Update app.py

app.py

@@ -21,15 +21,15 @@ else:
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
     pipe = pipe.to(device)
     #pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True)
-    refiner = refiner.to(device)
+    #refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True)
+    #refiner = refiner.to(device)
     #refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
 
 def genie (prompt, negative_prompt, height, width, scale, steps, seed, prompt_2, negative_prompt_2, high_noise_frac):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
-    int_image = pipe(prompt, prompt_2=prompt_2, height=height, width=width, num_inference_steps=steps, num_images_per_prompt=1, generator=generator, output_type="latent").images 
-    image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image, denoising_start=high_noise_frac).images[0]   
-    return image
+    int_image = pipe(prompt, prompt_2=prompt_2, height=height, width=width, num_inference_steps=steps, num_images_per_prompt=1, generator=generator).images[0] 
+    #image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image, denoising_start=high_noise_frac).images[0]   
+    return int_image
     
 gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
     gr.Textbox(label='What you Do Not want the AI to generate.'),