Update app.py

app.py

@@ -1,514 +1,352 @@
-import argparse
-import spaces
-from visualcloze import VisualClozeModel
+iimport os
+import uuid
 import gradio as gr
-import examples
+import spaces
+from clip_slider_pipeline import CLIPSliderFlux
+from diffusers import FluxPipeline, AutoencoderTiny
 import torch
-from functools import partial
-from data.prefix_instruction import get_layout_instruction
-from huggingface_hub import snapshot_download
-
-# Define the missing variables here
-GUIDANCE = """
-## How to use this demo:
-1. Select a task example from the right side, or prepare your own in-context examples and query.
-2. The grid will be filled with in-context examples and a query row.
-3. You can modify the task description or add content descriptions.
-4. Click "Generate" to create images following the pattern shown in examples.
-"""
-
-NOTE = """
-**Note:** The examples on the right side demonstrate various tasks. 
-Click on any example to load it into the interface. You can then modify images or prompts as needed.
-"""
+import numpy as np
+import cv2
+from PIL import Image
+from diffusers.utils import load_image
+from diffusers.utils import export_to_video
+import random
+
+# English menu labels
+english_labels = {
+    "Prompt": "Prompt",
+    "1st direction to steer": "1st Direction",
+    "2nd direction to steer": "2nd Direction",
+    "Strength": "Strength",
+    "Generate directions": "Generate Directions",
+    "Generated Images": "Generated Images",
+    "From 1st to 2nd direction": "From 1st to 2nd Direction",
+    "Strip": "Image Strip",
+    "Looping video": "Looping Video",
+    "Advanced options": "Advanced Options",
+    "Num of intermediate images": "Number of Intermediate Images",
+    "Num iterations for clip directions": "Number of CLIP Direction Iterations",
+    "Num inference steps": "Number of Inference Steps",
+    "Guidance scale": "Guidance Scale",
+    "Randomize seed": "Randomize Seed",
+    "Seed": "Seed"
+}
 
-CITATION = """
-## Paper Citation
-```
-@article{liu2024visualcloze,
-  title={VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning},
-  author={Liu, Zhaoyang and Lian, Yuheng and Wang, Jianfeng and Zhou, Aojun and Liu, Jiashi and Ye, Hang and Chen, Kai and Wang, Jingdong and Zhao, Deli},
-  journal={arXiv preprint arXiv:2504.07960},
-  year={2024}
+# Load pipelines
+base_model = "black-forest-labs/FLUX.1-schnell"
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to("cuda")
+pipe = FluxPipeline.from_pretrained(
+    base_model,
+    vae=taef1,
+    torch_dtype=torch.bfloat16
+)
+pipe.transformer.to(memory_format=torch.channels_last)
+clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda"))
+MAX_SEED = 2**32 - 1
+
+def save_images_with_unique_filenames(image_list, save_directory):
+    if not os.path.exists(save_directory):
+        os.makedirs(save_directory)
+    paths = []
+    for image in image_list:
+        unique_filename = f"{uuid.uuid4()}.png"
+        file_path = os.path.join(save_directory, unique_filename)
+        image.save(file_path)
+        paths.append(file_path)
+    return paths
+
+def convert_to_centered_scale(num):
+    if num % 2 == 0:  # even
+        start = -(num // 2 - 1)
+        end = num // 2
+    else:  # odd
+        start = -(num // 2)
+        end = num // 2
+    return tuple(range(start, end + 1))
+
+def is_korean(text):
+    """한글 포함 여부 확인"""
+    return any('\u3131' <= char <= '\u3163' or '\uac00' <= char <= '\ud7a3' for char in text)
+
+@spaces.GPU(duration=85)
+def generate(prompt,
+             concept_1,
+             concept_2,
+             scale,
+             randomize_seed=True,
+             seed=42,
+             recalc_directions=True,
+             iterations=200,
+             steps=3,
+             interm_steps=33,
+             guidance_scale=3.5,
+             x_concept_1="", x_concept_2="",
+             avg_diff_x=None,
+             total_images=[],
+             gradio_progress=gr.Progress()):
+    # Check if there is Korean text and warn if so
+    if is_korean(prompt) or is_korean(concept_1) or is_korean(concept_2):
+        print("Korean text detected. The model will use it directly without translation.")
+    
+    print(f"Prompt: {prompt}, ← {concept_2}, {concept_1} ➡️ . scale {scale}, interm steps {interm_steps}")
+    slider_x = [concept_2, concept_1]
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]) or recalc_directions:
+        gradio_progress(0, desc="Calculating directions...")
+        avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations)
+        x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
+    else:
+        avg_diff = avg_diff_x
+    images = []
+    high_scale = scale
+    low_scale = -1 * scale
+    for i in gradio_progress.tqdm(range(interm_steps), desc="Generating images"):
+        cur_scale = low_scale + (high_scale - low_scale) * i / (interm_steps - 1)
+        image = clip_slider.generate(
+            prompt,
+            width=768,
+            height=768,
+            guidance_scale=guidance_scale,
+            scale=cur_scale,
+            seed=seed,
+            num_inference_steps=steps,
+            avg_diff=avg_diff
+        )
+        images.append(image)
+    canvas = Image.new('RGB', (256 * interm_steps, 256))
+    for i, im in enumerate(images):
+        canvas.paste(im.resize((256, 256)), (256 * i, 0))
+    comma_concepts_x = f"{slider_x[1]}, {slider_x[0]}"
+    scale_total = convert_to_centered_scale(interm_steps)
+    scale_min = scale_total[0]
+    scale_max = scale_total[-1]
+    scale_middle = scale_total.index(0)
+    post_generation_slider_update = gr.update(label=comma_concepts_x, value=0, minimum=scale_min, maximum=scale_max, interactive=True)
+    avg_diff_x = avg_diff.cpu()
+    video_path = f"{uuid.uuid4()}.mp4"
+    print(video_path)
+    return x_concept_1, x_concept_2, avg_diff_x, export_to_video(images, video_path, fps=5), canvas, images, images[scale_middle], post_generation_slider_update, seed
+
+def update_pre_generated_images(slider_value, total_images):
+    number_images = len(total_images) if total_images else 0
+    if number_images > 0:
+        scale_tuple = convert_to_centered_scale(number_images)
+        return total_images[scale_tuple.index(slider_value)][0]
+    else:
+        return None
+
+def reset_recalc_directions():
+    return True
+
+# Five "Time Stream" themed examples (one Korean example included)
+examples = [
+    ["신선한 토마토가 부패한 토마토로 변해가는 과정", "Fresh", "Rotten", 2.0],
+    ["A blooming flower gradually withers into decay", "Bloom", "Wither", 1.5],
+    ["A vibrant cityscape transforms into a derelict ruin over time", "Modern", "Ruined", 2.5],
+    ["A lively forest slowly changes into an autumnal landscape", "Spring", "Autumn", 2.0],
+    ["A calm ocean evolves into a stormy seascape as time passes", "Calm", "Stormy", 3.0]
+]
+
+# CSS for a bright and modern UI with a background image
+css = """
+/* Bright and modern UI with background image */
+body {
+    background: #ffffff url('https://images.unsplash.com/photo-1506748686214-e9df14d4d9d0?ixlib=rb-1.2.1&auto=format&fit=crop&w=1600&q=80') no-repeat center center fixed;
+    background-size: cover;
+    font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+    color: #333;
+}
+footer {
+    visibility: hidden;
+}
+.container {
+    max-width: 1200px;
+    margin: 20px auto;
+    padding: 0 10px;
+}
+.main-panel {
+    background-color: rgba(255, 255, 255, 0.9);
+    border-radius: 12px;
+    padding: 20px;
+    margin-bottom: 20px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+.controls-panel {
+    background-color: rgba(255, 255, 255, 0.85);
+    border-radius: 8px;
+    padding: 16px;
+    box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.05);
+}
+.image-display {
+    min-height: 400px;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+}
+.slider-container {
+    padding: 10px 0;
+}
+.advanced-panel {
+    margin-top: 20px;
+    border-top: 1px solid #eaeaea;
+    padding-top: 20px;
 }
-```
 """
 
-max_grid_h = 5
-max_grid_w = 5
-default_grid_h = 2
-default_grid_w = 3
-default_upsampling_noise = 0.4
-default_steps = 30
-
-
-def create_demo(model):
-    with gr.Blocks(title="VisualCloze Demo") as demo:
-        gr.Markdown("# VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning")
-
-        gr.HTML("""
-        <div style="display:flex;column-gap:4px;">
-            <a href="https://github.com/lzyhha/VisualCloze">
-                <img src='https://img.shields.io/badge/GitHub-Repo-blue'>
-            </a> 
-            <a href="https://visualcloze.github.io/">
-                <img src='https://img.shields.io/badge/Project-Website-green'>
-            </a>
-            <a href="https://arxiv.org/abs/2504.07960">
-                <img src='https://img.shields.io/badge/ArXiv-Paper-red'>
-            </a>
-            <a href="https://huggingface.co/VisualCloze/VisualCloze">
-                <img src='https://img.shields.io/badge/VisualCloze%20checkpoint-HF%20Model-green?logoColor=violet&label=%F0%9F%A4%97%20Checkpoint'>
-            </a>
-            <a href="https://huggingface.co/datasets/VisualCloze/Graph200K">
-                <img src='https://img.shields.io/badge/VisualCloze%20datasets-HF%20Dataset-6B88E3?logoColor=violet&label=%F0%9F%A4%97%20Graph200k%20Dataset'>
-            </a>
-        </div>
-        """)
-        
-        gr.Markdown(GUIDANCE)
-
-        # Pre-create all possible image components
-        all_image_inputs = []
-        rows = []
-        row_texts = []
-        with gr.Row():
-
-            with gr.Column(scale=2):
-                # Image grid
-                for i in range(max_grid_h):
-                    # Add row label before each row
-                    row_texts.append(gr.Markdown(
-                        "## Query" if i == default_grid_h - 1 else f"## In-context Example {i + 1}",  
-                        elem_id=f"row_text_{i}", 
-                        visible=i < default_grid_h
-                    ))
-                    with gr.Row(visible=i < default_grid_h, elem_id=f"row_{i}") as row:
-                        rows.append(row)
-                        for j in range(max_grid_w):
-                            img_input = gr.Image(
-                                label=f"In-context Example {i + 1}/{j + 1}" if i != default_grid_h - 1 else f"Query {j + 1}",
-                                type="pil",
-                                visible= i < default_grid_h and j < default_grid_w,
-                                interactive=True,
-                                elem_id=f"img_{i}_{j}"
+# 여기에서 show_api=False를 추가해 Gradio의 OpenAPI 스키마 생성을 비활성화합니다.
+with gr.Blocks(css=css, title="Time Stream", show_api=False) as demo:
+    gr.Markdown("# Time Stream")
+    
+    x_concept_1 = gr.State("")
+    x_concept_2 = gr.State("")
+    total_images = gr.State([])
+    avg_diff_x = gr.State()
+    recalc_directions = gr.State(False)
+
+    with gr.Row(elem_classes="container"):
+        # Left Column - Controls
+        with gr.Column(scale=4):
+            with gr.Group(elem_classes="main-panel"):
+                gr.Markdown("### Image Generation Controls")
+                with gr.Group(elem_classes="controls-panel"):
+                    prompt = gr.Textbox(
+                        label=english_labels["Prompt"],
+                        info="Enter the description",
+                        placeholder="A dog in the park",
+                        lines=2
+                    )
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            concept_1 = gr.Textbox(
+                                label=english_labels["1st direction to steer"],
+                                info="Initial state",
+                                placeholder="Fresh"
                             )
-                            all_image_inputs.append(img_input)
-
-                # Prompts
-                layout_prompt = gr.Textbox(
-                    label="Layout Description (Auto-filled, Read-only)",
-                    placeholder="Layout description will be automatically filled based on grid size...",
-                    value=get_layout_instruction(default_grid_w, default_grid_h),
-                    elem_id="layout_prompt",
-                    interactive=False
-                )
-                
-                task_prompt = gr.Textbox(
-                    label="Task Description (Can be modified by referring to examples to perform custom tasks, but may lead to unstable results)",
-                    placeholder="Describe what task should be performed...",
-                    value="",
-                    elem_id="task_prompt"
-                )
-                
-                content_prompt = gr.Textbox(
-                    label="(Optional) Content Description (Image caption, Editing instructions, etc.)",
-                    placeholder="Describe the content requirements...",
-                    value="",
-                    elem_id="content_prompt"
-                )
-                
-                generate_btn = gr.Button("Generate", elem_id="generate_btn")
-                gr.Markdown(NOTE)
-
-                grid_h = gr.Slider(minimum=0, maximum=max_grid_h-1, value=default_grid_h-1, step=1, label="Number of In-context Examples", elem_id="grid_h")
-                grid_w = gr.Slider(minimum=1, maximum=max_grid_w, value=default_grid_w, step=1, label="Task Columns", elem_id="grid_w")
-                
-                with gr.Accordion("Advanced options", open=False):
-                    seed = gr.Number(label="Seed (0 for random)", value=0, precision=0)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=default_steps, step=1)
-                    cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=50.0, value=30, step=1)
-                    upsampling_steps = gr.Slider(label="Upsampling steps (SDEdit)", minimum=1, maximum=100.0, value=10, step=1)
-                    upsampling_noise = gr.Slider(label="Upsampling noise (SDEdit)", minimum=0, maximum=1.0, value=default_upsampling_noise, step=0.05)
-
-                gr.Markdown(CITATION)
-
-            # Output
-            with gr.Column(scale=2):
-                output_gallery = gr.Gallery(
-                    label="Generated Results",
-                    show_label=True,
-                    elem_id="output_gallery",
-                    columns=None,
-                    rows=None,
-                    height="auto",
-                    allow_preview=True,
-                    object_fit="contain"
-                )
-            
-                gr.Markdown("# Task Examples")
-                gr.Markdown("Each click on a task may result in different examples.")
-                text_dense_prediction_tasks = gr.Textbox(label="Task", visible=False)
-                dense_prediction_tasks = gr.Dataset(
-                    samples=examples.dense_prediction_text, 
-                    label='Dense Prediction', 
-                    samples_per_page=1000, 
-                    components=[text_dense_prediction_tasks])
-                
-                text_conditional_generation_tasks = gr.Textbox(label="Task", visible=False)
-                conditional_generation_tasks = gr.Dataset(
-                    samples=examples.conditional_generation_text, 
-                    label='Conditional Generation',
-                    samples_per_page=1000, 
-                    components=[text_conditional_generation_tasks])
-                
-                text_image_restoration_tasks = gr.Textbox(label="Task", visible=False)
-                image_restoration_tasks = gr.Dataset(
-                    samples=examples.image_restoration_text, 
-                    label='Image Restoration',
-                    samples_per_page=1000, 
-                    components=[text_image_restoration_tasks])
-                
-                text_style_transfer_tasks = gr.Textbox(label="Task", visible=False)
-                style_transfer_tasks = gr.Dataset(
-                    samples=examples.style_transfer_text, 
-                    label='Style Transfer', 
-                    samples_per_page=1000, 
-                    components=[text_style_transfer_tasks])
-                
-                text_style_condition_fusion_tasks = gr.Textbox(label="Task", visible=False)
-                style_condition_fusion_tasks = gr.Dataset(
-                    samples=examples.style_condition_fusion_text, 
-                    label='Style Condition Fusion', 
-                    samples_per_page=1000, 
-                    components=[text_style_condition_fusion_tasks]) 
-                
-                text_tryon_tasks = gr.Textbox(label="Task", visible=False)
-                tryon_tasks = gr.Dataset(
-                    samples=examples.tryon_text, 
-                    label='Virtual Try-On', 
-                    samples_per_page=1000, 
-                    components=[text_tryon_tasks])
-
-                text_relighting_tasks = gr.Textbox(label="Task", visible=False)
-                relighting_tasks = gr.Dataset(
-                    samples=examples.relighting_text, 
-                    label='Relighting', 
-                    samples_per_page=1000, 
-                    components=[text_relighting_tasks])
-                
-                text_photodoodle_tasks = gr.Textbox(label="Task", visible=False)
-                photodoodle_tasks = gr.Dataset(
-                    samples=examples.photodoodle_text, 
-                    label='Photodoodle', 
-                    samples_per_page=1000, 
-                    components=[text_photodoodle_tasks])   
-
-                text_editing_tasks = gr.Textbox(label="Task", visible=False)
-                editing_tasks = gr.Dataset(
-                    samples=examples.editing_text, 
-                    label='Editing', 
-                    samples_per_page=1000, 
-                    components=[text_editing_tasks])
-
-                text_unseen_tasks = gr.Textbox(label="Task", visible=False)
-                unseen_tasks = gr.Dataset(
-                    samples=examples.unseen_tasks_text, 
-                    label='Unseen Tasks (May produce unstable effects)', 
-                    samples_per_page=1000, 
-                    components=[text_unseen_tasks]) 
-                
-                gr.Markdown("# Subject-driven Tasks Examples")
-                text_subject_driven_tasks = gr.Textbox(label="Task", visible=False)
-                subject_driven_tasks = gr.Dataset(
-                    samples=examples.subject_driven_text, 
-                    label='Subject-driven Generation', 
-                    samples_per_page=1000, 
-                    components=[text_subject_driven_tasks])
-                
-                text_condition_subject_fusion_tasks = gr.Textbox(label="Task", visible=False)
-                condition_subject_fusion_tasks = gr.Dataset(
-                    samples=examples.condition_subject_fusion_text, 
-                    label='Condition+Subject Fusion', 
-                    samples_per_page=1000, 
-                    components=[text_condition_subject_fusion_tasks])
-                
-                text_style_transfer_with_subject_tasks = gr.Textbox(label="Task", visible=False)
-                style_transfer_with_subject_tasks = gr.Dataset(
-                    samples=examples.style_transfer_with_subject_text, 
-                    label='Style Transfer with Subject', 
-                    samples_per_page=1000, 
-                    components=[text_style_transfer_with_subject_tasks])
-
-                text_condition_subject_style_fusion_tasks = gr.Textbox(label="Task", visible=False)
-                condition_subject_style_fusion_tasks = gr.Dataset(
-                    samples=examples.condition_subject_style_fusion_text, 
-                    label='Condition+Subject+Style Fusion', 
-                    samples_per_page=1000, 
-                    components=[text_condition_subject_style_fusion_tasks])
-
-                text_editing_with_subject_tasks = gr.Textbox(label="Task", visible=False)
-                editing_with_subject_tasks = gr.Dataset(
-                    samples=examples.editing_with_subject_text, 
-                    label='Editing with Subject', 
-                    samples_per_page=1000, 
-                    components=[text_editing_with_subject_tasks])
-
-                text_image_restoration_with_subject_tasks = gr.Textbox(label="Task", visible=False)
-                image_restoration_with_subject_tasks = gr.Dataset(
-                    samples=examples.image_restoration_with_subject_text, 
-                    label='Image Restoration with Subject', 
-                    samples_per_page=1000, 
-                    components=[text_image_restoration_with_subject_tasks])
-                
-        def update_grid(h, w):
-            actual_h = h + 1
-            model.set_grid_size(actual_h, w)
-            
-            updates = []
-            
-            # Update image component visibility
-            for i in range(max_grid_h * max_grid_w):
-                curr_row = i // max_grid_w
-                curr_col = i % max_grid_w
-                updates.append(
-                    gr.update(
-                        label=f"In-context Example {curr_row + 1}/{curr_col + 1}" if curr_row != actual_h - 1 else f"Query {curr_col + 1}",
-                        elem_id=f"img_{curr_row}_{curr_col}", 
-                        visible=(curr_row < actual_h and curr_col < w)))
-            
-            # Update row visibility and labels
-            updates_row = []
-            updates_row_text = []
-            for i in range(max_grid_h):
-                updates_row.append(gr.update(f"row_{i}", visible=(i < actual_h)))
-                updates_row_text.append(
-                    gr.update(
-                        elem_id=f"row_text_{i}", 
-                        visible=i < actual_h, 
-                        value="## Query" if i == actual_h - 1 else f"## In-context Example {i + 1}", 
+                        with gr.Column(scale=1):
+                            concept_2 = gr.Textbox(
+                                label=english_labels["2nd direction to steer"],
+                                info="Final state",
+                                placeholder="Rotten"
+                            )
+                    with gr.Row(elem_classes="slider-container"):
+                        x = gr.Slider(
+                            minimum=0,
+                            value=1.75,
+                            step=0.1,
+                            maximum=4.0,
+                            label=english_labels["Strength"],
+                            info="Maximum strength for each direction (above 2.5 may be unstable)"
+                        )
+                    submit = gr.Button(english_labels["Generate directions"], size="lg", variant="primary")
+            with gr.Accordion(label=english_labels["Advanced options"], open=False, elem_classes="advanced-panel"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        interm_steps = gr.Slider(
+                            label=english_labels["Num of intermediate images"],
+                            minimum=3,
+                            value=7,
+                            maximum=65,
+                            step=2
+                        )
+                    with gr.Column(scale=1):
+                        guidance_scale = gr.Slider(
+                            label=english_labels["Guidance scale"],
+                            minimum=0.1,
+                            maximum=10.0,
+                            step=0.1,
+                            value=3.5
+                        )
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        iterations = gr.Slider(
+                            label=english_labels["Num iterations for clip directions"],
+                            minimum=0,
+                            value=200,
+                            maximum=400,
+                            step=1
+                        )
+                    with gr.Column(scale=1):
+                        steps = gr.Slider(
+                            label=english_labels["Num inference steps"],
+                            minimum=1,
+                            value=3,
+                            maximum=4,
+                            step=1
+                        )
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        randomize_seed = gr.Checkbox(
+                            True,
+                            label=english_labels["Randomize seed"]
+                        )
+                    with gr.Column(scale=1):
+                        seed = gr.Slider(
+                            minimum=0,
+                            maximum=MAX_SEED,
+                            step=1,
+                            label=english_labels["Seed"],
+                            interactive=True,
+                            randomize=True
+                        )
+        # Right Column - Output
+        with gr.Column(scale=8):
+            with gr.Group(elem_classes="main-panel"):
+                gr.Markdown("### Generated Results")
+                # Swapped order: Image strip on top, video below (video is larger)
+                image_strip = gr.Image(label="Image Strip", type="filepath", elem_id="strip", height=200)
+                output_video = gr.Video(label=english_labels["Looping video"], elem_id="video", loop=True, autoplay=True, height=600)
+                with gr.Row():
+                    post_generation_image = gr.Image(
+                        label=english_labels["Generated Images"],
+                        type="filepath",
+                        elem_id="interactive",
+                        elem_classes="image-display"
+                    )
+                    post_generation_slider = gr.Slider(
+                        minimum=-10,
+                        maximum=10,
+                        value=0,
+                        step=1,
+                        label=english_labels["From 1st to 2nd direction"]
                     )
-                )
-            
-            updates.extend(updates_row)
-            updates.extend(updates_row_text)
-            updates.append(gr.update(elem_id="layout_prompt", value=get_layout_instruction(w, actual_h)))
-            return updates
-
-        def generate_image(*inputs):
-            images = []
-            if grid_h.value + 1 != model.grid_h or grid_w.value != model.grid_w:
-                raise gr.Error('Please wait for the loading to complete.')
-            for i in range(model.grid_h):
-                images.append([])
-                for j in range(model.grid_w):
-                    images[i].append(inputs[i * max_grid_w + j])
-                    if i != model.grid_h - 1:
-                        if inputs[i * max_grid_w + j] is None:
-                            raise gr.Error('Please upload in-context examples. Possible that the task examples have not finished loading yet, and you can try waiting a few seconds before clicking the button again.')
-            seed, cfg, steps, upsampling_steps, upsampling_noise, layout_text, task_text, content_text = inputs[-8:]
-            
-            try:
-                results = generate(
-                    images, 
-                    [layout_text, task_text, content_text], 
-                    seed=seed, cfg=cfg, steps=steps, 
-                    upsampling_steps=upsampling_steps, upsampling_noise=upsampling_noise
-                )
-            except Exception as e:
-                raise gr.Error('Process error. Possible that the task examples have not finished loading yet, and you can try waiting a few seconds before clicking the button again. Error: ' + str(e))
-
-            output = gr.update(
-                elem_id='output_gallery', 
-                value=results, 
-                columns=min(len(results), 2),
-                rows=int(len(results) / 2 + 0.5))
-            
-            return output
-
-        def process_tasks(task, func):
-            outputs = func(task)
-            mask = outputs[0]
-            state = outputs[1:8]
-            if state[5] is None:
-                state[5] = default_upsampling_noise
-            if state[6] is None:
-                state[6] = default_steps
-            images = outputs[8:-len(mask)]
-            output = outputs[-len(mask):]
-            for i in range(len(mask)):
-                if mask[i] == 1:
-                    images.append(None)
-                else:
-                    images.append(output[-len(mask) + i])
-            
-            state[0] = state[0] - 1
-            cur_hrid_h = state[0]
-            cur_hrid_w = state[1]
-
-            current_example = [None] * 25
-            for i, image in enumerate(images):
-                pos = (i // cur_hrid_w) * 5 + (i % cur_hrid_w)
-                if image is not None:
-                    current_example[pos] = image
-            update_grid(cur_hrid_h, cur_hrid_w)
-            output = gr.update(
-                elem_id='output_gallery', 
-                value=[o for o, m in zip(output, mask) if m == 1], 
-                columns=min(sum(mask), 2),
-                rows=int(sum(mask) / 2 + 0.5))
-            return [output] + current_example + state
-        
-        dense_prediction_tasks.click(
-            partial(process_tasks, func=examples.process_dense_prediction_tasks), 
-            inputs=[dense_prediction_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full", 
-            show_progress_on=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps] + [generate_btn])
-
-        conditional_generation_tasks.click(
-            partial(process_tasks, func=examples.process_conditional_generation_tasks), 
-            inputs=[conditional_generation_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        image_restoration_tasks.click(
-            partial(process_tasks, func=examples.process_image_restoration_tasks), 
-            inputs=[image_restoration_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        style_transfer_tasks.click(
-            partial(process_tasks, func=examples.process_style_transfer_tasks), 
-            inputs=[style_transfer_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        style_condition_fusion_tasks.click(
-            partial(process_tasks, func=examples.process_style_condition_fusion_tasks), 
-            inputs=[style_condition_fusion_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-            
-        relighting_tasks.click(
-            partial(process_tasks, func=examples.process_relighting_tasks), 
-            inputs=[relighting_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        tryon_tasks.click(
-            partial(process_tasks, func=examples.process_tryon_tasks), 
-            inputs=[tryon_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-        
-        photodoodle_tasks.click(
-            partial(process_tasks, func=examples.process_photodoodle_tasks), 
-            inputs=[photodoodle_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        editing_tasks.click(
-            partial(process_tasks, func=examples.process_editing_tasks), 
-            inputs=[editing_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-        
-        unseen_tasks.click(
-            partial(process_tasks, func=examples.process_unseen_tasks), 
-            inputs=[unseen_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        subject_driven_tasks.click(
-            partial(process_tasks, func=examples.process_subject_driven_tasks), 
-            inputs=[subject_driven_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        style_transfer_with_subject_tasks.click(
-            partial(process_tasks, func=examples.process_style_transfer_with_subject_tasks), 
-            inputs=[style_transfer_with_subject_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        condition_subject_fusion_tasks.click(
-            partial(process_tasks, func=examples.process_condition_subject_fusion_tasks), 
-            inputs=[condition_subject_fusion_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        condition_subject_style_fusion_tasks.click(
-            partial(process_tasks, func=examples.process_condition_subject_style_fusion_tasks), 
-            inputs=[condition_subject_style_fusion_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        editing_with_subject_tasks.click(
-            partial(process_tasks, func=examples.process_editing_with_subject_tasks), 
-            inputs=[editing_with_subject_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-
-        image_restoration_with_subject_tasks.click(
-            partial(process_tasks, func=examples.process_image_restoration_with_subject_tasks), 
-            inputs=[image_restoration_with_subject_tasks], 
-            outputs=[output_gallery] + all_image_inputs + [grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps], 
-            show_progress="full")
-        # Initialize grid
-        model.set_grid_size(default_grid_h, default_grid_w)
-        
-        # Connect event processing function to all components that need updating
-        output_components = all_image_inputs + rows + row_texts + [layout_prompt]
-
-        grid_h.change(fn=update_grid, inputs=[grid_h, grid_w], outputs=output_components)
-        grid_w.change(fn=update_grid, inputs=[grid_h, grid_w], outputs=output_components)
-        
-        # Modify generate button click event
-        generate_btn.click(
-            fn=generate_image,
-            inputs=all_image_inputs + [seed, cfg, steps, upsampling_steps, upsampling_noise] + [layout_prompt, task_prompt, content_prompt],
-            outputs=output_gallery
-        )
-        
-    return demo
-
-
-@spaces.GPU()
-def generate(
-    images, 
-    prompts, 
-    seed, cfg, steps, 
-    upsampling_steps, upsampling_noise):
-    with torch.no_grad():
-        return model.process_images(
-            images=images, 
-            prompts=prompts, 
-            seed=seed, 
-            cfg=cfg, 
-            steps=steps, 
-            upsampling_steps=upsampling_steps, 
-            upsampling_noise=upsampling_noise)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_path", type=str, default="checkpoints/visualcloze-384-lora.pth")
-    parser.add_argument("--precision", type=str, choices=["fp32", "bf16", "fp16"], default="bf16")
-    parser.add_argument("--resolution", type=int, default=384)
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = parse_args()
     
-    snapshot_download(repo_id="VisualCloze/VisualCloze", repo_type="model", local_dir="checkpoints")
-
-    # Initialize model
-    model = VisualClozeModel(resolution=args.resolution, model_path=args.model_path, precision=args.precision)
+    # Examples
+    gr.Examples(
+        examples=examples,
+        inputs=[prompt, concept_1, concept_2, x]
+    )
     
-    # Create Gradio demo
-    demo = create_demo(model)
+    # Event Handlers
+    submit.click(
+        fn=generate,
+        inputs=[
+            prompt, concept_1, concept_2, x, randomize_seed, seed,
+            recalc_directions, iterations, steps, interm_steps,
+            guidance_scale, x_concept_1, x_concept_2, avg_diff_x, total_images
+        ],
+        outputs=[
+            x_concept_1, x_concept_2, avg_diff_x,
+            output_video,      # video output
+            image_strip,       # canvas (image strip)
+            total_images,
+            post_generation_image,
+            post_generation_slider,
+            seed
+        ]
+    )
     
-    # Start Gradio server
-    demo.launch()
+    iterations.change(fn=reset_recalc_directions, outputs=[recalc_directions])
+    seed.change(fn=reset_recalc_directions, outputs=[recalc_directions])
+    post_generation_slider.change(
+        fn=update_pre_generated_images,
+        inputs=[post_generation_slider, total_images],
+        outputs=[post_generation_image],
+        queue=False,
+        show_progress="hidden",
+        concurrency_limit=None
+    )
+
+if __name__ == "__main__":
+    # Gradio API 스키마를 표시하지 않으려면 아래와 같이 show_api=False 옵션을 사용할 수 있습니다.
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)