app.py

import gradio as gr
import spaces
import torch
from diffusers import Krea2Pipeline


MODEL_ID = "krea/Krea-2-Turbo"
DTYPE = torch.bfloat16
MAX_SEED = 2**31 - 1

pipe = None


def get_pipe():
    global pipe
    if pipe is None:
        if not torch.cuda.is_available():
            raise RuntimeError("CUDA is not available. Set this Space hardware to ZeroGPU before generating.")
        print(f"Loading {MODEL_ID} pipeline...")
        pipe = Krea2Pipeline.from_pretrained(MODEL_ID, torch_dtype=DTYPE).to("cuda")
        print("Pipeline loaded!")
    return pipe


if torch.cuda.is_available():
    get_pipe()
else:
    print("CUDA is not available at startup. The UI will load, but generation requires ZeroGPU hardware.")


def gpu_duration(
    prompt,
    negative_prompt,
    height,
    width,
    num_inference_steps,
    guidance_scale,
    seed,
    randomize_seed,
    progress=None,
):
    megapixels = max(1.0, (int(width) * int(height)) / (1024 * 1024))
    return min(300, int(int(num_inference_steps) * 8 * megapixels * 4 + 180))


@spaces.GPU(duration=gpu_duration, size="xlarge")
def generate_image(
    prompt,
    negative_prompt,
    height,
    width,
    num_inference_steps,
    guidance_scale,
    seed,
    randomize_seed,
    progress=gr.Progress(track_tqdm=True),
):
    """Generate 4 Krea 2 Turbo images with seeds: seed, 2x, 3x, 4x."""
    if not prompt or not str(prompt).strip():
        raise gr.Error("Enter a prompt to generate images.")
    if not torch.cuda.is_available():
        raise gr.Error("CUDA is not available. Set this Space hardware to ZeroGPU before generating.")

    pipe = get_pipe()
    if randomize_seed:
        seed = torch.randint(0, MAX_SEED, (1,)).item()

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

    guidance = float(guidance_scale)
    neg_prompt = None
    if guidance > 0 and isinstance(negative_prompt, str) and negative_prompt.strip():
        neg_prompt = negative_prompt

    images = []
    try:
        for current_seed in seeds:
            generator = torch.Generator("cuda").manual_seed(int(current_seed))
            image = pipe(
                prompt=str(prompt).strip(),
                negative_prompt=neg_prompt,
                height=int(height),
                width=int(width),
                num_inference_steps=int(num_inference_steps),
                guidance_scale=guidance,
                generator=generator,
            ).images[0]
            images.append(image)
    except RuntimeError as exc:
        torch.cuda.empty_cache()
        raise gr.Error(
            f"Generation failed at {int(width)}x{int(height)}. Try 1024x1024, fewer images, or fewer steps."
        ) from exc

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


examples = [
    [
        "A russet harvest mouse clinging to a branch, macro photograph, shallow depth of field, "
        "creamy green bokeh, soft natural light"
    ],
    [
        'A quiet city bookstore at night, rain on the windows, a neon sign that reads "open late", '
        "cinematic warm lighting, detailed reflections"
    ],
    [
        "A fashion editorial portrait of a model wearing sculptural silver fabric, clean studio backdrop, "
        "softbox lighting, high-end magazine photography"
    ],
    [
        "A whimsical hand-drawn village built inside a giant teacup, watercolor texture, cozy evening light"
    ],
]


with gr.Blocks(title="Krea 2 Turbo Demo") as demo:
    gr.Markdown(
        """
        # 🎨 Krea 2 Turbo Demo

        Generate images with [krea/Krea-2-Turbo](https://huggingface.co/krea/Krea-2-Turbo).
        Turbo is the fast distilled Krea 2 checkpoint; the recommended default is 8 steps with CFG 0.0.
        """
    )

    with gr.Row():
        with gr.Column(scale=1):
            prompt = gr.Textbox(
                label="Prompt",
                placeholder='Describe the image in natural language. Wrap rendered text in quotes, e.g. a sign that reads "open late".',
                lines=4,
            )

            negative_prompt = gr.Textbox(
                label="Negative Prompt",
                placeholder="Only used when CFG Guidance Scale is above 0.",
                lines=3,
            )

            with gr.Row():
                height = gr.Slider(
                    minimum=512,
                    maximum=1536,
                    value=1024,
                    step=16,
                    label="Height",
                )
                width = gr.Slider(
                    minimum=512,
                    maximum=1536,
                    value=1024,
                    step=16,
                    label="Width",
                )

            with gr.Row():
                num_inference_steps = gr.Slider(
                    minimum=1,
                    maximum=28,
                    value=8,
                    step=1,
                    label="Inference Steps",
                    info="Krea 2 Turbo is designed for 8 steps.",
                )

            guidance_scale = gr.Slider(
                minimum=0.0,
                maximum=5.0,
                value=0.0,
                step=0.1,
                label="CFG Guidance Scale",
                info="Turbo default is 0.0. Negative prompt is ignored when CFG is 0.",
            )

            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(
        "Model by [Krea](https://huggingface.co/krea). "
        "This Space follows the Krea 2 Community License and uses the Turbo checkpoint for demo inference."
    )

    inputs = [prompt, negative_prompt, height, width, num_inference_steps, guidance_scale, seed, randomize_seed]
    outputs = [output_images, used_seeds]

    generate_btn.click(
        fn=generate_image,
        inputs=inputs,
        outputs=outputs,
        api_name="generate_image",
    )

    prompt.submit(
        fn=generate_image,
        inputs=inputs,
        outputs=outputs,
        api_name="generate_image_submit",
    )


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