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

device = 'cuda' #if torch.cuda.is_available() else 'cpu'
torch.cuda.max_memory_allocated(device=device)
torch.cuda.empty_cache()
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", variant="bf16", torch_dtype=torch.bfloat16)
prior.enable_xformers_memory_efficient_attention()
prior = prior.to(device)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.float16)
decoder.enable_xformers_memory_efficient_attention()
decoder.enable_model_cpu_offload()
torch.cuda.empty_cache()
                    
def genie (Prompt, negative_prompt, height, width, scale, steps, d_steps, seed):
    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
    torch.cuda.empty_cache()
    prior_image=prior(
    prompt=Prompt,
    height=height,
    width=width,
    negative_prompt=negative_prompt,
    guidance_scale=scale,
    num_images_per_prompt=1,
    num_inference_steps=steps)
    torch.cuda.empty_cache()
    image=decoder(
    image_embeddings=prior_image.image_embeddings.to(torch.float16),
    prompt=Prompt,
    negative_prompt=negative_prompt,
    guidance_scale=0.0,
    output_type="pil",
    num_inference_steps=d_steps).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, 1280, 1024, step=128, label='Height'),
                               gr.Slider(512, 1280, 1024, step=128, label='Width'),
                               gr.Slider(.5, maximum=15, value=3, step=.25, label='Guidance Scale'), 
                               gr.Slider(10, maximum=40, value=25, step=5, label='Number of Prior Iterations'), 
                               gr.Slider(5, maximum=20, value=10, step=5, label="Number of Decoder 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 V2.1 with Stable-Cascade - 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)