Update app.py

app.py

@@ -3,22 +3,26 @@ import torch
 import numpy as np
 import modin.pandas as pd
 from PIL import Image
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, StableDiffusionLatentUpscalePipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 pipe = DiffusionPipeline.from_pretrained("prompthero/openjourney-v4", safety_checker=None)
+upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, safety_checker=None)
+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]
-     return images
+     low_res_latents = pipe(prompt, generator=generator, output_type="latent").images
+     upscaled_image = upscaler(prompt=prompt, image=low_res_latents, num_inference_steps=20, guidance_scale=0, generator=generator).images[0]
+     return (images, 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), 
                                gr.Slider(1, maximum=50, value=25, step=1), 
                                gr.Slider(minimum=1, step=1, maximum=987654321, randomize=True)], 
-             outputs='image', 
+             outputs=('image', 'image')
              title="OpenJourney V4 CPU", 
              description="OJ V4 CPU. <b>WARNING:</b> Extremely Slow. 35s/Iteration. Expect 8-16mins an image for 15-30 iterations respectively. 50 iterations takes ~28mins.", 
              article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=True)