Update app.py

Testing using refiner after upscaler instead, to denoise more

app.py

@@ -40,10 +40,10 @@ def genie (prompt, negative_prompt, height, width, scale, steps, seed, upscaling
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
     int_image = pipe(prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, 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, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image).images[0]
-        upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
+        upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=int_image, num_inference_steps=5, guidance_scale=0).images[0]
+        image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=upscaled).images[0]
         torch.cuda.empty_cache()
-        return (image, upscaled)
+        return (upscaled, image)
     else:
         image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image, num_inference_steps=n_steps, denoising_start=high_noise_frac).images[0]   
         torch.cuda.empty_cache()