Update app.py

app.py

@@ -287,8 +287,9 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
                 return refined
         else:
             if upscale == "Yes":
-                image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
-            
+                image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0] 
+                torch.cuda.empty_cache()
+                torch.cuda.max_memory_allocated(device=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)
@@ -302,14 +303,16 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
                torch.cuda.empty_cache()
         
     if Model == 'FusionXL':
-        
+        torch.cuda.empty_cache()
+        torch.cuda.max_memory_allocated(device=device)
         pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
         pipe.enable_xformers_memory_efficient_attention()
         pipe = pipe.to(device)
         torch.cuda.empty_cache()
         if refine == "Yes":
+            torch.cuda.empty_cache()
+            torch.cuda.max_memory_allocated(device=device)
             int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
-           
             pipe = 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")
             pipe.enable_xformers_memory_efficient_attention()
             pipe = pipe.to(device)
@@ -317,6 +320,8 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
             image = pipe(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
             torch.cuda.empty_cache()
             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)
@@ -328,7 +333,11 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
                 return image
         else:
             if upscale == "Yes":
+                
                 image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+                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)