app.py

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

MAX_SEED = 2**32 - 1

# 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,
    negative_prompt,
    height,
    width,
    num_inference_steps,
    guidance_scale,
    seed,
    randomize_seed,
    progress=gr.Progress(track_tqdm=True),
):
    """Generate 4 images with seeds: seed, 2x, 3x, 4x (mod MAX_SEED)."""
    if randomize_seed:
        seed = torch.randint(0, MAX_SEED, (1,)).item()

    base_seed = int(seed) % MAX_SEED
    if base_seed < 0:
        base_seed += MAX_SEED

    seeds = [(base_seed * i) % MAX_SEED for i in range(1, 5)]

    neg_prompt = None
    if isinstance(negative_prompt, str) and negative_prompt.strip():
        neg_prompt = negative_prompt

    images = []
    for s in seeds:
        generator = torch.Generator("cuda").manual_seed(int(s))
        image = pipe(
            prompt=prompt,
            negative_prompt=neg_prompt,
            height=int(height),
            width=int(width),
            num_inference_steps=int(num_inference_steps),
            guidance_scale=float(guidance_scale),  # 0.0 is recommended default for Turbo
            generator=generator,
        ).images[0]
        images.append(image)

    return images, ", ".join(str(s) for s in seeds)


# Example prompts
examples = [
    ["Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp, bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda, blurred colorful distant lights."],
    ["A majestic dragon soaring through clouds at sunset, scales shimmering with iridescent colors, detailed fantasy art style"],
    ["Cozy coffee shop interior, warm lighting, rain on windows, plants on shelves, vintage aesthetic, photorealistic"],
    ["Astronaut riding a horse on Mars, cinematic lighting, sci-fi concept art, highly detailed"],
    ["Portrait of a wise old wizard with a long white beard, holding a glowing crystal staff, magical forest background"],
]

# Build the Gradio interface
with gr.Blocks(title="Z-Image-Turbo Demo") as demo:
    gr.Markdown(
        """
        # 🎨 Z-Image-Turbo Demo
        
        Generate high-quality images using the [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) model.
        This turbo model generates images in just 8 inference steps!
        """
    )
    
    with gr.Row():
        with gr.Column(scale=1):
            prompt = gr.Textbox(
                label="Prompt",
                placeholder="Enter your image description...",
                lines=4,
            )

            negative_prompt = gr.Textbox(
                label="Negative Prompt",
                placeholder="Things you don't want in the image...",
                lines=3,
            )

            with gr.Row():
                height = gr.Slider(
                    minimum=512,
                    maximum=2048,
                    value=1024,
                    step=64,
                    label="Height",
                )
                width = gr.Slider(
                    minimum=512,
                    maximum=2048,
                    value=1024,
                    step=64,
                    label="Width",
                )
            
            with gr.Row():
                num_inference_steps = gr.Slider(
                    minimum=1,
                    maximum=20,
                    value=9,
                    step=1,
                    label="Inference Steps",
                    info="9 steps results in 8 DiT forwards",
                )

            guidance_scale = gr.Slider(
                minimum=0.0,
                maximum=7.0,
                value=0.0,
                step=0.1,
                label="CFG Guidance Scale",
                info="0 = no CFG (recommended for Turbo models)",
            )
            
            with gr.Row():
                seed = gr.Number(
                    label="Seed",
                    value=42,
                    precision=0,
                )
                randomize_seed = gr.Checkbox(
                    label="Randomize Seed",
                    value=False,
                )
            
            generate_btn = gr.Button("🚀 Generate", variant="primary", size="lg")
        
        with gr.Column(scale=1):
            output_images = gr.Gallery(
                label="Generated Images",
                columns=2,
                rows=2,
                preview=True,
            )
            used_seeds = gr.Textbox(
                label="Seeds Used (base, 2x, 3x, 4x)",
                interactive=False,
            )
    
    gr.Markdown("### 💡 Example Prompts")
    gr.Examples(
        examples=examples,
        inputs=[prompt],
        cache_examples=False,
    )

    gr.Markdown("Demo by [mrfakename](https://x.com/realmrfakename). Model by Alibaba. The model is licensed under Apache 2.0, you can use generated images commercially! Thanks to [multimodalart](https://huggingface.co/multimodalart) for the FA3 + AoTI enhancements/speedups")
    
    # Connect the generate button
    generate_btn.click(
        fn=generate_image,
        inputs=[prompt, negative_prompt, height, width, num_inference_steps, guidance_scale, seed, randomize_seed],
        outputs=[output_images, used_seeds],
    )
    
    # Also allow generating by pressing Enter in the prompt box
    prompt.submit(
        fn=generate_image,
        inputs=[prompt, negative_prompt, height, width, num_inference_steps, guidance_scale, seed, randomize_seed],
        outputs=[output_images, used_seeds],
    )

if __name__ == "__main__":
    demo.launch(mcp_server=True, show_error=True)