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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
login(token=os.environ.get('HF_KEY'))
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.cuda.max_memory_allocated(device='cuda')
torch.cuda.empty_cache()
def genie (prompt, negative_prompt, height, width, scale, steps, seed, upscaler):
torch.cuda.max_memory_allocated(device='cuda')
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
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, height=height, width=width, guidance_scale=scale, num_images_per_prompt=1, generator=generator, output_type="latent").images
torch.cuda.empty_cache()
if upscaler == 'Yes':
torch.cuda.max_memory_allocated(device='cuda')
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
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()
return (image, upscaled)
else:
torch.cuda.empty_cache()
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe = pipe.to(device)
pipe.enable_xformers_memory_efficient_attention()
image = pipe(prompt=prompt, negative_prompt=negative_prompt, image=int_image).images[0]
torch.cuda.empty_cache()
return (image, image)
gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit. A Token is Any Word, Number, Symbol, or Punctuation. Everything Over 77 Will Be Truncated!'),
gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
gr.Slider(512, 1024, 768, step=128, label='Height'),
gr.Slider(512, 1024, 768, step=128, label='Width'),
gr.Slider(1, 15, 10, step=.25, label='Guidance Scale: How Closely the AI follows the Prompt'),
gr.Slider(25, maximum=100, value=50, step=25, label='Number of Iterations'),
gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True, label='Seed'),
gr.Radio(['Yes', 'No'], label='Upscale?')],
outputs=['image', 'image'],
title="Stable Diffusion XL 1.0 GPU",
description="SDXL 1.0 GPU. <br><br><b>WARNING: Capable of producing NSFW (Softcore) images.</b>",
article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(concurrency_count=1).launch(debug=True, max_threads=80)
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