import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline, AutoPipelineForText2Image#, 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()
#torch.cuda.max_memory_allocated(device=device)
#pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.bfloat16).to(device)
pipe = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float32).to(device) # or black-forest-labs/FLUX.1-schnell "black-forest-labs/FLUX.1-dev"


pipe.load_lora_weights("lustlyai/Flux_Lustly.ai_Uncensored_nsfw_v1",
                           weight_name="flux_lustly-ai_v1.safetensors",
                           adapter_name="v1")

pipe.set_adapters(["v1"], adapter_weights=[1])

pipe.enable_xformers_memory_efficient_attention()
# Open it for reduce GPU memory usage
pipe.enable_model_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
torch.cuda.empty_cache()

#torch.cuda.max_memory_allocated(device=device)
#refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.bfloat16, variant="fp16").to(device)
#refiner.enable_xformers_memory_efficient_attention()
#torch.cuda.empty_cache()


def genie (Prompt, negative_prompt, height, width, scale, steps, seed):
    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
    #generator=np.random.seed(0)
    #int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
    #image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
    image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).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=9, 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 V2.5 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)