import gradio as gr import torch import numpy as np import modin.pandas as pd from PIL import Image from diffusers import DiffusionPipeline, StableDiffusionLatentUpscalePipeline device = "cuda" if torch.cuda.is_available() else "cpu" pipe = DiffusionPipeline.from_pretrained("dreamlike-art/dreamlike-photoreal-2.0", torch_dtype=torch.float16, safety_checker=None) upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16) upscaler = upscaler.to(device) pipe = pipe.to(device) def genie (Prompt, scale, steps, Seed): generator = torch.Generator(device=device).manual_seed(Seed) #images = pipe(prompt, num_inference_steps=steps, guidance_scale=scale, generator=generator).images[0] low_res_latents = pipe(Prompt, num_inference_steps=steps, guidance_scale=scale, generator=generator, output_type="latent").images upscaled_image = upscaler(prompt='', image=low_res_latents, num_inference_steps=5, guidance_scale=0, generator=generator).images[0] return upscaled_image gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), gr.Slider(1, maximum=15, value=10, step=.25, label='Prompt Guidance Scale:', interactive=True), gr.Slider(1, maximum=100, value=50, step=1, label='Number of Iterations: 50 is typically fine.'), gr.Slider(minimum=1, step=10, maximum=999999999999999999, randomize=True, interactive=True)], outputs=gr.Image(label='512x512 Generated Image'), title="PhotoReal V2 with SD x2 Upscaler - GPU", description="

Warning: This Demo is capable of producing NSFW content.", article = "Code Monkey: Manjushri").launch(debug=True, max_threads=True)