import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline, StableDiffusion3Pipeline
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()

def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
    if Model == "SD3":
        #torch.cuda.max_memory_allocated(device=device)
        torch.cuda.empty_cache()
        SD3 = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16).to(device)
        torch.cuda.empty_cache()
        image=SD3(
        prompt=Prompt,
        height=height,
        width=width,
        negative_prompt=negative_prompt,
        guidance_scale=scale,
        num_images_per_prompt=1,
        num_inference_steps=steps).images[0]
    if Model == "FXL":

        torch.cuda.empty_cache()
        #torch.cuda.max_memory_allocated(device=device)
        pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16)
        pipe.enable_xformers_memory_efficient_attention()
        pipe = pipe.to(device)
        torch.cuda.empty_cache()

        #torch.cuda.max_memory_allocated(device=device)
        int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
        pipe = 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")
        pipe.enable_xformers_memory_efficient_attention()
        pipe = pipe.to(device)
        torch.cuda.empty_cache()
        image = pipe(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.Radio(["SD3", "FXL"], value='SD3', label='Choose Model'),
                               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, 1536, 1024, step=128, label='Height'),
                               gr.Slider(512, 1536, 1024, step=128, label='Width'),
                               gr.Slider(.5, maximum=15, value=7, step=.25, label='Guidance Scale'), 
                               gr.Slider(10, maximum=50, value=25, step=5, label='Number of Prior 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 V2.4 with Stable Diffusion 3 & 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)