Update app.py

Fixed mysterious indent issue

app.py

@@ -102,7 +102,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
             image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
             
-        if upscale == "Yes":
+            if upscale == "Yes":
                 refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
                 refiner.enable_xformers_memory_efficient_attention()
                 refiner = refiner.to(device)
@@ -141,7 +141,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
             image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
             
-        if upscale == "Yes":
+            if upscale == "Yes":
                 refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
                 refiner.enable_xformers_memory_efficient_attention()
                 refiner = refiner.to(device)
@@ -182,7 +182,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
             image = refiner(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
             
-        if upscale == "Yes":
+            if upscale == "Yes":
                 refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
                 refiner.enable_xformers_memory_efficient_attention()
                 refiner = refiner.to(device)
@@ -205,9 +205,9 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
                 return upscaled
             else:
                 
-               image = semi(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
-               torch.cuda.empty_cache()
-               return image
+                image = semi(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+                torch.cuda.empty_cache()
+                return image
 
     if Model == "Animagine XL 3.0":
         animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0")
@@ -226,7 +226,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
             image = animagine(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
             
-        if upscale == "Yes":
+            if upscale == "Yes":
                 animagine = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
                 animagine.enable_xformers_memory_efficient_attention()
                 animagine = animagine.to(device)
@@ -273,7 +273,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
             refined = sdxl(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
             
-        if upscale == "Yes":
+            if upscale == "Yes":
                 sdxl = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
                 sdxl.enable_xformers_memory_efficient_attention()
                 sdxl = sdxl.to(device)