Update app.py

app.py

@@ -251,7 +251,8 @@ def inference(source_prompt, target_prompt, source_guidance_scale=1, guidance_sc
                                      )
         ptp_utils.register_attention_control(pipe, controller)
     elif cross_attention_control == "None":
-        pass
+        controller = EmptyControl()
+        ptp_utils.register_attention_control(pipe, controller)
     else:
         raise ValueError("Unknown cross_attention_control: {}".format(cross_attention_control))
 
@@ -287,7 +288,7 @@ with gr.Blocks(css=css) as demo:
               <p>
                 Demo for CycleDiffusion with Stable Diffusion. <br>
                 CycleDiffusion (<a href="https://arxiv.org/abs/2210.05559">📄 Paper link</a> | <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cycle_diffusion">🧨 Pipeline doc</a>) is an image-to-image translation method that supports stochastic samplers for diffusion models. <br>
-                It also supports Cross Attention Control (<a href="https://arxiv.org/abs/2208.01626">📄 Paper link</a>), which is a technique to transfer the attention map from the source prompt to the target prompt. <br>
+                We also support the combination of CycleDiffusion and Cross Attention Control (CAC | <a href="https://arxiv.org/abs/2208.01626">📄 Paper link</a>). CAC is a technique to transfer the attention map from the source prompt to the target prompt. <br>
               </p>
               <p>
                 <b>How to use:</b> <br>