app.py

import spaces
import gradio as gr
import json
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel, FluxControlNetImg2ImgPipeline
from huggingface_hub import HfFileSystem, ModelCard
import random
import time

from env import models, num_loras, num_cns
from mod import (clear_cache, get_repo_safetensors, is_repo_name, is_repo_exists, get_model_trigger,
                 description_ui, compose_lora_json, is_valid_lora, fuse_loras, save_image, preprocess_i2i_image,
                 get_trigger_word, enhance_prompt, deselect_lora, set_control_union_image,
                 get_control_union_mode, set_control_union_mode, get_control_params, translate_to_en)
from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
                  download_my_lora, get_all_lora_tupled_list, apply_lora_prompt,
                  update_loras, get_t2i_model_info)
from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
from tagger.fl2flux import predict_tags_fl2_flux

# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
    loras = json.load(f)

# Initialize the base model
base_model = models[0]
controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
#controlnet_model_union_repo = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'
dtype = torch.bfloat16
#dtype = torch.float8_e4m3fn
#device = "cuda" if torch.cuda.is_available() else "cpu"
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1)
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model, vae=good_vae, transformer=pipe.transformer, text_encoder=pipe.text_encoder,
 tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype)
controlnet_union = None
controlnet = None
last_model = models[0]
last_cn_on = False
#controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
#controlnet = FluxMultiControlNetModel([controlnet_union])

MAX_SEED = 2**32-1

# https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union
# https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union
# https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux
#@spaces.GPU()
def change_base_model(repo_id: str, cn_on: bool, disable_model_cache: bool, progress=gr.Progress(track_tqdm=True)):
    global pipe
    global pipe_i2i
    global taef1
    global good_vae
    global controlnet_union
    global controlnet
    global last_model
    global last_cn_on
    global dtype
    try:
        if not disable_model_cache and (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True)
        pipe.to("cpu")
        pipe_i2i.to("cpu")
        good_vae.to("cpu")
        taef1.to("cpu")
        if controlnet is not None: controlnet.to("cpu")
        if controlnet_union is not None: controlnet_union.to("cpu")
        clear_cache()
        if cn_on:
            progress(0, desc=f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
            print(f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}")
            controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
            controlnet = FluxMultiControlNetModel([controlnet_union])
            pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype)
            pipe_i2i = FluxControlNetImg2ImgPipeline.from_pretrained(repo_id, controlnet=controlnet, vae=None, transformer=pipe.transformer, text_encoder=pipe.text_encoder,
             tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype)
            last_model = repo_id
            last_cn_on = cn_on
            progress(1, desc=f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
            print(f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
        else:
            progress(0, desc=f"Loading model: {repo_id}")
            print(f"Loading model: {repo_id}")
            pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype)
            pipe_i2i = AutoPipelineForImage2Image.from_pretrained(repo_id, vae=None, transformer=pipe.transformer, text_encoder=pipe.text_encoder,
             tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype)
            last_model = repo_id
            last_cn_on = cn_on
            progress(1, desc=f"Model loaded: {repo_id}")
            print(f"Model loaded: {repo_id}")
    except Exception as e:
        print(f"Model load Error: {e}")
        raise gr.Error(f"Model load Error: {e}") from e
    return gr.update(visible=True)

change_base_model.zerogpu = True

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def update_selection(evt: gr.SelectData, width, height):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    if "aspect" in selected_lora:
        if selected_lora["aspect"] == "portrait":
            width = 768
            height = 1024
        elif selected_lora["aspect"] == "landscape":
            width = 1024
            height = 768
        else:
            width = 1024
            height = 1024
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
    )

@spaces.GPU(duration=70)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)):
    global pipe
    global taef1
    global good_vae
    global controlnet
    global controlnet_union
    try:
        good_vae.to("cuda")
        taef1.to("cuda")
        generator = torch.Generator(device="cuda").manual_seed(int(float(seed)))
        
        with calculateDuration("Generating image"):
            # Generate image
            modes, images, scales = get_control_params()
            if not cn_on or len(modes) == 0:
                pipe.to("cuda")
                pipe.vae = taef1
                pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
                progress(0, desc="Start Inference.")
                for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
                    prompt=prompt_mash,
                    num_inference_steps=steps,
                    guidance_scale=cfg_scale,
                    width=width,
                    height=height,
                    generator=generator,
                    joint_attention_kwargs={"scale": lora_scale},
                    output_type="pil",
                    good_vae=good_vae,
                ):
                    yield img
            else:
                pipe.to("cuda")
                pipe.vae = good_vae
                if controlnet_union is not None: controlnet_union.to("cuda")
                if controlnet is not None: controlnet.to("cuda")
                pipe.enable_model_cpu_offload()
                progress(0, desc="Start Inference with ControlNet.")
                for img in pipe(
                    prompt=prompt_mash,
                    control_image=images,
                    control_mode=modes,
                    num_inference_steps=steps,
                    guidance_scale=cfg_scale,
                    width=width,
                    height=height,
                    controlnet_conditioning_scale=scales,
                    generator=generator,
                    joint_attention_kwargs={"scale": lora_scale},
                ).images:
                    yield img
    except Exception as e:
        print(e)
        raise gr.Error(f"Inference Error: {e}") from e

@spaces.GPU(duration=70)
def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed, cn_on, progress=gr.Progress(track_tqdm=True)):
    global pipe_i2i
    global good_vae
    global controlnet
    global controlnet_union
    try:
        good_vae.to("cuda")
        generator = torch.Generator(device="cuda").manual_seed(int(float(seed)))
        image_input = load_image(image_input_path)
        
        with calculateDuration("Generating image"):
            # Generate image
            modes, images, scales = get_control_params()
            if True or not cn_on or len(modes) == 0:
                pipe_i2i.to("cuda")
                pipe_i2i.vae = good_vae
                image_input = load_image(image_input_path)
                progress(0, desc="Start I2I Inference.")
                final_image = pipe_i2i(
                    prompt=prompt_mash,
                    image=image_input,
                    strength=image_strength,
                    num_inference_steps=steps,
                    guidance_scale=cfg_scale,
                    width=width,
                    height=height,
                    generator=generator,
                    joint_attention_kwargs={"scale": lora_scale},
                    output_type="pil",
                ).images[0]
                return final_image 
            else: # omitted
                pipe_i2i.to("cuda")
                pipe_i2i.vae = good_vae
                image_input = load_image(image_input_path)
                if controlnet_union is not None: controlnet_union.to("cuda")
                if controlnet is not None: controlnet.to("cuda")
                pipe_i2i.enable_model_cpu_offload()
                progress(0, desc="Start I2I Inference with ControlNet.")
                final_image = pipe_i2i(
                    prompt=prompt_mash,
                    control_image=images,
                    control_mode=modes,
                    image=image_input,
                    strength=image_strength,
                    num_inference_steps=steps,
                    guidance_scale=cfg_scale,
                    width=width,
                    height=height,
                    controlnet_conditioning_scale=scales,
                    generator=generator,
                    joint_attention_kwargs={"scale": lora_scale},
                    output_type="pil",
                ).images[0]
                return final_image
    except Exception as e:
        print(e)
        raise gr.Error(f"I2I Inference Error: {e}") from e

def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,

              lora_scale, lora_json, cn_on, translate_on, progress=gr.Progress(track_tqdm=True)):
    global pipe
    if selected_index is None and not is_valid_lora(lora_json):
        gr.Info("LoRA isn't selected.")
    #    raise gr.Error("You must select a LoRA before proceeding.")
    progress(0, desc="Preparing Inference.")

    with calculateDuration("Unloading LoRA"):
        try:
            pipe.unfuse_lora()
            pipe.unload_lora_weights()
            pipe_i2i.unfuse_lora()
            pipe_i2i.unload_lora_weights()
        except Exception as e:
            print(e)
    
    clear_cache() #

    if translate_on: prompt = translate_to_en(prompt)

    prompt_mash = prompt + get_model_trigger(last_model)
    if is_valid_lora(lora_json):
        # Load External LoRA weights
        with calculateDuration("Loading External LoRA weights"):
            fuse_loras(pipe, lora_json)
            trigger_word = get_trigger_word(lora_json)
            prompt_mash = f"{prompt} {trigger_word}"
    if selected_index is not None:
        selected_lora = loras[selected_index]
        lora_path = selected_lora["repo"]
        trigger_word = selected_lora["trigger_word"]
        if(trigger_word):
            if "trigger_position" in selected_lora:
                if selected_lora["trigger_position"] == "prepend":
                    prompt_mash = f"{trigger_word} {prompt_mash}"
                else:
                    prompt_mash = f"{prompt_mash} {trigger_word}"
            else:
                prompt_mash = f"{trigger_word} {prompt_mash}"
        else:
            prompt_mash = prompt_mash
        # Load LoRA weights
        with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
            if(image_input is not None):
                if "weights" in selected_lora:
                    pipe_i2i.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
                else:
                    pipe_i2i.load_lora_weights(lora_path)
            else:
                if "weights" in selected_lora:
                    pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
                else:
                    pipe.load_lora_weights(lora_path)
        
    # Set random seed for reproducibility
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
    
    progress(0, desc="Running Inference.")
    if(image_input is not None):
        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed, cn_on, progress)
        yield save_image(final_image, None, last_model, prompt_mash, height, width, steps, cfg_scale, seed), seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
        # Consume the generator to get the final image
        final_image = None
        step_counter = 0
        for image in image_generator:
            step_counter+=1
            final_image = image
            progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
            yield image, seed, gr.update(value=progress_bar, visible=True)
        yield save_image(final_image, None, last_model, prompt_mash, height, width, steps, cfg_scale, seed), seed, gr.update(value=progress_bar, visible=False)

def get_huggingface_safetensors(link):
  split_link = link.split("/")
  if(len(split_link) == 2):
            model_card = ModelCard.load(link)
            base_model = model_card.data.get("base_model")
            print(base_model)
            if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
                raise Exception("Not a FLUX LoRA!")
            image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
            trigger_word = model_card.data.get("instance_prompt", "")
            image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
            fs = HfFileSystem()
            try:
                list_of_files = fs.ls(link, detail=False)
                for file in list_of_files:
                    if(file.endswith(".safetensors")):
                        safetensors_name = file.split("/")[-1]
                    if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
                      image_elements = file.split("/")
                      image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
            except Exception as e:
              print(e)
              gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
              raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
            return split_link[1], link, safetensors_name, trigger_word, image_url

def check_custom_model(link):
    if(link.startswith("https://")):
        if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
    else: 
        return get_huggingface_safetensors(link)

def add_custom_lora(custom_lora):
    global loras
    if(custom_lora):
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            card = f'''

            <div class="custom_lora_card">

              <span>Loaded custom LoRA:</span>

              <div class="card_internal">

                <img src="{image}" />

                <div>

                    <h3>{title}</h3>

                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>

                </div>

              </div>

            </div>

            '''
            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
            if(not existing_item_index):
                new_item = {
                    "image": image,
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(new_item)
                existing_item_index = len(loras)
                loras.append(new_item)
        
            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
        except Exception as e:
            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None, ""
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

def remove_custom_lora():
    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

run_lora.zerogpu = True

css = '''

#gen_btn{height: 100%}

#title{text-align: center}

#title h1{font-size: 3em; display:inline-flex; align-items:center}

#title img{width: 100px; margin-right: 0.5em}

#gallery .grid-wrap{height: 10vh}

#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}

.card_internal{display: flex;height: 100px;margin-top: .5em}

.card_internal img{margin-right: 1em}

.styler{--form-gap-width: 0px !important}

#progress{height:30px}

#progress .generating{display:none}

.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}

.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}

.info {text-align:center; !important}

'''
with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css, delete_cache=(60, 3600)) as app:
    with gr.Tab("FLUX LoRA the Explorer"):
        title = gr.HTML(
            """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA">FLUX LoRA the Explorer Mod</h1>""",
            elem_id="title",
        )
        selected_index = gr.State(None)
        with gr.Row():
            with gr.Column(scale=3):
                with gr.Group():
                    with gr.Accordion("Generate Prompt from Image", open=False):
                        tagger_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
                        with gr.Accordion(label="Advanced options", open=False):
                            tagger_general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
                            tagger_character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
                            neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False)
                            v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2, visible=False)
                            v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2, visible=False)
                            v2_copy = gr.Button(value="Copy to clipboard", size="sm", interactive=False, visible=False)
                        tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-Flux"], label="Algorithms", value=["Use WD Tagger"])
                        tagger_generate_from_image = gr.Button(value="Generate Prompt from Image")
                    prompt = gr.Textbox(label="Prompt", lines=1, max_lines=8, placeholder="Type a prompt", show_copy_button=True)
                    with gr.Row():
                        prompt_enhance = gr.Button(value="Enhance your prompt", variant="secondary")
                        auto_trans = gr.Checkbox(label="Auto translate to English", value=False, elem_classes="info")
            with gr.Column(scale=1, elem_id="gen_column"):
                generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
        with gr.Row():
            with gr.Column():
                selected_info = gr.Markdown("")
                gallery = gr.Gallery(
                    [(item["image"], item["title"]) for item in loras],
                    label="LoRA Gallery",
                    allow_preview=False,
                    columns=3,
                    elem_id="gallery"
                )
                with gr.Group():
                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
                    gr.Markdown("[Check the list of FLUX LoRas](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
                custom_lora_info = gr.HTML(visible=False)
                custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
                deselect_lora_button = gr.Button("Deselect LoRA", variant="secondary")
            with gr.Column():
                progress_bar = gr.Markdown(elem_id="progress",visible=False)
                result = gr.Image(label="Generated Image", format="png", show_share_button=False)
        with gr.Group():
            model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id to want to use.", choices=models, value=models[0], allow_custom_value=True)
            model_info = gr.Markdown(elem_classes="info")
        with gr.Row():
            with gr.Accordion("Advanced Settings", open=False):
                with gr.Row():
                    input_image = gr.Image(label="Input image", type="filepath", height=256, sources=["upload", "clipboard"], show_share_button=False)
                    with gr.Column():
                        image_strength = gr.Slider(label="Image Strength", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
                        input_image_preprocess = gr.Checkbox(True, label="Preprocess Input image")
                with gr.Column():
                    with gr.Row():
                        lora_scale = gr.Slider(label="LoRA Scale", minimum=-3, maximum=3, step=0.01, value=0.95)
                        width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                        height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
                    with gr.Row():
                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
                        steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
                    with gr.Row():
                        randomize_seed = gr.Checkbox(True, label="Randomize seed")
                        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
                        disable_model_cache = gr.Checkbox(False, label="Disable model caching")
                    with gr.Accordion("External LoRA", open=True):
                        with gr.Column():
                            lora_repo_json = gr.JSON(value=[{}] * num_loras, visible=False)
                            lora_repo = [None] * num_loras
                            lora_weights = [None] * num_loras
                            lora_trigger = [None] * num_loras
                            lora_wt = [None] * num_loras
                            lora_info = [None] * num_loras
                            lora_copy = [None] * num_loras
                            lora_md = [None] * num_loras
                            lora_num = [None] * num_loras
                            with gr.Row():
                                for i in range(num_loras):
                                    with gr.Column():
                                        lora_repo[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Repo", choices=get_all_lora_tupled_list(), info="Input LoRA Repo ID", value="", allow_custom_value=True)
                                        with gr.Row():
                                            lora_weights[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Filename", choices=[], info="Optional", value="", allow_custom_value=True)
                                            lora_trigger[i] = gr.Textbox(label=f"LoRA {int(i+1)} Trigger Prompt", lines=1, max_lines=4, value="")
                                            lora_wt[i] = gr.Slider(label=f"LoRA {int(i+1)} Scale", minimum=-3, maximum=3, step=0.01, value=1.00)
                                        with gr.Row():
                                            lora_info[i] = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
                                            lora_copy[i] = gr.Button(value="Copy example to prompt", visible=False)
                                            lora_md[i] = gr.Markdown(value="", visible=False)
                                            lora_num[i] = gr.Number(i, visible=False)
                            with gr.Accordion("From URL", open=True, visible=True):
                                with gr.Row():
                                    lora_search_civitai_query = gr.Textbox(label="Query", placeholder="flux", lines=1)
                                    lora_search_civitai_submit = gr.Button("Search on Civitai")
                                    lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Flux.1 D", "Flux.1 S"], value=["Flux.1 D", "Flux.1 S"])
                                with gr.Row():
                                    lora_search_civitai_json = gr.JSON(value={}, visible=False)
                                    lora_search_civitai_desc = gr.Markdown(value="", visible=False)
                                lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
                                lora_download_url = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
                                with gr.Row():
                                    lora_download = [None] * num_loras
                                    for i in range(num_loras):
                                        lora_download[i] = gr.Button(f"Get and set LoRA to {int(i+1)}")
                    with gr.Accordion("ControlNet (extremely slow)", open=True, visible=True):
                        with gr.Column():
                            cn_on = gr.Checkbox(False, label="Use ControlNet")
                            cn_mode = [None] * num_cns
                            cn_scale = [None] * num_cns
                            cn_image = [None] * num_cns
                            cn_image_ref = [None] * num_cns
                            cn_res = [None] * num_cns
                            cn_num = [None] * num_cns
                            with gr.Row():
                                for i in range(num_cns):
                                    with gr.Column():
                                        cn_mode[i] = gr.Radio(label=f"ControlNet {int(i+1)} Mode", choices=get_control_union_mode(), value=get_control_union_mode()[0])
                                        with gr.Row():
                                            cn_scale[i] = gr.Slider(label=f"ControlNet {int(i+1)} Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.75)
                                            cn_res[i] = gr.Slider(label=f"ControlNet {int(i+1)} Preprocess resolution", minimum=128, maximum=512, value=384, step=1)
                                            cn_num[i] = gr.Number(i, visible=False)
                                        with gr.Row():
                                            cn_image_ref[i] = gr.Image(label="Image Reference", type="pil", format="png", height=256, sources=["upload", "clipboard"], show_share_button=False)
                                            cn_image[i] = gr.Image(label="Control Image", type="pil", format="png", height=256, show_share_button=False, interactive=False)
    
    gallery.select(
        update_selection,
        inputs=[width, height],
        outputs=[prompt, selected_info, selected_index, width, height],
        queue=False,
        show_api=False,
        trigger_mode="once",
    )
    custom_lora.input(
        add_custom_lora,
        inputs=[custom_lora],
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt],
        queue=False,
        show_api=False,
    )
    custom_lora_button.click(
        remove_custom_lora,
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora],
        queue=False,
        show_api=False,
    )
    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=change_base_model,
        inputs=[model_name, cn_on, disable_model_cache],
        outputs=[result],
        queue=True,
        show_api=False,
        trigger_mode="once",
    ).success(
        fn=run_lora,
        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
                 lora_scale, lora_repo_json, cn_on, auto_trans], 
        outputs=[result, seed, progress_bar],
        queue=True,
        show_api=True,
    )

    input_image.upload(preprocess_i2i_image, [input_image, input_image_preprocess, height, width], [input_image], queue=False, show_api=False)
    deselect_lora_button.click(deselect_lora, None, [prompt, selected_info, selected_index, width, height], queue=False, show_api=False)
    gr.on(
        triggers=[model_name.change, cn_on.change],
        fn=get_t2i_model_info,
        inputs=[model_name], 
        outputs=[model_info],
        queue=False,
        show_api=False,
        trigger_mode="once",
    ).then(change_base_model, [model_name, cn_on, disable_model_cache], [result], queue=True, show_api=False)
    prompt_enhance.click(enhance_prompt, [prompt], [prompt], queue=False, show_api=False)

    gr.on(
        triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
        fn=search_civitai_lora,
        inputs=[lora_search_civitai_query, lora_search_civitai_basemodel],
        outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query],
        scroll_to_output=True,
        queue=True,
        show_api=False,
    )
    lora_search_civitai_json.change(search_civitai_lora_json, [lora_search_civitai_query, lora_search_civitai_basemodel], [lora_search_civitai_json], queue=True, show_api=True)  # fn for api
    lora_search_civitai_result.change(select_civitai_lora, [lora_search_civitai_result], [lora_download_url, lora_search_civitai_desc], scroll_to_output=True, queue=False, show_api=False)

    for i, l in enumerate(lora_repo):
        deselect_lora_button.click(lambda: ("", 1.0), None, [lora_repo[i], lora_wt[i]], queue=False, show_api=False)
        gr.on(
            triggers=[lora_download[i].click],
            fn=download_my_lora,
            inputs=[lora_download_url, lora_repo[i]],
            outputs=[lora_repo[i]],
            scroll_to_output=True,
            queue=True,
            show_api=False,
        )
        gr.on(
            triggers=[lora_repo[i].change, lora_wt[i].change],
            fn=update_loras,
            inputs=[prompt, lora_repo[i], lora_wt[i]],
            outputs=[prompt, lora_repo[i], lora_wt[i], lora_info[i], lora_md[i]],
            queue=False,
            trigger_mode="once",
            show_api=False,
        ).success(get_repo_safetensors, [lora_repo[i]], [lora_weights[i]], queue=False, show_api=False
        ).success(apply_lora_prompt, [lora_info[i]], [lora_trigger[i]], queue=False, show_api=False
        ).success(compose_lora_json, [lora_repo_json, lora_num[i], lora_repo[i], lora_wt[i], lora_weights[i], lora_trigger[i]], [lora_repo_json], queue=False, show_api=False)
        
    for i, m in enumerate(cn_mode):
        gr.on(
            triggers=[cn_mode[i].change, cn_scale[i].change],
            fn=set_control_union_mode,
            inputs=[cn_num[i], cn_mode[i], cn_scale[i]],
            outputs=[cn_on],
            queue=True,
            show_api=False,
        ).success(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)
        cn_image_ref[i].upload(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)

    tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
    ).success(
        predict_tags_wd,
        [tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold],
        [v2_series, v2_character, prompt, v2_copy],
        show_api=False,
    ).success(predict_tags_fl2_flux, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
    ).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False)

    with gr.Tab("FLUX Prompt Generator"):
        from prompt import (PromptGenerator, HuggingFaceInferenceNode, florence_caption,
            ARTFORM, PHOTO_TYPE, ROLES, HAIRSTYLES, LIGHTING, COMPOSITION, POSE, BACKGROUND,
            PHOTOGRAPHY_STYLES, DEVICE, PHOTOGRAPHER, ARTIST, DIGITAL_ARTFORM, PLACE,
            FEMALE_DEFAULT_TAGS, MALE_DEFAULT_TAGS, FEMALE_BODY_TYPES, MALE_BODY_TYPES,
            FEMALE_CLOTHING, MALE_CLOTHING, FEMALE_ADDITIONAL_DETAILS, MALE_ADDITIONAL_DETAILS, pg_title)

        prompt_generator = PromptGenerator()
        huggingface_node = HuggingFaceInferenceNode()

        gr.HTML(pg_title)

        with gr.Row():
            with gr.Column(scale=2):
                with gr.Accordion("Basic Settings"):
                    pg_custom = gr.Textbox(label="Custom Input Prompt (optional)")
                    pg_subject = gr.Textbox(label="Subject (optional)")
                    pg_gender = gr.Radio(["female", "male"], label="Gender", value="female")
                    
                    # Add the radio button for global option selection
                    pg_global_option = gr.Radio(
                        ["Disabled", "Random", "No Figure Rand"],
                        label="Set all options to:",
                        value="Disabled"
                    )
                
                with gr.Accordion("Artform and Photo Type", open=False):
                    pg_artform = gr.Dropdown(["disabled", "random"] + ARTFORM, label="Artform", value="disabled")
                    pg_photo_type = gr.Dropdown(["disabled", "random"] + PHOTO_TYPE, label="Photo Type", value="disabled")
            
                with gr.Accordion("Character Details", open=False):
                    pg_body_types = gr.Dropdown(["disabled", "random"] + FEMALE_BODY_TYPES + MALE_BODY_TYPES, label="Body Types", value="disabled")
                    pg_default_tags = gr.Dropdown(["disabled", "random"] + FEMALE_DEFAULT_TAGS + MALE_DEFAULT_TAGS, label="Default Tags", value="disabled")
                    pg_roles = gr.Dropdown(["disabled", "random"] + ROLES, label="Roles", value="disabled")
                    pg_hairstyles = gr.Dropdown(["disabled", "random"] + HAIRSTYLES, label="Hairstyles", value="disabled")
                    pg_clothing = gr.Dropdown(["disabled", "random"] + FEMALE_CLOTHING + MALE_CLOTHING, label="Clothing", value="disabled")
            
                with gr.Accordion("Scene Details", open=False):
                    pg_place = gr.Dropdown(["disabled", "random"] + PLACE, label="Place", value="disabled")
                    pg_lighting = gr.Dropdown(["disabled", "random"] + LIGHTING, label="Lighting", value="disabled")
                    pg_composition = gr.Dropdown(["disabled", "random"] + COMPOSITION, label="Composition", value="disabled")
                    pg_pose = gr.Dropdown(["disabled", "random"] + POSE, label="Pose", value="disabled")
                    pg_background = gr.Dropdown(["disabled", "random"] + BACKGROUND, label="Background", value="disabled")
            
                with gr.Accordion("Style and Artist", open=False):
                    pg_additional_details = gr.Dropdown(["disabled", "random"] + FEMALE_ADDITIONAL_DETAILS + MALE_ADDITIONAL_DETAILS, label="Additional Details", value="disabled")
                    pg_photography_styles = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHY_STYLES, label="Photography Styles", value="disabled")
                    pg_device = gr.Dropdown(["disabled", "random"] + DEVICE, label="Device", value="disabled")
                    pg_photographer = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHER, label="Photographer", value="disabled")
                    pg_artist = gr.Dropdown(["disabled", "random"] + ARTIST, label="Artist", value="disabled")
                    pg_digital_artform = gr.Dropdown(["disabled", "random"] + DIGITAL_ARTFORM, label="Digital Artform", value="disabled")
                
                pg_generate_button = gr.Button("Generate Prompt")

            with gr.Column(scale=2):
                with gr.Accordion("Image and Caption", open=False):
                    pg_input_image = gr.Image(label="Input Image (optional)")
                    pg_caption_output = gr.Textbox(label="Generated Caption", lines=3)
                    pg_create_caption_button = gr.Button("Create Caption")
                    pg_add_caption_button = gr.Button("Add Caption to Prompt")

                with gr.Accordion("Prompt Generation", open=True):
                    pg_output = gr.Textbox(label="Generated Prompt / Input Text", lines=4)
                    pg_t5xxl_output = gr.Textbox(label="T5XXL Output", visible=True)
                    pg_clip_l_output = gr.Textbox(label="CLIP L Output", visible=True)
                    pg_clip_g_output = gr.Textbox(label="CLIP G Output", visible=True)
            
            with gr.Column(scale=2):
                with gr.Accordion("Prompt Generation with LLM", open=False):
                    pg_happy_talk = gr.Checkbox(label="Happy Talk", value=True)
                    pg_compress = gr.Checkbox(label="Compress", value=True)
                    pg_compression_level = gr.Radio(["soft", "medium", "hard"], label="Compression Level", value="hard")
                    pg_poster = gr.Checkbox(label="Poster", value=False)
                    pg_custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5)
                pg_generate_text_button = gr.Button("Generate Prompt with LLM (Llama 3.1 70B)")
                pg_text_output = gr.Textbox(label="Generated Text", lines=10)

        def create_caption(image):
            if image is not None:
                return florence_caption(image)
            return ""

        pg_create_caption_button.click(
            create_caption,
            inputs=[pg_input_image],
            outputs=[pg_caption_output]
        )

        def generate_prompt_with_dynamic_seed(*args):
            # Generate a new random seed
            dynamic_seed = random.randint(0, 1000000)
            
            # Call the generate_prompt function with the dynamic seed
            result = prompt_generator.generate_prompt(dynamic_seed, *args)
            
            # Return the result along with the used seed
            return [dynamic_seed] + list(result)

        pg_generate_button.click(
            generate_prompt_with_dynamic_seed,
            inputs=[pg_custom, pg_subject, pg_gender, pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles,
                    pg_additional_details, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform,
                    pg_place, pg_lighting, pg_clothing, pg_composition, pg_pose, pg_background, pg_input_image],
            outputs=[gr.Number(label="Used Seed", visible=False), pg_output, gr.Number(visible=False), pg_t5xxl_output, pg_clip_l_output, pg_clip_g_output]
        ) #

        pg_add_caption_button.click(
            prompt_generator.add_caption_to_prompt,
            inputs=[pg_output, pg_caption_output],
            outputs=[pg_output]
        )

        pg_generate_text_button.click(
            huggingface_node.generate,
            inputs=[pg_output, pg_happy_talk, pg_compress, pg_compression_level, pg_poster, pg_custom_base_prompt],
            outputs=pg_text_output
        )

        def update_all_options(choice):
            updates = {}
            if choice == "Disabled":
                for dropdown in [
                    pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
                    pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
                    pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
                ]:
                    updates[dropdown] = gr.update(value="disabled")
            elif choice == "Random":
                for dropdown in [
                    pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
                    pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
                    pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
                ]:
                    updates[dropdown] = gr.update(value="random")
            else:  # No Figure Random
                for dropdown in [pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing, pg_pose, pg_additional_details]:
                    updates[dropdown] = gr.update(value="disabled")
                for dropdown in [pg_artform, pg_place, pg_lighting, pg_composition, pg_background, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform]:
                    updates[dropdown] = gr.update(value="random")
            return updates
        
        pg_global_option.change(
            update_all_options,
            inputs=[pg_global_option],
            outputs=[
                pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
                pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
                pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
            ]
        )

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


app.queue()
app.launch()