import gradio as gr
import spaces
import random
import numpy as np

import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import AutoencoderTiny, AutoencoderKL

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
#taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
#good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)

# from bobber/bigasp2 to John6666/biglove-ponyv20-sdxl
good_vae = AutoencoderKL.from_pretrained("bobber/bigasp2", subfolder="vae", torch_dtype=dtype).to(device)
pipeline = StableDiffusionXLPipeline.from_pretrained("bobber/bigasp2", torch_dtype=dtype, vae=good_vae).to(device)

MAX_SEED = np.iinfo(np.int32).max

@spaces.GPU
def generate(prompt, negative_prompt, width, height, sample_steps, guidance_scale, seed):
    if seed ==0:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    return pipeline(prompt=prompt, generator=generator, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=sample_steps).images[0]

with gr.Blocks() as interface:
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    prompt = gr.Textbox(label="Prompt", info="What do you want?", value="selfie, holding phone, 18 years old, red and blonde hair, (tattoos), messy long hair, stockings, wet pussy, toned body, oni tattoo, spread pussy, basement bath room, vibrant colors, ", lines=4, interactive=True)
                    negative_prompt = gr.Textbox(label="Negative Prompt", info="What do you want to exclude from the image?", value="monochrome", lines=4, interactive=True)
                with gr.Column():
                    generate_button = gr.Button("Generate")
                    output = gr.Image()
            with gr.Row():
                with gr.Accordion(label="Advanced Settings", open=False):
                    with gr.Row():
                        with gr.Column():
                            width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=1248, minimum=128, maximum=4096, step=64, interactive=True)
                            height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=1824, minimum=128, maximum=4096, step=64, interactive=True)
                        with gr.Column():
                            sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=8, minimum=4, maximum=50, step=1, interactive=True)
                            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=163829704,)
                        with gr.Column():
                            guidance_scale = gr.Slider(label="Guidance Scale", info="Guidance scale.", value=2.5, minimum=1, maximum=10, step=0.1, interactive=True)
        
        generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps, guidance_scale, seed], outputs=[output])

if __name__ == "__main__":
    interface.launch()