Spaces:
Running
on
T4
Running
on
T4
File size: 2,723 Bytes
d8fcee4 5b148b6 8ec67ee d8fcee4 87a75a2 01c29bd 87a75a2 5b148b6 adff792 108b1be 1663d97 adff792 87a75a2 01c29bd bad37a2 87a75a2 5b148b6 8d3fd7c d8fcee4 5b148b6 d8fcee4 6334560 5b148b6 d8fcee4 5b148b6 d8fcee4 5b148b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
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()
pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1").to(device)
#pipe.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
def genie (Prompt, negative_prompt, height, width, scale, steps, seed):
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0").to(device)
#refiner.enable_xformers_memory_efficient_attention()
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, 1280, 768, step=128, label='Height'),
gr.Slider(512, 1280, 768, 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 V2.5 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) |