app.py

import os
import random
import gradio as gr
import PIL.Image
from gradio_client import Client
import numpy as np

DESCRIPTION = "# SDXL Pixelart"

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1"

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

def pixelate(input_file_path, pixel_size):
    image = Image.open(input_file_path)
    image = image.resize(
        (image.size[0] // pixel_size, image.size[1] // pixel_size),
        Image.NEAREST
    )
    image = image.resize(
        (image.size[0] * pixel_size, image.size[1] * pixel_size),
        Image.NEAREST
    )
    return image

def generate(
    prompt: str,
    negative_prompt: str = "",
    prompt_2: str = "",
    negative_prompt_2: str = "",
    use_negative_prompt: bool = False,
    use_prompt_2: bool = False,
    use_negative_prompt_2: bool = False,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale_base: float = 5.0,
    guidance_scale_refiner: float = 5.0,
    num_inference_steps_base: int = 25,
    num_inference_steps_refiner: int = 25,
    apply_refiner: bool = False,
):
    client = Client("hysts/SDXL")
    result = client.predict(
        prompt=prompt,
        negative_prompt=negative_prompt,
        prompt_2=prompt_2,
        negative_prompt_2=negative_prompt_2,
        use_negative_prompt=use_negative_prompt,
        use_prompt_2=use_prompt_2,
        use_negative_prompt_2=use_negative_prompt_2,
        seed=seed,
        width=width,
        height=height,
        guidance_scale_base=guidance_scale_base,
        guidance_scale_refiner=guidance_scale_refiner,
        num_inference_steps_base=num_inference_steps_base,
        num_inference_steps_refiner=num_inference_steps_refiner,
        apply_refiner=apply_refiner,
        api_name="/run",
        hf_token=os.environ['HF_TOKEN']
    )
    image = pixelate(result, 16)
    return image


examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
]

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Group():
        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)
        result = gr.Image(label="Result", show_label=False)
    with gr.Accordion("Advanced options", open=False):
        with gr.Row():
            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
        negative_prompt = gr.Text(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a negative prompt",
            visible=False,
        )
        prompt_2 = gr.Text(
            label="Prompt 2",
            max_lines=1,
            placeholder="Enter your prompt",
            visible=False,
        )
        negative_prompt_2 = gr.Text(
            label="Negative prompt 2",
            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=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
        apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
        with gr.Row():
            guidance_scale_base = gr.Slider(
                label="Guidance scale for base",
                minimum=1,
                maximum=20,
                step=0.1,
                value=5.0,
            )
            num_inference_steps_base = gr.Slider(
                label="Number of inference steps for base",
                minimum=10,
                maximum=100,
                step=1,
                value=25,
            )
        with gr.Row(visible=False) as refiner_params:
            guidance_scale_refiner = gr.Slider(
                label="Guidance scale for refiner",
                minimum=1,
                maximum=20,
                step=0.1,
                value=5.0,
            )
            num_inference_steps_refiner = gr.Slider(
                label="Number of inference steps for refiner",
                minimum=10,
                maximum=100,
                step=1,
                value=25,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=result,
        fn=generate,
    )

    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        queue=False,
        api_name=False,
    )
    use_prompt_2.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_prompt_2,
        outputs=prompt_2,
        queue=False,
        api_name=False,
    )
    use_negative_prompt_2.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt_2,
        outputs=negative_prompt_2,
        queue=False,
        api_name=False,
    )
    apply_refiner.change(
        fn=lambda x: gr.update(visible=x),
        inputs=apply_refiner,
        outputs=refiner_params,
        queue=False,
        api_name=False,
    )

    gr.on(
        triggers=[
            prompt.submit,
            negative_prompt.submit,
            prompt_2.submit,
            negative_prompt_2.submit,
            run_button.click,
        ],
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=[
            prompt,
            negative_prompt,
            prompt_2,
            negative_prompt_2,
            use_negative_prompt,
            use_prompt_2,
            use_negative_prompt_2,
            seed,
            width,
            height,
            guidance_scale_base,
            guidance_scale_refiner,
            num_inference_steps_base,
            num_inference_steps_refiner,
            apply_refiner,
        ],
        outputs=result,
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()