| 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" | |
| 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) | |
| torch.cuda.max_memory_allocated(device='cuda') | |
| torch.cuda.empty_cache() | |
| def genie (prompt, negative_prompt, height, width, scale, steps, seed, upscaler): | |
| 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': | |
| image = refiner(prompt=prompt, image=int_image).images[0] | |
| torch.cuda.empty_cache() | |
| 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='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) | |