Update app.py

app.py

@@ -113,12 +113,15 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
         animagine = animagine.to(device)
         torch.cuda.empty_cache()
         if upscale == "Yes":
-            refiner = 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")
-            refiner.enable_xformers_memory_efficient_attention()
-            refiner = refiner.to(device)
             torch.cuda.empty_cache()
+            torch.cuda.max_memory_allocated(device=device)
             int_image = animagine(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]
+            torch.cuda.empty_cache()
+            animagine = 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")
+            animagine.enable_xformers_memory_efficient_attention()
+            animagine = animagine.to(device)
+            torch.cuda.empty_cache()
+            image = animagine(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
             return image
         else:
@@ -139,12 +142,10 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
             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()
-            
             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()
-            
             refined = sdxl(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
             return refined