app.py

import os
import json
import copy
import time
import random
import logging
import numpy as np
from typing import Any, Dict, List, Optional, Union

import torch
from PIL import Image
import gradio as gr


from diffusers import (
    DiffusionPipeline,
    AutoencoderTiny,
    AutoencoderKL,
    AutoPipelineForImage2Image,
    FluxPipeline,
    FlowMatchEulerDiscreteScheduler)

from huggingface_hub import (
    hf_hub_download,
    HfFileSystem,
    ModelCard,
    snapshot_download)

from diffusers.utils import load_image

from modules.version_info import (
    versions_html,
    #initialize_cuda,
    #release_torch_resources,
    #get_torch_info
)
from modules.image_utils import (
    change_color,
    open_image,
    build_prerendered_images,
    upscale_image,
    lerp_imagemath,
    shrink_and_paste_on_blank,
    show_lut,
    apply_lut_to_image_path,
    multiply_and_blend_images,
    alpha_composite_with_control,
    crop_and_resize_image,
    convert_to_rgba_png
)
from modules.constants import (
    LORA_DETAILS, LORAS as loras, MODELS,
    default_lut_example_img, 
    lut_files, 
    MAX_SEED, 
    lut_folder,cards, 
    cards_alternating, 
    card_colors, 
    card_colors_alternating,
    pre_rendered_maps_paths
)
from modules.excluded_colors import (
    add_color,
    delete_color,
    build_dataframe,
    on_input,
    excluded_color_list,
    on_color_display_select
)
from modules.misc import (
    get_filename,
    convert_ratio_to_dimensions,
    update_dimensions_on_ratio
)
from modules.lora_details import (
    approximate_token_count,
    split_prompt_precisely,
)

import spaces

input_image_palette = []
current_prerendered_image = gr.State("./images/images/Beeuty-1.png")
#---if workspace = local or colab---

# Authenticate with Hugging Face
# from huggingface_hub import login

# Log in to Hugging Face using the provided token
# hf_token = 'hf-token-authentication'
# login(hf_token)

def calculate_shift(
    image_seq_len,
    base_seq_len: int = 256,
    max_seq_len: int = 4096,
    base_shift: float = 0.5,
    max_shift: float = 1.16,
):
    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
    b = base_shift - m * base_seq_len
    mu = image_seq_len * m + b
    return mu

def retrieve_timesteps(
    scheduler,
    num_inference_steps: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    timesteps: Optional[List[int]] = None,
    sigmas: Optional[List[float]] = None,
    **kwargs,
):
    if timesteps is not None and sigmas is not None:
        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
    if timesteps is not None:
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    elif sigmas is not None:
        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps

# FLUX pipeline
@torch.inference_mode()
def flux_pipe_call_that_returns_an_iterable_of_images(
    self,
    prompt: Union[str, List[str]] = None,
    prompt_2: Optional[Union[str, List[str]]] = None,
    height: Optional[int] = None,
    width: Optional[int] = None,
    num_inference_steps: int = 28,
    timesteps: List[int] = None,
    guidance_scale: float = 3.5,
    num_images_per_prompt: Optional[int] = 1,
    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
    latents: Optional[torch.FloatTensor] = None,
    prompt_embeds: Optional[torch.FloatTensor] = None,
    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = True,
    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    max_sequence_length: int = 512,
    good_vae: Optional[Any] = None,
):
    height = height or self.default_sample_size * self.vae_scale_factor
    width = width or self.default_sample_size * self.vae_scale_factor
    
    self.check_inputs(
        prompt,
        prompt_2,
        height,
        width,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        max_sequence_length=max_sequence_length,
    )

    self._guidance_scale = guidance_scale
    self._joint_attention_kwargs = joint_attention_kwargs
    self._interrupt = False

    batch_size = 1 if isinstance(prompt, str) else len(prompt)
    device = self._execution_device

    lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
        prompt=prompt,
        prompt_2=prompt_2,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        device=device,
        num_images_per_prompt=num_images_per_prompt,
        max_sequence_length=max_sequence_length,
        lora_scale=lora_scale,
    )
    
    num_channels_latents = self.transformer.config.in_channels // 4
    latents, latent_image_ids = self.prepare_latents(
        batch_size * num_images_per_prompt,
        num_channels_latents,
        height,
        width,
        prompt_embeds.dtype,
        device,
        generator,
        latents,
    )
    
    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
    image_seq_len = latents.shape[1]
    mu = calculate_shift(
        image_seq_len,
        self.scheduler.config.base_image_seq_len,
        self.scheduler.config.max_image_seq_len,
        self.scheduler.config.base_shift,
        self.scheduler.config.max_shift,
    )
    timesteps, num_inference_steps = retrieve_timesteps(
        self.scheduler,
        num_inference_steps,
        device,
        timesteps,
        sigmas,
        mu=mu,
    )
    self._num_timesteps = len(timesteps)

    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None

    for i, t in enumerate(timesteps):
        if self.interrupt:
            continue

        timestep = t.expand(latents.shape[0]).to(latents.dtype)

        noise_pred = self.transformer(
            hidden_states=latents,
            timestep=timestep / 1000,
            guidance=guidance,
            pooled_projections=pooled_prompt_embeds,
            encoder_hidden_states=prompt_embeds,
            txt_ids=text_ids,
            img_ids=latent_image_ids,
            joint_attention_kwargs=self.joint_attention_kwargs,
            return_dict=False,
        )[0]

        latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
        latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
        image = self.vae.decode(latents_for_image, return_dict=False)[0]
        yield self.image_processor.postprocess(image, output_type=output_type)[0]
        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
        torch.cuda.empty_cache()
        
    latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
    latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
    image = good_vae.decode(latents, return_dict=False)[0]
    self.maybe_free_model_hooks()
    torch.cuda.empty_cache()
    yield self.image_processor.postprocess(image, output_type=output_type)[0]

#--------------------------------------------------Model Initialization-----------------------------------------------------------------------------------------#

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "black-forest-labs/FLUX.1-dev"

#TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.#
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
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
                                                     )

MAX_SEED = 2**32-1

pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

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=120)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    if approximate_token_count(prompt_mash) > 76:
        prompt, prompt2 = split_prompt_precisely(prompt_mash)
    with calculateDuration("Generating image"):
        # Generate image
        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt,
            prompt2=prompt2,
            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

def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
    generator = torch.Generator(device="cuda").manual_seed(seed)
    pipe_i2i.to("cuda")
    image_input = load_image(image_input_path)
    if approximate_token_count(prompt_mash) > 76:
        prompt, prompt2 = split_prompt_precisely(prompt_mash)
    final_image = pipe_i2i(
        prompt=prompt,
        prompt2=prompt2,
        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 

@spaces.GPU(duration=120)
def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
    if selected_index is None:
        raise gr.Error("You must select a LoRA before proceeding.🧨")
    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}"
            else:
                prompt_mash = f"{prompt} {trigger_word}"
        else:
            prompt_mash = f"{trigger_word} {prompt}"
    else:
        prompt_mash = prompt

    with calculateDuration("Unloading LoRA"):
        pipe.unload_lora_weights()
        pipe_i2i.unload_lora_weights()
        
    #LoRA weights flow
    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
        pipe_to_use = pipe_i2i if image_input is not None else pipe
        weight_name = selected_lora.get("weights", None)
        
        pipe_to_use.load_lora_weights(
            lora_path, 
            weight_name=weight_name, 
            low_cpu_mem_usage=True
        )
            
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
            
    if(image_input is not None):
        print(f"\nGenerating image to image with seed: {seed}\n")
        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
        yield final_image, seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
    
        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 final_image, 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)
      
            #Allows Both
            if base_model not in MODELS:
            #if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
                raise Exception("Flux LoRA Not Found!")
                
            # Only allow "black-forest-labs/FLUX.1-dev"
            #if base_model != "black-forest-labs/FLUX.1-dev":
                #raise Exception("Only FLUX.1-dev is supported, other LoRA models are not allowed!")
                
            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=False), 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, ""
def on_prerendered_gallery_selection(event_data: gr.SelectData):
    global current_prerendered_image
    selected_index = event_data.index
    selected_image = pre_rendered_maps_paths[selected_index]
    print(f"Gallery Image Selected: {selected_image}\n")
    current_prerendered_image.value = selected_image
    return current_prerendered_image

run_lora.zerogpu = True

title = "Hex Game Maker"
with gr.Blocks(css_paths="style_20250128.css", title=title, theme='Surn/beeuty', delete_cache=(7200, 7200)) as app:
    with gr.Row():
        gr.Markdown("""
        # Hex Game Maker
        ## Transform Your Images into Mesmerizing Hexagon Grid Masterpieces! ⬢""", elem_classes="intro")
    with gr.Row():
        with gr.Accordion("Welcome to Hex Game Maker, the ultimate tool for transforming your images into stunning hexagon grid artworks. Whether you're a tabletop game enthusiast, a digital artist, or someone who loves unique patterns, HexaGrid Creator has something for you.", open=False, elem_classes="intro"):
            gr.Markdown ("""

            ## Drop an image into the Input Image and get started!

        

            ## What is Hex Game Maker?
            Hex Game Maker is a web-based application that allows you to apply a hexagon grid overlay to any image. You can customize the size, color, and opacity of the hexagons, as well as the background and border colors. The result is a visually striking image that looks like it was made from hexagonal tiles!

            ### What Can You Do?
            - **Generate Hexagon Grids:** Create beautiful hexagon grid overlays on any image with fully customizable parameters.
            - **AI-Powered Image Generation:** Use advanced AI models to generate images based on your prompts and apply hexagon grids to them.
            - **Color Exclusion:** Select and exclude specific colors from your hexagon grid for a cleaner and more refined look.
            - **Interactive Customization:** Adjust hexagon size, border size, rotation, background color, and more in real-time.
            - **Depth and 3D Model Generation:** Generate depth maps and 3D models from your images for enhanced visualization.
            - **Image Filter [Look-Up Table (LUT)] Application:** Apply filters (LUTs) to your images for color grading and enhancement.
            - **Pre-rendered Maps:** Access a library of pre-rendered hexagon maps for quick and easy customization.
            - **Add Margins:** Add customizable margins around your images for a polished finish.

            ### Why You'll Love It
            - **Fun and Easy to Use:** With an intuitive interface and real-time previews, creating hexagon grids has never been this fun!
            - **Endless Creativity:** Unleash your creativity with endless customization options and see your images transform in unique ways.
            - **Hexagon-Inspired Theme:** Enjoy a delightful yellow and purple theme inspired by hexagons! ⬢
            - **Advanced AI Models:** Leverage advanced AI models and LoRA weights for high-quality image generation and customization.

            ### Get Started
            1. **Upload or Generate an Image:** Start by uploading your own image or generate one using our AI-powered tool.
            2. **Customize Your Grid:** Play around with the settings to create the perfect hexagon grid overlay.
            3. **Download and Share:** Once you're happy with your creation, download it and share it with the world!

            ### Advanced Features
            - **Generative AI Integration:** Utilize models like `black-forest-labs/FLUX.1-dev` and various LoRA weights for generating unique images.
            - **Pre-rendered Maps:** Access a library of pre-rendered hexagon maps for quick and easy customization.
            - **Image Filter [Look-Up Table (LUT)] Application:** Apply filters (LUTs) to your images for color grading and enhancement.
            - **Depth and 3D Model Generation:** Create depth maps and 3D models from your images for enhanced visualization.
            - **Add Margins:** Customize margins around your images for a polished finish.

            Join the hive and start creating with HexaGrid Creator today!
        
            """, elem_classes="intro")
    selected_index = gr.State(None)
    with gr.Row():
        with gr.Column(scale=2):
            progress_bar = gr.Markdown(elem_id="progress",visible=False)
            input_image = gr.Image(
                label="Input Image",
                type="filepath",
                interactive=True,
                elem_classes="centered solid imgcontainer",
                key="imgInput",
                image_mode=None,
                format="PNG", 
                show_download_button=True,
            )
            def on_input_image_change(image_path):
                if image_path is None:
                    gr.Warning("Please upload an Input Image to get started.")
                    return None
                img, img_path = convert_to_rgba_png(image_path)
                return img_path

            input_image.input(
                fn=on_input_image_change,
                inputs=[input_image],
                outputs=[input_image], scroll_to_output=True,
            )
        with gr.Column(scale=0):
            with gr.Accordion("Hex Coloring and Exclusion", open = False):
                with gr.Row():
                    with gr.Column():
                        color_picker = gr.ColorPicker(label="Pick a color to exclude",value="#505050")
                    with gr.Column():
                        filter_color = gr.Checkbox(label="Filter Excluded Colors from Sampling", value=False,)
                exclude_color_button = gr.Button("Exclude Color", elem_id="exlude_color_button", elem_classes="solid")
                color_display = gr.DataFrame(label="List of Excluded RGBA Colors", headers=["R", "G", "B", "A"], elem_id="excluded_colors", type="array", value=build_dataframe(excluded_color_list), interactive=True, elem_classes="solid centered")
                selected_row = gr.Number(0, label="Selected Row", visible=False)
                delete_button = gr.Button("Delete Row", elem_id="delete_exclusion_button", elem_classes="solid")
                fill_hex = gr.Checkbox(label="Fill Hex with color from Image", value=True)
            with gr.Accordion("Image Filters", open = False):
                with gr.Row():
                    with gr.Column():
                        composite_color = gr.ColorPicker(label="Color", value="#ede9ac44")
                    with gr.Column():
                        composite_opacity = gr.Slider(label="Opacity %", minimum=0, maximum=100, value=50, interactive=True)
                with gr.Row():
                    composite_button = gr.Button("Composite", elem_classes="solid")
                with gr.Row():
                    with gr.Column():
                        lut_filename = gr.Textbox(
                            value="", 
                            label="Look Up Table (LUT) File Name",
                            elem_id="lutFileName")
                    with gr.Column():
                        lut_file = gr.File(
                            value=None,
                            file_count="single",
                            file_types=[".cube"],
                            type="filepath",
                            label="LUT cube File")
                with gr.Row():
                    lut_example_image = gr.Image(type="pil", label="Filter (LUT) Example Image", value=default_lut_example_img)
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("""
                        ### Included Filters (LUTs)
                        There are several included Filters:

                        Try them on the example image before applying to your Input Image.
                        """, elem_id="lut_markdown")
                    with gr.Column():
                        gr.Examples(elem_id="lut_examples",
                            examples=[[f] for f in lut_files],
                            inputs=[lut_filename],
                            outputs=[lut_filename],
                            label="Select a Filter (LUT) file. Populate the LUT File Name field"
                        )
                    
                with gr.Row():
                    apply_lut_button = gr.Button("Apply Filter (LUT)", elem_classes="solid", elem_id="apply_lut_button")
                    
                lut_file.change(get_filename, inputs=[lut_file], outputs=[lut_filename])
                lut_filename.change(show_lut, inputs=[lut_filename, lut_example_image], outputs=[lut_example_image])
                apply_lut_button.click(
                    lambda lut_filename, input_image: gr.Warning("Please upload an Input Image to get started.") if input_image is None else apply_lut_to_image_path(lut_filename, input_image)[0], 
                    inputs=[lut_filename, input_image], 
                    outputs=[input_image], 
                    scroll_to_output=True
                )
    with gr.Row():
        with gr.Accordion("Generative AI", open = False):
            with gr.Column(scale=3):
                prompt = gr.Textbox(label="Prompt", lines=1, placeholder=":/ choose the LoRA and type the prompt ", value="top-down, (rectangular tabletop_map) alien planet map, Battletech_boardgame scifi world with forests, lakes, oceans, continents and snow at the top and bottom, (middle is dark, no_reflections, no_shadows), from directly above. From 100,000 feet looking straight down")
            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(scale=0):
                    selected_info = gr.Markdown("")
                    gallery = gr.Gallery(
                        [(item["image"], item["title"]) for item in loras],
                        label="LoRA Styles",
                        allow_preview=False,
                        columns=3,
                        elem_id="gallery",
                        show_share_button=False
                    )
                    with gr.Group():
                        custom_lora = gr.Textbox(label="Enter Custom LoRA", placeholder="prithivMLmods/Canopus-LoRA-Flux-Anime")
                        gr.Markdown("[Check the list of FLUX LoRA's](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)
                with gr.Column(scale=2):
                    # conditioning_image = gr.Image(label="Conditioning Image",
                    #     type="filepath",
                    #     interactive=True,
                    #     elem_classes="centered solid imgcontainer",
                    #     key="imgConditioning",
                    #     image_mode=None,
                    #     format="PNG", 
                    #     show_download_button=True
                    # )
                    with gr.Row():
                        with gr.Column(scale=1):
                            # Gallery from PRE_RENDERED_IMAGES GOES HERE
                            prerendered_image_gallery = gr.Gallery(label="Image Gallery", show_label=True, value=build_prerendered_images(pre_rendered_maps_paths), elem_id="gallery", elem_classes="solid", type="filepath", columns=[3], rows=[3], preview=False ,object_fit="contain", height="auto", format="png",allow_preview=False)                            
                        with gr.Column(scale=1):
                            #image_guidance_stength = gr.Slider(label="Image Guidance Strength", minimum=0, maximum=1.0, value=0.25, step=0.01, interactive=True)
                            replace_input_image_button = gr.Button(
                                "Replace Input Image",
                                elem_id="prerendered_replace_input_image_button",
                                elem_classes="solid"
                            )                        
                            generate_input_image_from_gallery = gr.Button(
                                "Generate AI Image from Gallery",
                                elem_id="generate_input_image_from_gallery",
                                elem_classes="solid"
                            )
                    with gr.Accordion("Advanced Settings", open=False):
                        with gr.Row():
                            image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
                        with gr.Column():
                            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():
                                negative_prompt_textbox = gr.Textbox(
                                        label="Negative Prompt",
                                        visible=False,
                                        elem_classes="solid",
                                        value="Earth, low quality, bad anatomy, blurry, cropped, worst quality, shadows, people, humans, reflections, shadows, realistic map of the Earth, isometric, text"
                                )
                                # Add Dropdown for sizing of Images, height and width based on selection. Options are 16x9, 16x10, 4x5, 1x1
                                # The values of height and width are based on common resolutions for each aspect ratio
                                # Default to 16x9, 1024x576
                                image_size_ratio = gr.Dropdown(label="Image Size", choices=["16:9", "16:10", "4:5", "4:3", "2:1","3:2","1:1", "9:16", "10:16", "5:4", "3:4","1:2", "2:3"], value="16:9", elem_classes="solid", type="value", scale=0, interactive=True)
                                width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=576)
                                height = gr.Slider(label="Height", minimum=256, maximum=2560, step=16, value=1024, interactive=False)
                                image_size_ratio.change(
                                    fn=update_dimensions_on_ratio,
                                    inputs=[image_size_ratio, width],
                                    outputs=[width, height]
                                )
                                width.change(
                                    fn=lambda *args: update_dimensions_on_ratio(*args)[1],
                                    inputs=[image_size_ratio, width],
                                    outputs=[height]
                                )
                            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)
                                lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95)
    with gr.Row():
        gr.HTML(value=versions_html(), visible=True, elem_id="versions")

    # Event Handlers
    prerendered_image_gallery.select(
        fn=on_prerendered_gallery_selection, 
        inputs=None, 
        outputs=[gr.State(current_prerendered_image)],  # Update the state with the selected image
        show_api=False
    )
    # replace input image with selected gallery image
    replace_input_image_button.click(
        lambda: current_prerendered_image.value,
        inputs=None,
        outputs=[input_image], scroll_to_output=True
    )
    gallery.select(
        update_selection,
        inputs=[width, height],
        outputs=[prompt, selected_info, selected_index, width, height]
    )
    custom_lora.input(
        add_custom_lora,
        inputs=[custom_lora],
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
    )
    custom_lora_button.click(
        remove_custom_lora,
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
    )
    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=run_lora,
        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
        outputs=[input_image, seed, progress_bar]
    )

app.queue()
app.launch(allowed_paths=["assets","/","./assets","images","./images", "./images/prerendered"], favicon_path="./assets/favicon.ico", max_file_size="10mb")