app.py

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

from PIL import Image
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
from torchao.quantization import quantize_
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3

import time


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"
BASE_MODEL_ID = "Qwen/Qwen-Image-Edit-2511"
BASE_MODEL_REVISION = "6f3ccc0b56e431dc6a0c2b2039706d7d26f22cb9"
ACCELERATED_TRANSFORMER_ID = "Sneak-Moose/Qwen-Rapid-AIO-v18-NSFW-diffusers"
ACCELERATED_TRANSFORMER_REVISION = "5641245ab83ffd498c986485eb8c3e9f6f3f2184"

# This UI is tuned for four-step inference and must use the matching accelerated
# transformer. Do not silently fall back to the standard transformer: doing so
# produces misleading low-quality output while presenting the app as healthy.
try:
    transformer = QwenImageTransformer2DModel.from_pretrained(
        ACCELERATED_TRANSFORMER_ID,
        subfolder="transformer",
        revision=ACCELERATED_TRANSFORMER_REVISION,
        torch_dtype=dtype,
        device_map="cuda" if torch.cuda.is_available() else None,
    )
except Exception as exc:
    raise RuntimeError(
        "The pinned four-step accelerated transformer could not be loaded. "
        "Generation is disabled rather than silently using an incompatible fallback."
    ) from exc

pipe = QwenImageEditPlusPipeline.from_pretrained(
    BASE_MODEL_ID,
    revision=BASE_MODEL_REVISION,
    transformer=transformer,
    torch_dtype=dtype,
)

del transformer
if torch.cuda.is_available():
    torch.cuda.empty_cache()

# A full BF16 pipeline is roughly 58 GB and cannot be transferred into a
# standard ZeroGPU allocation. Dynamic FP8 cuts the transformer/text-encoder
# footprint enough to fit while preserving four-step Rapid-AIO behavior.
fp8_config = Float8DynamicActivationFloat8WeightConfig()
quantize_(pipe.transformer, fp8_config)
try:
    quantize_(pipe.text_encoder, fp8_config)
except Exception as exc:
    print(
        f"Text-encoder FP8 quantization unavailable ({type(exc).__name__}); "
        "continuing with the pinned text encoder.",
        flush=True,
    )

pipe = pipe.to(device)

# Apply the upstream FA3 optimization, but keep boot resilient if kernel support
# differs on duplicated Spaces or the public fallback transformer.
try:
    pipe.transformer.__class__ = QwenImageTransformer2DModel
    pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
except Exception as exc:
    print(f"FA3 attention processor unavailable; continuing with default attention: {exc}")

# 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()


# --- 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
MIN_INPUT_SIDE = 256

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)


# --- Main Inference Function (with hardcoded negative prompt) ---
@spaces.GPU(duration=180)
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 not pil_images:
        raise gr.Error("Upload at least one valid input image before editing.")

    for img in pil_images:
        if min(img.size) < MIN_INPUT_SIDE:
            raise gr.Error(
                f"Input images must be at least {MIN_INPUT_SIDE}px on each side. "
                "Very small source images can produce tiled or grid-like artifacts."
            )

    print(
        f"Starting generation: seed={seed}, steps={num_inference_steps}, "
        f"guidance={true_guidance_scale}, size={width}x{height}, inputs={len(pil_images)}",
        flush=True,
    )

    # 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

    # Let Gradio manage temporary result files so delete_cache can expire them.
    return images_pil, seed, 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, delete_cache=(3600, 86400)) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML("""
        <div id="logo-title">
            <h1>Pro Realism Edit Studio 🎨</h1>
            <h2 style="font-style: italic;color: #5b47d1">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,
                        )
                        
            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)

                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,
        ],
        outputs=[result, seed, use_output_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,

    )

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