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 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

# ===== Load models (original from your Space) =====
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)
loaded_keys = list(state_dict.keys())
result = ControlNetModel_Union._load_pretrained_model(
    controlnet_model, state_dict, model_file, "xinsir/controlnet-union-sdxl-1.0", loaded_keys
)
model = result[0].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)

# ===== Helpers (original) =====
def can_expand(source_width, source_height, target_width, target_height, 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)
    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)
    source = image.resize((new_width, new_height), Image.LANCZOS)

    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:
        resize_percentage = custom_resize_percentage

    resize_factor = resize_percentage / 100
    new_width = max(int(source.width * resize_factor), 64)
    new_height = max(int(source.height * resize_factor), 64)
    source = source.resize((new_width, new_height), Image.LANCZOS)

    overlap_x = max(int(new_width * (overlap_percentage / 100)), 1)
    overlap_y = max(int(new_height * (overlap_percentage / 100)), 1)

    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

    margin_x = max(0, min(margin_x, target_size[0] - new_width))
    margin_y = max(0, min(margin_y, target_size[1] - new_height))

    background = Image.new('RGB', target_size, (255, 255, 255))
    background.paste(source, (margin_x, margin_y))

    mask = Image.new('L', target_size, 255)
    mask_draw = ImageDraw.Draw(mask)

    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

    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)
    preview = background.copy().convert('RGBA')
    red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 64))
    red_mask = Image.new('RGBA', background.size, (0, 0, 0, 0))
    red_mask.paste(red_overlay, (0, 0), mask)
    return Image.alpha_composite(preview, red_mask)

# ===== Streaming infer (UI) =====
@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):
    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):
        alignment = "Middle"

    cnet_image = background.copy()
    cnet_image.paste(0, (0, 0), mask)

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

    with torch.autocast(device_type="cuda", dtype=torch.float16):
        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = pipe.encode_prompt(final_prompt, "cuda", True)

        for image 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=cnet_image,
            num_inference_steps=num_inference_steps
        ):
            yield cnet_image, image

    image = image.convert("RGBA")
    cnet_image.paste(image, (0, 0), mask)
    yield background, cnet_image

# ===== Non-streaming wrapper (returns final pair) =====
def infer_rest(image, width, height, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage,
               prompt_input, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    gen = 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)
    last = None
    for last in gen:
        pass
    return last  # (background, generated)

def clear_result():
    return gr.update(value=None)

def preload_presets(target_ratio, ui_width, ui_height):
    if target_ratio == "9:16":
        return 720, 1280, gr.update()
    elif target_ratio == "16:9":
        return 1280, 720, gr.update()
    elif target_ratio == "1:1":
        return 1024, 1024, gr.update()
    elif target_ratio == "Custom":
        return ui_width, ui_height, gr.update(open=True)

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):
    if history is None:
        history = []
    history.insert(0, new_image)
    return history

css = """
.gradio-container { width: 1200px !important; }
"""
title = """<h1 align="center">Re-Size Image Outpaint</h1>"""

# ---- Full UI (unchanged) ----
with gr.Blocks(theme="soft", css=css) as ui_app:
    with gr.Column():
        gr.HTML(title)
        with gr.Row():
            with gr.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)")
                    with gr.Column(scale=1):
                        run_button = gr.Button("Generate")
                with gr.Row():
                    target_ratio = gr.Radio(label="Expected 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="Alignment")
                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=720, maximum=1536, step=8, value=720)
                            height_slider = gr.Slider(label="Target Height", minimum=720, maximum=1536, step=8, value=1280)
                        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 (%)", minimum=1, maximum=50, value=10, step=1)
                            with gr.Row():
                                overlap_top = gr.Checkbox(label="Overlap Top", value=True)
                                overlap_right = gr.Checkbox(label="Overlap Right", value=True)
                            with gr.Row():
                                overlap_left = gr.Checkbox(label="Overlap Left", value=True)
                                overlap_bottom = gr.Checkbox(label="Overlap Bottom", value=True)
                        with gr.Row():
                            resize_option = gr.Radio(label="Resize input image", choices=["Full", "50%", "33%", "25%", "Custom"], value="Full")
                            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 and mask")

                gr.Examples(
                    examples=[
                        ["./examples/example_2.jpg", 1440, 810, "Left"],
                        ["./examples/example_3.jpg", 1024, 1024, "Top"],
                        ["./examples/example_3.jpg", 1024, 1024, "Bottom"],
                    ],
                    inputs=[input_image, width_slider, height_slider, alignment_dropdown],
                )

            with gr.Column():
                result = ImageSlider(interactive=False, label="Generated Image")
                use_as_input_button = gr.Button("Use as Input Image", visible=False)
                history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
                preview_image = gr.Image(label="Preview")

    def use_output_as_input(output_image):
        return gr.update(value=output_image[1])

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

    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)

    run_button.click(fn=clear_result, inputs=None, outputs=result) \
        .then(fn=infer,
              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],
              outputs=result) \
        .then(fn=lambda x, history: update_history(x[1], history) if x else history, inputs=[result, history_gallery], outputs=history_gallery) \
        .then(fn=lambda: gr.update(visible=True), inputs=None, outputs=use_as_input_button)

    prompt_input.submit(fn=clear_result, inputs=None, outputs=result) \
        .then(fn=infer,
              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],
              outputs=result) \
        .then(fn=lambda x, history: update_history(x[1], history) if x else history, inputs=[result, history_gallery], outputs=history_gallery) \
        .then(fn=lambda: gr.update(visible=True), inputs=None, outputs=use_as_input_button)

    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_image, queue=False)

# ---- Minimal Interface tab that DEFINITELY exposes /api/predict/infer ----
api_app = gr.Interface(
    fn=infer_rest,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Number(value=1024, label="Target Width", precision=0),
        gr.Number(value=1024, label="Target Height", precision=0),
        gr.Number(value=10, label="Mask overlap (%)"),
        gr.Number(value=8, label="Steps", precision=0),
        gr.Radio(choices=["Full", "50%", "33%", "25%", "Custom"], value="Full", label="Resize input image"),
        gr.Number(value=50, label="Custom resize (%)", precision=0),
        gr.Textbox(label="Prompt (Optional)"),
        gr.Dropdown(choices=["Middle", "Left", "Right", "Top", "Bottom"], value="Middle", label="Alignment"),
        gr.Checkbox(value=True, label="Overlap Left"),
        gr.Checkbox(value=True, label="Overlap Right"),
        gr.Checkbox(value=True, label="Overlap Top"),
        gr.Checkbox(value=True, label="Overlap Bottom"),
    ],
    outputs=[gr.Image(label="Background"), gr.Image(label="Generated")],
    allow_flagging="never",
    api_name="infer",  # <--- THIS creates /api/predict/infer
    title="Re-Size Image Outpaint API",
    description="Non-streaming endpoint for programmatic access.",
)

# Publish BOTH tabs — put API FIRST to be extra safe on older Gradio builds
demo = gr.TabbedInterface([api_app, ui_app], tab_names=["API", "App"])

# Open REST API
demo.queue(max_size=12, api_open=True).launch(share=False)