Update app.py

app.py

@@ -16,24 +16,23 @@ is_colab = utils.is_google_colab()
 colab_instruction = "" if is_colab else """
 <p>You can skip the queue using Colab: <a href="https://colab.research.google.com/gist/ChenWu98/0aa4fe7be80f6b45d3d055df9f14353a/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>"""
 
-if True:
-    model_id_or_path = "CompVis/stable-diffusion-v1-4"
-    if is_colab:
-        scheduler = DDIMScheduler.from_config(model_id_or_path, subfolder="scheduler")
-        pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler)
-    else:
-        import streamlit as st
-        scheduler = DDIMScheduler.from_config(model_id_or_path, use_auth_token=st.secrets["USER_TOKEN"], subfolder="scheduler")
-        torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-        pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, use_auth_token=st.secrets["USER_TOKEN"], scheduler=scheduler, torch_dtype=torch_dtype)
-    tokenizer = pipe.tokenizer
-
-    if torch.cuda.is_available():
-        pipe = pipe.to("cuda")
-
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+model_id_or_path = "CompVis/stable-diffusion-v1-4"
 device_print = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+if is_colab:
+    scheduler = DDIMScheduler.from_config(model_id_or_path, subfolder="scheduler")
+    pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler, torch_dtype=torch_dtype)
+else:
+    import streamlit as st
+    scheduler = DDIMScheduler.from_config(model_id_or_path, use_auth_token=st.secrets["USER_TOKEN"], subfolder="scheduler")
+    pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, use_auth_token=st.secrets["USER_TOKEN"], scheduler=scheduler, torch_dtype=torch_dtype)
+tokenizer = pipe.tokenizer
+
+if torch.cuda.is_available():
+    pipe = pipe.to("cuda")
+
 
 class LocalBlend: