Update app.py

app.py

@@ -39,20 +39,20 @@ def genie (prompt, negative_prompt, height, width, scale, steps, seed, upscaling
     if refining == 'Yes':
         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, num_inference_steps=n_steps, denoising_start=high_noise_frac).images[0] #num_inference_steps=n_steps,
-        upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
-        torch.cuda.empty_cache()
-        return (image, upscaled)
+            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] #num_inference_steps=n_steps,
+            upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
+            torch.cuda.empty_cache()
+            return (image, upscaled)
         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()
             return (image, image)
     else:
         if upscaling == 'Yes':
-        image = pipe(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] #num_inference_steps=n_steps,
-        upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
-        torch.cuda.empty_cache()
-        return (image, upscaled)
+            image = pipe(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] #num_inference_steps=n_steps,
+            upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
+            torch.cuda.empty_cache()
+            return (image, upscaled)
         else:
             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).images[0]
             torch.cuda.empty_cache()