Update app.py

app.py

@@ -20,8 +20,8 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
         
     if Model == "PhotoReal":
         pipe = DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
-        pipe = pipe.to(device)
         pipe.enable_xformers_memory_efficient_attention()
+        pipe = pipe.to(device)
         torch.cuda.empty_cache()
         if upscale == "Yes":
             int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
@@ -35,8 +35,8 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
     
     if Model == "Anime":
         anime = DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1")
-        anime = anime.to(device)
         anime.enable_xformers_memory_efficient_attention()
+        anime = anime.to(device)
         torch.cuda.empty_cache()
         if upscale == "Yes":
             int_image = anime(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
@@ -50,8 +50,8 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
             
     if Model == "Disney":
         disney = DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.8.1")
-        disney = disney.to(device)
         disney.enable_xformers_memory_efficient_attention()
+        disney = disney.to(device)
         torch.cuda.empty_cache()
         if upscale == "Yes":
             int_image = disney(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
@@ -65,8 +65,8 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
             
     if Model == "StoryBook":
         story = DiffusionPipeline.from_pretrained("circulus/canvers-story-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-story-v3.8.1")
-        story = story.to(device)
         story.enable_xformers_memory_efficient_attention()
+        story = story.to(device)
         torch.cuda.empty_cache()
         if upscale == "Yes":
             int_image = story(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
@@ -80,8 +80,8 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
 
     if Model == "SemiReal":
         semi = DiffusionPipeline.from_pretrained("circulus/canvers-semi-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-semi-v3.8.1")
-        semi = semi.to(device)
         semi.enable_xformers_memory_efficient_attention()
+        semi = semi.to(device)
         torch.cuda.empty_cache()
         if upscale == "Yes":
             image = semi(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
@@ -95,8 +95,8 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
 
     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")
-        animagine = animagine.to(device)
         animagine.enable_xformers_memory_efficient_attention()
+        animagine = animagine.to(device)
         torch.cuda.empty_cache()
         if upscale == "Yes":
             int_image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
@@ -111,11 +111,12 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
     if Model == "SDXL 1.0":
         torch.cuda.max_memory_allocated(device=device)
         sdxl = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-        sdxl = sdxl.to(device)   
         sdxl.enable_xformers_memory_efficient_attention()
+        sdxl = sdxl.to(device)   
         torch.cuda.empty_cache()
     
         if upscale == "Yes":
+            torch.cuda.max_memory_allocated(device=device)
             image = sdxl(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
             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()