app.py

import gradio as gr
import spaces
import torch
from diffusers import AutoencoderKL, TCDScheduler
from diffusers.models.model_loading_utils import load_state_dict
# Removed ImageSlider import
from huggingface_hub import hf_hub_download

# Ensure these custom modules are accessible in the environment
# If running locally, they should be in the same directory or installed
try:
    from controlnet_union import ControlNetModel_Union
    from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline
except ImportError as e:
    print(f"Error importing custom modules: {e}")
    print("Please ensure 'controlnet_union.py' and 'pipeline_fill_sd_xl.py' are in the working directory or installed.")
    # Optionally, try installing if running in a suitable environment
    # import os
    # os.system("pip install git+https://github.com/UNION-AI-Research/FILL-Context-Aware-Outpainting.git") # Or wherever the package is hosted
    # Re-try import might be needed depending on environment setup
    exit()


from PIL import Image, ImageDraw
import numpy as np
import os # For checking example files

# --- Model Loading ---
# Use environment variable for model cache if needed
# HUGGINGFACE_HUB_CACHE = os.environ.get("HUGGINGFACE_HUB_CACHE", None)

try:
    config_file = hf_hub_download(
        "xinsir/controlnet-union-sdxl-1.0",
        filename="config_promax.json",
        # cache_dir=HUGGINGFACE_HUB_CACHE
    )

    config = ControlNetModel_Union.load_config(config_file)
    controlnet_model = ControlNetModel_Union.from_config(config)
    model_file = hf_hub_download(
        "xinsir/controlnet-union-sdxl-1.0",
        filename="diffusion_pytorch_model_promax.safetensors",
        # cache_dir=HUGGINGFACE_HUB_CACHE
    )

    sstate_dict = load_state_dict(model_file)
    model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model(
        controlnet_model, sstate_dict, model_file, "xinsir/controlnet-union-sdxl-1.0"
    )
    model.to(device="cuda", dtype=torch.float16)
    print("ControlNet loaded successfully.")

    vae = AutoencoderKL.from_pretrained(
        "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, # cache_dir=HUGGINGFACE_HUB_CACHE
    ).to("cuda")
    print("VAE loaded successfully.")

    pipe = StableDiffusionXLFillPipeline.from_pretrained(
        "SG161222/RealVisXL_V5.0_Lightning",
        torch_dtype=torch.float16,
        vae=vae,
        controlnet=model,
        variant="fp16",
        # cache_dir=HUGGINGFACE_HUB_CACHE
    ).to("cuda")
    print("Pipeline loaded successfully.")

    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
    print("Scheduler configured.")

except Exception as e:
    print(f"Error during model loading: {e}")
    raise e

# --- Helper Functions ---
def can_expand(source_width, source_height, target_width, target_height, alignment):
    """Checks if the image can be expanded based on the alignment."""
    if alignment in ("Left", "Right") and source_width >= target_width:
        return False
    if alignment in ("Top", "Bottom") and source_height >= target_height:
        return False
    return True

def prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    if image is None:
        raise gr.Error("Input image not provided.")
    try:
        target_size = (width, height)

        # Calculate the scaling factor to fit the image within the target size
        scale_factor = min(target_size[0] / image.width, target_size[1] / image.height)
        new_width = int(image.width * scale_factor)
        new_height = int(image.height * scale_factor)

        # Resize the source image to fit within target size
        source = image.resize((new_width, new_height), Image.LANCZOS)

        # Apply resize option using percentages
        if resize_option == "Full":
            resize_percentage = 100
        elif resize_option == "50%":
            resize_percentage = 50
        elif resize_option == "33%":
            resize_percentage = 33
        elif resize_option == "25%":
            resize_percentage = 25
        elif resize_option == "Custom":
            resize_percentage = custom_resize_percentage
        else:
             raise ValueError(f"Invalid resize option: {resize_option}")


        # Calculate new dimensions based on percentage
        resize_factor = resize_percentage / 100
        new_width = int(source.width * resize_factor)
        new_height = int(source.height * resize_factor)

        # Ensure minimum size of 64 pixels
        new_width = max(new_width, 64)
        new_height = max(new_height, 64)

        # Ensure dimensions fit within target (can happen if original image is tiny and resize % is large)
        new_width = min(new_width, target_size[0])
        new_height = min(new_height, target_size[1])

        # Resize the image
        source = source.resize((new_width, new_height), Image.LANCZOS)

        # Calculate the overlap in pixels based on the percentage
        overlap_x = int(new_width * (overlap_percentage / 100))
        overlap_y = int(new_height * (overlap_percentage / 100))

        # Ensure minimum overlap of 1 pixel if overlap is enabled, otherwise 0
        overlap_x = max(overlap_x, 1) if overlap_left or overlap_right else 0
        overlap_y = max(overlap_y, 1) if overlap_top or overlap_bottom else 0

        # Calculate margins based on alignment
        if alignment == "Middle":
            margin_x = (target_size[0] - new_width) // 2
            margin_y = (target_size[1] - new_height) // 2
        elif alignment == "Left":
            margin_x = 0
            margin_y = (target_size[1] - new_height) // 2
        elif alignment == "Right":
            margin_x = target_size[0] - new_width
            margin_y = (target_size[1] - new_height) // 2
        elif alignment == "Top":
            margin_x = (target_size[0] - new_width) // 2
            margin_y = 0
        elif alignment == "Bottom":
            margin_x = (target_size[0] - new_width) // 2
            margin_y = target_size[1] - new_height
        else:
             raise ValueError(f"Invalid alignment: {alignment}")


        # Adjust margins to ensure image is fully within bounds (should be redundant with min check above)
        margin_x = max(0, min(margin_x, target_size[0] - new_width))
        margin_y = max(0, min(margin_y, target_size[1] - new_height))

        # Create a new background image and paste the resized source image
        background = Image.new('RGB', target_size, (255, 255, 255)) # White background
        background.paste(source, (margin_x, margin_y))

        # Create the mask (initially all black - meaning keep everything)
        mask_np = np.zeros(target_size[::-1], dtype=np.uint8) # Use numpy for easier slicing [::-1] for (height, width)

        # Calculate the coordinates of the *source image* area within the target canvas
        source_left = margin_x
        source_top = margin_y
        source_right = margin_x + new_width
        source_bottom = margin_y + new_height

        # Calculate the coordinates of the *unmasked* area (area to keep from source)
        unmasked_left = source_left + overlap_x if overlap_left else source_left
        unmasked_top = source_top + overlap_y if overlap_top else source_top
        unmasked_right = source_right - overlap_x if overlap_right else source_right
        unmasked_bottom = source_bottom - overlap_y if overlap_bottom else source_bottom

        # Special handling for edge alignments to ensure the edge itself is kept if overlap disabled
        if alignment == "Left" and not overlap_left:
            unmasked_left = source_left
        if alignment == "Right" and not overlap_right:
            unmasked_right = source_right
        if alignment == "Top" and not overlap_top:
            unmasked_top = source_top
        if alignment == "Bottom" and not overlap_bottom:
            unmasked_bottom = source_bottom

        # Ensure coordinates are valid and clipped to the source image area within the canvas
        unmasked_left = max(source_left, min(unmasked_left, source_right))
        unmasked_top = max(source_top, min(unmasked_top, source_bottom))
        unmasked_right = max(source_left, min(unmasked_right, source_right))
        unmasked_bottom = max(source_top, min(unmasked_bottom, source_bottom))

        # Create the final mask: White (255) = Area to inpaint/outpaint, Black (0) = Area to keep
        final_mask_np = np.ones(target_size[::-1], dtype=np.uint8) * 255 # Start with all white (change everything)
        if unmasked_right > unmasked_left and unmasked_bottom > unmasked_top:
             # Set the area to keep (calculated unmasked rectangle) to black (0)
             final_mask_np[unmasked_top:unmasked_bottom, unmasked_left:unmasked_right] = 0

        mask = Image.fromarray(final_mask_np)

        return background, mask
    except Exception as e:
        print(f"Error in prepare_image_and_mask: {e}")
        raise gr.Error(f"Failed to prepare image and mask: {e}")


def preview_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    if image is None:
        return None # Or return a placeholder image/message
    try:
        background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)

        # Create a preview image showing the mask
        preview = background.copy().convert('RGBA')

        # Create a semi-transparent red overlay for the masked (inpainting/outpainting) area
        red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 100)) # 100 alpha (~40% opacity)

        # The mask is white (255) where outpainting happens. Use this directly.
        preview.paste(red_overlay, (0, 0), mask) # Paste red where mask is white

        return preview
    except Exception as e:
        print(f"Error during preview generation: {e}")
        # Return the original background or an error placeholder
        if 'background' in locals():
             return background.convert('RGBA')
        else:
             return Image.new('RGBA', (width, height), (200, 200, 200, 255)) # Grey placeholder


@spaces.GPU(duration=60) # Adjusted duration slightly
def infer(image, width, height, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage, prompt_input, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom, progress=gr.Progress(track_tqdm=True)):
    if image is None:
         raise gr.Error("Please provide an input image.")

    try:
        # --- Preparation ---
        progress(0.1, desc="Preparing image and mask...")
        original_alignment = alignment
        background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)

        # --- Alignment Check & Correction ---
        # Get dimensions *after* initial placement and resize
        pasted_source_img_width = int(image.width * min(width / image.width, height / image.height) * (custom_resize_percentage if resize_option=='Custom' else {'Full':100, '50%':50, '33%':33, '25%':25}[resize_option])/100)
        pasted_source_img_height = int(image.height * min(width / image.width, height / image.height) * (custom_resize_percentage if resize_option=='Custom' else {'Full':100, '50%':50, '33%':33, '25%':25}[resize_option])/100)
        pasted_source_img_width = max(64, min(pasted_source_img_width, width))
        pasted_source_img_height = max(64, min(pasted_source_img_height, height))

        needs_reprepare = False
        if alignment in ("Left", "Right") and pasted_source_img_width >= width:
            print(f"Warning: Source width ({pasted_source_img_width}) >= target width ({width}) with {alignment} alignment. Forcing Middle alignment.")
            alignment = "Middle"
            needs_reprepare = True
        if alignment in ("Top", "Bottom") and pasted_source_img_height >= height:
            print(f"Warning: Source height ({pasted_source_img_height}) >= target height ({height}) with {alignment} alignment. Forcing Middle alignment.")
            alignment = "Middle"
            needs_reprepare = True

        if needs_reprepare and alignment != original_alignment:
            print("Re-preparing mask due to alignment change.")
            progress(0.15, desc="Re-preparing mask for Middle alignment...")
            background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)

        # ControlNet expects the image with the *original* content visible in the non-masked area
        cnet_image = background.copy()
        # In some ControlNet inpainting setups, you might mask the control image too,
        # but Union ControlNet Fill often works well with the unmasked source pasted onto the background.
        # cnet_image.paste(0, mask=ImageOps.invert(mask)) # Optional: Black out masked area in CNet image

        # --- Prompt Encoding ---
        progress(0.2, desc="Encoding prompt...")
        final_prompt = f"{prompt_input}, high quality, 4k" if prompt_input else "high quality, 4k" # Add default tags if no prompt
        negative_prompt = "low quality, blurry, noisy, text, words, letters, watermark, signature, username, artist name, deformed, distorted, disfigured, bad anatomy, extra limbs, missing limbs"


        # Note: TCD/Lightning pipelines often work better *without* explicit negative prompts encoded
        # Try encoding only the positive prompt first
        (
            prompt_embeds,
            _, # negative_prompt_embeds (set to None or handle differently for TCD)
            pooled_prompt_embeds,
            _, # negative_pooled_prompt_embeds
        ) = pipe.encode_prompt(final_prompt, "cuda", False) # do_classifier_free_guidance=False for TCD


        # --- Inference ---
        progress(0.3, desc="Starting diffusion process...")
        print(f"Running inference with {num_inference_steps} steps...")
        pipeline_output = pipe(
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=None, # Pass None for TCD/Lightning
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=None, # Pass None for TCD/Lightning
            image=background,           # Initial state for masked area (background with source)
            mask_image=mask,            # Mask (white = change)
            control_image=cnet_image,   # ControlNet input
            num_inference_steps=num_inference_steps,
            guidance_scale=0.0,         # Crucial for TCD/Lightning
            controlnet_conditioning_scale=0.8, # Default for FILL pipeline, adjust if needed
            output_type="pil"           # Ensure PIL output
            # Add tqdm=True if supported by the custom pipeline and using gr.Progress without track_tqdm
        )

        # --- Process Output ---
        progress(0.9, desc="Processing results...")
        # Check if the pipeline returned a standard output object or a generator
        output_image = None
        if hasattr(pipeline_output, 'images'): # Standard diffusers output
            print("Pipeline returned a standard output object.")
            if len(pipeline_output.images) > 0:
                output_image = pipeline_output.images[0]
            else:
                 raise ValueError("Pipeline output contained no images.")
        # Check if it's iterable (generator) - less likely with direct call and output_type='pil' but good practice
        elif hasattr(pipeline_output, '__iter__') and not isinstance(pipeline_output, dict):
            print("Pipeline returned a generator, iterating to get the final image.")
            last_item = None
            for item in pipeline_output:
                last_item = item
             # Try to extract image from the last yielded item (structure can vary)
            if isinstance(last_item, tuple) and len(last_item) > 0 and isinstance(last_item[0], Image.Image):
                output_image = last_item[0]
            elif isinstance(last_item, dict) and 'images' in last_item and len(last_item['images']) > 0:
                output_image = last_item['images'][0]
            elif isinstance(last_item, Image.Image):
                output_image = last_item
            elif hasattr(last_item, 'images') and len(last_item.images) > 0: # Handle case where object yielded early
                 output_image = last_item.images[0]

            if output_image is None:
                raise ValueError("Pipeline generator did not yield a valid final image structure.")
        else:
            raise TypeError(f"Unexpected pipeline output type: {type(pipeline_output)}. Cannot extract image.")

        print("Inference complete.")
        progress(1.0, desc="Done!")
        return output_image

    except Exception as e:
        print(f"Error during inference: {e}")
        import traceback
        traceback.print_exc() # Print full traceback to console/logs
        raise gr.Error(f"Inference failed: {e}")


def clear_result(*args):
    """Clears the result Image and related components."""
    updates = {
        result: gr.update(value=None),
        use_as_input_button: gr.update(visible=False),
    }
    # If preview image is passed as an arg, clear it too
    if len(args) > 0 and isinstance(args[0], gr.Image):
         updates[args[0]] = gr.update(value=None) # Assuming preview_image is the first optional arg
    return updates


# --- UI Helper Functions ---
def preload_presets(target_ratio, ui_width, ui_height):
    """Updates the width and height sliders based on the selected aspect ratio."""
    settings_update = gr.update() # Default: no change to accordion state
    if target_ratio == "9:16":
        changed_width = 720
        changed_height = 1280
    elif target_ratio == "16:9":
        changed_width = 1280
        changed_height = 720
    elif target_ratio == "1:1":
        changed_width = 1024
        changed_height = 1024
    elif target_ratio == "Custom":
        changed_width = ui_width   # Keep current slider values
        changed_height = ui_height
        settings_update = gr.update(open=True) # Open accordion for custom
    else: # Should not happen
         changed_width = ui_width
         changed_height = ui_height

    return changed_width, changed_height, settings_update

def select_the_right_preset(user_width, user_height):
    """Updates the radio button based on the current slider values."""
    if user_width == 720 and user_height == 1280:
        return "9:16"
    elif user_width == 1280 and user_height == 720:
        return "16:9"
    elif user_width == 1024 and user_height == 1024:
        return "1:1"
    else:
        return "Custom"

def toggle_custom_resize_slider(resize_option):
    """Shows/hides the custom resize slider."""
    return gr.update(visible=(resize_option == "Custom"))

def update_history(new_image, history):
    """Updates the history gallery with the new image."""
    if not isinstance(new_image, Image.Image): # Don't add if generation failed (None)
         return history or [] # Return current or empty list

    if history is None:
        history = []
    history.insert(0, new_image)
    # Limit history size (optional)
    max_history = 12
    if len(history) > max_history:
        history = history[:max_history]
    return history

# --- Gradio UI Definition ---
css = """
.gradio-container {
    max-width: 1200px !important; /* Use max-width for responsiveness */
    margin: auto !important; /* Center the container */
    padding: 10px; /* Add some padding */
}
h1 { text-align: center; margin-bottom: 15px;}
footer { display: none !important; /* More reliable way to hide footer */ }

/* Ensure result image takes reasonable space */
#result-image img {
    max-height: 768px; /* Adjust max height as needed */
    object-fit: contain;
    width: 100%; /* Allow image to use column width */
    height: auto;
    display: block; /* Prevent extra space below image */
    margin: auto; /* Center image within its container */
}
#input-image img {
     max-height: 400px;
     object-fit: contain;
     width: 100%;
     height: auto;
     display: block;
     margin: auto;
}
#preview-image img {
     max-height: 250px; /* Smaller preview */
     object-fit: contain;
     width: 100%;
     height: auto;
     display: block;
     margin: auto;
}

#history-gallery .thumbnail-item { /* Style history items */
    height: 100px !important;
    overflow: hidden; /* Hide overflow */
}
#history-gallery .gallery {
    grid-template-rows: repeat(auto-fill, 100px) !important;
    gap: 4px !important; /* Add small gap */
}
#history-gallery .thumbnail-item img {
    object-fit: contain !important; /* Ensure history previews fit */
    height: 100%;
    width: 100%;
}

/* Make Checkboxes smaller and closer */
.gradio-checkboxgroup .wrap {
    gap: 0.5rem 1rem !important; /* Adjust spacing */
}
.gradio-checkbox label span {
    font-size: 0.9em; /* Slightly smaller label text */
}
.gradio-checkbox input {
    transform: scale(0.9); /* Slightly smaller checkbox */
}

/* Style Accordion */
.gradio-accordion .label-wrap { /* Target the label wrapper */
    border: 1px solid #e0e0e0;
    border-radius: 5px;
    padding: 8px 12px;
    background-color: #f9f9f9;
}
"""

title = """<h1 align="center">🖼️ Diffusers Image Outpaint Lightning ⚡</h1>"""

# --- Example Files Handling ---
# Create examples directory if it doesn't exist
if not os.path.exists("./examples"):
    os.makedirs("./examples")

# Check for example images and provide defaults or placeholders if missing
example_files = {
    "ex1": "./examples/example_1.webp",
    "ex2": "./examples/example_2.jpg",
    "ex3": "./examples/example_3.jpg"
}
default_image_path = None # Will be set to the first available example

# You might want to download example images if they don't exist
# from huggingface_hub import hf_hub_download
# def download_example(repo_id, filename, local_path):
#     if not os.path.exists(local_path):
#         try:
#             hf_hub_download(repo_id=repo_id, filename=filename, local_dir="./examples", local_dir_use_symlinks=False)
#             print(f"Downloaded {filename}")
#         except Exception as e:
#             print(f"Failed to download example {filename}: {e}")
#             return False # Indicate failure
#     return os.path.exists(local_path)

# Example: download_example("path/to/your/example-repo", "example_1.webp", example_files["ex1"])
# For now, we just check existence

examples_available = {key: os.path.exists(path) for key, path in example_files.items()}

example_list = []
if examples_available["ex1"]:
    example_list.append([example_files["ex1"], "A wide landscape view of the mountains", 1280, 720, "Middle"])
    if default_image_path is None: default_image_path = example_files["ex1"]
if examples_available["ex2"]:
     example_list.append([example_files["ex2"], "Full body shot of the astronaut on the moon", 720, 1280, "Middle"])
     if default_image_path is None: default_image_path = example_files["ex2"]
if examples_available["ex3"]:
     example_list.append([example_files["ex3"], "Expanding the sky and ground around the subject", 1024, 1024, "Middle"])
     example_list.append([example_files["ex3"], "Expanding downwards from the subject", 1024, 1024, "Top"])
     example_list.append([example_files["ex3"], "Expanding upwards from the subject", 1024, 1024, "Bottom"])
     if default_image_path is None: default_image_path = example_files["ex3"]

if not example_list:
    print("Warning: No example images found in ./examples/. Examples section will be empty.")
    # Optionally create a placeholder image
    # placeholder = Image.new('RGB', (512, 512), color = 'grey')
    # placeholder_path = "./examples/placeholder.png"
    # placeholder.save(placeholder_path)
    # example_list.append([placeholder_path, "Placeholder", 1024, 1024, "Middle"])
    # default_image_path = placeholder_path

# --- UI ---
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo: # Added a theme
    gr.HTML(title)

    with gr.Row():
        with gr.Column(scale=1): # Left column for inputs
            input_image = gr.Image(
                value=default_image_path, # Load default example
                type="pil",
                label="Input Image",
                elem_id="input-image"
            )

            prompt_input = gr.Textbox(label="Prompt", placeholder="Describe the scene to expand (optional but recommended)...", lines=2)

            with gr.Row():
                target_ratio = gr.Radio(
                    label="Target Aspect Ratio",
                    choices=["9:16", "16:9", "1:1", "Custom"],
                    value="9:16",
                    scale=2
                )
                alignment_dropdown = gr.Dropdown(
                    choices=["Middle", "Left", "Right", "Top", "Bottom"],
                    value="Middle",
                    label="Align Source Image",
                    scale=1
                )

            with gr.Accordion(label="Advanced settings", open=False) as settings_panel:
                with gr.Row():
                     width_slider = gr.Slider(
                         label="Target Width", minimum=512, maximum=2048, step=64, value=720
                     )
                     height_slider = gr.Slider(
                         label="Target Height", minimum=512, maximum=2048, step=64, value=1280
                     )
                num_inference_steps = gr.Slider(
                    label="Steps (TCD/Lightning: 1-8)", minimum=1, maximum=12, step=1, value=4
                )

                with gr.Group():
                    overlap_percentage = gr.Slider(
                        label="Mask Overlap with Source (%)", minimum=0, maximum=50, value=12, step=1
                    )
                    gr.Markdown("Select edges to overlap:", scale=0) # Add context
                    with gr.Row(elem_classes="gradio-checkboxgroup"): # Apply CSS class
                        overlap_top = gr.Checkbox(label="Top", value=True, scale=1)
                        overlap_bottom = gr.Checkbox(label="Bottom", value=True, scale=1)
                        overlap_left = gr.Checkbox(label="Left", value=True, scale=1)
                        overlap_right = gr.Checkbox(label="Right", value=True, scale=1)


                with gr.Row():
                    resize_option = gr.Radio(
                        label="Resize source within target",
                        choices=["Full", "50%", "33%", "25%", "Custom"],
                        value="Full",
                         scale=2
                    )
                    custom_resize_percentage = gr.Slider(
                        label="Custom resize (%)", minimum=1, maximum=100, step=1, value=50, visible=False, scale=1
                    )

                preview_button = gr.Button("Preview Mask & Alignment")
                preview_image = gr.Image(label="Mask Preview (Red = Outpaint Area)", type="pil", interactive=False, elem_id="preview-image")

            if example_list:
                gr.Examples(
                    examples=example_list,
                    inputs=[input_image, prompt_input, width_slider, height_slider, alignment_dropdown],
                    label="Examples (Click to load)",
                    examples_per_page=10
                )
            else:
                 gr.Markdown("_(No example files found in ./examples)_")

            run_button = gr.Button("Generate", variant="primary")


        with gr.Column(scale=1): # Right column for output
            result = gr.Image(label="Generated Image", type="pil", interactive=False, elem_id="result-image")
            use_as_input_button = gr.Button("Use Result as Input Image", visible=False)

            history_gallery = gr.Gallery(
                label="History", columns=6, object_fit="contain", interactive=False,
                height=110, elem_id="history-gallery"
            )

    # --- Event Handling ---

    # Function to set result as input and clear result area
    def use_output_as_input_and_clear(output_image):
        return {
            input_image: gr.update(value=output_image),
            result: gr.update(value=None), # Clear result after using it
            use_as_input_button: gr.update(visible=False) # Hide button again
        }

    use_as_input_button.click(
        fn=use_output_as_input_and_clear,
        inputs=[result],
        outputs=[input_image, result, use_as_input_button]
    )

    target_ratio.change(
        fn=preload_presets,
        inputs=[target_ratio, width_slider, height_slider],
        outputs=[width_slider, height_slider, settings_panel],
        queue=False
    )

    width_slider.change(
        fn=select_the_right_preset,
        inputs=[width_slider, height_slider],
        outputs=[target_ratio],
        queue=False
    )
    height_slider.change(
        fn=select_the_right_preset,
        inputs=[width_slider, height_slider],
        outputs=[target_ratio],
        queue=False
    )

    resize_option.change(
        fn=toggle_custom_resize_slider,
        inputs=[resize_option],
        outputs=[custom_resize_percentage],
        queue=False
    )

    # Consolidate common inputs for generation
    gen_inputs = [
        input_image, width_slider, height_slider, overlap_percentage, num_inference_steps,
        resize_option, custom_resize_percentage, prompt_input, alignment_dropdown,
        overlap_left, overlap_right, overlap_top, overlap_bottom
    ]
    gen_outputs = [result] # Single output image

    # Chain generation logic for Run button
    run_trigger = run_button.click(
        fn=clear_result, # Clear previous result first
        inputs=[], # No inputs needed for clear
        outputs=[result, use_as_input_button], # Components to clear/hide
        queue=False
    ).then(
        fn=infer,
        inputs=gen_inputs,
        outputs=gen_outputs,
    )

    # After generation finishes (successfully or not), update history and button visibility
    run_trigger.then(
        fn=lambda res_img, hist: update_history(res_img, hist),
        inputs=[result, history_gallery],
        outputs=[history_gallery],
        queue=False # Update history immediately
    ).then(
        # Show the 'Use as Input' button only if generation was successful (result is not None)
        fn=lambda res_img: gr.update(visible=isinstance(res_img, Image.Image)),
        inputs=[result],
        outputs=[use_as_input_button],
        queue=False # Show button immediately
    )


    # Chain generation logic for Enter key in Prompt textbox
    submit_trigger = prompt_input.submit(
         fn=clear_result,
        inputs=[],
        outputs=[result, use_as_input_button],
        queue=False
    ).then(
        fn=infer,
        inputs=gen_inputs,
        outputs=gen_outputs,
    )

    submit_trigger.then(
        fn=lambda res_img, hist: update_history(res_img, hist),
        inputs=[result, history_gallery],
        outputs=[history_gallery],
         queue=False
    ).then(
        fn=lambda res_img: gr.update(visible=isinstance(res_img, Image.Image)),
        inputs=[result],
        outputs=[use_as_input_button],
        queue=False
    )

    # Preview button logic
    preview_inputs = [
        input_image, width_slider, height_slider, overlap_percentage, resize_option,
        custom_resize_percentage, alignment_dropdown, overlap_left, overlap_right,
        overlap_top, overlap_bottom
    ]
    preview_button.click(
        fn=preview_image_and_mask,
        inputs=preview_inputs,
        outputs=preview_image,
        queue=False
    )

# Launch the interface
demo.queue(max_size=10).launch(ssr_mode=False, show_error=True, debug=True) # Add debug=True for more logs