Update app.py

app.py

@@ -322,11 +322,11 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
             if upscale == "Yes":
                 torch.cuda.empty_cache()
                 torch.cuda.max_memory_allocated(device=device)
-                pipe = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
-                pipe.enable_xformers_memory_efficient_attention()
-                pipe = pipe.to(device)
+                upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+                upscaler.enable_xformers_memory_efficient_attention()
+                upscaler = upscaler.to(device)
                 torch.cuda.empty_cache()
-                upscaled = pipe(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
+                upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
                 torch.cuda.empty_cache()
                 return upscaled
             else:
@@ -338,11 +338,11 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
                 torch.cuda.empty_cache()
                 torch.cuda.max_memory_allocated(device=device)
 
-                pipe = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
-                pipe.enable_xformers_memory_efficient_attention()
-                pipe = pipe.to(device)
+                upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
+                upscaler.enable_xformers_memory_efficient_attention()
+                upscaler = upscaler.to(device)
                 torch.cuda.empty_cache()
-                upscaled = pipe(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
+                upscaled = upscaler(prompt=Prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=15, guidance_scale=0).images[0]
                 torch.cuda.empty_cache()
                 return upscaled
             else: