app.py

import gradio as gr
import torch
from diffusers import AutoencoderKL, FluxTransformer2DModel
from diffusers.utils import load_image
from controlnet_flux import FluxControlNetModel
from transformer_flux import FluxTransformer2DModel
from pipeline_flux_controlnet_inpaint import FluxControlNetInpaintingPipeline
from transformers import T5EncoderModel, CLIPTextModel
from PIL import Image, ImageDraw
import numpy as np
import spaces
from huggingface_hub import hf_hub_download

# Load fp8
#transformer = FluxTransformer2DModel.from_single_file("https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8.safetensors", torch_dtype=torch.bfloat16)
#quantize(transformer, weights=qfloat8)
#freeze(transformer)

# Load models
#controlnet = FluxControlNetModel.from_pretrained("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", torch_dtype=torch.bfloat16)
#quantize(controlnet, weights=qfloat8)
#freeze(controlnet)

text_encoder_2 = T5EncoderModel.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="text_encoder_2", torch_dtype=torch.bfloat16)
quantize(text_encoder_2, weights=qfloat8)
freeze(text_encoder_2)

pipe = FluxControlNetInpaintingPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    text_encoder_2=None,
    torch_dtype=torch.bfloat16
)
pipe.text_encoder_2 = text_encoder_2

repo_name = "ByteDance/Hyper-SD"
ckpt_name = "Hyper-FLUX.1-dev-8steps-lora.safetensors"
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.fuse_lora(lora_scale=0.125)
pipe.to("cuda")
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)

    # 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

@spaces.GPU
def inpaint(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)):
    
    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"

    generator = torch.Generator(device="cuda").manual_seed(42)

    result = pipe(
        prompt=final_prompt,
        height=height,
        width=width,
        control_image=cnet_image,
        control_mask=mask,
        num_inference_steps=num_inference_steps,
        generator=generator,
        controlnet_conditioning_scale=0.9,
        guidance_scale=3.5,
        negative_prompt="",
        true_guidance_scale=3.5,
    ).images[0]
   
    result = result.convert("RGBA")
    cnet_image.paste(result, (0, 0), mask)

    return cnet_image, background

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)
    preview = Image.alpha_composite(preview, red_mask)
    
    return preview

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">FLUX Image Outpaint</h1>
<div align="center">Drop an image you would like to extend, pick your expected ratio and hit Generate.</div>
<div align="center">Using <a href="https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha" target="_blank"><code>FLUX.1-dev-Controlnet-Inpainting-Beta</code></a></div>
"""

with gr.Blocks(css=css) as demo:
    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")
                            
            with gr.Column():
                result = gr.Image(
                    interactive=False,
                    label="Generated Image",
                )
                use_as_input_button = gr.Button("Use as Input Image", visible=False)
                with gr.Accordion("History and Mask", open=False):
                    history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
                    preview_image = gr.Image(label="Mask preview")

    def use_output_as_input(output_image):
        return output_image

    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=inpaint,
        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, preview_image],
    ).then(
        fn=lambda x, history: update_history(x[1], 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=inpaint,
        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, preview_image],
    ).then(
        fn=lambda x, history: update_history(x, 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
    )

demo.queue(max_size=12).launch(share=False)