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-Edit-2509-Multiple-angles", 
        weight_name="镜头转换.safetensors", adapter_name="angles"
    )

# pipe.load_lora_weights(
#         "lovis93/next-scene-qwen-image-lora-2509", 
#         weight_name="next-scene_lora-v2-3000.safetensors", adapter_name="next-scene"
#     )
pipe.set_adapters(["angles"], adapter_weights=[1.])
pipe.fuse_lora(adapter_names=["angles"], lora_scale=1.25)
# pipe.fuse_lora(adapter_names=["next-scene"], lora_scale=1.)
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_camera_prompt(rotate_deg, move_forward, vertical_tilt, wideangle):
    prompt_parts = []

    # Rotation
    if rotate_deg != 0:
        direction = "left" if rotate_deg > 0 else "right"
        if direction == "left":
            prompt_parts.append(f"将镜头向左旋转{abs(rotate_deg)}度 Rotate the camera {abs(rotate_deg)} degrees to the left.")
        else:
            prompt_parts.append(f"将镜头向右旋转{abs(rotate_deg)}度 Rotate the camera {abs(rotate_deg)} degrees to the right.")


    # Move forward / close-up
    if move_forward > 5:
        prompt_parts.append("将镜头转为特写镜头 Turn the camera to a close-up.")
    elif move_forward >= 1:
        prompt_parts.append("将镜头向前移动 Move the camera forward.")

    # Vertical tilt
    if vertical_tilt <= -1:
        prompt_parts.append("将相机转向鸟瞰视角 Turn the camera to a bird's-eye view.")
    elif vertical_tilt >= 1:
        prompt_parts.append("将相机切换到仰视视角 Turn the camera to a worm's-eye view.")

    # Lens option
    if wideangle:
        prompt_parts.append(" 将镜头转为广角镜头 Turn the camera to a wide-angle lens.")

    final_prompt = " ".join(prompt_parts).strip()
    return final_prompt if final_prompt else "no camera movement"


@spaces.GPU
def infer_camera_edit(
    image,
    rotate_deg,
    move_forward,
    vertical_tilt,
    wideangle,
    seed,
    randomize_seed,
    true_guidance_scale,
    num_inference_steps,
    height,
    width,
    prev_output = None,
    progress=gr.Progress(track_tqdm=True)
):
    prompt = build_camera_prompt(rotate_deg, move_forward, vertical_tilt, wideangle)
    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 == "no camera movement":
        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: 800px; margin: 0 auto; }
.dark .progress-text{color: white !important}
#examples{max-width: 800px; margin: 0 auto; }'''

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.Citrus(), css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("## 🎬 Qwen Image Edit — Camera Angle Control")
        gr.Markdown("""
            Qwen Image Edit 2509 for Camera Control ✨ 
            Using [dx8152's Qwen-Edit-2509-Multiple-angles LoRA](https://huggingface.co/dx8152/Qwen-Edit-2509-Multiple-angles) and [Phr00t/Qwen-Image-Edit-Rapid-AIO](https://huggingface.co/Phr00t/Qwen-Image-Edit-Rapid-AIO/tree/main) for 4-step inference 💨
            """
        )

        with gr.Row():
            with gr.Column():
                image = gr.Image(label="Input Image", type="pil")
                prev_output = gr.Image(value=None, visible=False)
                is_reset = gr.Checkbox(value=False, visible=False)

                with gr.Tab("Camera Controls"):
                    rotate_deg = gr.Slider(label="Rotate Right-Left (degrees °)", minimum=-90, maximum=90, step=45, value=0)
                    move_forward = gr.Slider(label="Move Forward → Close-Up", minimum=0, maximum=10, step=5, value=0)
                    vertical_tilt = gr.Slider(label="Vertical Angle (Bird ↔ Worm)", minimum=-1, maximum=1, step=1, value=0)
                    wideangle = gr.Checkbox(label="Wide-Angle Lens", value=False)
                with gr.Row():
                        reset_btn = gr.Button("Reset")
                        run_btn = gr.Button("Generate", variant="primary")

                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="True 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():
                result = gr.Image(label="Output Image", interactive=False)
                prompt_preview = gr.Textbox(label="Processed Prompt", interactive=False)
                create_video_button = gr.Button("🎥 Create Video Between Images", variant="secondary", visible=False)
                with gr.Group(visible=False) as video_group:
                    video_output = gr.Video(label="Generated Video", show_download_button=True, autoplay=True)
                    
    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()