Update app.py

app.py

@@ -18,41 +18,20 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
         pipe.enable_xformers_memory_efficient_attention()
         pipe = pipe.to(device)
         torch.cuda.empty_cache()
-        if refine == "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()
-            int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
-            image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
-            torch.cuda.empty_cache()
-            return image
-        else:
-            image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
-            torch.cuda.empty_cache()
-            return image
+    
+        image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+        torch.cuda.empty_cache()
+        return image
     
     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.enable_xformers_memory_efficient_attention()
         animagine = animagine.to(device)
         torch.cuda.empty_cache()
-        if refine == "Yes":
-            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
-            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:
-            image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
-            torch.cuda.empty_cache()
-            return image
+        
+        image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+        torch.cuda.empty_cache()
+        return image
 
     
     return image