app.py

import spaces 
import gradio as gr
import torch
import modin.pandas as pd
import numpy as np
from diffusers import DiffusionPipeline 

device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    torch.cuda.empty_cache()
    pipe = DiffusionPipeline.from_pretrained("mann-e/Mann-E_Dreams", torch_dtype=torch.float16)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
    torch.cuda.empty_cache()
else: 
    pipe = DiffusionPipeline.from_pretrained("mann-e/Mann-E_Dreams", use_safetensors=True)
    pipe = pipe.to(device)

@spaces.GPU
def genie (prompt, negative_prompt, steps, seed):
    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
    int_image = pipe(prompt=prompt, negative_prompt=negative_prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0]
    return int_image
    
gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 75 Token Limit.'),
    gr.Textbox(label='What you DO NOT want the AI to generate. 75 Token Limit.'),
    gr.Slider(1, maximum=8, value=6, step=1, label='Number of Iterations'), 
    gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=True),
    ],
    outputs='image', 
    title="Mann-E Dreams", 
    description="Mann-E Dreams <br><br><b>WARNING: This model is capable of producing NSFW (Softcore) images.</b>", 
    article = "").launch(debug=True, max_threads=80)