app.py

import spaces
import os
from stablepy import (
    Model_Diffusers,
    SCHEDULE_TYPE_OPTIONS,
    SCHEDULE_PREDICTION_TYPE_OPTIONS,
    check_scheduler_compatibility,
    TASK_AND_PREPROCESSORS,
)
from constants import (
    TASK_STABLEPY,
    TASK_MODEL_LIST,
    UPSCALER_DICT_GUI,
    UPSCALER_KEYS,
    PROMPT_W_OPTIONS,
    WARNING_MSG_VAE,
    SDXL_TASK,
    MODEL_TYPE_TASK,
    POST_PROCESSING_SAMPLER,
    DIFFUSERS_CONTROLNET_MODEL,

)
from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
import torch
import re
from stablepy import (
    scheduler_names,
    IP_ADAPTERS_SD,
    IP_ADAPTERS_SDXL,
)
import time
from PIL import ImageFile
from utils import (
    get_model_list,
    extract_parameters,
    get_model_type,
    extract_exif_data,
    create_mask_now,
    download_diffuser_repo,
    get_used_storage_gb,
    delete_model,
    progress_step_bar,
    html_template_message,
    escape_html,
)
from image_processor import preprocessor_tab
from datetime import datetime
import gradio as gr
import logging
import diffusers
import warnings
from stablepy import logger
from diffusers import FluxPipeline
# import urllib.parse

ImageFile.LOAD_TRUNCATED_IMAGES = True
torch.backends.cuda.matmul.allow_tf32 = True
# os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"] = "1"
print(os.getenv("SPACES_ZERO_GPU"))

## BEGIN MOD
from modutils import (list_uniq, download_private_repo, get_model_id_list, get_tupled_embed_list,
    get_lora_model_list, get_all_lora_tupled_list, update_loras, apply_lora_prompt, set_prompt_loras,
    get_my_lora, upload_file_lora, move_file_lora, search_civitai_lora, select_civitai_lora,
    update_civitai_selection, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
    set_textual_inversion_prompt, get_model_pipeline, change_interface_mode, get_t2i_model_info,
    get_tupled_model_list, save_gallery_images, save_gallery_history, set_optimization, set_sampler_settings,
    set_quick_presets, process_style_prompt, optimization_list, save_images, download_things, valid_model_name,
    preset_styles, preset_quality, preset_sampler_setting, translate_to_en, EXAMPLES_GUI, RESOURCES)
from env import (HF_TOKEN, CIVITAI_API_KEY, HF_LORA_ESSENTIAL_PRIVATE_REPO, HF_VAE_PRIVATE_REPO,
    HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO,
    DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_EMBEDS_SDXL,
    DIRECTORY_EMBEDS_POSITIVE_SDXL, LOAD_DIFFUSERS_FORMAT_MODEL,
    DOWNLOAD_MODEL_LIST, DOWNLOAD_LORA_LIST, DOWNLOAD_VAE_LIST, DOWNLOAD_EMBEDS)

download_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, DIRECTORY_LORAS, True)
download_private_repo(HF_VAE_PRIVATE_REPO, DIRECTORY_VAES, False)
## END MOD

# - **Download Models**
DOWNLOAD_MODEL = ", ".join(DOWNLOAD_MODEL_LIST)
# - **Download VAEs**
DOWNLOAD_VAE = ", ".join(DOWNLOAD_VAE_LIST)
# - **Download LoRAs**
DOWNLOAD_LORA = ", ".join(DOWNLOAD_LORA_LIST)

# Download stuffs
for url in [url.strip() for url in DOWNLOAD_MODEL.split(',')]:
    if not os.path.exists(f"./models/{url.split('/')[-1]}"):
        download_things(DIRECTORY_MODELS, url, HF_TOKEN, CIVITAI_API_KEY)
for url in [url.strip() for url in DOWNLOAD_VAE.split(',')]:
    if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
        download_things(DIRECTORY_VAES, url, HF_TOKEN, CIVITAI_API_KEY)
for url in [url.strip() for url in DOWNLOAD_LORA.split(',')]:
    if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
        download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY)

# Download Embeddings
for url_embed in DOWNLOAD_EMBEDS:
    if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
        download_things(DIRECTORY_EMBEDS, url_embed, HF_TOKEN, CIVITAI_API_KEY)

# Build list models
embed_list = get_model_list(DIRECTORY_EMBEDS)
lora_model_list = get_lora_model_list()
vae_model_list = get_model_list(DIRECTORY_VAES)
vae_model_list.insert(0, "BakedVAE")
vae_model_list.insert(0, "None")

## BEGIN MOD
single_file_model_list = get_model_list(DIRECTORY_MODELS)
model_list = list_uniq(get_model_id_list() + LOAD_DIFFUSERS_FORMAT_MODEL + single_file_model_list)
download_private_repo(HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, DIRECTORY_EMBEDS_SDXL, False)
download_private_repo(HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO, DIRECTORY_EMBEDS_POSITIVE_SDXL, False)
embed_sdxl_list = get_model_list(DIRECTORY_EMBEDS_SDXL) + get_model_list(DIRECTORY_EMBEDS_POSITIVE_SDXL)

def get_embed_list(pipeline_name):
    return get_tupled_embed_list(embed_sdxl_list if pipeline_name == "StableDiffusionXLPipeline" else embed_list)
## END MOD

print('\033[33m🏁 Download and listing of valid models completed.\033[0m')

flux_repo = "camenduru/FLUX.1-dev-diffusers"
flux_pipe = FluxPipeline.from_pretrained(
    flux_repo,
    transformer=None,
    torch_dtype=torch.bfloat16,
).to("cuda")
components = flux_pipe.components
components.pop("transformer", None)
delete_model(flux_repo)

#######################
# GUI
#######################
logging.getLogger("diffusers").setLevel(logging.ERROR)
diffusers.utils.logging.set_verbosity(40)
warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
## BEGIN MOD
#logger.setLevel(logging.CRITICAL)
logger.setLevel(logging.DEBUG)

from tagger.v2 import V2_ALL_MODELS, v2_random_prompt, v2_upsampling_prompt
from tagger.utils import (gradio_copy_text, COPY_ACTION_JS, gradio_copy_prompt,
    V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS, V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)
from tagger.tagger import (predict_tags_wd, convert_danbooru_to_e621_prompt,
    remove_specific_prompt, insert_recom_prompt, insert_model_recom_prompt,
    compose_prompt_to_copy, translate_prompt, select_random_character)
def description_ui():
    gr.Markdown(
        """

## Danbooru Tags Transformer V2 Demo with WD Tagger

(Image =>) Prompt => Upsampled longer prompt

- Mod of p1atdev's [Danbooru Tags Transformer V2 Demo](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2) and [WD Tagger with 🤗 transformers](https://huggingface.co/spaces/p1atdev/wd-tagger-transformers).

- Models: p1atdev's [wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf), [dart-v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft)

"""
    )
## END MOD

class GuiSD:
    def __init__(self, stream=True):
        self.model = None
        self.status_loading = False
        self.sleep_loading = 4
        self.last_load = datetime.now()
        self.inventory = []

    def update_storage_models(self, storage_floor_gb=24, required_inventory_for_purge=3):
        while get_used_storage_gb() > storage_floor_gb:
            if len(self.inventory) < required_inventory_for_purge:
                break
            removal_candidate = self.inventory.pop(0)
            delete_model(removal_candidate)

    def update_inventory(self, model_name):
        if model_name not in single_file_model_list:
            self.inventory = [
                m for m in self.inventory if m != model_name
            ] + [model_name]
        print(self.inventory)

    def load_new_model(self, model_name, vae_model, task, controlnet_model, progress=gr.Progress(track_tqdm=True)):

        # download link model > model_name

        model_name = valid_model_name(model_name) # MOD

        self.update_storage_models()

        vae_model = vae_model if vae_model != "None" else None
        model_type = get_model_type(model_name)
        dtype_model = torch.bfloat16 if model_type == "FLUX" else torch.float16

        if not os.path.exists(model_name):
            _ = download_diffuser_repo(
                repo_name=model_name,
                model_type=model_type,
                revision="main",
                token=True,
            )

        self.update_inventory(model_name)

        for i in range(68):
            if not self.status_loading:
                self.status_loading = True
                if i > 0:
                    time.sleep(self.sleep_loading)
                    print("Previous model ops...")
                break
            time.sleep(0.5)
            print(f"Waiting queue {i}")
            yield "Waiting queue"

        self.status_loading = True

        yield f"Loading model: {model_name}"

        if vae_model == "BakedVAE":
            if not os.path.exists(model_name):
                vae_model = model_name
            else:
                vae_model = None
        elif vae_model:
            vae_type = "SDXL" if "sdxl" in vae_model.lower() else "SD 1.5"
            if model_type != vae_type:
                gr.Warning(WARNING_MSG_VAE)

        print("Loading model...")

        try:
            start_time = time.time()

            if self.model is None:
                self.model = Model_Diffusers(
                    base_model_id=model_name,
                    task_name=TASK_STABLEPY[task],
                    vae_model=vae_model,
                    type_model_precision=dtype_model,
                    retain_task_model_in_cache=False,
                    controlnet_model=controlnet_model,
                    device="cpu",
                    env_components=components,
                )
                self.model.advanced_params(image_preprocessor_cuda_active=True)
            else:
                if self.model.base_model_id != model_name:
                    load_now_time = datetime.now()
                    elapsed_time = max((load_now_time - self.last_load).total_seconds(), 0)

                    if elapsed_time <= 9:
                        print("Waiting for the previous model's time ops...")
                        time.sleep(9 - elapsed_time)

                self.model.device = torch.device("cpu")
                self.model.load_pipe(
                    model_name,
                    task_name=TASK_STABLEPY[task],
                    vae_model=vae_model,
                    type_model_precision=dtype_model,
                    retain_task_model_in_cache=False,
                    controlnet_model=controlnet_model,
                )

            end_time = time.time()
            self.sleep_loading = max(min(int(end_time - start_time), 10), 4)
        except Exception as e:
            self.last_load = datetime.now()
            self.status_loading = False
            self.sleep_loading = 4
            raise e

        self.last_load = datetime.now()
        self.status_loading = False

        yield f"Model loaded: {model_name}"

    #@spaces.GPU
    @torch.inference_mode()
    def generate_pipeline(

        self,

        prompt,

        neg_prompt,

        num_images,

        steps,

        cfg,

        clip_skip,

        seed,

        lora1,

        lora_scale1,

        lora2,

        lora_scale2,

        lora3,

        lora_scale3,

        lora4,

        lora_scale4,

        lora5,

        lora_scale5,

        lora6,

        lora_scale6,

        lora7,

        lora_scale7,

        sampler,

        schedule_type,

        schedule_prediction_type,

        img_height,

        img_width,

        model_name,

        vae_model,

        task,

        image_control,

        preprocessor_name,

        preprocess_resolution,

        image_resolution,

        style_prompt,  # list []

        style_json_file,

        image_mask,

        strength,

        low_threshold,

        high_threshold,

        value_threshold,

        distance_threshold,

        recolor_gamma_correction,

        tile_blur_sigma,

        controlnet_output_scaling_in_unet,

        controlnet_start_threshold,

        controlnet_stop_threshold,

        textual_inversion,

        syntax_weights,

        upscaler_model_path,

        upscaler_increases_size,

        esrgan_tile,

        esrgan_tile_overlap,

        hires_steps,

        hires_denoising_strength,

        hires_sampler,

        hires_prompt,

        hires_negative_prompt,

        hires_before_adetailer,

        hires_after_adetailer,

        hires_schedule_type,

        hires_guidance_scale,

        controlnet_model,

        loop_generation,

        leave_progress_bar,

        disable_progress_bar,

        image_previews,

        display_images,

        save_generated_images,

        filename_pattern,

        image_storage_location,

        retain_compel_previous_load,

        retain_detailfix_model_previous_load,

        retain_hires_model_previous_load,

        t2i_adapter_preprocessor,

        t2i_adapter_conditioning_scale,

        t2i_adapter_conditioning_factor,

        xformers_memory_efficient_attention,

        freeu,

        generator_in_cpu,

        adetailer_inpaint_only,

        adetailer_verbose,

        adetailer_sampler,

        adetailer_active_a,

        prompt_ad_a,

        negative_prompt_ad_a,

        strength_ad_a,

        face_detector_ad_a,

        person_detector_ad_a,

        hand_detector_ad_a,

        mask_dilation_a,

        mask_blur_a,

        mask_padding_a,

        adetailer_active_b,

        prompt_ad_b,

        negative_prompt_ad_b,

        strength_ad_b,

        face_detector_ad_b,

        person_detector_ad_b,

        hand_detector_ad_b,

        mask_dilation_b,

        mask_blur_b,

        mask_padding_b,

        retain_task_cache_gui,

        guidance_rescale,

        image_ip1,

        mask_ip1,

        model_ip1,

        mode_ip1,

        scale_ip1,

        image_ip2,

        mask_ip2,

        model_ip2,

        mode_ip2,

        scale_ip2,

        pag_scale,

    ):
        info_state = html_template_message("Navigating latent space...")
        yield info_state, gr.update(), gr.update()

        vae_model = vae_model if vae_model != "None" else None
        loras_list = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
        vae_msg = f"VAE: {vae_model}" if vae_model else ""
        msg_lora = ""

## BEGIN MOD
        loras_list = [s if s else "None" for s in loras_list]
        global lora_model_list
        lora_model_list = get_lora_model_list()
        lora1, lora_scale1, lora2, lora_scale2, lora3, lora_scale3, lora4, lora_scale4, lora5, lora_scale5, lora6, lora_scale6, lora7, lora_scale7 = \
                set_prompt_loras(prompt, syntax_weights, model_name, lora1, lora_scale1, lora2, lora_scale2, lora3,
                                  lora_scale3, lora4, lora_scale4, lora5, lora_scale5, lora6, lora_scale6, lora7, lora_scale7)
## END MOD

        print("Config model:", model_name, vae_model, loras_list)

        task = TASK_STABLEPY[task]

        params_ip_img = []
        params_ip_msk = []
        params_ip_model = []
        params_ip_mode = []
        params_ip_scale = []

        all_adapters = [
            (image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
            (image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
        ]

        if not hasattr(self.model.pipe, "transformer"):
            for imgip, mskip, modelip, modeip, scaleip in all_adapters:
                if imgip:
                    params_ip_img.append(imgip)
                    if mskip:
                        params_ip_msk.append(mskip)
                    params_ip_model.append(modelip)
                    params_ip_mode.append(modeip)
                    params_ip_scale.append(scaleip)

        concurrency = 5
        self.model.stream_config(concurrency=concurrency, latent_resize_by=1, vae_decoding=False)

        if task != "txt2img" and not image_control:
            raise ValueError("No control image found: To use this function, you have to upload an image in 'Image ControlNet/Inpaint/Img2img'")

        if task == "inpaint" and not image_mask:
            raise ValueError("No mask image found: Specify one in 'Image Mask'")

        if upscaler_model_path in UPSCALER_KEYS[:9]:
            upscaler_model = upscaler_model_path
        else:
            directory_upscalers = 'upscalers'
            os.makedirs(directory_upscalers, exist_ok=True)

            url_upscaler = UPSCALER_DICT_GUI[upscaler_model_path]

            if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
                download_things(directory_upscalers, url_upscaler, HF_TOKEN)

            upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"

        logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)

        adetailer_params_A = {
            "face_detector_ad": face_detector_ad_a,
            "person_detector_ad": person_detector_ad_a,
            "hand_detector_ad": hand_detector_ad_a,
            "prompt": prompt_ad_a,
            "negative_prompt": negative_prompt_ad_a,
            "strength": strength_ad_a,
            # "image_list_task" : None,
            "mask_dilation": mask_dilation_a,
            "mask_blur": mask_blur_a,
            "mask_padding": mask_padding_a,
            "inpaint_only": adetailer_inpaint_only,
            "sampler": adetailer_sampler,
        }

        adetailer_params_B = {
            "face_detector_ad": face_detector_ad_b,
            "person_detector_ad": person_detector_ad_b,
            "hand_detector_ad": hand_detector_ad_b,
            "prompt": prompt_ad_b,
            "negative_prompt": negative_prompt_ad_b,
            "strength": strength_ad_b,
            # "image_list_task" : None,
            "mask_dilation": mask_dilation_b,
            "mask_blur": mask_blur_b,
            "mask_padding": mask_padding_b,
        }
        pipe_params = {
            "prompt": prompt,
            "negative_prompt": neg_prompt,
            "img_height": img_height,
            "img_width": img_width,
            "num_images": num_images,
            "num_steps": steps,
            "guidance_scale": cfg,
            "clip_skip": clip_skip,
            "pag_scale": float(pag_scale),
            "seed": seed,
            "image": image_control,
            "preprocessor_name": preprocessor_name,
            "preprocess_resolution": preprocess_resolution,
            "image_resolution": image_resolution,
            "style_prompt": style_prompt if style_prompt else "",
            "style_json_file": "",
            "image_mask": image_mask,  # only for Inpaint
            "strength": strength,  # only for Inpaint or ...
            "low_threshold": low_threshold,
            "high_threshold": high_threshold,
            "value_threshold": value_threshold,
            "distance_threshold": distance_threshold,
            "recolor_gamma_correction": float(recolor_gamma_correction),
            "tile_blur_sigma": int(tile_blur_sigma),
            "lora_A": lora1 if lora1 != "None" else None,
            "lora_scale_A": lora_scale1,
            "lora_B": lora2 if lora2 != "None" else None,
            "lora_scale_B": lora_scale2,
            "lora_C": lora3 if lora3 != "None" else None,
            "lora_scale_C": lora_scale3,
            "lora_D": lora4 if lora4 != "None" else None,
            "lora_scale_D": lora_scale4,
            "lora_E": lora5 if lora5 != "None" else None,
            "lora_scale_E": lora_scale5,
            "lora_F": lora6 if lora6 != "None" else None,
            "lora_scale_F": lora_scale6,
            "lora_G": lora7 if lora7 != "None" else None,
            "lora_scale_G": lora_scale7,
## BEGIN MOD
            "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [],
## END MOD
            "syntax_weights": syntax_weights,  # "Classic"
            "sampler": sampler,
            "schedule_type": schedule_type,
            "schedule_prediction_type": schedule_prediction_type,
            "xformers_memory_efficient_attention": xformers_memory_efficient_attention,
            "gui_active": True,
            "loop_generation": loop_generation,
            "controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
            "control_guidance_start": float(controlnet_start_threshold),
            "control_guidance_end": float(controlnet_stop_threshold),
            "generator_in_cpu": generator_in_cpu,
            "FreeU": freeu,
            "adetailer_A": adetailer_active_a,
            "adetailer_A_params": adetailer_params_A,
            "adetailer_B": adetailer_active_b,
            "adetailer_B_params": adetailer_params_B,
            "leave_progress_bar": leave_progress_bar,
            "disable_progress_bar": disable_progress_bar,
            "image_previews": image_previews,
            "display_images": display_images,
            "save_generated_images": save_generated_images,
            "filename_pattern": filename_pattern,
            "image_storage_location": image_storage_location,
            "retain_compel_previous_load": retain_compel_previous_load,
            "retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
            "retain_hires_model_previous_load": retain_hires_model_previous_load,
            "t2i_adapter_preprocessor": t2i_adapter_preprocessor,
            "t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
            "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
            "upscaler_model_path": upscaler_model,
            "upscaler_increases_size": upscaler_increases_size,
            "esrgan_tile": esrgan_tile,
            "esrgan_tile_overlap": esrgan_tile_overlap,
            "hires_steps": hires_steps,
            "hires_denoising_strength": hires_denoising_strength,
            "hires_prompt": hires_prompt,
            "hires_negative_prompt": hires_negative_prompt,
            "hires_sampler": hires_sampler,
            "hires_before_adetailer": hires_before_adetailer,
            "hires_after_adetailer": hires_after_adetailer,
            "hires_schedule_type": hires_schedule_type,
            "hires_guidance_scale": hires_guidance_scale,
            "ip_adapter_image": params_ip_img,
            "ip_adapter_mask": params_ip_msk,
            "ip_adapter_model": params_ip_model,
            "ip_adapter_mode": params_ip_mode,
            "ip_adapter_scale": params_ip_scale,
        }

        # kwargs for diffusers pipeline
        if guidance_rescale:
            pipe_params["guidance_rescale"] = guidance_rescale

        self.model.device = torch.device("cuda:0")
        if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * self.model.num_loras:
            self.model.pipe.transformer.to(self.model.device)
            print("transformer to cuda")

        actual_progress = 0
        info_images = gr.update()
        for img, [seed, image_path, metadata] in self.model(**pipe_params):
            info_state = progress_step_bar(actual_progress, steps)
            actual_progress += concurrency
            if image_path:
                info_images = f"Seeds: {str(seed)}"
                if vae_msg:
                    info_images = info_images + "<br>" + vae_msg

                if "Cannot copy out of meta tensor; no data!" in self.model.last_lora_error:
                    msg_ram = "Unable to process the LoRAs due to high RAM usage; please try again later."
                    print(msg_ram)
                    msg_lora += f"<br>{msg_ram}"

                for status, lora in zip(self.model.lora_status, self.model.lora_memory):
                    if status:
                        msg_lora += f"<br>Loaded: {lora}"
                    elif status is not None:
                        msg_lora += f"<br>Error with: {lora}"

                if msg_lora:
                    info_images += msg_lora

                info_images = info_images + "<br>" + "GENERATION DATA:<br>" + escape_html(metadata[-1]) + "<br>-------<br>"

                download_links = "<br>".join(
                    [
                        f'<a href="{path.replace("/images/", "/file=/home/user/app/images/")}" download="{os.path.basename(path)}">Download Image {i + 1}</a>'
                        for i, path in enumerate(image_path)
                    ]
                )
                if save_generated_images:
                    info_images += f"<br>{download_links}"

## BEGIN MOD
                img = save_images(img, metadata)
## END MOD

                info_state = "COMPLETE"

            yield info_state, img, info_images


def dynamic_gpu_duration(func, duration, *args):

    @spaces.GPU(duration=duration)
    def wrapped_func():
        yield from func(*args)

    return wrapped_func()


@spaces.GPU
def dummy_gpu():
    return None


def sd_gen_generate_pipeline(*args):
    gpu_duration_arg = int(args[-1]) if args[-1] else 59
    verbose_arg = int(args[-2])
    load_lora_cpu = args[-3]
    generation_args = args[:-3]
    lora_list = [
        None if item == "None" or item == "" else item # MOD
        for item in [args[7], args[9], args[11], args[13], args[15], args[17], args[19]]
    ]
    lora_status = [None] * sd_gen.model.num_loras

    msg_load_lora = "Updating LoRAs in GPU..."
    if load_lora_cpu:
        msg_load_lora = "Updating LoRAs in CPU..."

    if lora_list != sd_gen.model.lora_memory and lora_list != [None] * sd_gen.model.num_loras:
        yield msg_load_lora, gr.update(), gr.update()

    # Load lora in CPU
    if load_lora_cpu:
        lora_status = sd_gen.model.load_lora_on_the_fly(
            lora_A=lora_list[0], lora_scale_A=args[8],
            lora_B=lora_list[1], lora_scale_B=args[10],
            lora_C=lora_list[2], lora_scale_C=args[12],
            lora_D=lora_list[3], lora_scale_D=args[14],
            lora_E=lora_list[4], lora_scale_E=args[16],
            lora_F=lora_list[5], lora_scale_F=args[18],
            lora_G=lora_list[6], lora_scale_G=args[20],
        )
        print(lora_status)

    sampler_name = args[21]
    schedule_type_name = args[22]
    _, _, msg_sampler = check_scheduler_compatibility(
        sd_gen.model.class_name, sampler_name, schedule_type_name
    )
    if msg_sampler:
        gr.Warning(msg_sampler)

    if verbose_arg:
        for status, lora in zip(lora_status, lora_list):
            if status:
                gr.Info(f"LoRA loaded in CPU: {lora}")
            elif status is not None:
                gr.Warning(f"Failed to load LoRA: {lora}")

        if lora_status == [None] * sd_gen.model.num_loras and sd_gen.model.lora_memory != [None] * sd_gen.model.num_loras and load_lora_cpu:
            lora_cache_msg = ", ".join(
                str(x) for x in sd_gen.model.lora_memory if x is not None
            )
            gr.Info(f"LoRAs in cache: {lora_cache_msg}")

    msg_request = f"Requesting {gpu_duration_arg}s. of GPU time.\nModel: {sd_gen.model.base_model_id}"
    if verbose_arg:
        gr.Info(msg_request)
        print(msg_request)
    yield msg_request.replace("\n", "<br>"), gr.update(), gr.update()

    start_time = time.time()

    # yield from sd_gen.generate_pipeline(*generation_args)
    yield from dynamic_gpu_duration(
        sd_gen.generate_pipeline,
        gpu_duration_arg,
        *generation_args,
    )

    end_time = time.time()
    execution_time = end_time - start_time
    msg_task_complete = (
        f"GPU task complete in: {int(round(execution_time, 0) + 1)} seconds"
    )

    if verbose_arg:
        gr.Info(msg_task_complete)
        print(msg_task_complete)

    yield msg_task_complete, gr.update(), gr.update()


@spaces.GPU(duration=15)
def esrgan_upscale(image, upscaler_name, upscaler_size):
    if image is None: return None

    from stablepy.diffusers_vanilla.utils import save_pil_image_with_metadata
    from stablepy import UpscalerESRGAN

    exif_image = extract_exif_data(image)

    url_upscaler = UPSCALER_DICT_GUI[upscaler_name]
    directory_upscalers = 'upscalers'
    os.makedirs(directory_upscalers, exist_ok=True)
    if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
        download_things(directory_upscalers, url_upscaler, HF_TOKEN)

    scaler_beta = UpscalerESRGAN(0, 0)
    image_up = scaler_beta.upscale(image, upscaler_size, f"./upscalers/{url_upscaler.split('/')[-1]}")

    image_path = save_pil_image_with_metadata(image_up, f'{os.getcwd()}/up_images', exif_image)

    return image_path


# https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space/discussions/1#674969a022b99c122af5d407
dynamic_gpu_duration.zerogpu = True
sd_gen_generate_pipeline.zerogpu = True
sd_gen = GuiSD()


## BEGIN MOD
CSS ="""

.gradio-container, #main { width:100%; height:100%; max-width:100%; padding-left:0; padding-right:0; margin-left:0; margin-right:0; }

.contain { display:flex; flex-direction:column; }

#component-0 { width:100%; height:100%; }

#gallery { flex-grow:1; }

#load_model { height: 50px; }

.lora { min-width:480px; }

#model-info { text-align:center; }

.title { font-size: 3em; align-items: center; text-align: center; }

.info { align-items: center; text-align: center; }

.desc [src$='#float'] { float: right; margin: 20px; }

"""

with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, css=CSS, delete_cache=(60, 3600)) as app:
    gr.Markdown("# 🧩 DiffuseCraft Mod", elem_classes="title")
    gr.Markdown("This space is a modification of [r3gm's DiffuseCraft](https://huggingface.co/spaces/r3gm/DiffuseCraft).", elem_classes="info")
    with gr.Column():
        with gr.Tab("Generation"):
            with gr.Row():
                with gr.Column(scale=1):

                    def update_task_options(model_name, task_name):
                        new_choices = MODEL_TYPE_TASK[get_model_type(valid_model_name(model_name))]

                        if task_name not in new_choices:
                            task_name = "txt2img"

                        return gr.update(value=task_name, choices=new_choices)
                    
                    interface_mode_gui = gr.Radio(label="Quick settings", choices=["Simple", "Standard", "Fast", "LoRA"], value="Standard")
                    with gr.Accordion("Model and Task", open=False) as menu_model:
                        task_gui = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
                        with gr.Group():
                            model_name_gui = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.", choices=get_tupled_model_list(model_list), value="votepurchase/animagine-xl-3.1", allow_custom_value=True)
                            model_info_gui = gr.Markdown(elem_classes="info")
                        with gr.Row():
                            quick_model_type_gui = gr.Radio(label="Model Type", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)
                            quick_genre_gui = gr.Radio(label="Genre", choices=["Anime", "Photo"], value="Anime", interactive=True)
                            quick_speed_gui = gr.Radio(label="Speed", choices=["Fast", "Standard", "Heavy"], value="Standard", interactive=True)
                            quick_aspect_gui = gr.Radio(label="Aspect Ratio", choices=["1:1", "3:4"], value="1:1", interactive=True)
                        with gr.Row():
                            quality_selector_gui = gr.Dropdown(label="Quality Tags Presets", interactive=True, choices=list(preset_quality.keys()), value="None")
                            style_selector_gui = gr.Dropdown(label="Style Preset", interactive=True, choices=list(preset_styles.keys()), value="None")
                            sampler_selector_gui = gr.Dropdown(label="Sampler Quick Settings", interactive=True, choices=list(preset_sampler_setting.keys()), value="None")
                            optimization_gui = gr.Dropdown(label="Optimization for SDXL", choices=list(optimization_list.keys()), value="None", interactive=True)
                    with gr.Group():
                        with gr.Accordion("Prompt from Image", open=False) as menu_from_image:
                            input_image_gui = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
                            with gr.Accordion(label="Advanced options", open=False):
                                with gr.Row():
                                    general_threshold_gui = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
                                    character_threshold_gui = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
                                with gr.Row():
                                    tag_type_gui = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
                                    recom_prompt_gui = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
                                    keep_tags_gui = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
                                image_algorithms = gr.CheckboxGroup(["Use WD Tagger"], label="Algorithms", value=["Use WD Tagger"], visible=False)
                            generate_from_image_btn_gui = gr.Button(value="GENERATE TAGS FROM IMAGE")
                        prompt_gui = gr.Textbox(lines=6, placeholder="1girl, solo, ...", label="Prompt", show_copy_button=True)
                        with gr.Accordion("Negative prompt, etc.", open=False) as menu_negative:
                            neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt", value="lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, worst quality, low quality, very displeasing, (bad)", show_copy_button=True)
                            translate_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
                            with gr.Row():
                                insert_prompt_gui = gr.Radio(label="Insert reccomended positive / negative prompt", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)                          
                                prompt_type_gui = gr.Radio(label="Convert tags to", choices=["danbooru", "e621"], value="e621", visible=False)
                                prompt_type_button = gr.Button(value="Convert prompt to Pony e621 style", size="sm", variant="secondary")
                            with gr.Row():
                                character_dbt = gr.Textbox(lines=1, placeholder="kafuu chino, ...", label="Character names")
                                series_dbt = gr.Textbox(lines=1, placeholder="Is the order a rabbit?, ...", label="Series names")
                                random_character_gui = gr.Button(value="Random character 🎲", size="sm", variant="secondary")
                                model_name_dbt = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0], visible=False)
                                aspect_ratio_dbt = gr.Radio(label="Aspect ratio", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square", visible=False)
                                length_dbt = gr.Radio(label="Length", choices=list(V2_LENGTH_OPTIONS), value="very_long", visible=False)
                                identity_dbt = gr.Radio(label="Keep identity", choices=list(V2_IDENTITY_OPTIONS), value="lax", visible=False)                    
                                ban_tags_dbt = gr.Textbox(label="Ban tags", placeholder="alternate costumen, ...", value="futanari, censored, furry, furrification", visible=False)
                                copy_button_dbt = gr.Button(value="Copy to clipboard", visible=False)
                            rating_dbt = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="sfw")
                            generate_db_random_button = gr.Button(value="EXTEND PROMPT 🎲")
                        with gr.Row():
                            translate_prompt_gui = gr.Button(value="Translate Prompt 📝", variant="secondary", size="sm")
                            set_random_seed = gr.Button(value="Seed 🎲", variant="secondary", size="sm")
                            set_params_gui = gr.Button(value="Params ↙️", variant="secondary", size="sm")
                            clear_prompt_gui = gr.Button(value="Clear 🗑️", variant="secondary", size="sm")

                    generate_button = gr.Button(value="GENERATE IMAGE", size="lg", variant="primary")

                    model_name_gui.change(
                        update_task_options,
                        [model_name_gui, task_gui],
                        [task_gui],
                    )

                    load_model_gui = gr.HTML(elem_id="load_model", elem_classes="contain")

                    result_images = gr.Gallery(
                        label="Generated images",
                        show_label=False,
                        elem_id="gallery",
                        #columns=[2],
                        columns=[1],
                        #rows=[2],
                        rows=[1],
                        object_fit="contain",
                        # height="auto",
                        interactive=False,
                        preview=False,
                        show_share_button=False,
                        show_download_button=True,
                        selected_index=50,
                        format="png",
                    )

                    result_images_files = gr.Files(interactive=False, visible=False)

                    actual_task_info = gr.HTML()

                    with gr.Accordion("History", open=False):
                        history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", format="png", interactive=False, show_share_button=False,
                        show_download_button=True)
                        history_files = gr.Files(interactive=False, visible=False)
                        history_clear_button = gr.Button(value="Clear History", variant="secondary")
                        history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False)

                    with gr.Row(equal_height=False, variant="default"):
                        gpu_duration_gui = gr.Number(minimum=5, maximum=240, value=59, show_label=False, container=False, info="GPU time duration (seconds)")
                        with gr.Column():
                            verbose_info_gui = gr.Checkbox(value=False, container=False, label="Status info")
                            load_lora_cpu_gui = gr.Checkbox(value=False, container=False, label="Load LoRAs on CPU")

                with gr.Column(scale=1):
                    with gr.Accordion("Generation settings", open=False, visible=True) as menu_gen:
                        with gr.Row():
                            img_width_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Width")
                            img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Height")
                            steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=28, label="Steps")
                            cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.0, label="CFG")
                            guidance_rescale_gui = gr.Slider(label="CFG rescale:", value=0., step=0.01, minimum=0., maximum=1.5)
                        with gr.Row():
                            seed_gui = gr.Number(minimum=-1, maximum=2**32-1, value=-1, label="Seed")
                            pag_scale_gui = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
                            num_images_gui = gr.Slider(minimum=1, maximum=5, step=1, value=1, label="Images")
                            clip_skip_gui = gr.Checkbox(value=False, label="Layer 2 Clip Skip")
                            free_u_gui = gr.Checkbox(value=False, label="FreeU")
                        with gr.Row():
                            sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler")
                            schedule_type_gui = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0])
                            schedule_prediction_type_gui = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
                            vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list, value=vae_model_list[0])
                            prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1])

                        with gr.Row(equal_height=False):

                            def run_set_params_gui(base_prompt, name_model):
                                valid_receptors = {  # default values
                                    "prompt": gr.update(value=base_prompt),
                                    "neg_prompt": gr.update(value=""),
                                    "Steps": gr.update(value=30),
                                    "width": gr.update(value=1024),
                                    "height": gr.update(value=1024),
                                    "Seed": gr.update(value=-1),
                                    "Sampler": gr.update(value="Euler"),
                                    "CFG scale": gr.update(value=7.),  # cfg
                                    "Clip skip": gr.update(value=True),
                                    "Model": gr.update(value=name_model),
                                    "Schedule type": gr.update(value="Automatic"),
                                    "PAG": gr.update(value=.0),
                                    "FreeU": gr.update(value=False),
                                }
                                valid_keys = list(valid_receptors.keys())

                                parameters = extract_parameters(base_prompt)
                                # print(parameters)

                                if "Sampler" in parameters:
                                    value_sampler = parameters["Sampler"]
                                    for s_type in SCHEDULE_TYPE_OPTIONS:
                                        if s_type in value_sampler:
                                            value_sampler = value_sampler.replace(s_type, "").strip()
                                            parameters["Sampler"] = value_sampler
                                            parameters["Schedule type"] = s_type

                                for key, val in parameters.items():
                                    # print(val)
                                    if key in valid_keys:
                                        try:
                                            if key == "Sampler":
                                                if val not in scheduler_names:
                                                    continue
                                            if key == "Schedule type":
                                                if val not in SCHEDULE_TYPE_OPTIONS:
                                                    val = "Automatic"
                                            elif key == "Clip skip":
                                                if "," in str(val):
                                                    val = val.replace(",", "")
                                                if int(val) >= 2:
                                                    val = True
                                            if key == "prompt":
                                                if ">" in val and "<" in val:
                                                    val = re.sub(r'<[^>]+>', '', val)
                                                    print("Removed LoRA written in the prompt")
                                            if key in ["prompt", "neg_prompt"]:
                                                val = re.sub(r'\s+', ' ', re.sub(r',+', ',', val)).strip()
                                            if key in ["Steps", "width", "height", "Seed"]:
                                                val = int(val)
                                            if key == "FreeU":
                                                val = True
                                            if key in ["CFG scale", "PAG"]:
                                                val = float(val)
                                            if key == "Model":
                                                filtered_models = [m for m in model_list if val in m]
                                                if filtered_models:
                                                    val = filtered_models[0]
                                                else:
                                                    val = name_model
                                            if key == "Seed":
                                                continue
                                            valid_receptors[key] = gr.update(value=val)
                                            # print(val, type(val))
                                            # print(valid_receptors)
                                        except Exception as e:
                                            print(str(e))
                                return [value for value in valid_receptors.values()]

                            set_params_gui.click(
                                run_set_params_gui, [prompt_gui, model_name_gui], [
                                    prompt_gui,
                                    neg_prompt_gui,
                                    steps_gui,
                                    img_width_gui,
                                    img_height_gui,
                                    seed_gui,
                                    sampler_gui,
                                    cfg_gui,
                                    clip_skip_gui,
                                    model_name_gui,
                                    schedule_type_gui,
                                    pag_scale_gui,
                                    free_u_gui,
                                ],
                            )

                            def run_clear_prompt_gui():
                                return gr.update(value=""), gr.update(value="")
                            clear_prompt_gui.click(
                                run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui]
                            )

                            def run_set_random_seed():
                                return -1
                            set_random_seed.click(
                                run_set_random_seed, [], seed_gui
                            )

                    with gr.Accordion("LoRA", open=False, visible=True) as menu_lora:
                        def lora_dropdown(label, visible=True):
                            return gr.Dropdown(label=label, choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320, visible=visible)

                        def lora_scale_slider(label, visible=True):
                            return gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label=label, visible=visible)
                        
                        def lora_textbox(label):
                            return gr.Textbox(label=label, info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
                        
                        with gr.Row():
                            with gr.Column():
                                lora1_gui = lora_dropdown("LoRA1")
                                lora_scale_1_gui = lora_scale_slider("LoRA Scale 1")
                                with gr.Row():
                                    with gr.Group():
                                        lora1_info_gui = lora_textbox("LoRA1 prompts")
                                        lora1_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                                    lora1_desc_gui = gr.Markdown(value="", visible=False)
                            with gr.Column():
                                lora2_gui = lora_dropdown("LoRA2")
                                lora_scale_2_gui = lora_scale_slider("LoRA Scale 2")
                                with gr.Row():
                                    with gr.Group():
                                        lora2_info_gui = lora_textbox("LoRA2 prompts")
                                        lora2_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                                    lora2_desc_gui = gr.Markdown(value="", visible=False)
                            with gr.Column():
                                lora3_gui = lora_dropdown("LoRA3")
                                lora_scale_3_gui = lora_scale_slider("LoRA Scale 3")
                                with gr.Row():
                                    with gr.Group():
                                        lora3_info_gui = lora_textbox("LoRA3 prompts")
                                        lora3_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                                    lora3_desc_gui = gr.Markdown(value="", visible=False)
                            with gr.Column():
                                lora4_gui = lora_dropdown("LoRA4")
                                lora_scale_4_gui = lora_scale_slider("LoRA Scale 4")
                                with gr.Row():
                                    with gr.Group():
                                        lora4_info_gui = lora_textbox("LoRA4 prompts")
                                        lora4_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                                    lora4_desc_gui = gr.Markdown(value="", visible=False)
                            with gr.Column():
                                lora5_gui = lora_dropdown("LoRA5")
                                lora_scale_5_gui = lora_scale_slider("LoRA Scale 5")
                                with gr.Row():
                                    with gr.Group():
                                        lora5_info_gui = lora_textbox("LoRA5 prompts")
                                        lora5_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                                    lora5_desc_gui = gr.Markdown(value="", visible=False)
                            with gr.Column():
                                lora6_gui = lora_dropdown("LoRA6", visible=False)
                                lora_scale_6_gui = lora_scale_slider("LoRA Scale 6", visible=False)
                                with gr.Row():
                                    with gr.Group():
                                        lora6_info_gui = lora_textbox("LoRA6 prompts")
                                        lora6_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                                    lora6_desc_gui = gr.Markdown(value="", visible=False)
                            with gr.Column():
                                lora7_gui = lora_dropdown("LoRA7", visible=False)
                                lora_scale_7_gui = lora_scale_slider("LoRA Scale 7", visible=False)
                                with gr.Row():
                                    with gr.Group():
                                        lora7_info_gui = lora_textbox("LoRA7 prompts")
                                        lora7_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                                    lora7_desc_gui = gr.Markdown(value="", visible=False)
                        with gr.Accordion("From URL", open=True, visible=True):
                            with gr.Row():
                                search_civitai_basemodel_lora = gr.CheckboxGroup(label="Search LoRA for", choices=CIVITAI_BASEMODEL, value=["Pony", "Illustrious", "SDXL 1.0"])
                                search_civitai_sort_lora = gr.Radio(label="Sort", choices=CIVITAI_SORT, value="Highest Rated")
                                search_civitai_period_lora = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="AllTime")
                            with gr.Row():
                                search_civitai_query_lora = gr.Textbox(label="Query", placeholder="oomuro sakurako...", lines=1)
                                search_civitai_tag_lora = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True)
                                search_civitai_user_lora = gr.Textbox(label="Username", lines=1)
                            search_civitai_button_lora = gr.Button("Search on Civitai")
                            search_civitai_desc_lora = gr.Markdown(value="", visible=False, elem_classes="desc")
                            with gr.Accordion("Select from Gallery", open=False):
                                search_civitai_gallery_lora = gr.Gallery([], label="Results", allow_preview=False, columns=5, show_share_button=False, interactive=False)
                            search_civitai_result_lora = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
                            with gr.Row():
                                text_lora = gr.Textbox(label="LoRA's download URL", placeholder="https://civitai.com/api/download/models/28907", info="It has to be .safetensors files, and you can also download them from Hugging Face.", lines=1, scale=4)
                                romanize_text = gr.Checkbox(value=False, label="Transliterate name", scale=1, visible=False)
                            button_lora = gr.Button("Get and Refresh the LoRA Lists")
                            new_lora_status = gr.HTML()
                        with gr.Accordion("From Local", open=True, visible=True):
                            file_output_lora = gr.File(label="Uploaded LoRA", file_types=['.ckpt', '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple", interactive=False, visible=False)
                            upload_button_lora = gr.UploadButton(label="Upload LoRA from your disk (very slow)", file_types=['.ckpt', '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple")

                    with gr.Column() as menu_advanced:
                        with gr.Accordion("Hires fix", open=False, visible=True) as menu_hires:
                            upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS, value=UPSCALER_KEYS[0])
                            with gr.Row():
                                upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.0, label="Upscale by")
                                esrgan_tile_gui = gr.Slider(minimum=0, value=100, maximum=500, step=1, label="ESRGAN Tile")
                                esrgan_tile_overlap_gui = gr.Slider(minimum=1, maximum=200, step=1, value=10, label="ESRGAN Tile Overlap")
                            with gr.Row():
                                hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
                                hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
                                hires_sampler_gui = gr.Dropdown(label="Hires Sampler", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
                                hires_schedule_list = ["Use same schedule type"] + SCHEDULE_TYPE_OPTIONS
                                hires_schedule_type_gui = gr.Dropdown(label="Hires Schedule type", choices=hires_schedule_list, value=hires_schedule_list[0])
                                hires_guidance_scale_gui = gr.Slider(minimum=-1., maximum=30., step=0.5, value=-1., label="Hires CFG", info="If the value is -1, the main CFG will be used")
                            hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
                            hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)

                        with gr.Accordion("Detailfix", open=False, visible=True) as menu_detail:
                            with gr.Row():

                                # Adetailer Inpaint Only
                                adetailer_inpaint_only_gui = gr.Checkbox(label="Inpaint only", value=True)

                                # Adetailer Verbose
                                adetailer_verbose_gui = gr.Checkbox(label="Verbose", value=False)

                                # Adetailer Sampler
                                adetailer_sampler_gui = gr.Dropdown(label="Adetailer sampler:", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])

                            with gr.Accordion("Detailfix A", open=True, visible=True):
                                # Adetailer A
                                adetailer_active_a_gui = gr.Checkbox(label="Enable Adetailer A", value=False)
                                prompt_ad_a_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
                                negative_prompt_ad_a_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
                                with gr.Row():
                                    strength_ad_a_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
                                    face_detector_ad_a_gui = gr.Checkbox(label="Face detector", value=False)
                                    person_detector_ad_a_gui = gr.Checkbox(label="Person detector", value=True)
                                    hand_detector_ad_a_gui = gr.Checkbox(label="Hand detector", value=False)
                                with gr.Row():
                                    mask_dilation_a_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
                                    mask_blur_a_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
                                    mask_padding_a_gui = gr.Number(label="Mask padding:", value=32, minimum=1)

                            with gr.Accordion("Detailfix B", open=True, visible=True):
                                # Adetailer B
                                adetailer_active_b_gui = gr.Checkbox(label="Enable Adetailer B", value=False)
                                prompt_ad_b_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
                                negative_prompt_ad_b_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
                                with gr.Row():
                                    strength_ad_b_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
                                    face_detector_ad_b_gui = gr.Checkbox(label="Face detector", value=False)
                                    person_detector_ad_b_gui = gr.Checkbox(label="Person detector", value=True)
                                    hand_detector_ad_b_gui = gr.Checkbox(label="Hand detector", value=False)
                                with gr.Row():
                                    mask_dilation_b_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
                                    mask_blur_b_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
                                    mask_padding_b_gui = gr.Number(label="Mask padding:", value=32, minimum=1)
                                    
                        with gr.Accordion("Textual inversion", open=False, visible=True) as menu_ti:
                            active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
                            use_textual_inversion_gui = gr.CheckboxGroup(choices=get_embed_list(get_model_pipeline(model_name_gui.value)) if active_textual_inversion_gui.value else [], value=None, label="Use Textual Invertion in prompt")
                            def update_textual_inversion_gui(active_textual_inversion_gui, model_name_gui):
                                return gr.update(choices=get_embed_list(get_model_pipeline(model_name_gui)) if active_textual_inversion_gui else [])
                            active_textual_inversion_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui])
                            model_name_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui])

                        with gr.Accordion("ControlNet / Img2img / Inpaint", open=False, visible=True) as menu_i2i:
                            with gr.Row():
                                image_control = gr.Image(label="Image ControlNet/Inpaint/Img2img", type="filepath")
                                image_mask_gui = gr.Image(label="Image Mask", type="filepath")
                            with gr.Row():
                                strength_gui = gr.Slider(
                                    minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
                                    info="This option adjusts the level of changes for img2img and inpainting."
                                )
                                image_resolution_gui = gr.Slider(
                                    minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution",
                                    info="The maximum proportional size of the generated image based on the uploaded image."
                                )
                            with gr.Row():
                                controlnet_model_gui = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0])
                                control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
                                control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
                                control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
                            with gr.Row():
                                preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=TASK_AND_PREPROCESSORS["canny"])
                                preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution")
                                low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold")
                                high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold")

                                def change_preprocessor_choices(task):
                                    task = TASK_STABLEPY[task]
                                    if task in TASK_AND_PREPROCESSORS.keys():
                                        choices_task = TASK_AND_PREPROCESSORS[task]
                                    else:
                                        choices_task = TASK_AND_PREPROCESSORS["canny"]
                                    return gr.update(choices=choices_task, value=choices_task[0])

                                task_gui.change(
                                    change_preprocessor_choices,
                                    [task_gui],
                                    [preprocessor_name_gui],
                                )

                            with gr.Row():
                                value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
                                distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
                                recolor_gamma_correction_gui = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
                                tile_blur_sigma_gui = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'TILE' blur sigma")

                        with gr.Accordion("IP-Adapter", open=False, visible=True) as menu_ipa:

                            IP_MODELS = sorted(list(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL)))
                            MODE_IP_OPTIONS = ["original", "style", "layout", "style+layout"]

                            with gr.Accordion("IP-Adapter 1", open=True, visible=True):
                                with gr.Row():
                                    image_ip1 = gr.Image(label="IP Image", type="filepath")
                                    mask_ip1 = gr.Image(label="IP Mask", type="filepath")
                                with gr.Row():
                                    model_ip1 = gr.Dropdown(value="plus_face", label="Model", choices=IP_MODELS)
                                    mode_ip1 = gr.Dropdown(value="original", label="Mode", choices=MODE_IP_OPTIONS)
                                scale_ip1 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
                            with gr.Accordion("IP-Adapter 2", open=True, visible=True):
                                with gr.Row():
                                    image_ip2 = gr.Image(label="IP Image", type="filepath")
                                    mask_ip2 = gr.Image(label="IP Mask (optional)", type="filepath")
                                with gr.Row():
                                    model_ip2 = gr.Dropdown(value="base", label="Model", choices=IP_MODELS)
                                    mode_ip2 = gr.Dropdown(value="style", label="Mode", choices=MODE_IP_OPTIONS)
                                scale_ip2 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")

                        with gr.Accordion("T2I adapter", open=False, visible=False) as menu_t2i:
                            t2i_adapter_preprocessor_gui = gr.Checkbox(value=True, label="T2i Adapter Preprocessor")
                            with gr.Row():
                                adapter_conditioning_scale_gui = gr.Slider(minimum=0, maximum=5., step=0.1, value=1, label="Adapter Conditioning Scale")
                                adapter_conditioning_factor_gui = gr.Slider(minimum=0, maximum=1., step=0.01, value=0.55, label="Adapter Conditioning Factor (%)")

                        with gr.Accordion("Styles", open=False, visible=True) as menu_styles:

                            try:
                                style_names_found = sd_gen.model.STYLE_NAMES
                            except Exception:
                                style_names_found = STYLE_NAMES

                            style_prompt_gui = gr.Dropdown(
                                style_names_found,
                                multiselect=True,
                                value=None,
                                label="Style Prompt",
                                interactive=True,
                            )
                            style_json_gui = gr.File(label="Style JSON File")
                            style_button = gr.Button("Load styles")

                            def load_json_style_file(json):
                                if not sd_gen.model:
                                    gr.Info("First load the model")
                                    return gr.update(value=None, choices=STYLE_NAMES)

                                sd_gen.model.load_style_file(json)
                                gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded")
                                return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES)

                            style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])

                        with gr.Accordion("Other settings", open=False, visible=True) as menu_other:
                            with gr.Row():
                                save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images")
                                filename_pattern_gui = gr.Textbox(label="Filename pattern", value="model,seed", placeholder="model,seed,sampler,schedule_type,img_width,img_height,guidance_scale,num_steps,vae,prompt_section,neg_prompt_section", lines=1)
                            with gr.Row():
                                hires_before_adetailer_gui = gr.Checkbox(value=False, label="Hires Before Adetailer")
                                hires_after_adetailer_gui = gr.Checkbox(value=True, label="Hires After Adetailer")
                                generator_in_cpu_gui = gr.Checkbox(value=False, label="Generator in CPU")

                        with gr.Accordion("More settings", open=False, visible=False):
                            loop_generation_gui = gr.Slider(minimum=1, value=1, label="Loop Generation")
                            retain_task_cache_gui = gr.Checkbox(value=True, label="Retain task model in cache")
                            leave_progress_bar_gui = gr.Checkbox(value=True, label="Leave Progress Bar")
                            disable_progress_bar_gui = gr.Checkbox(value=False, label="Disable Progress Bar")
                            display_images_gui = gr.Checkbox(value=False, label="Display Images")
                            image_previews_gui = gr.Checkbox(value=True, label="Image Previews")
                            image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location")
                            retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load")
                            retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Detailfix Model Previous Load")
                            retain_hires_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Hires Model Previous Load")
                            xformers_memory_efficient_attention_gui = gr.Checkbox(value=False, label="Xformers Memory Efficient Attention")

            with gr.Accordion("Examples and help", open=True, visible=True) as menu_example:
                gr.Examples(
                    examples=EXAMPLES_GUI,
                    fn=sd_gen.generate_pipeline,
                    inputs=[
                        prompt_gui,
                        neg_prompt_gui,
                        num_images_gui,
                        steps_gui,
                        cfg_gui,
                        clip_skip_gui,
                        seed_gui,
                        sampler_gui,
                        img_height_gui,
                        img_width_gui,
                        model_name_gui,
                    ],
                    outputs=[load_model_gui, result_images, actual_task_info],
                    cache_examples=False,
                    #elem_id="examples",
                )

            gr.Markdown(RESOURCES)
## END MOD

        with gr.Tab("Inpaint mask maker", render=True):

            def create_mask_now(img, invert):            
                import numpy as np
                import time

                time.sleep(0.5)

                transparent_image = img["layers"][0]

                # Extract the alpha channel
                alpha_channel = np.array(transparent_image)[:, :, 3]

                # Create a binary mask by thresholding the alpha channel
                binary_mask = alpha_channel > 1

                if invert:
                    print("Invert")
                    # Invert the binary mask so that the drawn shape is white and the rest is black
                    binary_mask = np.invert(binary_mask)

                # Convert the binary mask to a 3-channel RGB mask
                rgb_mask = np.stack((binary_mask,) * 3, axis=-1)

                # Convert the mask to uint8
                rgb_mask = rgb_mask.astype(np.uint8) * 255

                return img["background"], rgb_mask

            with gr.Row():
                with gr.Column(scale=2):
                    # image_base = gr.ImageEditor(label="Base image", show_label=True, brush=gr.Brush(colors=["#000000"]))
                    image_base = gr.ImageEditor(
                        sources=["upload", "clipboard"],
                        # crop_size="1:1",
                        # enable crop (or disable it)
                        # transforms=["crop"],
                        brush=gr.Brush(
                            default_size="16",  # or leave it as 'auto'
                            color_mode="fixed",  # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it
                            # default_color="black", # html names are supported
                            colors=[
                                "rgba(0, 0, 0, 1)",  # rgb(a)
                                "rgba(0, 0, 0, 0.1)",
                                "rgba(255, 255, 255, 0.1)",
                                # "hsl(360, 120, 120)" # in fact any valid colorstring
                            ]
                        ),
                        eraser=gr.Eraser(default_size="16")
                    )
                    invert_mask = gr.Checkbox(value=False, label="Invert mask")
                    btn = gr.Button("Create mask")
                with gr.Column(scale=1):
                    img_source = gr.Image(interactive=False)
                    img_result = gr.Image(label="Mask image", show_label=True, interactive=False)
                    btn_send = gr.Button("Send to the first tab")

                btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result])

                def send_img(img_source, img_result):
                    return img_source, img_result
                btn_send.click(send_img, [img_source, img_result], [image_control, image_mask_gui])

        with gr.Tab("PNG Info"):
            with gr.Row():
                with gr.Column():
                    image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])

                with gr.Column():
                    result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)

                    image_metadata.change(
                        fn=extract_exif_data,
                        inputs=[image_metadata],
                        outputs=[result_metadata],
                    )

        with gr.Tab("Upscaler"):
            with gr.Row():
                with gr.Column():
                    image_up_tab = gr.Image(label="Image", type="pil", sources=["upload"])
                    upscaler_tab = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS[9:], value=UPSCALER_KEYS[11])
                    upscaler_size_tab = gr.Slider(minimum=1., maximum=4., step=0.1, value=1.1, label="Upscale by")
                    generate_button_up_tab = gr.Button(value="START UPSCALE", variant="primary")

                with gr.Column():
                    result_up_tab = gr.Image(label="Result", type="pil", interactive=False, format="png")

                    generate_button_up_tab.click(
                        fn=esrgan_upscale,
                        inputs=[image_up_tab, upscaler_tab, upscaler_size_tab],
                        outputs=[result_up_tab],
                    )

        with gr.Tab("Preprocessor", render=True):
            preprocessor_tab()

## BEGIN MOD
        interface_mode_gui.change(
            change_interface_mode,
            [interface_mode_gui],
            [menu_model, menu_from_image, menu_negative, menu_gen, menu_hires, menu_lora, menu_advanced,
              menu_example, task_gui, quick_speed_gui],
            queue=False,
        )
        model_name_gui.change(get_t2i_model_info, [model_name_gui], [model_info_gui], queue=False)
        translate_prompt_gui.click(translate_to_en, [prompt_gui], [prompt_gui], queue=False)\
        .then(translate_to_en, [neg_prompt_gui], [neg_prompt_gui], queue=False)

        gr.on(
            triggers=[quick_model_type_gui.change, quick_genre_gui.change, quick_speed_gui.change, quick_aspect_gui.change],
            fn=set_quick_presets,
            inputs=[quick_genre_gui, quick_model_type_gui, quick_speed_gui, quick_aspect_gui],
            outputs=[quality_selector_gui, style_selector_gui, sampler_selector_gui, optimization_gui, insert_prompt_gui],
            queue=False,
            trigger_mode="once",
        )
        gr.on(
            triggers=[quality_selector_gui.change, style_selector_gui.change, insert_prompt_gui.change],
            fn=process_style_prompt,
            inputs=[prompt_gui, neg_prompt_gui, style_selector_gui, quality_selector_gui, insert_prompt_gui],
            outputs=[prompt_gui, neg_prompt_gui, quick_model_type_gui],
            queue=False,
            trigger_mode="once",
        )
        sampler_selector_gui.change(set_sampler_settings, [sampler_selector_gui], [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui], queue=False)
        optimization_gui.change(set_optimization, [optimization_gui, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], [steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], queue=False)

        gr.on(
            triggers=[lora1_gui.change, lora_scale_1_gui.change, lora2_gui.change, lora_scale_2_gui.change,
                       lora3_gui.change, lora_scale_3_gui.change, lora4_gui.change, lora_scale_4_gui.change,
                       lora5_gui.change, lora_scale_5_gui.change, lora6_gui.change, lora_scale_6_gui.change,
                       lora7_gui.change, lora_scale_7_gui.change, prompt_syntax_gui.change],
            fn=update_loras,
            inputs=[prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui,
                     lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui,
                     lora6_gui, lora_scale_6_gui, lora7_gui, lora_scale_7_gui],
            outputs=[prompt_gui, lora1_gui, lora_scale_1_gui, lora1_info_gui, lora1_copy_gui, lora1_desc_gui,
                    lora2_gui, lora_scale_2_gui, lora2_info_gui, lora2_copy_gui, lora2_desc_gui,
                    lora3_gui, lora_scale_3_gui, lora3_info_gui, lora3_copy_gui, lora3_desc_gui, 
                    lora4_gui, lora_scale_4_gui, lora4_info_gui, lora4_copy_gui, lora4_desc_gui,
                    lora5_gui, lora_scale_5_gui, lora5_info_gui, lora5_copy_gui, lora5_desc_gui,
                    lora6_gui, lora_scale_6_gui, lora6_info_gui, lora6_copy_gui, lora6_desc_gui,
                    lora7_gui, lora_scale_7_gui, lora7_info_gui, lora7_copy_gui, lora7_desc_gui],
            queue=False,
            trigger_mode="once",
        )
        lora1_copy_gui.click(apply_lora_prompt, [prompt_gui, lora1_info_gui], [prompt_gui], queue=False)
        lora2_copy_gui.click(apply_lora_prompt, [prompt_gui, lora2_info_gui], [prompt_gui], queue=False)
        lora3_copy_gui.click(apply_lora_prompt, [prompt_gui, lora3_info_gui], [prompt_gui], queue=False)
        lora4_copy_gui.click(apply_lora_prompt, [prompt_gui, lora4_info_gui], [prompt_gui], queue=False)
        lora5_copy_gui.click(apply_lora_prompt, [prompt_gui, lora5_info_gui], [prompt_gui], queue=False)
        lora6_copy_gui.click(apply_lora_prompt, [prompt_gui, lora6_info_gui], [prompt_gui], queue=False)
        lora7_copy_gui.click(apply_lora_prompt, [prompt_gui, lora7_info_gui], [prompt_gui], queue=False)
        gr.on(
            triggers=[search_civitai_button_lora.click, search_civitai_query_lora.submit],
            fn=search_civitai_lora,
            inputs=[search_civitai_query_lora, search_civitai_basemodel_lora, search_civitai_sort_lora, search_civitai_period_lora,
                    search_civitai_tag_lora, search_civitai_user_lora, search_civitai_gallery_lora],
            outputs=[search_civitai_result_lora, search_civitai_desc_lora, search_civitai_button_lora, search_civitai_query_lora, search_civitai_gallery_lora],
            queue=True,
            scroll_to_output=True,
        )
        search_civitai_result_lora.change(select_civitai_lora, [search_civitai_result_lora], [text_lora, search_civitai_desc_lora], queue=False, scroll_to_output=True)
        search_civitai_gallery_lora.select(update_civitai_selection, None, [search_civitai_result_lora], queue=False, show_api=False)
        button_lora.click(get_my_lora, [text_lora, romanize_text], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui, new_lora_status], scroll_to_output=True)
        upload_button_lora.upload(upload_file_lora, [upload_button_lora], [file_output_lora, upload_button_lora]).success(
            move_file_lora, [file_output_lora], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui], scroll_to_output=True)

        use_textual_inversion_gui.change(set_textual_inversion_prompt, [use_textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui], [prompt_gui, neg_prompt_gui])

        generate_from_image_btn_gui.click(
            lambda: ("", "", ""), None, [series_dbt, character_dbt, prompt_gui], queue=False,
        ).success(
            predict_tags_wd,
            [input_image_gui, prompt_gui, image_algorithms, general_threshold_gui, character_threshold_gui],
            [series_dbt, character_dbt, prompt_gui, copy_button_dbt],
        ).success(
            compose_prompt_to_copy, [character_dbt, series_dbt, prompt_gui], [prompt_gui], queue=False,
        ).success(
            remove_specific_prompt, [prompt_gui, keep_tags_gui], [prompt_gui], queue=False,
        ).success(
            convert_danbooru_to_e621_prompt, [prompt_gui, tag_type_gui], [prompt_gui], queue=False,
        ).success(
            insert_recom_prompt, [prompt_gui, neg_prompt_gui, recom_prompt_gui], [prompt_gui, neg_prompt_gui], queue=False,
        )
        
        prompt_type_button.click(convert_danbooru_to_e621_prompt, [prompt_gui, prompt_type_gui], [prompt_gui], queue=False)
        random_character_gui.click(select_random_character, [series_dbt, character_dbt], [series_dbt, character_dbt], queue=False)
        generate_db_random_button.click(
            v2_random_prompt,
            [prompt_gui, series_dbt, character_dbt,
            rating_dbt, aspect_ratio_dbt, length_dbt, identity_dbt, ban_tags_dbt, model_name_dbt],
            [prompt_gui, series_dbt, character_dbt],
        ).success(
            convert_danbooru_to_e621_prompt, [prompt_gui, tag_type_gui], [prompt_gui], queue=False,
        )

        translate_prompt_button.click(translate_prompt, [prompt_gui], [prompt_gui], queue=False)
        translate_prompt_button.click(translate_prompt, [character_dbt], [character_dbt], queue=False)
        translate_prompt_button.click(translate_prompt, [series_dbt], [series_dbt], queue=False)

        generate_button.click(
            fn=insert_model_recom_prompt,
            inputs=[prompt_gui, neg_prompt_gui, model_name_gui, recom_prompt_gui],
            outputs=[prompt_gui, neg_prompt_gui],
            queue=False,
        ).success(
            fn=sd_gen.load_new_model,
            inputs=[
                model_name_gui,
                vae_model_gui,
                task_gui,
                controlnet_model_gui,
            ],
            outputs=[load_model_gui],
            queue=True,
            show_progress="minimal",
        ).success(
            fn=sd_gen_generate_pipeline,
            inputs=[
                prompt_gui,
                neg_prompt_gui,
                num_images_gui,
                steps_gui,
                cfg_gui,
                clip_skip_gui,
                seed_gui,
                lora1_gui,
                lora_scale_1_gui,
                lora2_gui,
                lora_scale_2_gui,
                lora3_gui,
                lora_scale_3_gui,
                lora4_gui,
                lora_scale_4_gui,
                lora5_gui,
                lora_scale_5_gui,
                lora6_gui,
                lora_scale_6_gui,
                lora7_gui,
                lora_scale_7_gui,
                sampler_gui,
                schedule_type_gui,
                schedule_prediction_type_gui,
                img_height_gui,
                img_width_gui,
                model_name_gui,
                vae_model_gui,
                task_gui,
                image_control,
                preprocessor_name_gui,
                preprocess_resolution_gui,
                image_resolution_gui,
                style_prompt_gui,
                style_json_gui,
                image_mask_gui,
                strength_gui,
                low_threshold_gui,
                high_threshold_gui,
                value_threshold_gui,
                distance_threshold_gui,
                recolor_gamma_correction_gui,
                tile_blur_sigma_gui,
                control_net_output_scaling_gui,
                control_net_start_threshold_gui,
                control_net_stop_threshold_gui,
                active_textual_inversion_gui,
                prompt_syntax_gui,
                upscaler_model_path_gui,
                upscaler_increases_size_gui,
                esrgan_tile_gui,
                esrgan_tile_overlap_gui,
                hires_steps_gui,
                hires_denoising_strength_gui,
                hires_sampler_gui,
                hires_prompt_gui,
                hires_negative_prompt_gui,
                hires_before_adetailer_gui,
                hires_after_adetailer_gui,
                hires_schedule_type_gui,
                hires_guidance_scale_gui,
                controlnet_model_gui,
                loop_generation_gui,
                leave_progress_bar_gui,
                disable_progress_bar_gui,
                image_previews_gui,
                display_images_gui,
                save_generated_images_gui,
                filename_pattern_gui,
                image_storage_location_gui,
                retain_compel_previous_load_gui,
                retain_detailfix_model_previous_load_gui,
                retain_hires_model_previous_load_gui,
                t2i_adapter_preprocessor_gui,
                adapter_conditioning_scale_gui,
                adapter_conditioning_factor_gui,
                xformers_memory_efficient_attention_gui,
                free_u_gui,
                generator_in_cpu_gui,
                adetailer_inpaint_only_gui,
                adetailer_verbose_gui,
                adetailer_sampler_gui,
                adetailer_active_a_gui,
                prompt_ad_a_gui,
                negative_prompt_ad_a_gui,
                strength_ad_a_gui,
                face_detector_ad_a_gui,
                person_detector_ad_a_gui,
                hand_detector_ad_a_gui,
                mask_dilation_a_gui,
                mask_blur_a_gui,
                mask_padding_a_gui,
                adetailer_active_b_gui,
                prompt_ad_b_gui,
                negative_prompt_ad_b_gui,
                strength_ad_b_gui,
                face_detector_ad_b_gui,
                person_detector_ad_b_gui,
                hand_detector_ad_b_gui,
                mask_dilation_b_gui,
                mask_blur_b_gui,
                mask_padding_b_gui,
                retain_task_cache_gui,
                guidance_rescale_gui,
                image_ip1,
                mask_ip1,
                model_ip1,
                mode_ip1,
                scale_ip1,
                image_ip2,
                mask_ip2,
                model_ip2,
                mode_ip2,
                scale_ip2,
                pag_scale_gui,
                load_lora_cpu_gui,
                verbose_info_gui,
                gpu_duration_gui,
            ],
            outputs=[load_model_gui, result_images, actual_task_info], 
            queue=True,
            show_progress="full",
        ).success(save_gallery_images, [result_images, model_name_gui], [result_images, result_images_files], queue=False, show_api=False)\
        .success(save_gallery_history, [result_images, result_images_files, history_gallery, history_files], [history_gallery, history_files], queue=False, show_api=False)

        with gr.Tab("Danbooru Tags Transformer with WD Tagger", render=True):
            with gr.Column(scale=2):
                with gr.Group():
                    input_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
                    with gr.Accordion(label="Advanced options", open=False):
                        general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
                        character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
                        input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
                        recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
                    image_algorithms = gr.CheckboxGroup(["Use WD Tagger"], label="Algorithms", value=["Use WD Tagger"], visible=False)
                    keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
                    generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary")
                with gr.Group():
                    with gr.Row():
                        input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku")
                        input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid")
                        pick_random_character = gr.Button(value="Random character 🎲", size="sm")
                    input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="")
                    input_tags_to_copy = gr.Textbox(value="", visible=False)
                    with gr.Row():
                        copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
                        copy_prompt_btn_input = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
                    translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
                    tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False)
                    input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit")
                    with gr.Accordion(label="Advanced options", open=False):
                        input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square")
                        input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long")
                        input_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax")                    
                        input_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
                        model_name = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
                        dummy_np = gr.Textbox(label="Negative prompt", value="", visible=False)
                        recom_animagine = gr.Textbox(label="Animagine reccomended prompt", value="Animagine", visible=False)
                        recom_pony = gr.Textbox(label="Pony reccomended prompt", value="Pony", visible=False)
                    generate_btn = gr.Button(value="GENERATE TAGS", size="lg", variant="primary")
                with gr.Row():
                    with gr.Group():
                        output_text = gr.TextArea(label="Output tags", interactive=False, show_copy_button=True)
                        with gr.Row():
                            copy_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
                            copy_prompt_btn = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
                    with gr.Group():
                        output_text_pony = gr.TextArea(label="Output tags (Pony e621 style)", interactive=False, show_copy_button=True)
                        with gr.Row():
                            copy_btn_pony = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
                            copy_prompt_btn_pony = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
                description_ui()

        translate_input_prompt_button.click(translate_prompt, inputs=[input_general], outputs=[input_general], queue=False)
        translate_input_prompt_button.click(translate_prompt, inputs=[input_character], outputs=[input_character], queue=False)
        translate_input_prompt_button.click(translate_prompt, inputs=[input_copyright], outputs=[input_copyright], queue=False)

        generate_from_image_btn.click(
            lambda: ("", "", ""), None, [input_copyright, input_character, input_general], queue=False,
        ).success(
            predict_tags_wd,
            [input_image, input_general, image_algorithms, general_threshold, character_threshold],
            [input_copyright, input_character, input_general, copy_input_btn],
        ).success(
            remove_specific_prompt, inputs=[input_general, keep_tags], outputs=[input_general], queue=False,
        ).success(
            convert_danbooru_to_e621_prompt, inputs=[input_general, input_tag_type], outputs=[input_general], queue=False,
        ).success(
            insert_recom_prompt, inputs=[input_general, dummy_np, recom_prompt], outputs=[input_general, dummy_np], queue=False,
        ).success(lambda: gr.update(interactive=True), None, [copy_prompt_btn_input], queue=False)
        copy_input_btn.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy])\
            .success(gradio_copy_text, inputs=[input_tags_to_copy], js=COPY_ACTION_JS)
        copy_prompt_btn_input.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy])\
            .success(gradio_copy_prompt, inputs=[input_tags_to_copy], outputs=[prompt_gui])
        
        pick_random_character.click(select_random_character, [input_copyright, input_character], [input_copyright, input_character])

        generate_btn.click(
            v2_upsampling_prompt,
            [model_name, input_copyright, input_character, input_general,
            input_rating, input_aspect_ratio, input_length, input_identity, input_ban_tags],
            [output_text],
        ).success(
            convert_danbooru_to_e621_prompt, inputs=[output_text, tag_type], outputs=[output_text_pony], queue=False,
        ).success(
            insert_recom_prompt, inputs=[output_text, dummy_np, recom_animagine], outputs=[output_text, dummy_np], queue=False,
        ).success(
            insert_recom_prompt, inputs=[output_text_pony, dummy_np, recom_pony], outputs=[output_text_pony, dummy_np], queue=False,
        ).success(lambda: (gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)),
                   None, [copy_btn, copy_btn_pony, copy_prompt_btn, copy_prompt_btn_pony], queue=False)
        copy_btn.click(gradio_copy_text, inputs=[output_text], js=COPY_ACTION_JS)
        copy_btn_pony.click(gradio_copy_text, inputs=[output_text_pony], js=COPY_ACTION_JS)
        copy_prompt_btn.click(gradio_copy_prompt, inputs=[output_text], outputs=[prompt_gui])
        copy_prompt_btn_pony.click(gradio_copy_prompt, inputs=[output_text_pony], outputs=[prompt_gui])

    gr.LoginButton()
    gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")

app.queue()
app.launch(show_error=True, debug=True) # allowed_paths=["./images/"], show_error=True, debug=True
## END MOD