import gradio as gr import torch import numpy as np import modin.pandas as pd from PIL import Image from diffusers import DiffusionPipeline, StableDiffusionLatentUpscalePipeline import random random.seed(0) device = "cuda" if torch.cuda.is_available() else "cpu" pipe = DiffusionPipeline.from_pretrained("circulus/canvers-realistic-v3.6", torch_dtype=torch.float16, safety_checker=None) pipe = pipe.to(device) pipe.enable_xformers_memory_efficient_attention() upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, safety_checker=None) upscaler = upscaler.to(device) upscaler.enable_xformers_memory_efficient_attention() def genie (Prompt, negative_prompt, height, width, scale, steps, seed, upscale): generator = torch.manual_seed(seed) if upscale == "Yes": image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0] upscaled = upscaler(Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0] return (image, upscaled) else: image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0] return (image, image) 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. 77 Token Limit'), gr.Slider(512, 1024, 768, step=128, label='Height'), gr.Slider(512, 1024, 768, step=128, label='Width'), gr.Slider(1, maximum=15, value=7, step=.25, label='Guidance Scale'), gr.Slider(25, maximum=100, value=50, step=25, label='Number of Iterations'), gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'), gr.Radio(["Yes", "No"], label='Upscale?', value='No'), ], outputs=[gr.Image(label='Generated Image'), gr.Image(label='Generated Image')], title="PhotoReal V3.6 with SD x2 Upscaler - GPU", description="

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