import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline
from huggingface_hub import hf_hub_download

device = 'cuda' #if torch.cuda.is_available() else 'cpu'
torch.cuda.max_memory_allocated(device=device)
torch.cuda.empty_cache()
pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16, safety_checker=None)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
torch.cuda.empty_cache()
            
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)
torch.cuda.empty_cache()
            
def genie (Prompt, negative_prompt, height, width, scale, steps, seed, upscale):
    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
    torch.cuda.empty_cache()
    int_image = pipe(prompt=Prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=scale, width=width, height=height, output_type="latent").images #
    torch.cuda.empty_cache()
    image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
    torch.cuda.empty_cache()
    return image
    
gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
                               gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
                               gr.Slider(512, 1408, 1024, step=128, label='Height'),
                               gr.Slider(512, 1408, 1024, step=128, label='Width'),
                               gr.Slider(7.5, maximum=15, value=10, step=.25, label='Guidance Scale'), 
                               gr.Slider(10, maximum=50, value=25, step=5, label='Number of Iterations'), 
                               gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random')],
             outputs=gr.Image(label='Generated Image'), 
             title="Manju Dream Booth V2.0 with Fusion XL - GPU", 
             description="<br><br><b/>Warning: This Demo is capable of producing NSFW content.", 
             article = "If You Enjoyed this Demo and would like to Donate, you can send any amount to any of these Wallets. <br><br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>DOGE: D9QdVPtcU1EFH8jDC8jhU9uBcSTqUiA8h6<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)