Spaces:
Runtime error
Runtime error
import gradio as gr | |
import torch | |
import numpy as np | |
import modin.pandas as pd | |
from PIL import Image | |
from diffusers import DiffusionPipeline | |
device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
if torch.cuda.is_available(): | |
PYTORCH_CUDA_ALLOC_CONF={'max_split_size_mb': 6000} | |
torch.cuda.max_memory_allocated(device=device) | |
torch.cuda.empty_cache() | |
pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
pipe.enable_xformers_memory_efficient_attention() | |
pipe = pipe.to(device) | |
torch.cuda.empty_cache() | |
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") | |
refiner.enable_xformers_memory_efficient_attention() | |
refiner = refiner.to(device) | |
torch.cuda.empty_cache() | |
upscaler = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True) | |
upscaler.enable_xformers_memory_efficient_attention() | |
upscaler = upscaler.to(device) | |
torch.cuda.empty_cache() | |
else: | |
pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", use_safetensors=True) | |
pipe = pipe.to(device) | |
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True) | |
refiner = refiner.to(device) | |
def genie (prompt, negative_prompt, height, width, scale, steps, seed, upscaling): | |
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 | |
if upscaling == 'Yes': | |
image = refiner(prompt=prompt, image=int_image).images[0] | |
upscaled = upscaler(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: | |
image = refiner(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='Что вы хотите, чтобы ИИ генерировал'), | |
gr.Textbox(label='Что вы не хотите, чтобы ИИ генерировал'), | |
gr.Slider(512, 1024, 768, step=128, label='Высота картинки'), | |
gr.Slider(512, 1024, 768, step=128, label='Ширина картинки'), | |
gr.Slider(1, 15, 10, step=.25, label='Шкала расхождения'), | |
gr.Slider(25, maximum=100, value=50, step=25, label='Количество итераций'), | |
gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True, label='Зерно'), | |
gr.Radio(['Да', 'Нет'], label='Ремастеринг?')], | |
outputs=['image', 'image'], | |
title="Стабильная Диффузия - SDXL - Upscaler", | |
description="", | |
article = "<br><br><br><br><br><br><br><br><br><br>").launch(debug=True, max_threads=80) | |