app.py

import torch
import spaces
import gradio as gr
from diffusers import DiffusionPipeline

# Load the pipeline once at startup
print("Loading Z-Image-Turbo pipeline...")
pipe = DiffusionPipeline.from_pretrained(
    "Tongyi-MAI/Z-Image-Turbo",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=False,
)
pipe.to("cuda")

# ======== AoTI compilation + FA3 ========
# pipe.transformer.layers._repeated_blocks = ["ZImageTransformerBlock"]
# spaces.aoti_blocks_load(pipe.transformer.layers, "zerogpu-aoti/Z-Image", variant="fa3")

print("Pipeline loaded!")

@spaces.GPU
def generate_image(prompt, height, width, num_inference_steps, seed, randomize_seed, progress=gr.Progress(track_tqdm=True)):
    """Generate an image from the given prompt."""
    if randomize_seed:
        seed = torch.randint(0, 2**32 - 1, (1,)).item()
    
    generator = torch.Generator("cuda").manual_seed(int(seed))
    image = pipe(
        prompt=prompt,
        height=int(height),
        width=int(width),
        num_inference_steps=int(num_inference_steps),
        guidance_scale=0.0,
        generator=generator,
    ).images[0]
    
    return image, seed

# Example prompts
examples = [
    
    
    
    
    
]

# Custom theme with modern aesthetics (Gradio 6)
custom_theme = gr.themes.Soft(
    primary_hue="green",
    secondary_hue="amber",
    neutral_hue="slate",
    font=gr.themes.GoogleFont("Inter"),
    text_size="lg",
    spacing_size="md",
    radius_size="lg"
).set(
    button_primary_background_fill="*primary_500",
    button_primary_background_fill_hover="*primary_600",
    block_title_text_weight="600",
)

# Build the Gradio interface
with gr.Blocks(fill_height=True) as demo:
    # Header
    gr.Markdown(
        """
        # Z-Image-Turbo
        """,
        elem_classes="header-text"
    )
    
    with gr.Row(equal_height=False):
        # Left column - Input controls
        with gr.Column(scale=1, min_width=600):
            prompt = gr.Textbox(
                label="✨ Your Prompt",
                placeholder="Describe the image you want to create...",
                lines=5,
                max_lines=10,
                autofocus=True,
            )
            
            with gr.Accordion("⚙️ Advanced Settings", open=True):
                with gr.Row():
                    height = gr.Slider(
                        minimum=512,
                        maximum=2048,
                        value=1024,
                        step=64,
                        label="Height",
                        info="Pixels_______________________"
                    )
                    width = gr.Slider(
                        minimum=512,
                        maximum=2048,
                        value=1024,
                        step=64,
                        label="Width",
                        info="Pixels________________________"
                    )
                
                num_inference_steps = gr.Slider(
                    minimum=1,
                    maximum=20,
                    value=9,
                    step=1,
                    label="Inference Steps",
                    info="9 steps = 8 DiT (recommended)"
                )
                
                with gr.Row():
                    randomize_seed = gr.Checkbox(
                        label="🎲 Random Seed",
                        value=True,
                    )
                    seed = gr.Number(
                        label="Seed",
                        value=42,
                        precision=0,
                        visible=False,
                    )
                
                def toggle_seed(randomize):
                    return gr.Number(visible=not randomize)
                
                randomize_seed.change(
                    toggle_seed,
                    inputs=[randomize_seed],
                    outputs=[seed]
                )
            
            generate_btn = gr.Button(
                "🚀 Generate Image",
                variant="primary",
                size="lg",
                scale=1
            )
            
            # Example prompts
            gr.Examples(
                examples=examples,
                inputs=[prompt],
                label=" ",
                examples_per_page=5,
            )
        
        # Right column - Output
        with gr.Column(scale=1, min_width=320):
            output_image = gr.Image(
                label="Generated Image",
                type="pil",
                format="png",
                show_label=False,
                height=600,
                buttons=["download", "share"],
            )
            
            used_seed = gr.Number(
                label="🎲 Seed Used",
                interactive=False,
                container=True,
            )
    
    # Footer credits
    gr.Markdown(
        """
        ---
        <div style="text-align: center; opacity: 0.7; font-size: 0.9em; margin-top: 1rem;">
        <strong>Model:</strong> <a href="https://huggingface.co/Tongyi-MAI/Z-Image-Turbo" target="_blank">Tongyi-MAI/Z-Image-Turbo</a> (Apache 2.0 License) • 
        
        
        
        </div>
        """,
        elem_classes="footer-text"
    )
    
    # Connect the generate button
    generate_btn.click(
        fn=generate_image,
        inputs=[prompt, height, width, num_inference_steps, seed, randomize_seed],
        outputs=[output_image, used_seed],
    )
    
    # Also allow generating by pressing Enter in the prompt box
    prompt.submit(
        fn=generate_image,
        inputs=[prompt, height, width, num_inference_steps, seed, randomize_seed],
        outputs=[output_image, used_seed],
    )

if __name__ == "__main__":
    demo.launch(
        theme=custom_theme,
        css="""
        .header-text h1 {
            font-size: 2.5rem !important;
            font-weight: 700 !important;
            margin-bottom: 0.5rem !important;
            background: linear-gradient(135deg, #fbbf24 0%, #f59e0b 100%);
            -webkit-background-clip: text;
            -webkit-text-fill-color: transparent;
            background-clip: text;
        }
        
        .header-text p {
            font-size: 1.1rem !important;
            color: #64748b !important;
            margin-top: 0 !important;
        }
        
        .footer-text {
            padding: 1rem 0;
        }
        
        .footer-text a {
            color: #f59e0b !important;
            text-decoration: none !important;
            font-weight: 500;
        }
        
        .footer-text a:hover {
            text-decoration: underline !important;
        }
        
        /* Mobile optimizations */
        @media (max-width: 768px) {
            .header-text h1 {
                font-size: 1.8rem !important;
            }
            
            .header-text p {
                font-size: 1rem !important;
            }
        }
        
        /* Smooth transitions */
        button, .gr-button {
            transition: all 0.2s ease !important;
        }
        
        button:hover, .gr-button:hover {
            transform: translateY(-1px);
            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15) !important;
        }
        
        /* Better spacing */
        .gradio-container {
            max-width: 1400px !important;
            margin: 0 auto !important;
        }
        """,
        footer_links=[
            "api",
            "gradio"
        ],
        mcp_server=True
    )