iasam_app.py

import argparse
# import math
import gc
import os
import platform

if platform.system() == "Darwin":
    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"

if platform.system() == "Windows":
    os.environ["XFORMERS_FORCE_DISABLE_TRITON"] = "1"

import random
import traceback
from importlib.util import find_spec

import cv2
import gradio as gr
import numpy as np
import torch
from diffusers import (DDIMScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
                       KDPM2AncestralDiscreteScheduler, KDPM2DiscreteScheduler,
                       StableDiffusionInpaintPipeline)
from PIL import Image, ImageFilter
from PIL.PngImagePlugin import PngInfo
from torch.hub import download_url_to_file
from torchvision import transforms

import inpalib
from ia_check_versions import ia_check_versions
from ia_config import IAConfig, get_ia_config_index, set_ia_config, setup_ia_config_ini
from ia_devices import devices
from ia_file_manager import IAFileManager, download_model_from_hf, ia_file_manager
from ia_logging import ia_logging
from ia_threading import clear_cache_decorator
from ia_ui_gradio import reload_javascript
from ia_ui_items import (get_cleaner_model_ids, get_inp_model_ids, get_padding_mode_names,
                         get_sam_model_ids, get_sampler_names)
from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config, HDStrategy, LDMSampler, SDSampler

print("platform:", platform.system())

reload_javascript()

if find_spec("xformers") is not None:
    xformers_available = True
else:
    xformers_available = False

parser = argparse.ArgumentParser(description="Inpaint Anything")
parser.add_argument("--save-seg", action="store_true", help="Save the segmentation image generated by SAM.")
parser.add_argument("--offline", action="store_true", help="Execute inpainting using an offline network.")
parser.add_argument("--sam-cpu", action="store_true", help="Perform the Segment Anything operation on CPU.")
args = parser.parse_args()
IAConfig.global_args.update(args.__dict__)


@clear_cache_decorator
def download_model(sam_model_id):
    """Download SAM model.



    Args:

        sam_model_id (str): SAM model id



    Returns:

        str: download status

    """
    if "_hq_" in sam_model_id:
        url_sam = "https://huggingface.co/Uminosachi/sam-hq/resolve/main/" + sam_model_id
    elif "FastSAM" in sam_model_id:
        url_sam = "https://huggingface.co/Uminosachi/FastSAM/resolve/main/" + sam_model_id
    elif "mobile_sam" in sam_model_id:
        url_sam = "https://huggingface.co/Uminosachi/MobileSAM/resolve/main/" + sam_model_id
    elif "sam2_" in sam_model_id:
        url_sam = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/" + sam_model_id
    else:
        url_sam = "https://dl.fbaipublicfiles.com/segment_anything/" + sam_model_id

    sam_checkpoint = os.path.join(ia_file_manager.models_dir, sam_model_id)
    if not os.path.isfile(sam_checkpoint):
        try:
            download_url_to_file(url_sam, sam_checkpoint)
        except Exception as e:
            ia_logging.error(str(e))
            return str(e)

        return IAFileManager.DOWNLOAD_COMPLETE
    else:
        return "Model already exists"


sam_dict = dict(sam_masks=None, mask_image=None, cnet=None, orig_image=None, pad_mask=None)


def save_mask_image(mask_image, save_mask_chk=False):
    """Save mask image.



    Args:

        mask_image (np.ndarray): mask image

        save_mask_chk (bool, optional): If True, save mask image. Defaults to False.



    Returns:

        None

    """
    if save_mask_chk:
        save_name = "_".join([ia_file_manager.savename_prefix, "created_mask"]) + ".png"
        save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
        Image.fromarray(mask_image).save(save_name)


@clear_cache_decorator
def input_image_upload(input_image, sam_image, sel_mask):
    global sam_dict
    sam_dict["orig_image"] = input_image
    sam_dict["pad_mask"] = None

    if (sam_dict["mask_image"] is None or not isinstance(sam_dict["mask_image"], np.ndarray) or
            sam_dict["mask_image"].shape != input_image.shape):
        sam_dict["mask_image"] = np.zeros_like(input_image, dtype=np.uint8)

    ret_sel_image = cv2.addWeighted(input_image, 0.5, sam_dict["mask_image"], 0.5, 0)

    if sam_image is None or not isinstance(sam_image, dict) or "image" not in sam_image:
        sam_dict["sam_masks"] = None
        ret_sam_image = np.zeros_like(input_image, dtype=np.uint8)
    elif sam_image["image"].shape == input_image.shape:
        ret_sam_image = gr.update()
    else:
        sam_dict["sam_masks"] = None
        ret_sam_image = gr.update(value=np.zeros_like(input_image, dtype=np.uint8))

    if sel_mask is None or not isinstance(sel_mask, dict) or "image" not in sel_mask:
        ret_sel_mask = ret_sel_image
    elif sel_mask["image"].shape == ret_sel_image.shape and np.all(sel_mask["image"] == ret_sel_image):
        ret_sel_mask = gr.update()
    else:
        ret_sel_mask = gr.update(value=ret_sel_image)

    return ret_sam_image, ret_sel_mask, gr.update(interactive=True)


@clear_cache_decorator
def run_padding(input_image, pad_scale_width, pad_scale_height, pad_lr_barance, pad_tb_barance, padding_mode="edge"):
    global sam_dict
    if input_image is None or sam_dict["orig_image"] is None:
        sam_dict["orig_image"] = None
        sam_dict["pad_mask"] = None
        return None, "Input image not found"

    orig_image = sam_dict["orig_image"]

    height, width = orig_image.shape[:2]
    pad_width, pad_height = (int(width * pad_scale_width), int(height * pad_scale_height))
    ia_logging.info(f"resize by padding: ({height}, {width}) -> ({pad_height}, {pad_width})")

    pad_size_w, pad_size_h = (pad_width - width, pad_height - height)
    pad_size_l = int(pad_size_w * pad_lr_barance)
    pad_size_r = pad_size_w - pad_size_l
    pad_size_t = int(pad_size_h * pad_tb_barance)
    pad_size_b = pad_size_h - pad_size_t

    pad_width = [(pad_size_t, pad_size_b), (pad_size_l, pad_size_r), (0, 0)]
    if padding_mode == "constant":
        fill_value = 127
        pad_image = np.pad(orig_image, pad_width=pad_width, mode=padding_mode, constant_values=fill_value)
    else:
        pad_image = np.pad(orig_image, pad_width=pad_width, mode=padding_mode)

    mask_pad_width = [(pad_size_t, pad_size_b), (pad_size_l, pad_size_r)]
    pad_mask = np.zeros((height, width), dtype=np.uint8)
    pad_mask = np.pad(pad_mask, pad_width=mask_pad_width, mode="constant", constant_values=255)
    sam_dict["pad_mask"] = dict(segmentation=pad_mask.astype(bool))

    return pad_image, "Padding done"


@clear_cache_decorator
def run_sam(input_image, sam_model_id, sam_image, anime_style_chk=False):
    global sam_dict
    if not inpalib.sam_file_exists(sam_model_id):
        ret_sam_image = None if sam_image is None else gr.update()
        return ret_sam_image, f"{sam_model_id} not found, please download"

    if input_image is None:
        ret_sam_image = None if sam_image is None else gr.update()
        return ret_sam_image, "Input image not found"

    set_ia_config(IAConfig.KEYS.SAM_MODEL_ID, sam_model_id, IAConfig.SECTIONS.USER)

    if sam_dict["sam_masks"] is not None:
        sam_dict["sam_masks"] = None
        gc.collect()

    ia_logging.info(f"input_image: {input_image.shape} {input_image.dtype}")

    try:
        sam_masks = inpalib.generate_sam_masks(input_image, sam_model_id, anime_style_chk)
        sam_masks = inpalib.sort_masks_by_area(sam_masks)
        sam_masks = inpalib.insert_mask_to_sam_masks(sam_masks, sam_dict["pad_mask"])

        seg_image = inpalib.create_seg_color_image(input_image, sam_masks)

        sam_dict["sam_masks"] = sam_masks

    except Exception as e:
        print(traceback.format_exc())
        ia_logging.error(str(e))
        ret_sam_image = None if sam_image is None else gr.update()
        return ret_sam_image, "Segment Anything failed"

    if IAConfig.global_args.get("save_seg", False):
        save_name = "_".join([ia_file_manager.savename_prefix, os.path.splitext(sam_model_id)[0]]) + ".png"
        save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
        Image.fromarray(seg_image).save(save_name)

    if sam_image is None:
        return seg_image, "Segment Anything complete"
    else:
        if sam_image["image"].shape == seg_image.shape and np.all(sam_image["image"] == seg_image):
            return gr.update(), "Segment Anything complete"
        else:
            return gr.update(value=seg_image), "Segment Anything complete"


@clear_cache_decorator
def select_mask(input_image, sam_image, invert_chk, ignore_black_chk, sel_mask):
    global sam_dict
    if sam_dict["sam_masks"] is None or sam_image is None:
        ret_sel_mask = None if sel_mask is None else gr.update()
        return ret_sel_mask
    sam_masks = sam_dict["sam_masks"]

    # image = sam_image["image"]
    mask = sam_image["mask"][:, :, 0:1]

    try:
        seg_image = inpalib.create_mask_image(mask, sam_masks, ignore_black_chk)
        if invert_chk:
            seg_image = inpalib.invert_mask(seg_image)

        sam_dict["mask_image"] = seg_image

    except Exception as e:
        print(traceback.format_exc())
        ia_logging.error(str(e))
        ret_sel_mask = None if sel_mask is None else gr.update()
        return ret_sel_mask

    if input_image is not None and input_image.shape == seg_image.shape:
        ret_image = cv2.addWeighted(input_image, 0.5, seg_image, 0.5, 0)
    else:
        ret_image = seg_image

    if sel_mask is None:
        return ret_image
    else:
        if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
            return gr.update()
        else:
            return gr.update(value=ret_image)


@clear_cache_decorator
def expand_mask(input_image, sel_mask, expand_iteration=1):
    global sam_dict
    if sam_dict["mask_image"] is None or sel_mask is None:
        return None

    new_sel_mask = sam_dict["mask_image"]

    expand_iteration = int(np.clip(expand_iteration, 1, 100))

    new_sel_mask = cv2.dilate(new_sel_mask, np.ones((3, 3), dtype=np.uint8), iterations=expand_iteration)

    sam_dict["mask_image"] = new_sel_mask

    if input_image is not None and input_image.shape == new_sel_mask.shape:
        ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
    else:
        ret_image = new_sel_mask

    if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
        return gr.update()
    else:
        return gr.update(value=ret_image)


@clear_cache_decorator
def apply_mask(input_image, sel_mask):
    global sam_dict
    if sam_dict["mask_image"] is None or sel_mask is None:
        return None

    sel_mask_image = sam_dict["mask_image"]
    sel_mask_mask = np.logical_not(sel_mask["mask"][:, :, 0:3].astype(bool)).astype(np.uint8)
    new_sel_mask = sel_mask_image * sel_mask_mask

    sam_dict["mask_image"] = new_sel_mask

    if input_image is not None and input_image.shape == new_sel_mask.shape:
        ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
    else:
        ret_image = new_sel_mask

    if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
        return gr.update()
    else:
        return gr.update(value=ret_image)


@clear_cache_decorator
def add_mask(input_image, sel_mask):
    global sam_dict
    if sam_dict["mask_image"] is None or sel_mask is None:
        return None

    sel_mask_image = sam_dict["mask_image"]
    sel_mask_mask = sel_mask["mask"][:, :, 0:3].astype(bool).astype(np.uint8)
    new_sel_mask = sel_mask_image + (sel_mask_mask * np.invert(sel_mask_image, dtype=np.uint8))

    sam_dict["mask_image"] = new_sel_mask

    if input_image is not None and input_image.shape == new_sel_mask.shape:
        ret_image = cv2.addWeighted(input_image, 0.5, new_sel_mask, 0.5, 0)
    else:
        ret_image = new_sel_mask

    if sel_mask["image"].shape == ret_image.shape and np.all(sel_mask["image"] == ret_image):
        return gr.update()
    else:
        return gr.update(value=ret_image)


def auto_resize_to_pil(input_image, mask_image):
    init_image = Image.fromarray(input_image).convert("RGB")
    mask_image = Image.fromarray(mask_image).convert("RGB")
    assert init_image.size == mask_image.size, "The sizes of the image and mask do not match"
    width, height = init_image.size

    new_height = (height // 8) * 8
    new_width = (width // 8) * 8
    if new_width < width or new_height < height:
        if (new_width / width) < (new_height / height):
            scale = new_height / height
        else:
            scale = new_width / width
        resize_height = int(height*scale+0.5)
        resize_width = int(width*scale+0.5)
        if height != resize_height or width != resize_width:
            ia_logging.info(f"resize: ({height}, {width}) -> ({resize_height}, {resize_width})")
            init_image = transforms.functional.resize(init_image, (resize_height, resize_width), transforms.InterpolationMode.LANCZOS)
            mask_image = transforms.functional.resize(mask_image, (resize_height, resize_width), transforms.InterpolationMode.LANCZOS)
        if resize_height != new_height or resize_width != new_width:
            ia_logging.info(f"center_crop: ({resize_height}, {resize_width}) -> ({new_height}, {new_width})")
            init_image = transforms.functional.center_crop(init_image, (new_height, new_width))
            mask_image = transforms.functional.center_crop(mask_image, (new_height, new_width))

    return init_image, mask_image


@clear_cache_decorator
def run_inpaint(input_image, sel_mask, prompt, n_prompt, ddim_steps, cfg_scale, seed, inp_model_id, save_mask_chk, composite_chk,

                sampler_name="DDIM", iteration_count=1):
    global sam_dict
    if input_image is None or sam_dict["mask_image"] is None or sel_mask is None:
        ia_logging.error("The image or mask does not exist")
        return

    mask_image = sam_dict["mask_image"]
    if input_image.shape != mask_image.shape:
        ia_logging.error("The sizes of the image and mask do not match")
        return

    set_ia_config(IAConfig.KEYS.INP_MODEL_ID, inp_model_id, IAConfig.SECTIONS.USER)

    save_mask_image(mask_image, save_mask_chk)

    ia_logging.info(f"Loading model {inp_model_id}")
    config_offline_inpainting = IAConfig.global_args.get("offline", False)
    if config_offline_inpainting:
        ia_logging.info("Run Inpainting on offline network: {}".format(str(config_offline_inpainting)))
    local_files_only = False
    local_file_status = download_model_from_hf(inp_model_id, local_files_only=True)
    if local_file_status != IAFileManager.DOWNLOAD_COMPLETE:
        if config_offline_inpainting:
            ia_logging.warning(local_file_status)
            return
    else:
        local_files_only = True
        ia_logging.info("local_files_only: {}".format(str(local_files_only)))

    if platform.system() == "Darwin" or devices.device == devices.cpu or ia_check_versions.torch_on_amd_rocm:
        torch_dtype = torch.float32
    else:
        torch_dtype = torch.float16

    try:
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            inp_model_id, torch_dtype=torch_dtype, local_files_only=local_files_only, use_safetensors=True)
    except Exception as e:
        ia_logging.error(str(e))
        if not config_offline_inpainting:
            try:
                pipe = StableDiffusionInpaintPipeline.from_pretrained(
                    inp_model_id, torch_dtype=torch_dtype, use_safetensors=True)
            except Exception as e:
                ia_logging.error(str(e))
                try:
                    pipe = StableDiffusionInpaintPipeline.from_pretrained(
                        inp_model_id, torch_dtype=torch_dtype, force_download=True, use_safetensors=True)
                except Exception as e:
                    ia_logging.error(str(e))
                    return
        else:
            return
    pipe.safety_checker = None

    ia_logging.info(f"Using sampler {sampler_name}")
    if sampler_name == "DDIM":
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "Euler":
        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "Euler a":
        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "DPM2 Karras":
        pipe.scheduler = KDPM2DiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "DPM2 a Karras":
        pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    else:
        ia_logging.info("Sampler fallback to DDIM")
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)

    if platform.system() == "Darwin":
        pipe = pipe.to("mps" if ia_check_versions.torch_mps_is_available else "cpu")
        pipe.enable_attention_slicing()
        torch_generator = torch.Generator(devices.cpu)
    else:
        if ia_check_versions.diffusers_enable_cpu_offload and devices.device != devices.cpu:
            ia_logging.info("Enable model cpu offload")
            pipe.enable_model_cpu_offload()
        else:
            pipe = pipe.to(devices.device)
        if xformers_available:
            ia_logging.info("Enable xformers memory efficient attention")
            pipe.enable_xformers_memory_efficient_attention()
        else:
            ia_logging.info("Enable attention slicing")
            pipe.enable_attention_slicing()
        if "privateuseone" in str(getattr(devices.device, "type", "")):
            torch_generator = torch.Generator(devices.cpu)
        else:
            torch_generator = torch.Generator(devices.device)

    init_image, mask_image = auto_resize_to_pil(input_image, mask_image)
    width, height = init_image.size

    output_list = []
    iteration_count = iteration_count if iteration_count is not None else 1
    for count in range(int(iteration_count)):
        gc.collect()
        if seed < 0 or count > 0:
            seed = random.randint(0, 2147483647)

        generator = torch_generator.manual_seed(seed)

        pipe_args_dict = {
            "prompt": prompt,
            "image": init_image,
            "width": width,
            "height": height,
            "mask_image": mask_image,
            "num_inference_steps": ddim_steps,
            "guidance_scale": cfg_scale,
            "negative_prompt": n_prompt,
            "generator": generator,
        }

        output_image = pipe(**pipe_args_dict).images[0]

        if composite_chk:
            dilate_mask_image = Image.fromarray(cv2.dilate(np.array(mask_image), np.ones((3, 3), dtype=np.uint8), iterations=4))
            output_image = Image.composite(output_image, init_image, dilate_mask_image.convert("L").filter(ImageFilter.GaussianBlur(3)))

        generation_params = {
            "Steps": ddim_steps,
            "Sampler": sampler_name,
            "CFG scale": cfg_scale,
            "Seed": seed,
            "Size": f"{width}x{height}",
            "Model": inp_model_id,
        }

        generation_params_text = ", ".join([k if k == v else f"{k}: {v}" for k, v in generation_params.items() if v is not None])
        prompt_text = prompt if prompt else ""
        negative_prompt_text = "\nNegative prompt: " + n_prompt if n_prompt else ""
        infotext = f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()

        metadata = PngInfo()
        metadata.add_text("parameters", infotext)

        save_name = "_".join([ia_file_manager.savename_prefix, os.path.basename(inp_model_id), str(seed)]) + ".png"
        save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
        output_image.save(save_name, pnginfo=metadata)

        output_list.append(output_image)

        yield output_list, max([1, iteration_count - (count + 1)])


@clear_cache_decorator
def run_cleaner(input_image, sel_mask, cleaner_model_id, cleaner_save_mask_chk):
    global sam_dict
    if input_image is None or sam_dict["mask_image"] is None or sel_mask is None:
        ia_logging.error("The image or mask does not exist")
        return None

    mask_image = sam_dict["mask_image"]
    if input_image.shape != mask_image.shape:
        ia_logging.error("The sizes of the image and mask do not match")
        return None

    save_mask_image(mask_image, cleaner_save_mask_chk)

    ia_logging.info(f"Loading model {cleaner_model_id}")
    if platform.system() == "Darwin":
        model = ModelManager(name=cleaner_model_id, device=devices.cpu)
    else:
        model = ModelManager(name=cleaner_model_id, device=devices.device)

    init_image, mask_image = auto_resize_to_pil(input_image, mask_image)
    width, height = init_image.size

    init_image = np.array(init_image)
    mask_image = np.array(mask_image.convert("L"))

    config = Config(
        ldm_steps=20,
        ldm_sampler=LDMSampler.ddim,
        hd_strategy=HDStrategy.ORIGINAL,
        hd_strategy_crop_margin=32,
        hd_strategy_crop_trigger_size=512,
        hd_strategy_resize_limit=512,
        prompt="",
        sd_steps=20,
        sd_sampler=SDSampler.ddim
    )

    output_image = model(image=init_image, mask=mask_image, config=config)
    output_image = cv2.cvtColor(output_image.astype(np.uint8), cv2.COLOR_BGR2RGB)
    output_image = Image.fromarray(output_image)

    save_name = "_".join([ia_file_manager.savename_prefix, os.path.basename(cleaner_model_id)]) + ".png"
    save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
    output_image.save(save_name)

    del model
    return [output_image]


@clear_cache_decorator
def run_get_alpha_image(input_image, sel_mask):
    global sam_dict
    if input_image is None or sam_dict["mask_image"] is None or sel_mask is None:
        ia_logging.error("The image or mask does not exist")
        return None, ""

    mask_image = sam_dict["mask_image"]
    if input_image.shape != mask_image.shape:
        ia_logging.error("The sizes of the image and mask do not match")
        return None, ""

    alpha_image = Image.fromarray(input_image).convert("RGBA")
    mask_image = Image.fromarray(mask_image).convert("L")

    alpha_image.putalpha(mask_image)

    save_name = "_".join([ia_file_manager.savename_prefix, "rgba_image"]) + ".png"
    save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
    alpha_image.save(save_name)

    return alpha_image, f"saved: {save_name}"


@clear_cache_decorator
def run_get_mask(sel_mask):
    global sam_dict
    if sam_dict["mask_image"] is None or sel_mask is None:
        return None

    mask_image = sam_dict["mask_image"]

    save_name = "_".join([ia_file_manager.savename_prefix, "created_mask"]) + ".png"
    save_name = os.path.join(ia_file_manager.outputs_dir, save_name)
    Image.fromarray(mask_image).save(save_name)

    return mask_image


def on_ui_tabs():
    setup_ia_config_ini()
    sampler_names = get_sampler_names()
    sam_model_ids = get_sam_model_ids()
    sam_model_index = get_ia_config_index(IAConfig.KEYS.SAM_MODEL_ID, IAConfig.SECTIONS.USER)
    inp_model_ids = get_inp_model_ids()
    inp_model_index = get_ia_config_index(IAConfig.KEYS.INP_MODEL_ID, IAConfig.SECTIONS.USER)
    cleaner_model_ids = get_cleaner_model_ids()
    padding_mode_names = get_padding_mode_names()

    out_gallery_kwargs = dict(columns=2, height=520, object_fit="contain", preview=True)

    block = gr.Blocks(analytics_enabled=False).queue()
    block.title = "Inpaint Anything"
    with block as inpaint_anything_interface:
        with gr.Row():
            gr.Markdown("## Inpainting with Segment Anything")
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    with gr.Column():
                        sam_model_id = gr.Dropdown(label="Segment Anything Model ID", elem_id="sam_model_id", choices=sam_model_ids,
                                                   value=sam_model_ids[sam_model_index], show_label=True)
                    with gr.Column():
                        with gr.Row():
                            load_model_btn = gr.Button("Download model", elem_id="load_model_btn")
                        with gr.Row():
                            status_text = gr.Textbox(label="", elem_id="status_text", max_lines=1, show_label=False, interactive=False)
                with gr.Row():
                    input_image = gr.Image(label="Input image", elem_id="ia_input_image", source="upload", type="numpy", interactive=True)

                with gr.Row():
                    with gr.Accordion("Padding options", elem_id="padding_options", open=False):
                        with gr.Row():
                            with gr.Column():
                                pad_scale_width = gr.Slider(label="Scale Width", elem_id="pad_scale_width", minimum=1.0, maximum=1.5, value=1.0, step=0.01)
                            with gr.Column():
                                pad_lr_barance = gr.Slider(label="Left/Right Balance", elem_id="pad_lr_barance", minimum=0.0, maximum=1.0, value=0.5, step=0.01)
                        with gr.Row():
                            with gr.Column():
                                pad_scale_height = gr.Slider(label="Scale Height", elem_id="pad_scale_height", minimum=1.0, maximum=1.5, value=1.0, step=0.01)
                            with gr.Column():
                                pad_tb_barance = gr.Slider(label="Top/Bottom Balance", elem_id="pad_tb_barance", minimum=0.0, maximum=1.0, value=0.5, step=0.01)
                        with gr.Row():
                            with gr.Column():
                                padding_mode = gr.Dropdown(label="Padding Mode", elem_id="padding_mode", choices=padding_mode_names, value="edge")
                            with gr.Column():
                                padding_btn = gr.Button("Run Padding", elem_id="padding_btn")

                with gr.Row():
                    with gr.Column():
                        anime_style_chk = gr.Checkbox(label="Anime Style (Up Detection, Down mask Quality)", elem_id="anime_style_chk",
                                                      show_label=True, interactive=True)
                    with gr.Column():
                        sam_btn = gr.Button("Run Segment Anything", elem_id="sam_btn", variant="primary", interactive=False)

                with gr.Tab("Inpainting", elem_id="inpainting_tab"):
                    prompt = gr.Textbox(label="Inpainting Prompt", elem_id="sd_prompt")
                    n_prompt = gr.Textbox(label="Negative Prompt", elem_id="sd_n_prompt")
                    with gr.Accordion("Advanced options", elem_id="inp_advanced_options", open=False):
                        composite_chk = gr.Checkbox(label="Mask area Only", elem_id="composite_chk", value=True, show_label=True, interactive=True)
                        with gr.Row():
                            with gr.Column():
                                sampler_name = gr.Dropdown(label="Sampler", elem_id="sampler_name", choices=sampler_names,
                                                           value=sampler_names[0], show_label=True)
                            with gr.Column():
                                ddim_steps = gr.Slider(label="Sampling Steps", elem_id="ddim_steps", minimum=1, maximum=100, value=20, step=1)
                        cfg_scale = gr.Slider(label="Guidance Scale", elem_id="cfg_scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                        seed = gr.Slider(
                            label="Seed",
                            elem_id="sd_seed",
                            minimum=-1,
                            maximum=2147483647,
                            step=1,
                            value=-1,
                        )
                    with gr.Row():
                        with gr.Column():
                            inp_model_id = gr.Dropdown(label="Inpainting Model ID", elem_id="inp_model_id",
                                                       choices=inp_model_ids, value=inp_model_ids[inp_model_index], show_label=True)
                        with gr.Column():
                            with gr.Row():
                                inpaint_btn = gr.Button("Run Inpainting", elem_id="inpaint_btn", variant="primary")
                            with gr.Row():
                                save_mask_chk = gr.Checkbox(label="Save mask", elem_id="save_mask_chk",
                                                            value=False, show_label=False, interactive=False, visible=False)
                                iteration_count = gr.Slider(label="Iterations", elem_id="iteration_count", minimum=1, maximum=10, value=1, step=1)

                    with gr.Row():
                        if ia_check_versions.gradio_version_is_old:
                            out_image = gr.Gallery(label="Inpainted image", elem_id="ia_out_image", show_label=False
                                                   ).style(**out_gallery_kwargs)
                        else:
                            out_image = gr.Gallery(label="Inpainted image", elem_id="ia_out_image", show_label=False,
                                                   **out_gallery_kwargs)

                with gr.Tab("Cleaner", elem_id="cleaner_tab"):
                    with gr.Row():
                        with gr.Column():
                            cleaner_model_id = gr.Dropdown(label="Cleaner Model ID", elem_id="cleaner_model_id",
                                                           choices=cleaner_model_ids, value=cleaner_model_ids[0], show_label=True)
                        with gr.Column():
                            with gr.Row():
                                cleaner_btn = gr.Button("Run Cleaner", elem_id="cleaner_btn", variant="primary")
                            with gr.Row():
                                cleaner_save_mask_chk = gr.Checkbox(label="Save mask", elem_id="cleaner_save_mask_chk",
                                                                    value=False, show_label=False, interactive=False, visible=False)

                    with gr.Row():
                        if ia_check_versions.gradio_version_is_old:
                            cleaner_out_image = gr.Gallery(label="Cleaned image", elem_id="ia_cleaner_out_image", show_label=False
                                                           ).style(**out_gallery_kwargs)
                        else:
                            cleaner_out_image = gr.Gallery(label="Cleaned image", elem_id="ia_cleaner_out_image", show_label=False,
                                                           **out_gallery_kwargs)

                with gr.Tab("Mask only", elem_id="mask_only_tab"):
                    with gr.Row():
                        with gr.Column():
                            get_alpha_image_btn = gr.Button("Get mask as alpha of image", elem_id="get_alpha_image_btn")
                        with gr.Column():
                            get_mask_btn = gr.Button("Get mask", elem_id="get_mask_btn")

                    with gr.Row():
                        with gr.Column():
                            alpha_out_image = gr.Image(label="Alpha channel image", elem_id="alpha_out_image", type="pil", image_mode="RGBA", interactive=False)
                        with gr.Column():
                            mask_out_image = gr.Image(label="Mask image", elem_id="mask_out_image", type="numpy", interactive=False)

                    with gr.Row():
                        with gr.Column():
                            get_alpha_status_text = gr.Textbox(label="", elem_id="get_alpha_status_text", max_lines=1, show_label=False, interactive=False)
                        with gr.Column():
                            gr.Markdown("")

            with gr.Column():
                with gr.Row():
                    gr.Markdown("Mouse over image: Press `S` key for Fullscreen mode, `R` key to Reset zoom")
                with gr.Row():
                    if ia_check_versions.gradio_version_is_old:
                        sam_image = gr.Image(label="Segment Anything image", elem_id="ia_sam_image", type="numpy", tool="sketch", brush_radius=8,
                                             show_label=False, interactive=True).style(height=480)
                    else:
                        sam_image = gr.Image(label="Segment Anything image", elem_id="ia_sam_image", type="numpy", tool="sketch", brush_radius=8,
                                             show_label=False, interactive=True, height=480)

                with gr.Row():
                    with gr.Column():
                        select_btn = gr.Button("Create Mask", elem_id="select_btn", variant="primary")
                    with gr.Column():
                        with gr.Row():
                            invert_chk = gr.Checkbox(label="Invert mask", elem_id="invert_chk", show_label=True, interactive=True)
                            ignore_black_chk = gr.Checkbox(label="Ignore black area", elem_id="ignore_black_chk", value=True, show_label=True, interactive=True)

                with gr.Row():
                    if ia_check_versions.gradio_version_is_old:
                        sel_mask = gr.Image(label="Selected mask image", elem_id="ia_sel_mask", type="numpy", tool="sketch", brush_radius=12,
                                            show_label=False, interactive=True).style(height=480)
                    else:
                        sel_mask = gr.Image(label="Selected mask image", elem_id="ia_sel_mask", type="numpy", tool="sketch", brush_radius=12,
                                            show_label=False, interactive=True, height=480)

                with gr.Row():
                    with gr.Column():
                        expand_mask_btn = gr.Button("Expand mask region", elem_id="expand_mask_btn")
                        expand_mask_iteration_count = gr.Slider(label="Expand Mask Iterations",
                                                                elem_id="expand_mask_iteration_count", minimum=1, maximum=100, value=1, step=1)
                    with gr.Column():
                        apply_mask_btn = gr.Button("Trim mask by sketch", elem_id="apply_mask_btn")
                        add_mask_btn = gr.Button("Add mask by sketch", elem_id="add_mask_btn")

            load_model_btn.click(download_model, inputs=[sam_model_id], outputs=[status_text])
            input_image.upload(input_image_upload, inputs=[input_image, sam_image, sel_mask], outputs=[sam_image, sel_mask, sam_btn]).then(
                fn=None, inputs=None, outputs=None, _js="inpaintAnything_initSamSelMask")
            padding_btn.click(run_padding, inputs=[input_image, pad_scale_width, pad_scale_height, pad_lr_barance, pad_tb_barance, padding_mode],
                              outputs=[input_image, status_text])
            sam_btn.click(run_sam, inputs=[input_image, sam_model_id, sam_image, anime_style_chk], outputs=[sam_image, status_text]).then(
                fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSamMask")
            select_btn.click(select_mask, inputs=[input_image, sam_image, invert_chk, ignore_black_chk, sel_mask], outputs=[sel_mask]).then(
                fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSelMask")
            expand_mask_btn.click(expand_mask, inputs=[input_image, sel_mask, expand_mask_iteration_count], outputs=[sel_mask]).then(
                fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSelMask")
            apply_mask_btn.click(apply_mask, inputs=[input_image, sel_mask], outputs=[sel_mask]).then(
                fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSelMask")
            add_mask_btn.click(add_mask, inputs=[input_image, sel_mask], outputs=[sel_mask]).then(
                fn=None, inputs=None, outputs=None, _js="inpaintAnything_clearSelMask")

            inpaint_btn.click(
                run_inpaint,
                inputs=[input_image, sel_mask, prompt, n_prompt, ddim_steps, cfg_scale, seed, inp_model_id, save_mask_chk, composite_chk,
                        sampler_name, iteration_count],
                outputs=[out_image, iteration_count])
            cleaner_btn.click(
                run_cleaner,
                inputs=[input_image, sel_mask, cleaner_model_id, cleaner_save_mask_chk],
                outputs=[cleaner_out_image])
            get_alpha_image_btn.click(
                run_get_alpha_image,
                inputs=[input_image, sel_mask],
                outputs=[alpha_out_image, get_alpha_status_text])
            get_mask_btn.click(
                run_get_mask,
                inputs=[sel_mask],
                outputs=[mask_out_image])

    return [(inpaint_anything_interface, "Inpaint Anything", "inpaint_anything")]


block, _, _ = on_ui_tabs()[0]
block.launch(share=True)