app.py

import gradio as gr
import numpy as np
import random
import torch
import spaces

from PIL import Image
from diffusers import FlowMatchEulerDiscreteScheduler
from optimization import optimize_pipeline_
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3

import math
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

from PIL import Image
import os
import gradio as gr
from gradio_client import Client, handle_file
import tempfile


# --- Model Loading ---
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", 
                                                transformer= QwenImageTransformer2DModel.from_pretrained("linoyts/Qwen-Image-Edit-Rapid-AIO", 
                                                                                                         subfolder='transformer',
                                                                                                         torch_dtype=dtype,
                                                                                                         device_map='cuda'),torch_dtype=dtype).to(device)

pipe.load_lora_weights(
        "dx8152/Qwen-Image-Edit-2509-Light_restoration", 
        weight_name="light_restoration.safetensors", adapter_name="light_restoration"
    )

pipe.set_adapters(["light_restoration"], adapter_weights=[1.])
pipe.fuse_lora(adapter_names=["light_restoration"], lora_scale=1.0)
pipe.unload_lora_weights()



pipe.transformer.__class__ = QwenImageTransformer2DModel
pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())

optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")


MAX_SEED = np.iinfo(np.int32).max

def _generate_video_segment(input_image_path: str, output_image_path: str, prompt: str, request: gr.Request) -> str:
    """Generates a single video segment using the external service."""
    x_ip_token = request.headers['x-ip-token']
    video_client = Client("multimodalart/wan-2-2-first-last-frame", headers={"x-ip-token": x_ip_token})
    result = video_client.predict(
        start_image_pil=handle_file(input_image_path),
        end_image_pil=handle_file(output_image_path),
        prompt=prompt, api_name="/generate_video",
    )
    return result[0]["video"]

def build_light_restoration_prompt(brightness, contrast, exposure, saturation):
    prompt_parts = []

    # Brightness adjustment
    if brightness > 0:
        prompt_parts.append(f"Increase brightness by {brightness}%")
    elif brightness < 0:
        prompt_parts.append(f"Decrease brightness by {abs(brightness)}%")

    # Contrast adjustment
    if contrast > 0:
        prompt_parts.append(f"Increase contrast by {contrast}%")
    elif contrast < 0:
        prompt_parts.append(f"Decrease contrast by {abs(contrast)}%")

    # Exposure adjustment
    if exposure > 0:
        prompt_parts.append(f"Increase exposure by {exposure}%")
    elif exposure < 0:
        prompt_parts.append(f"Decrease exposure by {abs(exposure)}%")

    # Saturation adjustment
    if saturation > 0:
        prompt_parts.append(f"Increase saturation by {saturation}%")
    elif saturation < 0:
        prompt_parts.append(f"Decrease saturation by {abs(saturation)}%")

    final_prompt = ", ".join(prompt_parts).strip()
    return final_prompt if final_prompt else "Restore image lighting"


@spaces.GPU
def infer_light_restoration(
    image,
    brightness,
    contrast,
    exposure,
    saturation,
    seed,
    randomize_seed,
    true_guidance_scale,
    num_inference_steps,
    height,
    width,
    prev_output = None,
    progress=gr.Progress(track_tqdm=True)
):
    prompt = build_light_restoration_prompt(brightness, contrast, exposure, saturation)
    print(f"Generated Prompt: {prompt}")

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device=device).manual_seed(seed)

    # Choose input image (prefer uploaded, else last output)
    pil_images = []
    if image is not None:
        if isinstance(image, Image.Image):
            pil_images.append(image.convert("RGB"))
        elif hasattr(image, "name"):
            pil_images.append(Image.open(image.name).convert("RGB"))
    elif prev_output:
        pil_images.append(prev_output.convert("RGB"))

    if len(pil_images) == 0:
        raise gr.Error("Please upload an image first.")

    if prompt == "Restore image lighting":
        return image, seed, prompt
    result = pipe(
        image=pil_images,
        prompt=prompt,
        height=height if height != 0 else None,
        width=width if width != 0 else None,
        num_inference_steps=num_inference_steps,
        generator=generator,
        true_cfg_scale=true_guidance_scale,
        num_images_per_prompt=1,
    ).images[0]

    return result, seed, prompt

def create_video_between_images(input_image, output_image, prompt: str, request: gr.Request) -> str:
    """Create a video between the input and output images."""
    if input_image is None or output_image is None:
        raise gr.Error("Both input and output images are required to create a video.")
    
    try:
        
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
            input_image.save(tmp.name)
            input_image_path = tmp.name
        
        output_pil = Image.fromarray(output_image.astype('uint8'))
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
            output_pil.save(tmp.name)
            output_image_path = tmp.name
            
        video_path = _generate_video_segment(
            input_image_path, 
            output_image_path, 
            prompt if prompt else "Camera movement transformation",
            request
        )
        return video_path
    except Exception as e:
        raise gr.Error(f"Video generation failed: {e}")


# --- UI ---
css = '''
#col-container { 
    max-width: 900px; 
    margin: 0 auto; 
    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
}
.dark .progress-text{color: white !important}
#examples{max-width: 900px; margin: 0 auto; }
.gradio-container {
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
}
.gr-button-primary {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
    border: none !important;
    border-radius: 12px !important;
    padding: 12px 24px !important;
    font-weight: 600 !important;
}
.gr-button {
    border-radius: 12px !important;
    padding: 10px 20px !important;
}
.gr-box {
    border-radius: 16px !important;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1) !important;
}
'''

def reset_all():
    return [0, 0, 0, 0, False, True]

def end_reset():
    return False

def update_dimensions_on_upload(image):
    if image is None:
        return 1024, 1024
    
    original_width, original_height = image.size
    
    if original_width > original_height:
        new_width = 1024
        aspect_ratio = original_height / original_width
        new_height = int(new_width * aspect_ratio)
    else:
        new_height = 1024
        aspect_ratio = original_width / original_height
        new_width = int(new_height * aspect_ratio)
        
    # Ensure dimensions are multiples of 8
    new_width = (new_width // 8) * 8
    new_height = (new_height // 8) * 8
    
    return new_width, new_height


with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# ✨ Light Restoration Studio")
        gr.Markdown("""
            Professional image light restoration powered by Qwen Image Edit 2509 
            Using [dx8152's Light Restoration LoRA](https://huggingface.co/dx8152/Qwen-Image-Edit-2509-Light_restoration) 
            and [Phr00t/Qwen-Image-Edit-Rapid-AIO](https://huggingface.co/Phr00t/Qwen-Image-Edit-Rapid-AIO) for fast inference 💨
            """
        )

        with gr.Row():
            with gr.Column(scale=1):
                image = gr.Image(label="📸 Upload Image", type="pil", height=400)
                prev_output = gr.Image(value=None, visible=False)
                is_reset = gr.Checkbox(value=False, visible=False)

                gr.Markdown("### 🎨 Light Adjustments")
                brightness = gr.Slider(label="☀️ Brightness", minimum=-50, maximum=50, step=5, value=0)
                contrast = gr.Slider(label="🌓 Contrast", minimum=-50, maximum=50, step=5, value=0)
                exposure = gr.Slider(label="💡 Exposure", minimum=-50, maximum=50, step=5, value=0)
                saturation = gr.Slider(label="🎨 Saturation", minimum=-50, maximum=50, step=5, value=0)
                
                with gr.Row():
                    reset_btn = gr.Button("🔄 Reset", size="lg")
                    run_btn = gr.Button("✨ Restore", variant="primary", size="lg")

                with gr.Accordion("⚙️ Advanced Settings", open=False):
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                    randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                    true_guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
                    num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=40, step=1, value=4)
                    height = gr.Slider(label="Height", minimum=256, maximum=2048, step=8, value=1024)
                    width = gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024)

            with gr.Column(scale=1):
                result = gr.Image(label="✨ Restored Image", interactive=False, height=400)
                prompt
                    
    inputs = [
        image,rotate_deg, move_forward,
        vertical_tilt, wideangle,
        seed, randomize_seed, true_guidance_scale, num_inference_steps, height, width, prev_output
    ]
    outputs = [result, seed, prompt_preview]

    # Reset behavior
    reset_btn.click(
        fn=reset_all,
        inputs=None,
        outputs=[rotate_deg, move_forward, vertical_tilt, wideangle, is_reset],
        queue=False
    ).then(fn=end_reset, inputs=None, outputs=[is_reset], queue=False)

    # Manual generation with video button visibility control
    def infer_and_show_video_button(*args):
        result_img, result_seed, result_prompt = infer_camera_edit(*args)
        # Show video button if we have both input and output images
        show_button = args[0] is not None and result_img is not None
        return result_img, result_seed, result_prompt, gr.update(visible=show_button)
    
    run_event = run_btn.click(
        fn=infer_and_show_video_button, 
        inputs=inputs, 
        outputs=outputs + [create_video_button]
    )

    # Video creation
    create_video_button.click(
        fn=lambda: gr.update(visible=True), 
        outputs=[video_group],
        api_name=False
    ).then(
        fn=create_video_between_images,
        inputs=[image, result, prompt_preview],
        outputs=[video_output],
        api_name=False
    )

    # Examples
    gr.Examples(
        examples=[
            ["tool_of_the_sea.png", 90, 0, 0, False, 0, True, 1.0, 4, 568, 1024],
            ["monkey.jpg", -90, 0, 0, False, 0, True, 1.0, 4, 704, 1024],
            ["metropolis.jpg", 0, 0, -1, False, 0, True, 1.0, 4, 816, 1024],
            ["disaster_girl.jpg", -45, 0, 1, False, 0, True, 1.0, 4, 768, 1024],
            ["grumpy.png", 90, 0, 1, False, 0, True, 1.0, 4, 576, 1024]
        ],
        inputs=[image,rotate_deg, move_forward,
        vertical_tilt, wideangle,
        seed, randomize_seed, true_guidance_scale, num_inference_steps, height, width],
        outputs=outputs,
        fn=infer_camera_edit,
        cache_examples="lazy",
        elem_id="examples"
    )
    
    # Image upload triggers dimension update and control reset
    image.upload(
        fn=update_dimensions_on_upload,
        inputs=[image],
        outputs=[width, height]
    ).then(
        fn=reset_all,
        inputs=None,
        outputs=[rotate_deg, move_forward, vertical_tilt, wideangle, is_reset],
        queue=False
    ).then(
        fn=end_reset, 
        inputs=None, 
        outputs=[is_reset], 
        queue=False
    )


    # Live updates
    def maybe_infer(is_reset, progress=gr.Progress(track_tqdm=True), *args):
        if is_reset:
            return gr.update(), gr.update(), gr.update(), gr.update()
        else:
            result_img, result_seed, result_prompt = infer_camera_edit(*args)
            # Show video button if we have both input and output
            show_button = args[0] is not None and result_img is not None
            return result_img, result_seed, result_prompt, gr.update(visible=show_button)

    control_inputs = [
        image, rotate_deg, move_forward,
        vertical_tilt, wideangle,
        seed, randomize_seed, true_guidance_scale, num_inference_steps, height, width, prev_output
    ]
    control_inputs_with_flag = [is_reset] + control_inputs

    for control in [rotate_deg, move_forward, vertical_tilt]:
        control.release(fn=maybe_infer, inputs=control_inputs_with_flag, outputs=outputs + [create_video_button])
    
    wideangle.input(fn=maybe_infer, inputs=control_inputs_with_flag, outputs=outputs + [create_video_button])
    
    run_event.then(lambda img, *_: img, inputs=[result], outputs=[prev_output])

demo.launch()