Update app.py

app.py

@@ -1,24 +1,733 @@
-# app.py
+
+
+##############################
+# ===== Standard Imports =====
+##############################
 import os
+import sys
+import time
+import random
+import json
+from math import floor
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+import numpy as np
+import requests
+from PIL import Image
+
+# Diffusers imports
+from diffusers import (
+    DiffusionPipeline,
+    AutoencoderTiny,
+    AutoencoderKL,
+    AutoPipelineForImage2Image,
+)
+from diffusers.utils import load_image
+
+# Hugging Face Hub
+from huggingface_hub import ModelCard, HfFileSystem
+
+# Gradio (UI)
+import gradio as gr
+
+##############################
+# ===== config.py =====
+##############################
+# Configuration parameters
+DTYPE = torch.bfloat16
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+BASE_MODEL = "black-forest-labs/FLUX.1-dev"
+TAEF1_MODEL = "madebyollin/taef1"
+MAX_SEED = 2**32 - 1
+
+##############################
+# ===== utilities.py =====
+##############################
+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
+
+def load_image_from_path(image_path: str):
+    """Loads an image from a given file path."""
+    return load_image(image_path)
+
+def randomize_seed_if_needed(randomize_seed: bool, seed: int, max_seed: int) -> int:
+    """Randomizes the seed if requested."""
+    if randomize_seed:
+        return random.randint(0, max_seed)
+    return seed
+
+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")
+
+##############################
+# ===== enhance.py =====
+##############################
+def generate(message, max_new_tokens=256, temperature=0.9, top_p=0.95, repetition_penalty=1.0):
+    """
+    Generates an enhanced prompt using a streaming Hugging Face API.
+    Enhances the given prompt under 100 words without changing its essence.
+    """
+    SYSTEM_PROMPT = (
+        "You are a prompt enhancer and your work is to enhance the given prompt under 100 words "
+        "without changing the essence, only write the enhanced prompt and nothing else."
+    )
+    timestamp = time.time()
+    formatted_prompt = (
+        f"<s>[INST] SYSTEM: {SYSTEM_PROMPT} [/INST]"
+        f"[INST] {message} {timestamp} [/INST]"
+    )
+    
+    api_url = "https://ruslanmv-hf-llm-api.hf.space/api/v1/chat/completions"
+    headers = {"Content-Type": "application/json"}
+    
+    payload = {
+        "model": "mixtral-8x7b",
+        "messages": [{"role": "user", "content": formatted_prompt}],
+        "temperature": temperature,
+        "top_p": top_p,
+        "max_tokens": max_new_tokens,
+        "use_cache": False,
+        "stream": True
+    }
+    
+    try:
+        response = requests.post(api_url, headers=headers, json=payload, stream=True)
+        response.raise_for_status()
+        full_output = ""
+        
+        for line in response.iter_lines():
+            if not line:
+                continue
+            decoded_line = line.decode("utf-8").strip()
+            if decoded_line.startswith("data:"):
+                decoded_line = decoded_line[len("data:"):].strip()
+            if decoded_line == "[DONE]":
+                break
+            try:
+                json_data = json.loads(decoded_line)
+                for choice in json_data.get("choices", []):
+                    delta = choice.get("delta", {})
+                    content = delta.get("content", "")
+                    full_output += content
+                    yield full_output
+                    if choice.get("finish_reason") == "stop":
+                        return
+            except json.JSONDecodeError:
+                continue
+    except requests.exceptions.RequestException as e:
+        yield f"Error during generation: {str(e)}"
+
+##############################
+# ===== lora_handling.py =====
+##############################
+# A default list of LoRAs for the UI (this would normally be loaded from a separate module)
+loras = [
+    {"image": "placeholder.jpg", "title": "Placeholder LoRA", "repo": "placeholder/repo", "weights": None, "trigger_word": ""}
+]
+
+@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]
+
+def get_huggingface_safetensors(link: str) -> tuple:
+    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 not in ("black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"):
+            raise Exception("Flux LoRA Not Found!")
+
+        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]}"
+            return split_link[1], link, safetensors_name, trigger_word, image_url
+        except Exception as e:
+            print(e)
+            raise Exception("You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
+    else:
+        raise Exception("You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
+
+def check_custom_model(link: str) -> tuple:
+    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])
+    return get_huggingface_safetensors(link)
+
+def create_lora_card(title: str, repo: str, trigger_word: str, image: str) -> str:
+    trigger_word_info = (
+        f"Using: <code><b>{trigger_word}</b></code> as the trigger word"
+        if trigger_word
+        else "No trigger word found. If there's a trigger word, include it in your prompt"
+    )
+    return f'''
+    <div class="custom_lora_card">
+        <span>Loaded custom LoRA:</span>
+        <div class="card_internal">
+            <img src="{image}" />
+            <div>
+                <h3>{title}</h3>
+                <small>{trigger_word_info}<br></small>
+            </div>
+        </div>
+    </div>
+    '''
+
+def add_custom_lora(custom_lora: str, loras_list: list) -> tuple:
+    if custom_lora:
+        try:
+            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
+            print(f"Loaded custom LoRA: {repo}")
+            card = create_lora_card(title, repo, trigger_word, image)
 
-from flux_app.frontend import Frontend
-from flux_app.backend import ModelManager
+            existing_item_index = next((index for (index, item) in enumerate(loras_list) if item['repo'] == repo), None)
+            if existing_item_index is None:
+                new_item = {
+                    "image": image,
+                    "title": title,
+                    "repo": repo,
+                    "weights": path,
+                    "trigger_word": trigger_word
+                }
+                print(new_item)
+                loras_list.append(new_item)
+                existing_item_index = len(loras_list) - 1
 
-def main():
-    # Get the Hugging Face token from an environment variable
+            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:
+            print(f"Error loading LoRA: {e}")
+            return gr.update(visible=True, value="Invalid 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() -> tuple:
+    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
+
+def prepare_prompt(prompt: str, selected_index: Optional[int], loras_list: list) -> str:
+    if selected_index is None:
+        raise gr.Error("You must select a LoRA before proceeding.🧨")
+
+    selected_lora = loras_list[selected_index]
+    trigger_word = selected_lora.get("trigger_word")
+    if trigger_word:
+        trigger_position = selected_lora.get("trigger_position", "append")
+        if trigger_position == "prepend":
+            prompt_mash = f"{trigger_word} {prompt}"
+        else:
+            prompt_mash = f"{prompt} {trigger_word}"
+    else:
+        prompt_mash = prompt
+    return prompt_mash
+
+def unload_lora_weights(pipe, pipe_i2i):
+    if pipe is not None:
+        pipe.unload_lora_weights()
+    if pipe_i2i is not None:
+        pipe_i2i.unload_lora_weights()
+
+def load_lora_weights_into_pipeline(pipe_to_use, lora_path: str, weight_name: Optional[str]):
+    pipe_to_use.load_lora_weights(
+        lora_path,
+        weight_name=weight_name,
+        low_cpu_mem_usage=True
+    )
+
+def update_selection(evt: gr.SelectData, width, height, loras_list):
+    selected_lora = loras_list[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,
+    )
+
+##############################
+# ===== backend.py =====
+##############################
+class ModelManager:
+    def __init__(self, hf_token=None):
+        self.hf_token = hf_token
+        self.pipe = None
+        self.pipe_i2i = None
+        self.good_vae = None
+        self.taef1 = None
+        self.initialize_models()
+
+    def initialize_models(self):
+        """Initializes the diffusion pipelines and autoencoders."""
+        self.taef1 = AutoencoderTiny.from_pretrained(TAEF1_MODEL, torch_dtype=DTYPE).to(DEVICE)
+        self.good_vae = AutoencoderKL.from_pretrained(BASE_MODEL, subfolder="vae", torch_dtype=DTYPE).to(DEVICE)
+        # Optionally, if your model is private, you can pass `use_auth_token=self.hf_token` here.
+        self.pipe = DiffusionPipeline.from_pretrained(BASE_MODEL, torch_dtype=DTYPE, vae=self.taef1)
+        self.pipe = self.pipe.to(DEVICE)
+        self.pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
+            BASE_MODEL,
+            vae=self.good_vae,
+            transformer=self.pipe.transformer,
+            text_encoder=self.pipe.text_encoder,
+            tokenizer=self.pipe.tokenizer,
+            text_encoder_2=self.pipe.text_encoder_2,
+            tokenizer_2=self.pipe.tokenizer_2,
+            torch_dtype=DTYPE,
+        ).to(DEVICE)
+        # Bind the custom LoRA call to the pipeline.
+        self.pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(self.pipe)
+
+    def generate_image(self, prompt_mash, steps, seed, cfg_scale, width, height, lora_scale):
+        """Generates an image using the text-to-image pipeline."""
+        self.pipe.to(DEVICE)
+        generator = torch.Generator(device=DEVICE).manual_seed(seed)
+        with calculateDuration("Generating image"):
+            for img in self.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=self.good_vae,
+            ):
+                yield img
+
+    def generate_image_to_image(self, prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
+        """Generates an image using the image-to-image pipeline."""
+        generator = torch.Generator(device=DEVICE).manual_seed(seed)
+        self.pipe_i2i.to(DEVICE)
+        image_input = load_image_from_path(image_input_path)
+        with calculateDuration("Generating image to image"):
+            final_image = self.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
+
+##############################
+# ===== frontend.py =====
+##############################
+# The original code used a decorator from a module named `spaces`.
+# If unavailable, we define a dummy decorator.
+try:
+    import spaces
+except ImportError:
+    class spaces:
+        @staticmethod
+        def GPU(duration):
+            def decorator(func):
+                return func
+            return decorator
+
+class Frontend:
+    def __init__(self, model_manager: ModelManager):
+        self.model_manager = model_manager
+        self.loras = loras  # Use the default LoRA list defined above.
+        self.load_initial_loras()
+        self.css = self.define_css()
+
+    def define_css(self):
+        # Clean and professional CSS styling.
+        return '''
+        /* Title Styling */
+        #title {
+            text-align: center;
+            margin-bottom: 20px;
+        }
+        #title h1 {
+            font-size: 2.5rem;
+            margin: 0;
+            color: #333;
+        }
+        /* Button and Column Styling */
+        #gen_btn {
+            width: 100%;
+            padding: 12px;
+            font-weight: bold;
+            border-radius: 5px;
+        }
+        #gen_column {
+            display: flex;
+            align-items: center;
+            justify-content: center;
+        }
+        /* Gallery and List Styling */
+        #gallery .grid-wrap {
+            margin-top: 15px;
+        }
+        #lora_list {
+            background-color: #f5f5f5;
+            padding: 10px;
+            border-radius: 4px;
+            font-size: 0.9rem;
+        }
+        .card_internal {
+            display: flex;
+            align-items: center;
+            height: 100px;
+            margin-top: 10px;
+        }
+        .card_internal img {
+            margin-right: 10px;
+        }
+        .styler {
+            --form-gap-width: 0px !important;
+        }
+        /* Progress Bar Styling */
+        .progress-container {
+            width: 100%;
+            height: 20px;
+            background-color: #e0e0e0;
+            border-radius: 10px;
+            overflow: hidden;
+            margin-bottom: 20px;
+        }
+        .progress-bar {
+            height: 100%;
+            background-color: #4f46e5;
+            transition: width 0.3s ease-in-out;
+            width: calc(var(--current) / var(--total) * 100%);
+        }
+        '''
+
+    def load_initial_loras(self):
+        try:
+            from lora import loras as loras_list
+            self.loras = loras_list
+        except ImportError:
+            print("Warning: lora.py not found, using placeholder LoRAs.")
+            pass
+
+    @spaces.GPU(duration=300)
+    def run_lora(self, prompt, image_input, image_strength, cfg_scale, steps, selected_index,
+                 randomize_seed, seed, width, height, lora_scale, use_enhancer,
+                 progress=gr.Progress(track_tqdm=True)):
+        seed = randomize_seed_if_needed(randomize_seed, seed, MAX_SEED)
+        # Prepare the prompt using the selected LoRA trigger word.
+        prompt_mash = prepare_prompt(prompt, selected_index, self.loras)
+        enhanced_text = ""
+        
+        # Optionally enhance the prompt.
+        if use_enhancer:
+            for enhanced_chunk in generate(prompt_mash):
+                enhanced_text = enhanced_chunk
+                yield None, seed, gr.update(visible=False), enhanced_text
+            prompt_mash = enhanced_text
+        else:
+            enhanced_text = ""
+        
+        selected_lora = self.loras[selected_index]
+        unload_lora_weights(self.model_manager.pipe, self.model_manager.pipe_i2i)
+        pipe_to_use = self.model_manager.pipe_i2i if image_input is not None else self.model_manager.pipe
+        load_lora_weights_into_pipeline(pipe_to_use, selected_lora["repo"], selected_lora.get("weights"))
+
+        if image_input is not None:
+            final_image = self.model_manager.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), enhanced_text
+        else:
+            image_generator = self.model_manager.generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale)
+            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), enhanced_text
+            yield final_image, seed, gr.update(value=progress_bar, visible=False), enhanced_text
+
+    def create_ui(self):
+        with gr.Blocks(theme=gr.themes.Base(), css=self.css, title="Flux LoRA Generation") as app:
+            title = gr.HTML(
+                """<h1>Flux LoRA Generation</h1>""",
+                elem_id="title",
+            )
+            selected_index = gr.State(None)
+
+            with gr.Row():
+                with gr.Column(scale=3):
+                    prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Choose the LoRA and type the prompt")
+                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 self.loras],
+                        label="LoRA Collection",
+                        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():
+                    progress_bar = gr.Markdown(elem_id="progress", visible=False)
+                    result = gr.Image(label="Generated Image")
+
+            with gr.Row():
+                with gr.Accordion("Advanced Settings", open=False):
+                    with gr.Row():
+                        input_image = gr.Image(label="Input image", type="filepath")
+                        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():
+                            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():
+                            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():
+                            use_enhancer = gr.Checkbox(value=False, label="Use Prompt Enhancer")
+                            show_enhanced_prompt = gr.Checkbox(value=False, label="Display Enhanced Prompt")
+                    enhanced_prompt_box = gr.Textbox(label="Enhanced Prompt", visible=False)
+
+            gallery.select(
+                update_selection,
+                inputs=[width, height, gr.State(self.loras)],
+                outputs=[prompt, selected_info, selected_index, width, height]
+            )
+            custom_lora.input(
+                add_custom_lora,
+                inputs=[custom_lora, gr.State(self.loras)],
+                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]
+            )
+
+            show_enhanced_prompt.change(fn=lambda show: gr.update(visible=show),
+                                        inputs=show_enhanced_prompt,
+                                        outputs=enhanced_prompt_box)
+
+            gr.on(
+                triggers=[generate_button.click, prompt.submit],
+                fn=self.run_lora,
+                inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index,
+                        randomize_seed, seed, width, height, lora_scale, use_enhancer],
+                outputs=[result, seed, progress_bar, enhanced_prompt_box]
+            )
+
+            with gr.Row():
+                gr.HTML("<div style='text-align:center; font-size:0.9em; margin-top:20px;'>Credits: <a href='https://ruslanmv.com' target='_blank'>ruslanmv.com</a></div>")
+            
+            return app
+
+##############################
+# ===== Main app.py =====
+##############################
+if __name__ == "__main__":
+    # Get the Hugging Face token from the environment.
     hf_token = os.environ.get("HF_TOKEN")
     if not hf_token:
         raise ValueError("Hugging Face token (HF_TOKEN) not found in environment variables. Please set it.")
-        
     model_manager = ModelManager(hf_token=hf_token)
     frontend = Frontend(model_manager)
     app = frontend.create_ui()
-
-    # Enable request queuing. (No extra keyword arguments here.)
     app.queue()
-
-    # Launch the app with the specified server configuration.
-    app.launch(server_name="0.0.0.0", server_port=7860, share=False)
-
-if __name__ == "__main__":
-    main()
+    app.launch()