Duplicate from SergeyMovrody/SDXL-1.0

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README.md

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+---
+title: SDXL-1.0
+emoji: ⚡
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: SergeyMovrody/SDXL-1.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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 
+
+
+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("stabilityai/stable-diffusion-xl-base-1.0", 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-refiner-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/sd-x2-latent-upscaler", 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("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True)
+    pipe = pipe.to(device)
+    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-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="Stable Diffusion SDXL 1.0 GPU Upscaler", 
+    description="", 
+    article = "<br><br><br><br><br><br><br><br><br><br>").launch(debug=True, max_threads=80)

requirements.txt

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+transformers
+diffusers
+accelerate
+torch
+ftfy
+modin[all]
+xformers
+invisible_watermark