Update app.py

app.py

@@ -114,14 +114,17 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
         sdxl = sdxl.to(device)   
         sdxl.enable_xformers_memory_efficient_attention()
         torch.cuda.empty_cache()
+        image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
+        torch.cuda.empty_cache()
+        
         if upscale == "Yes":
-            int_image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
-            image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
+            sdxl = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+            sdxl.enable_xformers_memory_efficient_attention()
+            sdxl = sdxl.to(device)
             torch.cuda.empty_cache()
-            return image
-        else:
-            image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+            refined = sdxl(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
+            return refined
         
     return image