Update app.py

app.py

@@ -43,25 +43,14 @@ def set_timesteps_patched(self, num_inference_steps: int, device = None):
 
 # Image Editor
 edit_file = hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl_edit.safetensors")
-normal_file = hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl.safetensors")
-
 EDMEulerScheduler.set_timesteps = set_timesteps_patched
-
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-
 pipe_edit = StableDiffusionXLInstructPix2PixPipeline.from_single_file(
     edit_file, num_in_channels=8, is_cosxl_edit=True, vae=vae, torch_dtype=torch.float16,
 )
 pipe_edit.scheduler = EDMEulerScheduler(sigma_min=0.002, sigma_max=120.0, sigma_data=1.0, prediction_type="v_prediction")
 pipe_edit.to("cuda")
 
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
 # Generator
 @spaces.GPU(duration=30, queue=False)
 def king(type ,
@@ -98,7 +87,7 @@ def king(type ,
         generator = torch.Generator().manual_seed(seed)
         image = pipe(
             prompt = instruction,
-            guidance_scale = guidance_scale, 
+            guidance_scale = 7, 
             num_inference_steps = steps, 
             width = width, 
             height = height,
@@ -205,7 +194,7 @@ with gr.Blocks(css=css) as demo:
         inputs=[type,input_image, instruction],
         fn=king,
         outputs=[input_image],
-        cache_examples=False,
+        cache_examples=True,
     )
 
     gr.Markdown(help_text)