import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
import torch

pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16, variant="fp16").to("cuda")

pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")

@spaces.GPU(duration=50)
def generate_image(prompt, negative_prompt):
    # Run the diffusion model to generate an image
    output = pipe(prompt, negative_prompt, num_inference_steps=7, guidance_scale=3.5)
    return output.images[0]

prompt = gr.Textbox(label = "Prompt", info = "Describe the subject, the background and the style of image", placeholder = "Describe what you want to see", lines = 2)
negative_prompt = gr.Textbox(label = "Negative prompt", placeholder = "Describe what you do NOT want to see", value = "Ugly, malformed, noise, blur, watermark")

gr_interface = gr.Interface(
    fn=generate_image,
    inputs=[prompt, negative_prompt],
    outputs="image",
    title="Real-time Image Generation with Diffusion",
    description="Enter a prompt to generate an image",
    theme="soft"
)

# Launch the Gradio app
gr_interface.launch()