Update app.py

app.py

@@ -5,8 +5,19 @@ import logging
 import torch
 from PIL import Image
 import spaces
-from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
-from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+from diffusers import (
+    DiffusionPipeline,
+    AutoencoderTiny,
+    AutoencoderKL,
+    AutoPipelineForImage2Image,
+    FluxControlNetModel,
+    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
@@ -15,19 +26,13 @@ import time
 import requests
 import pandas as pd
 from transformers import pipeline
-
-import logging
 import warnings
-import numpy as np
-from diffusers import FluxControlNetModel
-from diffusers.pipelines import FluxControlNetPipeline
-from PIL import Image
-from huggingface_hub import snapshot_download
+from gradio_imageslider import ImageSlider
 
 # 번역 모델 로드
 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
 
-#Load prompts for randomization
+# Load prompts for randomization
 df = pd.read_csv('prompts.csv', header=None)
 prompt_values = df.values.flatten()
 
@@ -40,14 +45,9 @@ dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 base_model = "black-forest-labs/FLUX.1-dev"
 
-# FLUX 모델 한 번만 로드
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to(device)
-
-# VAE 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)
-
-# Image2Image 파이프라인 설정
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
 pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
     base_model,
     vae=good_vae,
@@ -57,27 +57,32 @@ pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
     text_encoder_2=pipe.text_encoder_2,
     tokenizer_2=pipe.tokenizer_2,
     torch_dtype=dtype
+)
+
+# Load controlnet model for upscaling
+controlnet = FluxControlNetModel.from_pretrained(
+    "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=dtype
+).to(device)
+
+pipe_controlnet = FluxControlNetPipeline(
+    vae=pipe.vae,
+    text_encoder=pipe.text_encoder,
+    tokenizer=pipe.tokenizer,
+    text_encoder_2=pipe.text_encoder_2,
+    tokenizer_2=pipe.tokenizer_2,
+    unet=pipe.unet,
+    controlnet=controlnet,
+    scheduler=pipe.scheduler,
+    safety_checker=pipe.safety_checker,
+    feature_extractor=pipe.feature_extractor,
+    torch_dtype=dtype
 ).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)
 
-# ControlNet 모델과 파이프라인 (필요할 때만 로드)
-controlnet = None
-pipe_controlnet = None
-
-def load_controlnet():
-    global controlnet, pipe_controlnet
-    if controlnet is None:
-        controlnet = FluxControlNetModel.from_pretrained(
-            "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16
-        ).to(device)
-    if pipe_controlnet is None:
-        pipe_controlnet = FluxControlNetPipeline.from_pretrained(
-            base_model, controlnet=controlnet, torch_dtype=torch.bfloat16
-        ).to(device)
-
 class calculateDuration:
     def __init__(self, activity_name=""):
         self.activity_name = activity_name
@@ -97,23 +102,23 @@ class calculateDuration:
 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 []
@@ -221,7 +226,7 @@ def add_custom_lora(custom_lora, selected_indices, current_loras):
                 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
@@ -371,7 +376,7 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
     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 = []
@@ -484,43 +489,7 @@ def update_history(new_image, history):
     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;}
-'''
-
-huggingface_token = os.getenv("HF_TOKEN")
-
-model_path = snapshot_download(
-    repo_id="black-forest-labs/FLUX.1-dev", 
-    repo_type="model", 
-    ignore_patterns=["*.md", "*..gitattributes"],
-    local_dir="FLUX.1-dev",
-    token=huggingface_token,
-)
-
-MAX_SEED = 1000000
-
-def process_input(input_image, upscale_factor):
+def process_input(input_image, upscale_factor, **kwargs):
     w, h = input_image.size
     w_original, h_original = w, h
     aspect_ratio = w / h
@@ -549,155 +518,237 @@ def process_input(input_image, upscale_factor):
 
     return input_image.resize((w, h)), w_original, h_original, was_resized
 
-MAX_PIXEL_BUDGET = 1024 * 1024
+@spaces.GPU#(duration=42)
+def infer(
+    seed,
+    randomize_seed,
+    input_image,
+    num_inference_steps,
+    upscale_factor,
+    controlnet_conditioning_scale,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    true_input_image = input_image
+    input_image, w_original, h_original, was_resized = process_input(
+        input_image, upscale_factor
+    )
 
-@spaces.GPU
-def upscale(input_image, progress=gr.Progress(track_tqdm=True)):
-    if input_image is None:
-        raise gr.Error("No image to upscale. Please generate an image first.")
-    
-    load_controlnet()  # ControlNet 필요 시 로드
-    
-    # 입력 이미지 처리
-    input_image, w_original, h_original, was_resized = process_input(input_image, 4)
-    
-    # 4096x4096 크기로 조정
-    control_image = input_image.resize((4096, 4096))
-
-    generator = torch.Generator(device=device).manual_seed(random.randint(0, MAX_SEED))
-
-    gr.Info("Upscaling image to 4096x4096...")
-    upscaled_image = pipe_controlnet(
+    # rescale with upscale factor
+    w, h = input_image.size
+    control_image = input_image.resize((w * upscale_factor, h * upscale_factor))
+
+    generator = torch.Generator().manual_seed(seed)
+
+    gr.Info("Upscaling image...")
+    image = pipe_controlnet(
         prompt="",
-        image=control_image,
-        controlnet_conditioning_scale=0.6,
-        num_inference_steps=28,
+        control_image=control_image,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        num_inference_steps=num_inference_steps,
         guidance_scale=3.5,
-        height=4096,
-        width=4096,
+        height=control_image.size[1],
+        width=control_image.size[0],
         generator=generator,
     ).images[0]
 
-    return upscaled_image  # 업스케일된 이미지만 반환
+    if was_resized:
+        gr.Info(
+            f"Resizing output image to targeted {w_original * upscale_factor}x{h_original * upscale_factor} size."
+        )
+
+    # resize to target desired size
+    image = image.resize((w_original * upscale_factor, h_original * upscale_factor))
+    image.save("output.jpg")
+    # convert to numpy
+    return [true_input_image, image, seed]
+
+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;}
+'''
 
 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"])
-            upscale_button = gr.Button("업스케일(4096X4096픽셀)", variant="secondary")
-    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", elem_id="result_image")        
-        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.Tab("Generate"):
+        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():
-                    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.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():
-                    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)
-
+                    remove_button_1 = gr.Button("Remove", size="sm")
+            with gr.Column(scale=8):
                 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)
-
-    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]
-    )
+                    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)
 
-    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(  # Update the history gallery
-        fn=lambda x, history: update_history(x, history),
-        inputs=[result, history_gallery],
-        outputs=history_gallery,
-    )
+        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)
+
+        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(  # Update the history gallery
+            fn=lambda x, history: update_history(x, history),
+            inputs=[result, history_gallery],
+            outputs=history_gallery,
+        )
 
-    upscale_button.click(
-        upscale,
-        inputs=[result],
-        outputs=[result]
-    )
+    with gr.Tab("Upscale"):
+        with gr.Row():
+            input_image_upscale = gr.Image(label="Input Image", type="pil")
+            result_upscale = ImageSlider(label="Input / Output", type="pil", interactive=True)
+        with gr.Row():
+            num_inference_steps_upscale = gr.Slider(
+                label="Number of Inference Steps",
+                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=1.5,
+                step=0.1,
+                value=0.6,
+            )
+            seed_upscale = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+            randomize_seed_upscale = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row():
+            upscale_button = gr.Button("Upscale", variant="primary")
+
+        upscale_button.click(
+            infer,
+            inputs=[
+                seed_upscale,
+                randomize_seed_upscale,
+                input_image_upscale,
+                num_inference_steps_upscale,
+                upscale_factor,
+                controlnet_conditioning_scale,
+            ],
+            outputs=result_upscale,
+        )
 
 app.queue()
-app.launch()    
+app.launch()