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import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline
from huggingface_hub import login
import os
from diffusers.models import AutoencoderKL
login(token=os.environ.get('HF_KEY'))
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.cuda.max_memory_allocated(device='cuda')
vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float16)
torch.cuda.empty_cache()
def genie (prompt, negative_prompt, scale, steps, seed, upscale):
torch.cuda.max_memory_allocated(device='cuda')
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, vae=vae)
pipe = pipe.to(device)
pipe.enable_xformers_memory_efficient_attention()
torch.cuda.empty_cache()
generator = torch.Generator(device=device).manual_seed(seed)
int_image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, generator=generator).images
torch.cuda.empty_cache()
if upscaler == 'Yes':
torch.cuda.max_memory_allocated(device='cuda')
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, vae=vae)
pipe = pipe.to(device)
pipe.enable_xformers_memory_efficient_attention()
image = pipe(prompt=prompt, image=int_image).images[0]
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device='cuda')
pipe = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
pipe.to("cuda")
pipe.enable_xformers_memory_efficient_attention()
upscaled = pipe(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
torch.cuda.empty_cache()
else:
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, vae=vae)
pipe = pipe.to(device)
pipe.enable_xformers_memory_efficient_attention()
image = pipe(prompt=prompt, image=int_image).images[0]
torch.cuda.empty_cache()
return (image, upscaled)
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.'),
gr.Slider(1, 15, 10), gr.Slider(25, maximum=100, value=50, step=1),
gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True),
gr.Radio(['Yes', 'No'], label='Upscale?')],
outputs=['image', 'image'],
title="Stable Diffusion XL 0.9 GPU",
description="SDXL 0.9 GPU. <b>WARNING:</b> Capable of producing NSFW images.",
article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)
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