from diffusers import StableDiffusionXLPipeline
import torch
from gradio import Interface, Image, Dropdown, Slider
import gradio as gr
import spaces

model_id = "RunDiffusion/Juggernaut-X-v10"
pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

@spaces.GPU()
def text_to_image(prompt, negative_prompt, steps, guidance_scale, progress=gr.Progress(track_tqdm=True)):
    image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=guidance_scale).images[0]
    return image


gradio_interface = Interface(
    fn=text_to_image,
    inputs=[
        gr.Textbox(label="Prompt", lines=2, placeholder="Enter your prompt here..."),
        gr.Textbox(label="Negative Prompt", lines=2, placeholder="What to exclude from the image..."),
        gr.Slider(minimum=1, maximum=100, value=50, label="Steps", step=1),
        gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale", step=0.1)
    ],
    outputs=Image(type="pil", show_download_button=True),
    examples=[
        ["magical kitten, 4k, high quality, (masterpiece)"],
    ],
    cache_examples=False
    theme=gr.themes.Soft()
)
gradio_interface.launch()