []
        if 'images' in r.keys():
            images = [Image.open(io.BytesIO(base64.b64decode(i))) for i in r['images']]
        elif 'image' in r.keys():
            images = [Image.open(io.BytesIO(base64.b64decode(r['image'])))]

        info = ''
        if 'info' in r.keys():
            try:
                info = json.loads(r['info'])
            except:
                info = r['info']
        elif 'html_info' in r.keys():
            info = r['html_info']

        parameters = ''
        if 'parameters' in r.keys():
            parameters = r['parameters']

        return WebUIApiResult(images, parameters, info)

    def txt2img(self,
                enable_hr=False,
                denoising_strength=0.7,
                firstphase_width=0,
                firstphase_height=0,
                hr_scale=2,
                hr_upscaler=HiResUpscaler.Latent,
                hr_second_pass_steps=0,
                hr_resize_x=0,
                hr_resize_y=0,
                prompt="",
                styles=[],
                seed=-1,
                subseed=-1,
                subseed_strength=0.0,
                seed_resize_from_h=0,
                seed_resize_from_w=0,
                sampler_name=None,  # use this instead of sampler_index
                batch_size=1,
                n_iter=1,
                steps=None,
                cfg_scale=7.0,
                width=512,
                height=512,
                restore_faces=False,
                tiling=False,
                do_not_save_samples=False,
                do_not_save_grid=False,
                negative_prompt="",
                eta=1.0,
                s_churn=0,
                s_tmax=0,
                s_tmin=0,
                s_noise=1,
                override_settings={},
                override_settings_restore_afterwards=True,
                script_args=None,  # List of arguments for the script "script_name"
                script_name=None,
                send_images=True,
                save_images=False,
                alwayson_scripts={},
                controlnet_units: List[ControlNetUnit] = [],
                sampler_index=None, # deprecated: use sampler_name
                use_deprecated_controlnet=False,
                ):
        if sampler_index is None:
            sampler_index = self.default_sampler
        if sampler_name is None:
            sampler_name = self.default_sampler
        if steps is None:
            steps = self.default_steps
        if script_args is None:
            script_args = []
        payload = {
            "enable_hr": enable_hr,
            "hr_scale" : hr_scale,
            "hr_upscaler" : hr_upscaler,
            "hr_second_pass_steps" : hr_second_pass_steps,
            "hr_resize_x": hr_resize_x,
            "hr_resize_y": hr_resize_y,
            "denoising_strength": denoising_strength,
            "firstphase_width": firstphase_width,
            "firstphase_height": firstphase_height,
            "prompt": prompt,
            "styles": styles,
            "seed": seed,
            "subseed": subseed,
            "subseed_strength": subseed_strength,
            "seed_resize_from_h": seed_resize_from_h,
            "seed_resize_from_w": seed_resize_from_w,
            "batch_size": batch_size,
            "n_iter": n_iter,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "width": width,
            "height": height,
            "restore_faces": restore_faces,
            "tiling": tiling,
            "do_not_save_samples": do_not_save_samples,
            "do_not_save_grid": do_not_save_grid,
            "negative_prompt": negative_prompt,
            "eta": eta,
            "s_churn": s_churn,
            "s_tmax": s_tmax,
            "s_tmin": s_tmin,
            "s_noise": s_noise,
            "override_settings": override_settings,
            "override_settings_restore_afterwards": override_settings_restore_afterwards,
            "sampler_name": sampler_name,
            "sampler_index": sampler_index,
            "script_name": script_name,
            "script_args": script_args,
            "send_images": send_images,
            "save_images": save_images,
            "alwayson_scripts": alwayson_scripts,
        }

        if use_deprecated_controlnet and controlnet_units and len(controlnet_units)>0:
            payload["controlnet_units"] = [x.to_dict() for x in controlnet_units]
            return self.custom_post('controlnet/txt2img', payload=payload)

        if controlnet_units and len(controlnet_units)>0:
            payload["alwayson_scripts"]["ControlNet"] = {
                "args": [x.to_dict() for x in controlnet_units]
            }

        response = self.session.post(url=f'{self.baseurl}/txt2img', json=payload)
        return self._to_api_result(response)

    def img2img(self,
                images=[],  # list of PIL Image
                resize_mode=0,
                denoising_strength=0.75,
                image_cfg_scale=1.5,
                mask_image=None,  # PIL Image mask
                mask_blur=4,
                inpainting_fill=0,
                inpaint_full_res=True,
                inpaint_full_res_padding=0,
                inpainting_mask_invert=0,
                initial_noise_multiplier=1,
                prompt="",
                styles=[],
                seed=-1,
                subseed=-1,
                subseed_strength=0,
                seed_resize_from_h=0,
                seed_resize_from_w=0,
                sampler_name=None,  # use this instead of sampler_index
                batch_size=1,
                n_iter=1,
                steps=None,
                cfg_scale=7.0,
                width=512,
                height=512,
                restore_faces=False,
                tiling=False,
                do_not_save_samples=False,
                do_not_save_grid=False,
                negative_prompt="",
                eta=1.0,
                s_churn=0,
                s_tmax=0,
                s_tmin=0,
                s_noise=1,
                override_settings={},
                override_settings_restore_afterwards=True,
                script_args=None,  # List of arguments for the script "script_name"
                sampler_index=None,  # deprecated: use sampler_name
                include_init_images=False,
                script_name=None,
                send_images=True,
                save_images=False,
                alwayson_scripts={},
                controlnet_units: List[ControlNetUnit] = [],
                use_deprecated_controlnet = False,
                ):
        if sampler_name is None:
            sampler_name = self.default_sampler
        if sampler_index is None:
            sampler_index = self.default_sampler
        if steps is None:
            steps = self.default_steps
        if script_args is None:
            script_args = []

        payload = {
            "init_images": [b64_img(x) for x in images],
            "resize_mode": resize_mode,
            "denoising_strength": denoising_strength,
            "mask_blur": mask_blur,
            "inpainting_fill": inpainting_fill,
            "inpaint_full_res": inpaint_full_res,
            "inpaint_full_res_padding": inpaint_full_res_padding,
            "inpainting_mask_invert": inpainting_mask_invert,
            "initial_noise_multiplier": initial_noise_multiplier,
            "prompt": prompt,
            "styles": styles,
            "seed": seed,
            "subseed": subseed,
            "subseed_strength": subseed_strength,
            "seed_resize_from_h": seed_resize_from_h,
            "seed_resize_from_w": seed_resize_from_w,
            "batch_size": batch_size,
            "n_iter": n_iter,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "image_cfg_scale": image_cfg_scale,
            "width": width,
            "height": height,
            "restore_faces": restore_faces,
            "tiling": tiling,
            "do_not_save_samples": do_not_save_samples,
            "do_not_save_grid": do_not_save_grid,
            "negative_prompt": negative_prompt,
            "eta": eta,
            "s_churn": s_churn,
            "s_tmax": s_tmax,
            "s_tmin": s_tmin,
            "s_noise": s_noise,
            "override_settings": override_settings,
            "override_settings_restore_afterwards": override_settings_restore_afterwards,
            "sampler_name": sampler_name,
            "sampler_index": sampler_index,
            "include_init_images": include_init_images,
            "script_name": script_name,
            "script_args": script_args,
            "send_images": send_images,
            "save_images": save_images,
            "alwayson_scripts": alwayson_scripts,
        }
        if mask_image is not None:
            payload['mask'] = b64_img(mask_image)
            
        if use_deprecated_controlnet and controlnet_units and len(controlnet_units)>0:
            payload["controlnet_units"] = [x.to_dict() for x in controlnet_units]
            return self.custom_post('controlnet/img2img', payload=payload)

        if controlnet_units and len(controlnet_units)>0:
            payload["alwayson_scripts"]["ControlNet"] = {
                "args": [x.to_dict() for x in controlnet_units]
            }
        response = self.session.post(url=f'{self.baseurl}/img2img', json=payload)
        return self._to_api_result(response)

    def extra_single_image(self,
                           image,  # PIL Image
                           resize_mode=0,
                           show_extras_results=True,
                           gfpgan_visibility=0,
                           codeformer_visibility=0,
                           codeformer_weight=0,
                           upscaling_resize=2,
                           upscaling_resize_w=512,
                           upscaling_resize_h=512,
                           upscaling_crop=True,
                           upscaler_1="None",
                           upscaler_2="None",
                           extras_upscaler_2_visibility=0,
                           upscale_first=False,
                           ):
        payload = {
            "resize_mode": resize_mode,
            "show_extras_results": show_extras_results,
            "gfpgan_visibility": gfpgan_visibility,
            "codeformer_visibility": codeformer_visibility,
            "codeformer_weight": codeformer_weight,
            "upscaling_resize": upscaling_resize,
            "upscaling_resize_w": upscaling_resize_w,
            "upscaling_resize_h": upscaling_resize_h,
            "upscaling_crop": upscaling_crop,
            "upscaler_1": upscaler_1,
            "upscaler_2": upscaler_2,
            "extras_upscaler_2_visibility": extras_upscaler_2_visibility,
            "upscale_first": upscale_first,
            "image": b64_img(image),
        }

        response = self.session.post(url=f'{self.baseurl}/extra-single-image', json=payload)
        return self._to_api_result(response)

    def extra_batch_images(self,
                           images,  # list of PIL images
                           name_list=None,  # list of image names
                           resize_mode=0,
                           show_extras_results=True,
                           gfpgan_visibility=0,
                           codeformer_visibility=0,
                           codeformer_weight=0,
                           upscaling_resize=2,
                           upscaling_resize_w=512,
                           upscaling_resize_h=512,
                           upscaling_crop=True,
                           upscaler_1="None",
                           upscaler_2="None",
                           extras_upscaler_2_visibility=0,
                           upscale_first=False,
                           ):
        if name_list is not None:
            if len(name_list) != len(images):
                raise RuntimeError('len(images) != len(name_list)')
        else:
            name_list = [f'image{i + 1:05}' for i in range(len(images))]
        images = [b64_img(x) for x in images]

        image_list = []
        for name, image in zip(name_list, images):
            image_list.append({
                "data": image,
                "name": name
            })

        payload = {
            "resize_mode": resize_mode,
            "show_extras_results": show_extras_results,
            "gfpgan_visibility": gfpgan_visibility,
            "codeformer_visibility": codeformer_visibility,
            "codeformer_weight": codeformer_weight,
            "upscaling_resize": upscaling_resize,
            "upscaling_resize_w": upscaling_resize_w,
            "upscaling_resize_h": upscaling_resize_h,
            "upscaling_crop": upscaling_crop,
            "upscaler_1": upscaler_1,
            "upscaler_2": upscaler_2,
            "extras_upscaler_2_visibility": extras_upscaler_2_visibility,
            "upscale_first": upscale_first,
            "imageList": image_list,
        }

        response = self.session.post(url=f'{self.baseurl}/extra-batch-images', json=payload)
        return self._to_api_result(response)

    # XXX 500 error (2022/12/26)
    def png_info(self, image):
        payload = {
            "image": b64_img(image),
        }

        response = self.session.post(url=f'{self.baseurl}/png-info', json=payload)
        return self._to_api_result(response)

    # XXX always returns empty info (2022/12/26)
    def interrogate(self, image):
        payload = {
            "image": b64_img(image),
        }

        response = self.session.post(url=f'{self.baseurl}/interrogate', json=payload)
        return self._to_api_result(response)

    def get_options(self):
        response = self.session.get(url=f'{self.baseurl}/options')
        return response.json()
    def set_options(self, options):
        response = self.session.post(url=f'{self.baseurl}/options', json=options)
        return response.json()

    def get_progress(self):
        response = self.session.get(url=f'{self.baseurl}/progress')
        return response.json()

    def get_cmd_flags(self):
        response = self.session.get(url=f'{self.baseurl}/cmd-flags')
        return response.json()
    def get_samplers(self):        
        response = self.session.get(url=f'{self.baseurl}/samplers')
        return response.json()
    def get_upscalers(self):        
        response = self.session.get(url=f'{self.baseurl}/upscalers')
        return response.json()
    def get_sd_models(self):        
        response = self.session.get(url=f'{self.baseurl}/sd-models')
        return response.json()
    def get_hypernetworks(self):        
        response = self.session.get(url=f'{self.baseurl}/hypernetworks')
        return response.json()
    def get_face_restorers(self):        
        response = self.session.get(url=f'{self.baseurl}/face-restorers')
        return response.json()
    def get_realesrgan_models(self):        
        response = self.session.get(url=f'{self.baseurl}/realesrgan-models')
        return response.json()
    def get_prompt_styles(self):        
        response = self.session.get(url=f'{self.baseurl}/prompt-styles')
        return response.json()
    def get_artist_categories(self):        
        response = self.session.get(url=f'{self.baseurl}/artist-categories')
        return response.json()
    def get_artists(self):        
        response = self.session.get(url=f'{self.baseurl}/artists')
        return response.json()
    def refresh_checkpoints(self):
        response = self.session.post(url=f'{self.baseurl}/refresh-checkpoints')
        return response.json()

    def get_endpoint(self, endpoint, baseurl):
        if baseurl:
            return f'{self.baseurl}/{endpoint}'
        else:
            from urllib.parse import urlparse, urlunparse
            parsed_url = urlparse(self.baseurl)
            basehost = parsed_url.netloc
            parsed_url2 = (parsed_url[0], basehost, endpoint, '', '', '')
            return urlunparse(parsed_url2)
    def custom_get(self, endpoint, baseurl=False):
        url = self.get_endpoint(endpoint, baseurl)
        response = self.session.get(url=url)
        return response.json()
    def custom_post(self, endpoint, payload={}, baseurl=False):
        url = self.get_endpoint(endpoint, baseurl)
        response = self.session.post(url=url, json=payload)
        return self._to_api_result(response)

    def util_get_model_names(self):
        return sorted([x['title'] for x in self.get_sd_models()])
    def util_set_model(self, name, find_closest=True):
        if find_closest:
            name = name.lower()
        models = self.util_get_model_names()
        found_model = None
        if name in models:
            found_model = name
        elif find_closest:
            import difflib
            def str_simularity(a, b):
                return difflib.SequenceMatcher(None, a, b).ratio()
            max_sim = 0.0
            max_model = models[0]
            for model in models:
                sim = str_simularity(name, model)
                if sim >= max_sim:
                    max_sim = sim
                    max_model = model
            found_model = max_model
        if found_model:
            print(f'loading {found_model}')
            options = {}
            options['sd_model_checkpoint'] = found_model
            self.set_options(options)
            print(f'model changed to {found_model}')
        else:
            print('model not found')

    def util_get_current_model(self):
        return self.get_options()['sd_model_checkpoint']

    def util_wait_for_ready(self, check_interval=5.0):
        import time
        while True:
            result =  self.get_progress()
            progress = result['progress']
            job_count = result['state']['job_count']
            if progress == 0.0 and job_count == 0:
                break
            else:
                print(f'[WAIT]: progress = {progress:.4f}, job_count = {job_count}')
                time.sleep(check_interval)



## Interface for extensions

# https://github.com/mix1009/model-keyword
@dataclass
class ModelKeywordResult:
    keywords: list
    model: str
    oldhash: str
    match_source: str
    
class ModelKeywordInterface:
    def __init__(self, webuiapi):
        self.api = webuiapi
    def get_keywords(self):
        result = self.api.custom_get('model_keyword/get_keywords')
        keywords = result['keywords']
        model = result['model']
        oldhash = result['hash']
        match_source = result['match_source']
        return ModelKeywordResult(keywords, model, oldhash, match_source)

# https://github.com/Klace/stable-diffusion-webui-instruct-pix2pix
class InstructPix2PixInterface:
    def __init__(self, webuiapi):
        self.api = webuiapi
    def img2img(self, 
        images=[],
        prompt: str = '',
        negative_prompt: str = '',
        output_batches: int = 1,
        sampler: str = 'Euler a',
        steps: int = 20,
        seed: int = 0,
        randomize_seed: bool = True,
        text_cfg: float = 7.5,
        image_cfg: float = 1.5,
        randomize_cfg: bool = False,
        output_image_width: int = 512
        ):
        init_images = [b64_img(x) for x in images]
        payload = {
            "init_images": init_images,
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "output_batches": output_batches,
            "sampler": sampler,
            "steps": steps,
            "seed": seed,
            "randomize_seed": randomize_seed,
            "text_cfg": text_cfg,
            "image_cfg": image_cfg,
            "randomize_cfg": randomize_cfg,
            "output_image_width": output_image_width
        }
        return self.api.custom_post('instruct-pix2pix/img2img', payload=payload)


# https://github.com/Mikubill/sd-webui-controlnet
class ControlNetInterface:
    def __init__(self, webuiapi, show_deprecation_warning=True):
        self.api = webuiapi
        self.show_deprecation_warning = show_deprecation_warning
        
    def print_deprecation_warning(self):
        print('ControlNetInterface txt2img/img2img is deprecated. Please use normal txt2img/img2img with controlnet_units param')
        
    def txt2img(self,
        prompt: str = "",
        negative_prompt: str = "",
        controlnet_input_image: [] = [],
        controlnet_mask: [] = [],
        controlnet_module: str = "",
        controlnet_model: str = "",
        controlnet_weight: float = 0.5,
        controlnet_resize_mode: str = "Scale to Fit (Inner Fit)",
        controlnet_lowvram: bool = False,
        controlnet_processor_res: int = 512,
        controlnet_threshold_a: int = 64,
        controlnet_threshold_b: int = 64,
        controlnet_guidance: float = 1.0,
        enable_hr: bool = False, # hiresfix
        denoising_strength: float = 0.5,
        hr_scale: float = 1.5,
        hr_upscale: str = "Latent",
        guess_mode: bool = True,
        seed: int = -1,
        subseed: int = -1,
        subseed_strength: int = -1,
        sampler_index: str = "Euler a",
        batch_size: int = 1,
        n_iter: int = 1, # Iteration
        steps: int = 20,
        cfg_scale: float = 7,
        width: int = 512,
        height: int = 512,
        restore_faces: bool = False,
        override_settings: Dict[str, Any] = None, 
        override_settings_restore_afterwards: bool = True):
        
        if self.show_deprecation_warning:
            self.print_deprecation_warning()
        
        controlnet_input_image_b64 = [raw_b64_img(x) for x in controlnet_input_image]
        controlnet_mask_b64 = [raw_b64_img(x) for x in controlnet_mask]

        payload = {
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "controlnet_input_image": controlnet_input_image_b64,
            "controlnet_mask": controlnet_mask_b64,
            "controlnet_module": controlnet_module,
            "controlnet_model": controlnet_model,
            "controlnet_weight": controlnet_weight,
            "controlnet_resize_mode": controlnet_resize_mode,
            "controlnet_lowvram": controlnet_lowvram,
            "controlnet_processor_res": controlnet_processor_res,
            "controlnet_threshold_a": controlnet_threshold_a,
            "controlnet_threshold_b": controlnet_threshold_b,
            "controlnet_guidance": controlnet_guidance,
            "enable_hr": enable_hr,
            "denoising_strength": denoising_strength,
            "hr_scale": hr_scale,
            "hr_upscale": hr_upscale,
            "guess_mode": guess_mode,
            "seed": seed,
            "subseed": subseed,
            "subseed_strength": subseed_strength,
            "sampler_index": sampler_index,
            "batch_size": batch_size,
            "n_iter": n_iter,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "width": width,
            "height": height,
            "restore_faces": restore_faces,
            "override_settings": override_settings,
            "override_settings_restore_afterwards": override_settings_restore_afterwards,
        }
        return self.api.custom_post('controlnet/txt2img', payload=payload)
    
    def img2img(self,
        init_images: [] = [],
        mask: str = None,
        mask_blur: int = 30,
        inpainting_fill: int = 0,
        inpaint_full_res: bool = True,
        inpaint_full_res_padding: int = 1,
        inpainting_mask_invert: int = 1,
        resize_mode: int = 0,
        denoising_strength: float = 0.7,
        prompt: str = "",
        negative_prompt: str = "",
        controlnet_input_image: [] = [],
        controlnet_mask: [] = [],
        controlnet_module: str = "",
        controlnet_model: str = "",
        controlnet_weight: float = 1.0,
        controlnet_resize_mode: str = "Scale to Fit (Inner Fit)",
        controlnet_lowvram: bool = False,
        controlnet_processor_res: int = 512,
        controlnet_threshold_a: int = 64,
        controlnet_threshold_b: int = 64,
        controlnet_guidance: float = 1.0,
        guess_mode: bool = True,
        seed: int = -1,
        subseed: int = -1,
        subseed_strength: int = -1,
        sampler_index: str = "",
        batch_size: int = 1,
        n_iter: int = 1, # Iteration
        steps: int = 20,
        cfg_scale: float = 7,
        width: int = 512,
        height: int = 512,
        restore_faces: bool = False,
        include_init_images: bool = True,
        override_settings: Dict[str, Any] = None, 
        override_settings_restore_afterwards: bool = True):
        
        if self.show_deprecation_warning:
            self.print_deprecation_warning()

        init_images_b64 = [raw_b64_img(x) for x in init_images]
        controlnet_input_image_b64 = [raw_b64_img(x) for x in controlnet_input_image]
        controlnet_mask_b64 = [raw_b64_img(x) for x in controlnet_mask]

        payload = {
            "init_images": init_images_b64,
            "mask": raw_b64_img(mask) if mask else None,
            "mask_blur": mask_blur,
            "inpainting_fill": inpainting_fill,
            "inpaint_full_res": inpaint_full_res,
            "inpaint_full_res_padding": inpaint_full_res_padding,
            "inpainting_mask_invert": inpainting_mask_invert,
            "resize_mode": resize_mode,
            "denoising_strength": denoising_strength,
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "controlnet_input_image": controlnet_input_image_b64,
            "controlnet_mask": controlnet_mask_b64,
            "controlnet_module": controlnet_module,
            "controlnet_model": controlnet_model,
            "controlnet_weight": controlnet_weight,
            "controlnet_resize_mode": controlnet_resize_mode,
            "controlnet_lowvram": controlnet_lowvram,
            "controlnet_processor_res": controlnet_processor_res,
            "controlnet_threshold_a": controlnet_threshold_a,
            "controlnet_threshold_b": controlnet_threshold_b,
            "controlnet_guidance": controlnet_guidance,
            "guess_mode": guess_mode,
            "seed": seed,
            "subseed": subseed,
            "subseed_strength": subseed_strength,
            "sampler_index": sampler_index,
            "batch_size": batch_size,
            "n_iter": n_iter,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "width": width,
            "height": height,
            "restore_faces": restore_faces,
            "include_init_images": include_init_images,
            "override_settings": override_settings,
            "override_settings_restore_afterwards": override_settings_restore_afterwards,
        }
        return self.api.custom_post('controlnet/img2img', payload=payload)
    
    def model_list(self):
        r = self.api.custom_get('controlnet/model_list')
        return r['model_list']