app.py

import spaces
import gradio as gr
import torch
from PIL import Image
from diffusers import DiffusionPipeline
import random
import uuid
from typing import Union, List, Optional
import numpy as np
import time
import zipfile

# Description for the app
DESCRIPTION = """## Qwen Image Hpc/."""

# Helper functions
def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name

def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

# Load Qwen/Qwen-Image pipeline
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe_qwen = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=dtype).to(device)

# Aspect ratios
aspect_ratios = {
    "1:1": (1328, 1328),
    "16:9": (1664, 928),
    "9:16": (928, 1664),
    "4:3": (1472, 1140),
    "3:4": (1140, 1472)
}

# Generation function for Qwen/Qwen-Image
@spaces.GPU
def generate_qwen(
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 4.0,
    randomize_seed: bool = False,
    num_inference_steps: int = 50,
    num_images: int = 1,
    zip_images: bool = False,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device).manual_seed(seed)
    
    start_time = time.time()
    
    images = pipe_qwen(
        prompt=prompt,
        negative_prompt=negative_prompt if negative_prompt else None,
        height=height,
        width=width,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=num_images,
        generator=generator,
        output_type="pil",
    ).images
    
    end_time = time.time()
    duration = end_time - start_time
    
    image_paths = [save_image(img) for img in images]
    zip_path = None
    if zip_images:
        zip_name = str(uuid.uuid4()) + ".zip"
        with zipfile.ZipFile(zip_name, 'w') as zipf:
            for i, img_path in enumerate(image_paths):
                zipf.write(img_path, arcname=f"Img_{i}.png")
        zip_path = zip_name
    
    return image_paths, seed, f"{duration:.2f}", zip_path

# Wrapper function to handle UI logic
@spaces.GPU
def generate(
    prompt: str,
    negative_prompt: str,
    use_negative_prompt: bool,
    seed: int,
    width: int,
    height: int,
    guidance_scale: float,
    randomize_seed: bool,
    num_inference_steps: int,
    num_images: int,
    zip_images: bool,
    progress=gr.Progress(track_tqdm=True),
):
    final_negative_prompt = negative_prompt if use_negative_prompt else ""
    return generate_qwen(
        prompt=prompt,
        negative_prompt=final_negative_prompt,
        seed=seed,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        randomize_seed=randomize_seed,
        num_inference_steps=num_inference_steps,
        num_images=num_images,
        zip_images=zip_images,
        progress=progress,
    )

# Examples
examples = [
    "Realistic still life photography style: A single, fresh apple resting on a clean, soft-textured surface. The apple is slightly off-center, softly backlit to highlight its natural gloss and subtle color gradients—deep crimson red blending into light golden hues. Fine details such as small blemishes, dew drops, and a few light highlights enhance its lifelike appearance. A shallow depth of field gently blurs the neutral background, drawing full attention to the apple. Hyper-detailed 8K resolution, studio lighting, photorealistic render, emphasizing texture and form.",
    "一幅精致细腻的工笔画，画面中心是一株蓬勃生长的红色牡丹，花朵繁茂，既有盛开的硕大花瓣，也有含苞待放的花蕾，层次丰富，色彩艳丽而不失典雅。牡丹枝叶舒展，叶片浓绿饱满，脉络清晰可见，与红花相映成趣。一只蓝紫色蝴蝶仿佛被画中花朵吸引，停驻在画面中央的一朵盛开牡丹上，流连忘返，蝶翼轻展，细节逼真，仿佛随时会随风飞舞。整幅画作笔触工整严谨，色彩浓郁鲜明，展现出中国传统工笔画的精妙与神韵，画面充满生机与灵动之感。",
    "A young girl wearing school uniform stands in a classroom, writing on a chalkboard. The text "Introducing Qwen-Image, a foundational image generation model that excels in complex text rendering and precise image editing" appears in neat white chalk at the center of the blackboard. Soft natural light filters through windows, casting gentle shadows. The scene is rendered in a realistic photography style with fine details, shallow depth of field, and warm tones. The girl's focused expression and chalk dust in the air add dynamism. Background elements include desks and educational posters, subtly blurred to emphasize the central action. Ultra-detailed 32K resolution, DSLR-quality, soft bokeh effect, documentary-style composition",
    "A capybara wearing a suit holding a sign that reads Hello World"
]

css = '''
.gradio-container {
    max-width: 590px !important;
    margin: 0 auto !important;
}
h1 {
    text-align: center;
}
footer {
    visibility: hidden;
}
'''

# Gradio interface
with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Row():
        prompt = gr.Text(
            label="Prompt",
            show_label=False,
            max_lines=1,
            placeholder="Enter your prompt",
            container=False,
        )
        run_button = gr.Button("Run", scale=0, variant="primary")
    result = gr.Gallery(label="Result", columns=1, show_label=False, preview=True)
    
    with gr.Row():
        aspect_ratio = gr.Dropdown(
            label="Aspect Ratio",
            choices=list(aspect_ratios.keys()),
            value="1:1",
        )
    with gr.Accordion("Additional Options", open=False):
        use_negative_prompt = gr.Checkbox(
            label="Use negative prompt",
            value=False,
            visible=True
        )
        negative_prompt = gr.Text(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a negative prompt",
            visible=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():
            width = gr.Slider(
                label="Width",
                minimum=512,
                maximum=2048,
                step=64,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=512,
                maximum=2048,
                step=64,
                value=1024,
            )
        guidance_scale = gr.Slider(
            label="Guidance Scale",
            minimum=0.0,
            maximum=20.0,
            step=0.1,
            value=4.0,
        )
        num_inference_steps = gr.Slider(
            label="Number of inference steps",
            minimum=1,
            maximum=100,
            step=1,
            value=50,
        )
        num_images = gr.Slider(
            label="Number of images",
            minimum=1,
            maximum=5,
            step=1,
            value=1,
        )
        zip_images = gr.Checkbox(label="Zip generated images", value=False)
        
        gr.Markdown("### Output Information")
        seed_display = gr.Textbox(label="Seed used", interactive=False)
        generation_time = gr.Textbox(label="Generation time (seconds)", interactive=False)
        zip_file = gr.File(label="Download ZIP")

    # Update aspect ratio
    def set_dimensions(ar):
        w, h = aspect_ratios[ar]
        return gr.update(value=w), gr.update(value=h)
    
    aspect_ratio.change(
        fn=set_dimensions,
        inputs=aspect_ratio,
        outputs=[width, height]
    )

    # Negative prompt visibility
    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt
    )

    # Run button and prompt submit
    gr.on(
        triggers=[prompt.submit, run_button.click],
        fn=generate,
        inputs=[
            prompt,
            negative_prompt,
            use_negative_prompt,
            seed,
            width,
            height,
            guidance_scale,
            randomize_seed,
            num_inference_steps,
            num_images,
            zip_images,
        ],
        outputs=[result, seed_display, generation_time, zip_file],
        api_name="run",
    )

    # Examples
    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=[result, seed_display, generation_time, zip_file],
        fn=generate,
        cache_examples=False,
    )

if __name__ == "__main__":
    demo.queue(max_size=30).launch(mcp_server=True, ssr_mode=False, show_error=True)