predict.py

from modules import timer
from modules import initialize_util
from modules import initialize
from modules.tiling.img_utils import convert_pil_img_to_binary, \
                                    convert_binary_img_to_pil, \
                                    convert_pil_img_to_base64, \
                                    convert_base64_img_to_pil, \
                                    shift_image, \
                                    draw_center_cross_image
from modules.debugging.debug_image import debug_tiling_image, \
                                          expand_canvas_tiling, \
                                          save_output_img
from handfix.handfix import (detect_and_crop_hand_from_binary, insert_cropped_hand_into_image)
from urllib.parse import urlparse
from fastapi import FastAPI
from io import BytesIO
from PIL import Image, ImageFilter

import os, json
import numpy as np
import requests
import base64
import uuid
import time
import cv2
import mimetypes
import subprocess

from cog import BasePredictor, Input, Path

mimetypes.add_type("image/webp", ".webp")

# Fixing the "DecompressionBombWarning" warning
Image.MAX_IMAGE_PIXELS = None

class Predictor(BasePredictor):
    def setup(self) -> None:
        """Load the model into memory to make running multiple predictions efficient"""

        os.environ['IGNORE_CMD_ARGS_ERRORS'] = '1'

        startup_timer = timer.startup_timer
        startup_timer.record("launcher")

        initialize.imports()
        initialize.check_versions()
        initialize.initialize()

        app = FastAPI()
        initialize_util.setup_middleware(app)

        from modules.api.api import Api
        from modules.call_queue import queue_lock

        self.api = Api(app, queue_lock)

        model_response = self.api.get_sd_models()
        print("Available checkpoints: ", str(model_response))

        from modules import script_callbacks
        script_callbacks.before_ui_callback()
        script_callbacks.app_started_callback(None, app)

        from modules.api.models import StableDiffusionImg2ImgProcessingAPI
        self.StableDiffusionImg2ImgProcessingAPI = StableDiffusionImg2ImgProcessingAPI

        file_path = Path("init.png")
        base64_encoded_data = base64.b64encode(file_path.read_bytes())
        base64_image = base64_encoded_data.decode('utf-8')

        payload = {
           "override_settings": {
                "sd_model_checkpoint": "juggernaut_reborn.safetensors",
                "sd_vae": "vae-ft-mse-840000-ema-pruned.safetensors",
                 "CLIP_stop_at_last_layers": 1,
            },
            "override_settings_restore_afterwards": False,
            "prompt": "office building",
            "steps": 1,
            "init_images": [base64_image],
            "denoising_strength": 0.1,
            "do_not_save_samples": True,
            "alwayson_scripts": {
                "Tiled Diffusion": {
                    "args": [
                        True,
                        "MultiDiffusion",
                        True,
                        True,
                        1,
                        1,
                        112,
                        144,
                        4,
                        8,
                        "4x-UltraSharp",
                        1.1,
                        False,
                        0,
                        0.0,
                        3,
                    ]
                },
                "Tiled VAE": {
                    "args": [
                        True,
                        3072,
                        192,
                        True,
                        True,
                        True,
                        True,
                    ]

                },
                "controlnet": {
                    "args": [
                        {
                            "enabled": True,
                            "module": "tile_resample",
                            "model": "control_v11f1e_sd15_tile",
                            "weight": 0.2,
                            "image": base64_image,
                            "resize_mode": 1,
                            "lowvram": False,
                            "downsample": 1.0,
                            "guidance_start": 0.0,
                            "guidance_end": 1.0,
                            "control_mode": 1,
                            "pixel_perfect": True,
                            "threshold_a": 1,
                            "threshold_b": 1,
                            "save_detected_map": False,
                            "processor_res": 512,
                        }
                    ]
                }
            }
        }

        req = StableDiffusionImg2ImgProcessingAPI(**payload)
        self.api.img2imgapi(req)

        print(f"Startup time: {startup_timer.summary()}.")

    def download_lora_weights(self, url: str):
        folder_path = "models/Lora"

        parsed_url = urlparse(url)
        filename = os.path.basename(parsed_url.path)

        if "civitai.com" in parsed_url.netloc:
            filename = f"{os.path.basename(parsed_url.path)}.safetensors"

        os.makedirs(folder_path, exist_ok=True)

        file_path = os.path.join(folder_path, filename)

        response = requests.get(url)
        response.raise_for_status()

        with open(file_path, "wb") as file:
            file.write(response.content)

        print("Lora saved under:", file_path)
        return file_path

    def download_safetensors(self, url: str):
        start_time_custom = time.time()

        safetensors_path = f"models/Stable-diffusion/custom-{uuid.uuid1()}.safetensors"

        subprocess.check_output(["pget", url, safetensors_path])

        print(f"Checkpoint downloading with pget took {round(time.time() - start_time_custom, 2)} seconds")

        return safetensors_path

    def calc_scale_factors(self, value):
        lst = []
        while value >= 2:
            lst.append(2)
            value /= 2
        if value > 1:
            lst.append(value)
        return lst

    def predict(
        self,
        image: Path = Input(description="input image"),
        prompt: str = Input(description="Prompt", default="masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>"),
        negative_prompt: str = Input(description="Negative Prompt", default="(worst quality, low quality, normal quality:2) JuggernautNegative-neg"),
        scale_factor: float = Input(
            description="Scale factor", default=2
        ),
        dynamic: float = Input(
            description="HDR, try from 3 - 9", ge=1, le=50, default=6
        ),
        creativity: float = Input(
            description="Creativity, try from 0.3 - 0.9", ge=0, le=1, default=0.35
        ),
        resemblance: float = Input(
            description="Resemblance, try from 0.3 - 1.6", ge=0, le=3, default=0.6
        ),
        tiling_width: int = Input(
            description="Fractality, set lower tile width for a high Fractality",
            choices=[16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256],
            default=112
        ),
        tiling_height: int = Input(
            description="Fractality, set lower tile height for a high Fractality",
            choices=[16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256],
            default=144
        ),
        sd_model: str = Input(
            description="Stable Diffusion model checkpoint",
            choices=['epicrealism_naturalSinRC1VAE.safetensors [84d76a0328]', 'juggernaut_reborn.safetensors [338b85bc4f]', 'flat2DAnimerge_v45Sharp.safetensors'],
            default="juggernaut_reborn.safetensors [338b85bc4f]",
        ),
        scheduler: str = Input(
            description="scheduler",
            choices=['DPM++ 2M Karras', 'DPM++ SDE Karras', 'DPM++ 2M SDE Exponential', 'DPM++ 2M SDE Karras', 'Euler a', 'Euler', 'LMS', 'Heun', 'DPM2', 'DPM2 a', 'DPM++ 2S a', 'DPM++ 2M', 'DPM++ SDE', 'DPM++ 2M SDE', 'DPM++ 2M SDE Heun', 'DPM++ 2M SDE Heun Karras', 'DPM++ 2M SDE Heun Exponential', 'DPM++ 3M SDE', 'DPM++ 3M SDE Karras', 'DPM++ 3M SDE Exponential', 'DPM fast', 'DPM adaptive', 'LMS Karras', 'DPM2 Karras', 'DPM2 a Karras', 'DPM++ 2S a Karras', 'Restart', 'DDIM', 'PLMS', 'UniPC'],
            default="DPM++ 3M SDE Karras",
        ),
        num_inference_steps: int = Input(
            description="Number of denoising steps", ge=1, le=100, default=18
        ),
        seed: int = Input(
            description="Random seed. Leave blank to randomize the seed", default=1337
        ),
        multistep_factor: float = Input(
            description="Multiplier for the number of denoising steps. 0.9 for 90% less steps, 1.1 for 10% more steps", ge=0, le=2, default=0.8
        ),
        downscaling: bool = Input(
            description="Downscale the image before upscaling. Can improve quality and speed for images with high resolution but lower quality", default=False
        ),
        downscaling_resolution: int = Input(
            description="Downscaling resolution", default=768
        ),
        lora_links: str = Input(
            description="Link to a lora file you want to use in your upscaling. Multiple links possible, seperated by comma",
            default=""
        ),
        custom_sd_model: str = Input(
            default=""
        ),
        sharpen: float = Input(
            description="Sharpen the image after upscaling. The higher the value, the more sharpening is applied. 0 for no sharpening", ge=0, le=10, default=0
        ),
        mask: Path = Input(
            description="Mask image to mark areas that should be preserved during upscaling", default=None
        ),
        handfix: str = Input(
            description="Use clarity to fix hands in the image",
            choices=['disabled', 'hands_only', 'image_and_hands'],
            default="disabled",
        ),
        pattern: bool = Input(
            description="Upscale a pattern with seamless tiling",
            default=False,
        ),
        output_format: str = Input(
            description="Format of the output images",
            choices=["webp", "jpg", "png"],
            default="png",
        )
    ) -> list[Path]:
        """Run a single prediction on the model"""
        print("Running prediction")
        start_time = time.time()

        outputs = [] ## init at the start so we can grab the initial image wrangling for debugging

        # checkpoint name changed bc hashing is deactivated so name is corrected here to old name to avoid breaking api calls
        if sd_model == "epicrealism_naturalSinRC1VAE.safetensors [84d76a0328]":
            sd_model = "epicrealism_naturalSinRC1VAE.safetensors"
        if sd_model == "juggernaut_reborn.safetensors [338b85bc4f]":
            sd_model = "juggernaut_reborn.safetensors"

        if lora_links:
            lora_link = [link.strip() for link in lora_links.split(",")]
            for link in lora_link:
                self.download_lora_weights(link)

        if custom_sd_model:
            path_to_custom_checkpoint = self.download_safetensors(custom_sd_model)
            sd_model = os.path.basename(path_to_custom_checkpoint)
            self.api.refresh_checkpoints()

        image_file_path = image

        with open(image_file_path, "rb") as image_file:
            binary_image_data = image_file.read()

        if mask:
            with Image.open(image_file_path) as img:
                original_resolution = img.size

        if downscaling:
            image_np_array = np.frombuffer(binary_image_data, dtype=np.uint8)

            image = cv2.imdecode(image_np_array, cv2.IMREAD_UNCHANGED)

            height, width = image.shape[:2]

            if height > width:
                scaling_factor = downscaling_resolution / float(height)
            else:
                scaling_factor = downscaling_resolution / float(width)

            new_width = int(width * scaling_factor)
            new_height = int(height * scaling_factor)

            resized_image = cv2.resize(image, (new_width, new_height))

            _, binary_resized_image = cv2.imencode('.jpg', resized_image)
            binary_image_data = binary_resized_image.tobytes()

        if handfix == "hands_only":
            print("Trying to fix hands")
            binary_image_data_full_image = binary_image_data
            cropped_hand_img, hand_coords = detect_and_crop_hand_from_binary(binary_image_data_full_image)
            if cropped_hand_img is not None:
                print("Hands detected")
                _, buffer = cv2.imencode('.jpg', cropped_hand_img)
                binary_image_data = buffer.tobytes()

                cropped_hand_img_rgb = cv2.cvtColor(cropped_hand_img, cv2.COLOR_BGR2RGB)
                cropped_hand_img_pil = Image.fromarray(cropped_hand_img_rgb)

            else:
                print("No hands detected")
                return

        base64_encoded_data = base64.b64encode(binary_image_data)
        base64_image = base64_encoded_data.decode('utf-8')

        multipliers = [scale_factor]
        if scale_factor > 2:
            multipliers = self.calc_scale_factors(scale_factor)
            print("Upscale your image " + str(len(multipliers)) + " times")

        first_iteration = True

        for i, multiplier in enumerate(multipliers):
            print("Upscaling with scale_factor: ", multiplier)

            if not first_iteration:
                creativity = creativity * multistep_factor
                seed = seed +1

            first_iteration = False

            if pattern:
                print('--- preparing seamless tiling process')
                init_img = convert_base64_img_to_pil(base64_image)
                ## now lets expand the canvas.
                expanded_img = expand_canvas_tiling(init_img, div=8, darken=False)

                ## now update the original base64 image data.
                base64_image = convert_pil_img_to_base64(expanded_img)

                # seamless_tiling_debug_mode = False
                # if seamless_tiling_debug_mode:
                #     ## and here we save the outputs
                #     out1 = save_output_img(init_img,
                #                            f"010_init_img.{output_format}",
                #                            info_text="1. initial image")
                #     out2 = save_output_img(expanded_img,
                #                            f"020_expanded.{output_format}",
                #                            info_text="2. expanded canvas")
                #     outputs += [out1, out2]

            payload = get_clarity_upscaler_payload(sd_model, tiling_width, tiling_height, multiplier, base64_image,
                                resemblance, prompt, negative_prompt, num_inference_steps, dynamic, seed, scheduler,
                                creativity)

            req = self.StableDiffusionImg2ImgProcessingAPI(**payload)
            resp = self.api.img2imgapi(req)
            info = json.loads(resp.info)

            base64_image = resp.images[0]

            if pattern:
                print('--- starting seamless tiling process')
                image_data = base64.b64decode(base64_image)
                upscaled_img = Image.open(BytesIO(image_data))

                ## crop back
                width = upscaled_img.width
                height = upscaled_img.height
                border_size = int(width / 10)
                cropped_back = upscaled_img.crop((border_size, border_size, width - border_size, height - border_size))

                ## now lets create a final debug tile.
                # debug_tiling_A = debug_tiling_image(cropped_back)

                ## now lets shift the pixels 50% to get the seam in the middle
                shift_x = cropped_back.width // 2
                shift_y = cropped_back.height // 2
                seamless_tiling_overlap_width = 1.0
                seamless_tiling_overlap_blur = 1.0
                shifted_img_A = shift_image(cropped_back, shift_x, shift_y)
                shifted_img_A_base64 = convert_pil_img_to_base64(shifted_img_A)
                inpaint_mask_A_base64, inpaint_mask_A = get_seamless_tiling_mask(shifted_img_A_base64,
                                                                        seamless_tiling_overlap_width,
                                                                        seamless_tiling_overlap_blur)
                ## get payload to do API inpainting
                payload = get_clarity_upscaler_payload(sd_model, tiling_width, tiling_height, multiplier,
                                                    shifted_img_A_base64,
                                                    resemblance, prompt, negative_prompt, num_inference_steps,
                                                    dynamic, seed, scheduler, creativity,
                                                    seamfix_mask=inpaint_mask_A_base64)
                req = self.StableDiffusionImg2ImgProcessingAPI(**payload)
                resp = self.api.img2imgapi(req)
                info = json.loads(resp.info)

                ## now we have our resulting image
                base64_image = resp.images[0]
                gen_bytes = BytesIO(base64.b64decode(base64_image))
                seam_fix_A = Image.open(gen_bytes)

                ## we can shift the pixels back to their original place
                shiftback_img_A = shift_image(seam_fix_A, -shift_x, -shift_y)
                #shiftback_img_A.save(optimised_file_path)  ### overwrite the final output with the shiftedback image

                ## now lets create a final debug tile.
                # debug_tiling_B = debug_tiling_image(shiftback_img_A)

                ## here lets do one more pass, as our cross mask will have never repainted
                ## the outer edges of our image.. so we going to offset the pixels 33% and
                ## do another round of inpainting.
                shift_x = shiftback_img_A.width // 3
                shift_y = shiftback_img_A.height // 3
                shifted_img_B = shift_image(shiftback_img_A, shift_x, shift_y)
                shifted_img_B_base64 = convert_pil_img_to_base64(shifted_img_B)

                ## calculate how much to offset the inpaint mask at 33%
                fourth = (shifted_img_B.width // 4)
                third = (shifted_img_B.width // 3)
                fraction = (shifted_img_B.width // 20)
                offset_x =  (fourth + fourth) - (third + third)
                offset_y =  (fourth + fourth) - (third + third)

                ## now draw the offset cross image
                inpaint_mask_B_base64, inpaint_mask_B = get_seamless_tiling_mask(shifted_img_B_base64,
                                                                        seamless_tiling_overlap_width,
                                                                        seamless_tiling_overlap_blur * 1.2,
                                                                        offset_x=offset_x,
                                                                        offset_y=offset_y,
                                                                        x_start=fourth+fourth+third-fraction,
                                                                        x_end=fourth+fourth+third+fraction,
                                                                        y_start=fourth+fourth+third-fraction,
                                                                        y_end=fourth+fourth+third+fraction,
                                                                        boost=False)

                ## now we can finally do our last inpainting call.
                ## let's drop the creativity way down, and push the resemblance up.
                payload = get_clarity_upscaler_payload(sd_model, tiling_width, tiling_height, multiplier,
                                                    shifted_img_B_base64,
                                                    1.0, prompt, negative_prompt, num_inference_steps,
                                                    dynamic, seed, scheduler, 0.35,
                                                    seamfix_mask=inpaint_mask_B_base64)

                req = self.StableDiffusionImg2ImgProcessingAPI(**payload)
                resp = self.api.img2imgapi(req)
                info = json.loads(resp.info)

                ## so here we should get our result..
                base64_image = resp.images[0]
                gen_bytes = BytesIO(base64.b64decode(base64_image))
                seam_fix_B = Image.open(gen_bytes)

                ## now lets shift the image back again
                shiftback_img_B = shift_image(seam_fix_B, -shift_x, -shift_y)
               
                buffered = BytesIO()
                shiftback_img_B.save(buffered, format="PNG")
                base64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")

                resp.images[0] = base64_image

                # debug_tiling_C = debug_tiling_image(shiftback_img_B)
                # output_tiling = expand_canvas_tiling(shiftback_img_B, div=1, darken=False)

                # if seamless_tiling_debug_mode:
                #     out3 = save_output_img(upscaled_img,
                #                             f"030_upscaled.{output_format}",
                #                             info_text="3. upscaled (canvas expanded)")
                #     out4 = save_output_img(cropped_back,
                #                             f"040_cropped_back.{output_format}",
                #                             info_text="4. upscaled (cropped back)")
                #     out5 = save_output_img(debug_tiling_A,
                #                             f'050_debug_tile_not_fixed.{output_format}',
                #                             info_text="5. tiling debug (before fix)")
                #     out6 = save_output_img(shifted_img_A,
                #                             f'060_shifted_center.{output_format}',
                #                             info_text="6. center seam (shifted 50%)")
                #     out7 = save_output_img(inpaint_mask_A,
                #                             f'070_inpaint_mask.{output_format}',
                #                             '7. inpainting mask')
                #     out8 = save_output_img(seam_fix_A,
                #                             f'080_inpainted_seam_fix.{output_format}',
                #                             info_text="8. seam fix 1")
                #     out9 = save_output_img(shiftback_img_A,
                #                             f'090_shifted_back_fix.{output_format}',
                #                             info_text="9. upscaled (shifted back 50%)")
                #     out10 = save_output_img(debug_tiling_B,
                #                             f'100_debug_tile.{output_format}',
                #                             info_text="10. tiling debug (after 1st fix)")
                #     out11 = save_output_img(shifted_img_B,
                #                             f'110_shift_30pct.{output_format}',
                #                             info_text="11. shift pixels 30%")
                #     out12 = save_output_img(inpaint_mask_B,
                #                             f'120_inpaint_mask.{output_format}',
                #                             '12. inpainting mask')
                #     out13 = save_output_img(seam_fix_B,
                #                             f'130_inpainted_seam_fix.{output_format}',
                #                             info_text="13. seam fix 2 ")
                #     out14 = save_output_img(shiftback_img_B,
                #                             f"140_shifted_back_fix.{output_format}",
                #                             info_text="14. upscaled (shifted back 30%)")
                #     out15 = save_output_img(debug_tiling_C,
                #                             f'150_debug_tile.{output_format}',
                #                             info_text="15. tiling debug (after 2nd fix)")
                #     out16 = save_output_img(output_tiling,
                #                             f'160_resulting_tile.{output_format}',
                #                             info_text="16. tiling result")
                #     outputs += [out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16]


        for i, image in enumerate(resp.images):
            seed = info.get("all_seeds", [])[i] or "unknown_seed"

            gen_bytes = BytesIO(base64.b64decode(image))
            imageObject = Image.open(gen_bytes)

            if handfix == "hands_only":
                imageObject = insert_cropped_hand_into_image(binary_image_data_full_image, imageObject, hand_coords, cropped_hand_img_pil)

            if mask:
                imageObject = imageObject.resize(original_resolution, Image.LANCZOS)
                original_image = Image.open(image_file_path).resize(original_resolution, Image.LANCZOS)
                mask_image = Image.open(mask).convert("L").resize(original_resolution, Image.LANCZOS)

                blur_radius = 5
                mask_image = mask_image.filter(ImageFilter.GaussianBlur(blur_radius))
                combined_image = Image.composite(original_image, imageObject, mask_image)

                imageObject = combined_image

            if sharpen > 0:
                a = -sharpen / 10
                b = 1 - 8 * a
                kernel = [a, a, a, a, b, a, a, a, a]
                kernel_filter = ImageFilter.Kernel((3, 3), kernel, scale=1, offset=0)

                imageObject = imageObject.filter(kernel_filter)

            optimised_file_path = Path(f"{seed}-{uuid.uuid1()}.{output_format}")

            if output_format in ["webp", "jpg"]:
                imageObject.save(
                    optimised_file_path,
                    quality=95,
                    optimize=True,
                )
            else:
                imageObject.save(optimised_file_path)
            
            outputs.append(optimised_file_path)

        if custom_sd_model:
            os.remove(path_to_custom_checkpoint)
            print(f"Custom checkpoint {path_to_custom_checkpoint} has been removed.")

        print(f"Prediction took {round(time.time() - start_time,2)} seconds")
        return outputs

def get_seamless_tiling_mask(base64_image, width_mult, blur_mult,
                            offset_x=0, offset_y=0,
                            x_start=0, x_end=0, y_start=0, y_end=0,
                            boost=True):
    gen_bytes = BytesIO(base64.b64decode(base64_image))
    img = Image.open(gen_bytes)
    mask_pil = draw_center_cross_image(img, thickness_mult=width_mult, blur_mult=blur_mult,
                                    offset_x=offset_x, offset_y=offset_y,
                                    x_start=x_start, x_end=x_end, y_start=y_start, y_end=y_end,
                                    boost=boost)
    mask_base64 = convert_pil_img_to_base64(mask_pil)

    return mask_base64, mask_pil

def get_clarity_upscaler_payload(sd_model,
                                tiling_width,
                                tiling_height,
                                multiplier,
                                base64_image,
                                resemblance,
                                prompt,
                                negative_prompt,
                                num_inference_steps,
                                dynamic,
                                seed,
                                scheduler,
                                creativity,
                                seamfix_mask=None):
    if seamfix_mask:
        multiplier = 1.0 ## set the multiplier to 1 as we're not upscaling in this round.

    override_settings = {
        "sd_model_checkpoint": sd_model,
        "sd_vae": "vae-ft-mse-840000-ema-pruned.safetensors",
        "CLIP_stop_at_last_layers": 1,
    }
    alwayson_scripts = {
        "Tiled Diffusion": {"args": get_tiled_diffusion_args(tiling_width, tiling_height, multiplier)},
        "Tiled VAE": {"args": get_tiled_vae_args()},
        "controlnet": {"args": get_controlnet_args(base64_image, resemblance)}
    }

    if seamfix_mask:
        payload_dict = {
            "override_settings": override_settings,
            "override_settings_restore_afterwards": False,
            "init_images": [base64_image],
            "mask": seamfix_mask,
            "mask_blur": 0,
            "inpainting_fill": 1,  ## [fill, original, latent noise, latent nothing]
            "inpaint_full_res": True,
            "inpaint_full_res_padding": 0,
            "inpainting_mask_invert": 0,
            "include_init_images": True,
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "steps": num_inference_steps,
            "cfg_scale": dynamic,
            "seed": seed,
            "tiling": True,
            "do_not_save_samples": True,
            "sampler_name": scheduler,
            "denoising_strength": creativity,
            "alwayson_scripts": alwayson_scripts,
        }
    else:
        payload_dict = {
            "override_settings": override_settings,
            "override_settings_restore_afterwards": False,
            "init_images": [base64_image],
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "steps": num_inference_steps,
            "cfg_scale": dynamic,
            "seed": seed,
            "do_not_save_samples": True,
            "sampler_name": scheduler,
            "denoising_strength": creativity,
            "alwayson_scripts": alwayson_scripts,
        }

    return payload_dict

def get_tiled_diffusion_args(tiling_width, tiling_height, multiplier):
    arg_list = [
        True,
        "MultiDiffusion",
        True,
        True,
        1,
        1,
        tiling_width,
        tiling_height,
        4,
        8,
        "4x-UltraSharp",
        multiplier,
        False,
        0,
        0.0,
        3,
    ]

    return arg_list

def get_tiled_vae_args():
    arg_list =  [
        True,
        2048,
        128,
        True,
        True,
        True,
        True,
    ]
    return arg_list

def get_controlnet_args(base64_image, resemblance):
    arg_dict = {
        "enabled": True,
        "module": "tile_resample",
        "model": "control_v11f1e_sd15_tile",
        "weight": resemblance,
        "image": base64_image,
        "resize_mode": 1,
        "lowvram": False,
        "downsample": 1.0,
        "guidance_start": 0.0,
        "guidance_end": 1.0,
        "control_mode": 1,
        "pixel_perfect": True,
        "threshold_a": 1,
        "threshold_b": 1,
        "save_detected_map": False,
        "processor_res": 512,
    }
    arg_list = [arg_dict]

    return arg_list