app.py

# -*- coding: utf-8 -*-
import gradio as gr
import spaces
import torch
from diffusers import AutoencoderKL, TCDScheduler
from diffusers.models.model_loading_utils import load_state_dict
# Remove ImageSlider import as it's no longer needed
# from gradio_imageslider import ImageSlider
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

# --- Model Loading (Keep as is) ---
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",
)
state_dict = load_state_dict(model_file)
model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model(
    controlnet_model, state_dict, model_file, "xinsir/controlnet-union-sdxl-1.0"
)
model.to(device="cuda", dtype=torch.float16)

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

pipe = StableDiffusionXLFillPipeline.from_pretrained(
    "SG161222/RealVisXL_V5.0_Lightning",
    torch_dtype=torch.float16,
    vae=vae,
    controlnet=model,
    variant="fp16",
).to("cuda")

pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)

# --- Helper Functions (Keep as is, except infer) ---

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):
    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

    # 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)
    mask_draw = ImageDraw.Draw(mask)

    # Calculate overlap areas
    white_gaps_patch = 2

    left_overlap = margin_x + overlap_x if overlap_left else margin_x + white_gaps_patch
    right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width - white_gaps_patch
    top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch
    bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height - white_gaps_patch

    if alignment == "Left":
        left_overlap = margin_x + overlap_x if overlap_left else margin_x
    elif alignment == "Right":
        right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width
    elif alignment == "Top":
        top_overlap = margin_y + overlap_y if overlap_top else margin_y
    elif alignment == "Bottom":
        bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height


    # Draw the mask
    mask_draw.rectangle([
        (left_overlap, top_overlap),
        (right_overlap, bottom_overlap)
    ], fill=0)

    return background, mask

def preview_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    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
    red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 64))  # Reduced alpha to 64 (25% opacity)

    # Convert black pixels in the mask to semi-transparent red
    red_mask = Image.new('RGBA', background.size, (0, 0, 0, 0))
    red_mask.paste(red_overlay, (0, 0), mask)

    # Overlay the red mask on the background
    preview = Image.alpha_composite(preview, red_mask)

    return preview

@spaces.GPU(duration=24)
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):
    if image is None:
        raise gr.Error("Please upload an input image.")

    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)

    if not can_expand(background.width, background.height, width, height, alignment):
        # Optionally provide feedback or default to middle
        # gr.Warning(f"Cannot expand image with '{alignment}' alignment as source dimension is larger than target. Defaulting to 'Middle'.")
        alignment = "Middle"
        # Recalculate background and mask if alignment changed due to this check
        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)


    cnet_image = background.copy()
    # Apply mask to create the input for controlnet (black out non-masked area)
    # cnet_image.paste(0, (0, 0), mask) # This line seems incorrect for inpainting/outpainting, usually the unmasked area is kept
    # The pipeline expects the original image content where mask=0 and potentially noise/latents where mask=1
    # Let's keep the original image content in the unmasked area and let the pipeline handle the masked area.
    # The `StableDiffusionXLFillPipeline` likely uses the `image` input and `mask` differently than standard inpainting.
    # Based on typical diffusers pipelines, `image` is often the *original* content placed on the canvas.
    # Let's pass `background` as the image input for the pipeline.

    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, negative_prompt="") # Add default negative prompt

    # The pipeline expects the `image` and `mask_image` arguments for inpainting/outpainting
    # `image` should be the canvas with the original image placed.
    # `mask_image` defines the area to be filled (white=fill, black=keep).
    # Our mask is inverted (black=keep, white=fill). Invert it.
    inverted_mask = Image.fromarray(255 - np.array(mask))

    # Run the pipeline
    # Note: The generator inside the pipeline call is not used here as we only need the final result.
    # We iterate once to get the final image.
    generated_image = None
    for img_output in 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, # Pass the background with the source image placed
        mask_image=inverted_mask, # Pass the inverted mask (white = area to fill)
        control_image=background, # ControlNet Union might need the full image context
        num_inference_steps=num_inference_steps,
        output_type="pil" # Ensure PIL images are returned
    ):
        generated_image = img_output[0] # The pipeline returns a list containing the image

    if generated_image is None:
         raise gr.Error("Image generation failed.")

    # The pipeline should return the complete image already composited.
    # No need to manually paste.
    final_image = generated_image.convert("RGB")

    # Yield only the final generated image
    yield final_image


def clear_result():
    """Clears the result Image component."""
    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
    elif target_ratio == "16:9":
        changed_width = 1280
        changed_height = 720
        return changed_width, changed_height, gr.update(open=False) # Close accordion
    elif target_ratio == "1:1":
        changed_width = 1024
        changed_height = 1024
        return changed_width, changed_height, gr.update(open=False) # Close accordion
    elif target_ratio == "Custom":
        # Keep current slider values but open the accordion
        return ui_width, ui_height, gr.update(open=True)

def select_the_right_preset(user_width, user_height):
    """Selects the preset radio button based on current width/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):
    """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 new_image is None: # Don't add None to history
        return history
    if history is None:
        history = []
    # Prepend the new image (as PIL) to the history list
    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

# --- Gradio UI ---

css = """
.gradio-container {
    max-width: 1200px !important; /* Limit overall width */
    margin: auto; /* Center the container */
}
/* Ensure gallery items are reasonably sized */
#history_gallery .thumbnail-item {
    height: 100px !important; /* Adjust as needed */
}
#history_gallery .gallery {
    grid-template-columns: repeat(auto-fill, minmax(100px, 1fr)) !important; /* Adjust column size */
}

"""

title = """<h1 align="center">Diffusers Image Outpaint</h1>
<div align="center">Drop an image you would like to extend, pick your expected ratio and hit Generate.</div>
<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
    <p style="display: flex;gap: 6px;">
         <a href="https://huggingface.co/spaces/fffiloni/diffusers-image-outpaint?duplicate=true">
            <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg" alt="Duplicate this Space">
        </a> to skip the queue and enjoy faster inference on the GPU of your choice
    </p>
</div>
"""

with gr.Blocks(css=css) as demo:
    with gr.Column():
        gr.HTML(title)

        with gr.Row():
            with gr.Column(scale=1): # Input column
                input_image = gr.Image(
                    type="pil",
                    label="Input Image"
                )

                with gr.Row():
                    with gr.Column(scale=2):
                        prompt_input = gr.Textbox(label="Prompt (Optional)", placeholder="Describe the desired extended scene...")
                    with gr.Column(scale=1, min_width=150):
                        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",
                        scale=2
                    )

                    alignment_dropdown = gr.Dropdown(
                        choices=["Middle", "Left", "Right", "Top", "Bottom"],
                        value="Middle",
                        label="Align Source 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 (px)",
                                minimum=512, # Lowered min slightly
                                maximum=2048, # Increased max slightly
                                step=64, # SDXL optimal step
                                value=720,
                            )
                            height_slider = gr.Slider(
                                label="Target Height (px)",
                                minimum=512, # Lowered min slightly
                                maximum=2048, # Increased max slightly
                                step=64, # SDXL optimal step
                                value=1280,
                            )

                        num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=20, step=1, value=8) # Increased max steps slightly
                        with gr.Group():
                            overlap_percentage = gr.Slider(
                                label="Mask overlap (%)",
                                minimum=1,
                                maximum=50,
                                value=10,
                                step=1,
                                info="How much the new area overlaps the original image."
                            )
                            gr.Markdown("Select sides to overlap (influences mask generation):")
                            with gr.Row():
                                overlap_top = gr.Checkbox(label="Top", value=True)
                                overlap_right = gr.Checkbox(label="Right", value=True)
                            with gr.Row():
                                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",
                                choices=["Full", "50%", "33%", "25%", "Custom"],
                                value="Full",
                                info="Scales the source image down before placing it on the target canvas."
                            )
                            custom_resize_percentage = gr.Slider(
                                label="Custom resize (%)",
                                minimum=1,
                                maximum=100,
                                step=1,
                                value=50,
                                visible=False
                            )

                        with gr.Column():
                            preview_button = gr.Button("Preview Alignment & Mask")


                gr.Examples(
                    examples=[
                        ["./examples/example_1.webp", 1280, 720, "Middle", "A wide landscape view of the mountains"],
                        ["./examples/example_2.jpg", 1440, 810, "Left", "Full body shot of the cat sitting on a rug"],
                        ["./examples/example_3.jpg", 1024, 1024, "Top", "The cloudy sky above the building"],
                        ["./examples/example_3.jpg", 1024, 1024, "Bottom", "The street below the building"],
                    ],
                    inputs=[input_image, width_slider, height_slider, alignment_dropdown, prompt_input],
                    label="Examples (Click to load)"
                )

            with gr.Column(scale=1): # Output column
                # Replace ImageSlider with gr.Image
                result_image = gr.Image(
                    label="Generated Image",
                    interactive=False,
                    show_download_button=True,
                    type="pil" # Ensure output is PIL for history
                )
                with gr.Row():
                    use_as_input_button = gr.Button("Use as Input", visible=False)
                    clear_button = gr.Button("Clear Output") # Added clear button

                preview_mask_image = gr.Image(label="Alignment & Mask Preview", interactive=False)

                history_gallery = gr.Gallery(
                    label="History",
                    columns=4, # Adjust columns as needed
                    object_fit="contain",
                    interactive=False,
                    show_label=True,
                    elem_id="history_gallery",
                    height=300 # Set a fixed height for the gallery area
                    )


    # --- Event Handlers ---

    def use_output_as_input(output_pil_image):
        """Sets the generated output PIL image as the new input image."""
        # output_image comes directly from result_image which is PIL type
        return gr.update(value=output_pil_image)

    use_as_input_button.click(
        fn=use_output_as_input,
        inputs=[result_image], # Input is the single result image
        outputs=[input_image]
    )

    clear_button.click(
        fn=lambda: (gr.update(value=None), gr.update(visible=False), gr.update(value=None)), # Clear image, hide button, clear preview
        inputs=None,
        outputs=[result_image, use_as_input_button, preview_mask_image],
        queue=False
    )

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

    # Link sliders back to ratio selector and potentially open accordion
    width_slider.change(
        fn=lambda w, h: (select_the_right_preset(w, h), gr.update() if select_the_right_preset(w, h) == "Custom" else gr.update()),
        inputs=[width_slider, height_slider],
        outputs=[target_ratio, settings_panel],
        queue=False
    )

    height_slider.change(
         fn=lambda w, h: (select_the_right_preset(w, h), gr.update() if select_the_right_preset(w, h) == "Custom" else gr.update()),
        inputs=[width_slider, height_slider],
        outputs=[target_ratio, settings_panel],
        queue=False
    )

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

    # Define 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
    ]

    # Define common steps after generation
    def handle_output(generated_image, current_history):
        # generated_image is the single PIL image from infer
        new_history = update_history(generated_image, current_history)
        button_visibility = gr.update(visible=True) if generated_image else gr.update(visible=False)
        return generated_image, new_history, button_visibility

    run_button.click(
        fn=lambda: (gr.update(value=None), gr.update(visible=False)), # Clear result and hide button first
        inputs=None,
        outputs=[result_image, use_as_input_button],
        queue=False # Don't queue the clearing part
    ).then(
        fn=infer, # Run the generation
        inputs=gen_inputs,
        outputs=result_image, # Output is the single generated image
    ).then(
        fn=handle_output, # Process output: update history, show button
        inputs=[result_image, history_gallery],
        outputs=[result_image, history_gallery, use_as_input_button] # Update result again (no change), history, and button visibility
    )

    prompt_input.submit(
         fn=lambda: (gr.update(value=None), gr.update(visible=False)), # Clear result and hide button first
        inputs=None,
        outputs=[result_image, use_as_input_button],
        queue=False # Don't queue the clearing part
    ).then(
        fn=infer, # Run the generation
        inputs=gen_inputs,
        outputs=result_image, # Output is the single generated image
    ).then(
        fn=handle_output, # Process output: update history, show button
        inputs=[result_image, history_gallery],
        outputs=[result_image, history_gallery, use_as_input_button] # Update result again (no change), history, and button visibility
    )


    preview_button.click(
        fn=preview_image_and_mask,
        inputs=[input_image, width_slider, height_slider, overlap_percentage, resize_option, custom_resize_percentage, alignment_dropdown,
                overlap_left, overlap_right, overlap_top, overlap_bottom],
        outputs=preview_mask_image, # Output to the preview image component
        queue=False # Preview should be fast
    )

# Launch the app
demo.queue(max_size=12).launch(share=False, ssr_mode=False, show_error=True)