Create app-backup.py

app-backup.py

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+import os
+import gradio as gr
+import json
+import logging
+import torch
+from PIL import Image
+import spaces
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image, FluxControlNetModel
+from diffusers.pipelines import FluxControlNetPipeline
+from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+from diffusers.utils import load_image
+from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
+import copy
+import random
+import time
+import requests
+import pandas as pd
+from transformers import pipeline
+from gradio_imageslider import ImageSlider
+import numpy as np
+import warnings
+
+
+huggingface_token = os.getenv("HUGGINFACE_TOKEN")
+
+
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu")
+
+
+        
+#Load prompts for randomization
+df = pd.read_csv('prompts.csv', header=None)
+prompt_values = df.values.flatten()
+
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
+
+# Initialize the base model
+dtype = torch.bfloat16
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# 공통 FLUX 모델 로드
+base_model = "black-forest-labs/FLUX.1-dev"
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to(device)
+
+# LoRA를 위한 설정
+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)
+
+# Image-to-Image 파이프라인 설정
+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
+).to(device)
+
+# Upscale을 위한 ControlNet 설정
+controlnet = FluxControlNetModel.from_pretrained(
+    "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16
+).to(device)
+
+# Upscale 파이프라인 설정 (기존 pipe 재사용)
+pipe_upscale = FluxControlNetPipeline(
+    vae=pipe.vae,
+    text_encoder=pipe.text_encoder,
+    text_encoder_2=pipe.text_encoder_2,
+    tokenizer=pipe.tokenizer,
+    tokenizer_2=pipe.tokenizer_2,
+    transformer=pipe.transformer,
+    scheduler=pipe.scheduler,
+    controlnet=controlnet
+).to(device)
+
+MAX_SEED = 2**32 - 1
+MAX_PIXEL_BUDGET = 1024 * 1024
+
+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 download_file(url, directory=None):
+    if directory is None:
+        directory = os.getcwd()  # Use current working directory if not specified
+    
+    # Get the filename from the URL
+    filename = url.split('/')[-1]
+    
+    # Full path for the downloaded file
+    filepath = os.path.join(directory, filename)
+    
+    # Download the file
+    response = requests.get(url)
+    response.raise_for_status()  # Raise an exception for bad status codes
+    
+    # Write the content to the file
+    with open(filepath, 'wb') as file:
+        file.write(response.content)
+    
+    return filepath
+            
+def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
+    selected_index = evt.index
+    selected_indices = selected_indices or []
+    if selected_index in selected_indices:
+        selected_indices.remove(selected_index)
+    else:
+        if len(selected_indices) < 2:
+            selected_indices.append(selected_index)
+        else:
+            gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
+            return gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), width, height, gr.update(), gr.update()
+
+    selected_info_1 = "Select a LoRA 1"
+    selected_info_2 = "Select a LoRA 2"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = loras_state[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = loras_state[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+
+    if selected_indices:
+        last_selected_lora = loras_state[selected_indices[-1]]
+        new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
+    else:
+        new_placeholder = "Type a prompt after selecting a LoRA"
+
+    return gr.update(placeholder=new_placeholder), selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2
+
+def remove_lora_1(selected_indices, loras_state):
+    if len(selected_indices) >= 1:
+        selected_indices.pop(0)
+    selected_info_1 = "Select a LoRA 1"
+    selected_info_2 = "Select a LoRA 2"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = loras_state[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = loras_state[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
+
+def remove_lora_2(selected_indices, loras_state):
+    if len(selected_indices) >= 2:
+        selected_indices.pop(1)
+    selected_info_1 = "Select LoRA 1"
+    selected_info_2 = "Select LoRA 2"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = loras_state[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = loras_state[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
+
+def randomize_loras(selected_indices, loras_state):
+    try:
+        if len(loras_state) < 2:
+            raise gr.Error("Not enough LoRAs to randomize.")
+        selected_indices = random.sample(range(len(loras_state)), 2)
+        lora1 = loras_state[selected_indices[0]]
+        lora2 = loras_state[selected_indices[1]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
+        lora_scale_1 = 1.15
+        lora_scale_2 = 1.15
+        lora_image_1 = lora1['image']
+        lora_image_2 = lora2['image']
+        random_prompt = random.choice(prompt_values)
+        return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, random_prompt
+    except Exception as e:
+        print(f"Error in randomize_loras: {str(e)}")
+        return "Error", "Error", [], 1.15, 1.15, None, None, ""
+
+def add_custom_lora(custom_lora, selected_indices, current_loras):
+    if custom_lora:
+        try:
+            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
+            print(f"Loaded custom LoRA: {repo}")
+            existing_item_index = next((index for (index, item) in enumerate(current_loras) if item['repo'] == repo), None)
+            if existing_item_index is None:
+                if repo.endswith(".safetensors") and repo.startswith("http"):
+                    repo = download_file(repo)
+                new_item = {
+                    "image": image if image else "/home/user/app/custom.png",
+                    "title": title,
+                    "repo": repo,
+                    "weights": path,
+                    "trigger_word": trigger_word
+                }
+                print(f"New LoRA: {new_item}")
+                existing_item_index = len(current_loras)
+                current_loras.append(new_item)
+            
+            # Update gallery
+            gallery_items = [(item["image"], item["title"]) for item in current_loras]
+            # Update selected_indices if there's room
+            if len(selected_indices) < 2:
+                selected_indices.append(existing_item_index)
+            else:
+                gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
+
+            # Update selected_info and images
+            selected_info_1 = "Select a LoRA 1"
+            selected_info_2 = "Select a LoRA 2"
+            lora_scale_1 = 1.15
+            lora_scale_2 = 1.15
+            lora_image_1 = None
+            lora_image_2 = None
+            if len(selected_indices) >= 1:
+                lora1 = current_loras[selected_indices[0]]
+                selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨"
+                lora_image_1 = lora1['image'] if lora1['image'] else None
+            if len(selected_indices) >= 2:
+                lora2 = current_loras[selected_indices[1]]
+                selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨"
+                lora_image_2 = lora2['image'] if lora2['image'] else None
+            print("Finished adding custom LoRA")
+            return (
+                current_loras,
+                gr.update(value=gallery_items),
+                selected_info_1, 
+                selected_info_2,
+                selected_indices,
+                lora_scale_1,
+                lora_scale_2,
+                lora_image_1,
+                lora_image_2
+            )
+        except Exception as e:
+            print(e)
+            gr.Warning(str(e))
+            return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
+    else:
+        return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
+
+def remove_custom_lora(selected_indices, current_loras):
+    if current_loras:
+        custom_lora_repo = current_loras[-1]['repo']
+        # Remove from loras list
+        current_loras = current_loras[:-1]
+        # Remove from selected_indices if selected
+        custom_lora_index = len(current_loras)
+        if custom_lora_index in selected_indices:
+            selected_indices.remove(custom_lora_index)
+    # Update gallery
+    gallery_items = [(item["image"], item["title"]) for item in current_loras]
+    # Update selected_info and images
+    selected_info_1 = "Select a LoRA 1"
+    selected_info_2 = "Select a LoRA 2"
+    lora_scale_1 = 1.15
+    lora_scale_2 = 1.15
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = current_loras[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = current_loras[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+    return (
+        current_loras,
+        gr.update(value=gallery_items),
+        selected_info_1,
+        selected_info_2,
+        selected_indices,
+        lora_scale_1,
+        lora_scale_2,
+        lora_image_1,
+        lora_image_2
+    )
+
+@spaces.GPU(duration=75)
+def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
+    print("Generating image...")
+    pipe.to("cuda")
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    with calculateDuration("Generating image"):
+        # Generate image
+        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": 1.0},
+            output_type="pil",
+            good_vae=good_vae,
+        ):
+            yield img
+
+@spaces.GPU(duration=75)
+def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
+    pipe_i2i.to("cuda")
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    image_input = load_image(image_input_path)
+    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": 1.0},
+        output_type="pil",
+    ).images[0]
+    return final_image
+
+def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
+    try:
+        # 한글 감지 및 번역
+        if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
+            translated = translator(prompt, max_length=512)[0]['translation_text']
+            print(f"Original prompt: {prompt}")
+            print(f"Translated prompt: {translated}")
+            prompt = translated
+
+        if not selected_indices:
+            raise gr.Error("You must select at least one LoRA before proceeding.")
+
+        selected_loras = [loras_state[idx] for idx in selected_indices]
+
+        # Build the prompt with trigger words
+        prepends = []
+        appends = []
+        for lora in selected_loras:
+            trigger_word = lora.get('trigger_word', '')
+            if trigger_word:
+                if lora.get("trigger_position") == "prepend":
+                    prepends.append(trigger_word)
+                else:
+                    appends.append(trigger_word)
+        prompt_mash = " ".join(prepends + [prompt] + appends)
+        print("Prompt Mash: ", prompt_mash)
+
+        # Unload previous LoRA weights
+        with calculateDuration("Unloading LoRA"):
+            pipe.unload_lora_weights()
+            pipe_i2i.unload_lora_weights()
+            
+        print(pipe.get_active_adapters())
+        # Load LoRA weights with respective scales
+        lora_names = []
+        lora_weights = []
+        with calculateDuration("Loading LoRA weights"):
+            for idx, lora in enumerate(selected_loras):
+                lora_name = f"lora_{idx}"
+                lora_names.append(lora_name)
+                lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
+                lora_path = lora['repo']
+                weight_name = lora.get("weights")
+                print(f"Lora Path: {lora_path}")
+                if image_input is not None:
+                    if weight_name:
+                        pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
+                    else:
+                        pipe_i2i.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
+                else:
+                    if weight_name:
+                        pipe.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
+                    else:
+                        pipe.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
+            print("Loaded LoRAs:", lora_names)
+            print("Adapter weights:", lora_weights)
+            if image_input is not None:
+                pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
+            else:
+                pipe.set_adapters(lora_names, adapter_weights=lora_weights)
+        print(pipe.get_active_adapters())
+        # Set random seed for reproducibility
+        with calculateDuration("Randomizing seed"):
+            if randomize_seed:
+                seed = random.randint(0, MAX_SEED)
+
+        # Generate image
+        if image_input is not None:
+            final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
+        else:
+            image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, 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)
+            
+
+
+        if final_image is None:
+            raise Exception("Failed to generate image")
+        
+        return final_image, seed, gr.update(visible=False)
+    except Exception as e:
+        print(f"Error in run_lora: {str(e)}")
+        return None, seed, gr.update(visible=False)
+
+
+
+run_lora.zerogpu = True
+
+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(f"Base model: {base_model}")
+        if base_model not in ["black-forest-labs/FLUX.1-dev", "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()
+        safetensors_name = None
+        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)
+            raise gr.Error("Invalid Hugging Face repository with a *.safetensors LoRA")
+        if not safetensors_name:
+            raise gr.Error("No *.safetensors file found in the repository")
+        return split_link[1], link, safetensors_name, trigger_word, image_url
+    else:
+        raise gr.Error("Invalid Hugging Face repository link")
+
+def check_custom_model(link):
+    if link.endswith(".safetensors"):
+        # Treat as direct link to the LoRA weights
+        title = os.path.basename(link)
+        repo = link
+        path = None  # No specific weight name
+        trigger_word = ""
+        image_url = None
+        return title, repo, path, trigger_word, image_url
+    elif link.startswith("https://"):
+        if "huggingface.co" in link:
+            link_split = link.split("huggingface.co/")
+            return get_huggingface_safetensors(link_split[1])
+        else:
+            raise Exception("Unsupported URL")
+    else:
+        # Assume it's a Hugging Face model path
+        return get_huggingface_safetensors(link)
+
+def update_history(new_image, history):
+    """Updates the history gallery with the new image."""
+    if history is None:
+        history = []
+    if new_image is not None:
+        history.insert(0, new_image)
+    return history
+
+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: 0.25em}
+#gallery .grid-wrap{height: 5vh}
+#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
+.custom_lora_card{margin-bottom: 1em}
+.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}
+#component-8, .button_total{height: 100%; align-self: stretch;}
+#loaded_loras [data-testid="block-info"]{font-size:80%}
+#custom_lora_structure{background: var(--block-background-fill)}
+#custom_lora_btn{margin-top: auto;margin-bottom: 11px}
+#random_btn{font-size: 300%}
+#component-11{align-self: stretch;}
+footer {visibility: hidden;}
+'''
+
+# 업스케일 관련 함수 추가
+def process_input(input_image, upscale_factor, **kwargs):
+    w, h = input_image.size
+    w_original, h_original = w, h
+    aspect_ratio = w / h
+
+    was_resized = False
+
+    max_size = int(np.sqrt(MAX_PIXEL_BUDGET / (upscale_factor ** 2)))
+    if w > max_size or h > max_size:
+        if w > h:
+            w_new = max_size
+            h_new = int(w_new / aspect_ratio)
+        else:
+            h_new = max_size
+            w_new = int(h_new * aspect_ratio)
+        
+        input_image = input_image.resize((w_new, h_new), Image.LANCZOS)
+        was_resized = True
+        gr.Info(f"Input image resized to {w_new}x{h_new} to fit within pixel budget after upscaling.")
+
+    # resize to multiple of 8
+    w, h = input_image.size
+    w = w - w % 8
+    h = h - h % 8
+
+    return input_image.resize((w, h)), w_original, h_original, was_resized
+    
+from PIL import Image
+import numpy as np
+
+@spaces.GPU
+def infer_upscale(
+    seed,
+    randomize_seed,
+    input_image,
+    num_inference_steps,
+    upscale_factor,
+    controlnet_conditioning_scale,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if input_image is None:
+        return None, seed, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(visible=True, value="Please upload an image for upscaling.")
+
+    try:
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+        
+        input_image, w_original, h_original, was_resized = process_input(input_image, upscale_factor)
+
+        # rescale with upscale factor
+        w, h = input_image.size
+        control_image = input_image.resize((w * upscale_factor, h * upscale_factor), Image.LANCZOS)
+
+        generator = torch.Generator(device=device).manual_seed(seed)
+
+        gr.Info("Upscaling image...")
+        # 모든 텐서를 동일한 디바이스로 이동
+        pipe_upscale.to(device)
+        
+        # Ensure the image is in RGB format
+        if control_image.mode != 'RGB':
+            control_image = control_image.convert('RGB')
+        
+        # Convert to tensor and add batch dimension
+        control_image = torch.from_numpy(np.array(control_image)).permute(2, 0, 1).float().unsqueeze(0).to(device) / 255.0
+        
+        with torch.no_grad():
+            image = pipe_upscale(
+                prompt="",
+                control_image=control_image,
+                controlnet_conditioning_scale=controlnet_conditioning_scale,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=3.5,
+                generator=generator,
+            ).images[0]
+
+        # Convert the image back to PIL Image
+        if isinstance(image, torch.Tensor):
+            image = image.cpu().permute(1, 2, 0).numpy()
+        
+        # Ensure the image data is in the correct range
+        image = np.clip(image * 255, 0, 255).astype(np.uint8)
+        image = Image.fromarray(image)
+
+        if was_resized:
+            gr.Info(
+                f"Resizing output image to targeted {w_original * upscale_factor}x{h_original * upscale_factor} size."
+            )
+            image = image.resize((w_original * upscale_factor, h_original * upscale_factor), Image.LANCZOS)
+
+        return image, seed, num_inference_steps, upscale_factor, controlnet_conditioning_scale, gr.update(), gr.update(visible=False)
+    except Exception as e:
+        print(f"Error in infer_upscale: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return None, seed, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(visible=True, value=f"Error: {str(e)}")
+        
+def check_upscale_input(input_image, *args):
+    if input_image is None:
+        return gr.update(interactive=False), *args, gr.update(visible=True, value="Please upload an image for upscaling.")
+    return gr.update(interactive=True), *args, gr.update(visible=False)
+    
+with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as app:
+    loras_state = gr.State(loras)
+    selected_indices = gr.State([])
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
+        with gr.Column(scale=1):
+            generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])
+    
+    with gr.Row(elem_id="loaded_loras"):
+        with gr.Column(scale=1, min_width=25):
+            randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
+        with gr.Column(scale=8):
+            with gr.Row():
+                with gr.Column(scale=0, min_width=50):
+                    lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
+                with gr.Column(scale=3, min_width=100):
+                    selected_info_1 = gr.Markdown("Select a LoRA 1")
+                with gr.Column(scale=5, min_width=50):
+                    lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
+            with gr.Row():
+                remove_button_1 = gr.Button("Remove", size="sm")
+        with gr.Column(scale=8):
+            with gr.Row():
+                with gr.Column(scale=0, min_width=50):
+                    lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
+                with gr.Column(scale=3, min_width=100):
+                    selected_info_2 = gr.Markdown("Select a LoRA 2")
+                with gr.Column(scale=5, min_width=50):
+                    lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
+            with gr.Row():
+                remove_button_2 = gr.Button("Remove", size="sm")
+    
+    with gr.Row():
+        with gr.Column():
+            with gr.Group():
+                with gr.Row(elem_id="custom_lora_structure"):
+                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3, min_width=150)
+                    add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
+                remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
+                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")
+            gallery = gr.Gallery(
+                [(item["image"], item["title"]) for item in loras],
+                label="Or pick from the LoRA Explorer gallery",
+                allow_preview=False,
+                columns=4,
+                elem_id="gallery"
+            )
+        with gr.Column():
+            progress_bar = gr.Markdown(elem_id="progress", visible=False)
+            result = gr.Image(label="Generated Image", interactive=False)
+            with gr.Accordion("History", open=False):
+                history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
+
+    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)
+
+# 업스케일 관련 UI 추가
+    with gr.Row():
+        upscale_button = gr.Button("Upscale", interactive=False)
+
+    with gr.Row():
+        with gr.Column(scale=4):
+            upscale_input = gr.Image(label="Input Image for Upscaling", type="pil")
+        with gr.Column(scale=1):
+            upscale_steps = gr.Slider(
+                label="Number of Inference Steps for Upscaling",
+                minimum=8,
+                maximum=50,
+                step=1,
+                value=28,
+            )
+            upscale_factor = gr.Slider(
+                label="Upscale Factor",
+                minimum=1,
+                maximum=4,
+                step=1,
+                value=4,
+            )
+            controlnet_conditioning_scale = gr.Slider(
+                label="Controlnet Conditioning Scale",
+                minimum=0.1,
+                maximum=1.0,
+                step=0.05,
+                value=0.5,  # 기본값을 0.5로 낮춤
+            )            
+            upscale_seed = gr.Slider(
+                label="Seed for Upscaling",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+            upscale_randomize_seed = gr.Checkbox(label="Randomize seed for Upscaling", value=True)
+            upscale_error = gr.Markdown(visible=False, value="Please provide an input image for upscaling.")
+    
+    with gr.Row():
+        upscale_result = gr.Image(label="Upscaled Image", type="pil")
+        upscale_seed_output = gr.Number(label="Seed Used", precision=0)
+
+
+    gallery.select(
+        update_selection,
+        inputs=[selected_indices, loras_state, width, height],
+        outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2]
+    )
+    remove_button_1.click(
+        remove_lora_1,
+        inputs=[selected_indices, loras_state],
+        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
+    )
+    remove_button_2.click(
+        remove_lora_2,
+        inputs=[selected_indices, loras_state],
+        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
+    )
+    randomize_button.click(
+        randomize_loras,
+        inputs=[selected_indices, loras_state],
+        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, prompt]
+    )
+    add_custom_lora_button.click(
+        add_custom_lora,
+        inputs=[custom_lora, selected_indices, loras_state],
+        outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
+    )
+    remove_custom_lora_button.click(
+        remove_custom_lora,
+        inputs=[selected_indices, loras_state],
+        outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
+    )
+
+    gr.on(
+        triggers=[generate_button.click, prompt.submit],
+        fn=run_lora,
+        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
+        outputs=[result, seed, progress_bar]
+    ).then(
+        fn=lambda x, history: update_history(x, history) if x is not None else history,
+        inputs=[result, history_gallery],
+        outputs=history_gallery,
+    )
+
+    upscale_input.upload(
+        lambda x: gr.update(interactive=x is not None),
+        inputs=[upscale_input],
+        outputs=[upscale_button]
+    )
+    
+    upscale_error = gr.Markdown(visible=False, value="")
+
+    upscale_button.click(
+        infer_upscale,
+        inputs=[
+            upscale_seed,
+            upscale_randomize_seed,
+            upscale_input,
+            upscale_steps,
+            upscale_factor,
+            controlnet_conditioning_scale,
+        ],
+        outputs=[
+            upscale_result,
+            upscale_seed_output,
+            upscale_steps,
+            upscale_factor,
+            controlnet_conditioning_scale,
+            upscale_randomize_seed,
+            upscale_error
+        ],
+
+    ).then(
+        infer_upscale,
+        inputs=[
+        upscale_seed,
+            upscale_randomize_seed,
+            upscale_input,
+            upscale_steps,
+            upscale_factor,
+            controlnet_conditioning_scale,
+        ],
+        outputs=[upscale_result, upscale_seed_output]
+    )
+
+
+if __name__ == "__main__":
+    app.queue(max_size=20)
+    app.launch(debug=True)