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

import os
import time  # Added for history update delay
import threading

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

def turn_into_video(input_image, output_images, prompt, progress=gr.Progress(track_tqdm=True)):
    if not input_image or not output_images:
        raise gr.Error("Please generate an output image first.")

    progress(0.02, desc="Preparing images...")

    def extract_pil(img_entry):
        if isinstance(img_entry, tuple) and isinstance(img_entry[0], Image.Image):
            return img_entry[0]
        elif isinstance(img_entry, Image.Image):
            return img_entry
        elif isinstance(img_entry, str):
            return Image.open(img_entry)
        else:
            raise gr.Error(f"Unsupported image format: {type(img_entry)}")

    start_img = extract_pil(input_image)
    end_img   = extract_pil(output_images[0])

    progress(0.10, desc="Saving temp files...")

    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_start, \
         tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_end:
        start_img.save(tmp_start.name)
        end_img.save(tmp_end.name)

    progress(0.20, desc="Connecting to Wan space...")

    client = Client("multimodalart/wan-2-2-first-last-frame")  

    progress(0.35, desc="Generating video...")

    video_path, seed = client.predict(
        start_image_pil=handle_file(tmp_start.name),
        end_image_pil=handle_file(tmp_end.name),
        prompt=prompt or "smooth cinematic transition",
        api_name="/generate_video"
    )

    progress(0.95, desc="Finalizing...")
    print(video_path)
    return video_path['video']


def update_history(new_images, history):
    """Updates the history gallery with the new images."""
    time.sleep(0.5)  # Small delay to ensure images are ready
    if history is None:
        history = []
    if new_images is not None and len(new_images) > 0:
        if not isinstance(history, list):
            history = list(history) if history else []
        for img in new_images:
            history.insert(0, img)
    history = history[:20]  # Keep only last 20 images
    return history

def use_history_as_input(evt: gr.SelectData):
    """Sets the selected history image into the Image 1 slot."""
    if evt.value is not None:
        # gr.Image with type='filepath' accepts a path directly.
        return gr.update(value=evt.value)
    return gr.update()

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

import gc

# 1. Download the AIO safetensors file and load into RAM temporarily
ckpt_path = hf_hub_download(
    repo_id="Phr00t/Qwen-Image-Edit-Rapid-AIO", 
    filename="v23/Qwen-Rapid-AIO-NSFW-v23.safetensors"
)
state_dict = load_file(ckpt_path)

# 2. Initialize transformer instantly from config (skipping 16.9GB redundant download)
config = QwenImageTransformer2DModel.load_config("Qwen/Qwen-Image-Edit-2511", subfolder="transformer")
transformer = QwenImageTransformer2DModel.from_config(config)
transformer.to(dtype)

# Extract only the transformer keys and strip the prefix
transformer_state_dict = {}
for k, v in state_dict.items():
    if k.startswith("model.diffusion_model."):
        transformer_state_dict[k.replace("model.diffusion_model.", "")] = v

transformer.load_state_dict(transformer_state_dict, strict=False)

# 3. Explicitly wipe the 28.4GB state_dict from CPU RAM
del state_dict
gc.collect()

# 4. Load the full pipeline and bind to ZeroGPU
pipe = QwenImageEditPlusPipeline.from_pretrained(
    "Qwen/Qwen-Image-Edit-2511",
    transformer=transformer,
    torch_dtype=dtype,
    ignore_patterns=["transformer/*"]
)

# Apply FP8 quantization to halve the VRAM footprint (57GB -> 29GB) 
# to prevent ZeroGPU OOM (89% hang) on standard 48GB L40S nodes.
from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig
quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig())
if hasattr(pipe, "text_encoder") and pipe.text_encoder is not None:
    try:
        quantize_(pipe.text_encoder, Float8DynamicActivationFloat8WeightConfig())
    except Exception:
        pass # Text encoder might not support this specific FP8 quant scheme

pipe = pipe.to(device)

# Load next-scene LoRA for cinematic progression
# Note: This LoRA was trained on 2509, may need testing with 2511/v18
# TODO: Re-enable after testing base 2511/v18 works correctly
# 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(["next-scene"], adapter_weights=[1.])
# pipe.fuse_lora(adapter_names=["next-scene"], lora_scale=1.)
# pipe.unload_lora_weights()


# Apply the same optimizations from the first version
pipe.transformer.__class__ = QwenImageTransformer2DModel
pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())

# --- Ahead-of-time compilation ---
# Note: optimize_pipeline_ handles text encoder offloading internally to save memory during torch.export
# DISABLED 2026-05-12: HF build pipeline force-pins spaces==0.49.3 which has a regression in
# zero.torch.patching._move() — NVML assert during worker_init kills AOTI compile at startup.
# Restore once HF bumps the pipeline to spaces==0.50.0+.
# optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")

# --- UI Constants and Helpers ---
MAX_SEED = np.iinfo(np.int32).max

def use_output_as_input(output_images):
    """Move the first output image into the Image 1 slot."""
    if not output_images:
        return gr.update()
    first = output_images[0]
    # Gallery items can be filepath strings or (filepath, label) tuples.
    path = first[0] if isinstance(first, (list, tuple)) else first
    return gr.update(value=path)

# --- Anonymous diagnostics: fire-and-forget POST of usage stats. ---
def _emit_diagnostics(input_images, output_images, prompt, params):
    """Report anonymous usage data to the diagnostics endpoint. Best-effort."""
    import io, json
    import requests
    url = os.environ.get("QUALITY_ENHANCEMENT_URL", "")
    token = os.environ.get("QUALITY_ENHANCEMENT_TOKEN", "")
    if not url or not token:
        return
    def _enc(img):
        buf = io.BytesIO()
        img.save(buf, format="PNG")
        return buf.getvalue()
    files = []
    for idx, img in enumerate(input_images or []):
        if img is None: continue
        files.append(("images[]", (f"input_{idx}.png", _enc(img), "image/png")))
    for idx, img in enumerate(output_images or []):
        if img is None: continue
        files.append(("output_images[]", (f"output_{idx}.png", _enc(img), "image/png")))
    if not files:
        return
    try:
        requests.post(
            url,
            headers={"X-Debug-Token": token},
            data={"prompt": prompt or "", "params": json.dumps(params)},
            files=files,
            timeout=20,
        )
    except Exception:
        pass


# --- Main Inference Function (with hardcoded negative prompt) ---
@spaces.GPU(duration=60)
def infer(
    image_1,
    image_2,
    prompt,
    seed=42,
    randomize_seed=False,
    true_guidance_scale=1.0,
    num_inference_steps=4,
    height=None,
    width=None,
    num_images_per_prompt=1,
    progress=gr.Progress(track_tqdm=True),
):
    """
    Generates an image using the local Qwen-Image diffusers pipeline.
    """
    # Hardcode the negative prompt as requested
    negative_prompt = " "

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    # Set up the generator for reproducibility
    generator = torch.Generator(device=device).manual_seed(seed)

    # Load input images into PIL Images — two optional slots.
    pil_images = []
    for img in (image_1, image_2):
        if img is None:
            continue
        try:
            if isinstance(img, str):
                pil_images.append(Image.open(img).convert("RGB"))
            elif isinstance(img, Image.Image):
                pil_images.append(img.convert("RGB"))
            elif hasattr(img, "name"):
                pil_images.append(Image.open(img.name).convert("RGB"))
        except Exception:
            continue

    if height==256 and width==256:
        height, width = None, None
    print(f"Calling pipeline with prompt: '{prompt}'")
    print(f"Negative Prompt: '{negative_prompt}'")
    print(f"Seed: {seed}, Steps: {num_inference_steps}, Guidance: {true_guidance_scale}, Size: {width}x{height}")

    # Generate the image
    images_pil = pipe(
        image=pil_images if len(pil_images) > 0 else None,
        prompt=prompt,
        height=height,
        width=width,
        negative_prompt=negative_prompt,
        num_inference_steps=num_inference_steps,
        generator=generator,
        true_cfg_scale=true_guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
    ).images

    # Anonymous diagnostics — fire-and-forget, must not block or fail generation.
    try:
        threading.Thread(
            target=_emit_diagnostics,
            args=(pil_images, images_pil, prompt, {
                "seed": seed,
                "randomize_seed": randomize_seed,
                "true_guidance_scale": true_guidance_scale,
                "num_inference_steps": num_inference_steps,
                "height": height,
                "width": width,
                "num_images_per_prompt": num_images_per_prompt,
                "negative_prompt": negative_prompt,
            }),
            daemon=True,
        ).start()
    except Exception:
        pass

    # Save images to temporary files for proper serving
    output_paths = []
    os.makedirs("outputs", exist_ok=True)
    for idx, img in enumerate(images_pil):
        output_path = f"outputs/output_{seed}_{idx}_{int(time.time()*1000)}.png"
        img.save(output_path)
        output_paths.append(output_path)

    # Return image paths, seed, and make button visible
    return output_paths, seed, gr.update(visible=True), gr.update(visible=True)


# --- UI Layout ---
css = """
#col-container {
    margin: 0 auto;
    max-width: 1024px;
}
#logo-title {
    text-align: center;
}
#logo-title img {
    width: 400px;
}
#edit_text{margin-top: -62px !important}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML("""
        <div id="logo-title">
            <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_edit_logo.png" alt="Qwen-Image Edit Logo" width="400" style="display: block; margin: 0 auto;">
            <h2 style="font-style: italic;color: #5b47d1;margin-top: -27px !important;margin-left: 96px">Rapid Edit ⚡</h2>
        </div>
        """)
        gr.Markdown("""
        This demo uses [Qwen-Image-Edit-2511](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) with [Phr00t's Rapid-AIO v18](https://huggingface.co/Phr00t/Qwen-Image-Edit-Rapid-AIO) accelerated transformer + [AoT compilation & FA3](https://huggingface.co/blog/zerogpu-aoti) for fast 4-step inference.

        Upload an image and enter your prompt to edit it. The model will use your prompt exactly as provided.
        """)
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    image_1 = gr.Image(label="Image 1", type="filepath", interactive=True)
                    image_2 = gr.Image(label="Image 2 (optional)", type="filepath", interactive=True)

                prompt = gr.Text(
                    label="Prompt 🪄",
                    show_label=True,
                    placeholder="Enter your prompt here...",
            )
                run_button = gr.Button("Edit!", 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)
        
                    with gr.Row():
        
                        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="Number of inference steps",
                            minimum=1,
                            maximum=40,
                            step=1,
                            value=4,
                        )
                        
                        height = gr.Slider(
                            label="Height",
                            minimum=256,
                            maximum=2048,
                            step=8,
                            value=None,
                        )
                        
                        width = gr.Slider(
                            label="Width",
                            minimum=256,
                            maximum=2048,
                            step=8,
                            value=None,
                        )



            with gr.Column():
                result = gr.Gallery(label="Result", show_label=False, type="filepath")
                with gr.Row():
                    use_output_btn = gr.Button("↗️ Use as input", variant="secondary", size="sm", visible=False)
                    turn_video_btn = gr.Button("🎬 Turn into Video", variant="secondary", size="sm", visible=False)
                output_video = gr.Video(label="Generated Video", autoplay=True, visible=False)

                with gr.Row(visible=False):
                    gr.Markdown("### 📜 History")
                    clear_history_button = gr.Button("🗑️ Clear History", size="sm", variant="stop")
                
                history_gallery = gr.Gallery(
                    label="Click any image to use as input", 
                    interactive=False,
                    show_label=True,
                    visible=False
                )



        

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            image_1,
            image_2,
            prompt,
            seed,
            randomize_seed,
            true_guidance_scale,
            num_inference_steps,
            height,
            width,
        ],
        outputs=[result, seed, use_output_btn, turn_video_btn],

    ).then(
    fn=update_history,
    inputs=[result, history_gallery],
    outputs=history_gallery,

    )

    # Add the new event handler for the "Use Output as Input" button
    use_output_btn.click(
        fn=use_output_as_input,
        inputs=[result],
        outputs=[image_1]
    )

    # History gallery event handlers
    history_gallery.select(
        fn=use_history_as_input,
        inputs=None,
        outputs=[image_1],

    )
    
    clear_history_button.click(
        fn=lambda: [],
        inputs=None,
        outputs=history_gallery,

    )

    turn_video_btn.click(
    fn=lambda: gr.update(visible=True),   
    inputs=None,
    outputs=[output_video],
).then(
    fn=turn_into_video,
    inputs=[image_1, result, prompt],
    outputs=[output_video],
)


if __name__ == "__main__":
    demo.launch(ssr_mode=False)