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
from huggingface_hub import hf_hub_download

from controlnet_union import ControlNetModel_Union
from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline

from PIL import Image, ImageDraw
import numpy as np

# --- Configuration and Model Loading ---

# Load ControlNet Union
config_file = hf_hub_download(
    "xinsir/controlnet-union-sdxl-1.0",
    filename="config_promax.json",
)
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",
)
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)

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
).to("cuda")

# --- Load Multiple Pipelines ---
pipelines = {}

# Load RealVisXL V5.0 Lightning
pipe_v5 = StableDiffusionXLFillPipeline.from_pretrained(
    "SG161222/RealVisXL_V5.0_Lightning",
    torch_dtype=torch.float16,
    vae=vae,
    controlnet=model, # Use the same controlnet
    variant="fp16",
).to("cuda")
pipe_v5.scheduler = TCDScheduler.from_config(pipe_v5.scheduler.config)
pipelines["RealVisXL V5.0 Lightning"] = pipe_v5

# Load RealVisXL V4.0 Lightning
pipe_v4 = StableDiffusionXLFillPipeline.from_pretrained(
    "SG161222/RealVisXL_V4.0_Lightning",
    torch_dtype=torch.float16,
    vae=vae, # Use the same VAE
    controlnet=model, # Use the same controlnet
    variant="fp16",
).to("cuda")
pipe_v4.scheduler = TCDScheduler.from_config(pipe_v4.scheduler.config)
pipelines["RealVisXL V4.0 Lightning"] = pipe_v4

# --- Helper Functions ---

def prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    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
    else:  # Custom
        resize_percentage = custom_resize_percentage

    # 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)

    # 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
    overlap_x = max(overlap_x, 1)
    overlap_y = max(overlap_y, 1)

    # 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: # Default to Middle if alignment is somehow invalid
        margin_x = (target_size[0] - new_width) // 2
        margin_y = (target_size[1] - new_height) // 2


    # Adjust margins to eliminate gaps
    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))
    background.paste(source, (margin_x, margin_y))

    # Create the mask
    mask = Image.new('L', target_size, 255) # White background (area to be filled)
    mask_draw = ImageDraw.Draw(mask)

    # Calculate overlap areas (where the mask should be black = keep original)
    white_gaps_patch = 2 # Small value to ensure no tiny gaps at edges if overlap is off

    # Determine the coordinates for the black rectangle (the non-masked area)
    # Start with the full area covered by the pasted image
    left_black = margin_x
    top_black = margin_y
    right_black = margin_x + new_width
    bottom_black = margin_y + new_height

    # Adjust the black area based on overlap checkboxes
    if overlap_left:
        left_black += overlap_x
    else:
        # If not overlapping left, ensure the black mask starts exactly at the image edge or slightly inside
        left_black += white_gaps_patch if alignment != "Left" else 0

    if overlap_right:
        right_black -= overlap_x
    else:
         # If not overlapping right, ensure the black mask ends exactly at the image edge or slightly inside
        right_black -= white_gaps_patch if alignment != "Right" else 0

    if overlap_top:
        top_black += overlap_y
    else:
        # If not overlapping top, ensure the black mask starts exactly at the image edge or slightly inside
        top_black += white_gaps_patch if alignment != "Top" else 0

    if overlap_bottom:
        bottom_black -= overlap_y
    else:
        # If not overlapping bottom, ensure the black mask ends exactly at the image edge or slightly inside
        bottom_black -= white_gaps_patch if alignment != "Bottom" else 0

    # Ensure coordinates are valid (left < right, top < bottom)
    left_black = min(left_black, target_size[0])
    top_black = min(top_black, target_size[1])
    right_black = max(left_black, right_black) # Ensure right >= left
    bottom_black = max(top_black, bottom_black) # Ensure bottom >= top
    right_black = min(right_black, target_size[0])
    bottom_black = min(bottom_black, target_size[1])


    # Draw the black rectangle onto the white mask
    # The area *inside* this rectangle will be kept (mask value 0)
    # The area *outside* this rectangle will be filled (mask value 255)
    if right_black > left_black and bottom_black > top_black:
         mask_draw.rectangle(
             [(left_black, top_black), (right_black, bottom_black)],
             fill=0 # Black means keep this area
         )

    return background, mask


@spaces.GPU(duration=24)
def infer(selected_model_name, image, width, height, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage, prompt_input, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    if image is None:
        raise gr.Error("Please upload an input image.")
    try:
        # Select the pipeline based on the dropdown choice
        pipe = pipelines[selected_model_name]

        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 the controlnet input image (original image pasted on white bg, with masked area blacked out)
        cnet_image = background.copy()
        # Create a black image of the same size as the mask
        black_fill = Image.new('RGB', mask.size, (0, 0, 0))
        # Paste the black fill using the mask (where mask is 255/white, paste black)
        cnet_image.paste(black_fill, (0, 0), mask)


        final_prompt = f"{prompt_input} , high quality, 4k" if prompt_input else "high quality, 4k"

        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = pipe.encode_prompt(final_prompt, "cuda", True)

        # Generate the image
        generator = torch.Generator(device="cuda").manual_seed(np.random.randint(0, 2**32)) # Add random seed

        # The pipeline expects the 'image' argument to be the background with the original content
        # and the 'mask_image' argument to define the area to *inpaint* (white area in our mask)
        result_image = pipe(
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            image=background, # The background containing the original image
            mask_image=mask, # The mask (white = fill, black = keep)
            control_image=cnet_image, # ControlNet input image
            num_inference_steps=num_inference_steps,
            generator=generator, # Use generator for reproducibility if needed
            output_type="pil" # Ensure PIL output
        ).images[0]

        # The pipeline directly returns the final composited image.
        # No need for manual pasting like before.

        return result_image
    except Exception as e:
        print(f"Error during inference: {e}")
        import traceback
        traceback.print_exc()
        # Return the background image or raise a Gradio error for clarity
        # raise gr.Error(f"Inference failed: {e}")
        # Or return the prepared background/mask for debugging
        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)
        # Combine background and mask for visualization
        debug_img = Image.blend(background.convert("RGBA"), mask.convert("RGBA"), 0.5)
        return debug_img # Return a debug image or None


def clear_result():
    """Clears the result Image."""
    return gr.update(value=None)

def preload_presets(target_ratio, ui_width, ui_height):
    """Updates the width and height sliders based on the selected aspect ratio."""
    if target_ratio == "9:16":
        changed_width = 720
        changed_height = 1280
        return changed_width, changed_height, gr.update(open=False) # Close accordion on preset
    elif target_ratio == "16:9":
        changed_width = 1280
        changed_height = 720
        return changed_width, changed_height, gr.update(open=False) # Close accordion on preset
    elif target_ratio == "1:1":
        changed_width = 1024
        changed_height = 1024
        return changed_width, changed_height, gr.update(open=False) # Close accordion on preset
    elif target_ratio == "Custom":
        # When switching to Custom, keep current slider values but open accordion
        return ui_width, ui_height, gr.update(open=True)
    # Should not happen, but return current values if it does
    return ui_width, ui_height, gr.update()


def select_the_right_preset(user_width, user_height):
    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):
    return gr.update(visible=(resize_option == "Custom"))

def update_history(new_image, history):
    """Updates the history gallery with the new image."""
    if new_image is None: # Don't add None to history (e.g., on clear or error)
        return history
    if history is None:
        history = []
    # Prepend the new image (as PIL or path depending on Gallery config)
    history.insert(0, new_image)
    # Limit history size if desired (e.g., keep last 12)
    max_history = 12
    if len(history) > max_history:
        history = history[:max_history]
    return history

# --- CSS and Title ---
css = """
h1 {
    text-align: center;
    display: block;
}
.gradio-container {
    max-width: 1280px !important;
    margin: auto !important;
}
"""

title = """<h1 align="center">Diffusers Image Outpaint Lightning</h1>
<p align="center">Expand images using ControlNet Union and Lightning models. Choose a base model below.</p>
"""

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

        with gr.Row():
            with gr.Column(scale=2): # Input column
                input_image = gr.Image(
                    type="pil",
                    label="Input Image"
                )

                # --- Model Selector ---
                model_selector = gr.Dropdown(
                    label="Select Model",
                    choices=list(pipelines.keys()),
                    value="RealVisXL V5.0 Lightning", # Default model
                )

                with gr.Row():
                    with gr.Column(scale=2):
                        prompt_input = gr.Textbox(label="Prompt (Describe the desired output)", placeholder="e.g., beautiful landscape, photorealistic")
                    with gr.Column(scale=1, min_width=120):
                        run_button = gr.Button("Generate", variant="primary")

                with gr.Row():
                    target_ratio = gr.Radio(
                        label="Target Ratio",
                        choices=["9:16", "16:9", "1:1", "Custom"],
                        value="9:16", # Default ratio
                        scale=2
                    )

                    alignment_dropdown = gr.Dropdown(
                        choices=["Middle", "Left", "Right", "Top", "Bottom"],
                        value="Middle",
                        label="Align Input Image"
                    )

                with gr.Accordion(label="Advanced settings", open=False) as settings_panel:
                    with gr.Column():
                        with gr.Row():
                            width_slider = gr.Slider(
                                label="Target Width",
                                minimum=512, # Lowered minimum slightly
                                maximum=1536,
                                step=64, # Steps of 64 common for SDXL
                                value=720, # Default width
                            )
                            height_slider = gr.Slider(
                                label="Target Height",
                                minimum=512, # Lowered minimum slightly
                                maximum=1536,
                                step=64, # Steps of 64
                                value=1280, # Default height
                            )

                        num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8)

                        with gr.Group():
                            overlap_percentage = gr.Slider(
                                label="Mask overlap (%)",
                                info="Percentage of the input image edge to keep (reduces seams)",
                                minimum=1,
                                maximum=50,
                                value=10, # Default overlap
                                step=1
                            )
                            gr.Markdown("Select edges to apply overlap:")
                            with gr.Row():
                                overlap_top = gr.Checkbox(label="Top", value=True)
                                overlap_right = gr.Checkbox(label="Right", value=True)
                                overlap_left = gr.Checkbox(label="Left", value=True)
                                overlap_bottom = gr.Checkbox(label="Bottom", value=True)

                        with gr.Row():
                            resize_option = gr.Radio(
                                label="Resize input image before placing",
                                info="Scale the input image relative to its fitted size",
                                choices=["Full", "50%", "33%", "25%", "Custom"],
                                value="Full" # Default resize option
                            )
                            custom_resize_percentage = gr.Slider(
                                label="Custom resize (%)",
                                minimum=1,
                                maximum=100,
                                step=1,
                                value=50,
                                visible=False # Initially hidden
                            )

                gr.Examples(
                    examples=[
                        ["./examples/example_1.webp", "RealVisXL V5.0 Lightning", 1280, 720, "Middle"],
                        ["./examples/example_2.jpg", "RealVisXL V4.0 Lightning", 1440, 810, "Left"],
                        ["./examples/example_3.jpg", "RealVisXL V5.0 Lightning", 1024, 1024, "Top"],
                        ["./examples/example_3.jpg", "RealVisXL V5.0 Lightning", 1024, 1024, "Bottom"],
                    ],
                    inputs=[input_image, model_selector, width_slider, height_slider, alignment_dropdown],
                    label="Examples (Prompt is optional)"
                )

            with gr.Column(scale=3): # Output column
                result = gr.Image(
                    interactive=False,
                    label="Generated Image",
                    format="png",
                )
                history_gallery = gr.Gallery(
                    label="History",
                    columns=4, # Adjust columns as needed
                    object_fit="contain",
                    interactive=False,
                    show_label=True,
                    allow_preview=True,
                    preview=True
                 )


    # --- Event Listeners ---

    # Update sliders and accordion based on ratio selection
    target_ratio.change(
        fn=preload_presets,
        inputs=[target_ratio, width_slider, height_slider],
        outputs=[width_slider, height_slider, settings_panel],
        queue=False
    )

    # Update ratio selection based on slider changes
    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
    )

    # Show/hide custom resize slider
    resize_option.change(
        fn=toggle_custom_resize_slider,
        inputs=[resize_option],
        outputs=[custom_resize_percentage],
        queue=False
    )

    # Define inputs for the main inference function
    infer_inputs = [
        model_selector, 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
    ]

    # --- Run Button Click ---
    run_button.click(
        fn=clear_result,
        inputs=None,
        outputs=[result], # Clear only the main result image
        queue=False # Clearing should be fast
    ).then(
        fn=infer,
        inputs=infer_inputs,
        outputs=[result], # Output to the main result image
    ).then(
        fn=update_history, # Use the specific update function
        inputs=[result, history_gallery], # Pass the result and current history
        outputs=[history_gallery], # Update the history gallery
    )

    # --- Prompt Submit (Enter Key) ---
    prompt_input.submit(
         fn=clear_result,
        inputs=None,
        outputs=[result],
        queue=False
    ).then(
        fn=infer,
        inputs=infer_inputs,
        outputs=[result],
    ).then(
        fn=update_history,
        inputs=[result, history_gallery],
        outputs=[history_gallery],
    )

# --- Launch App ---
# Make sure you have example images at the specified paths or remove/update the gr.Examples section
# Create an 'examples' directory and place images like 'example_1.webp', 'example_2.jpg', 'example_3.jpg' inside it.
demo.queue(max_size=20).launch(share=False, ssr_mode=False, show_error=True)