add

app.py

@@ -333,7 +333,7 @@ class BaseTrainer(object):
         return result
 
 
-@spaces.GPU(duration=149)
+@spaces.GPU(duration=140)
 def _warp(args,model, batch_data,joints,joint_mask_upper,joint_mask_hands,joint_mask_lower,use_trans,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std):
     diffusion = create_gaussian_diffusion(use_ddim=args.use_ddim)
     args,model,vq_model_upper,vq_model_hands,vq_model_lower,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std,vqvae_latent_scale=_warp_create_cuda_model(args,model,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std)
@@ -777,7 +777,7 @@ demo = gr.Interface(
     title='SynTalker: Enabling Synergistic Full-Body Control in Prompt-Based Co-Speech Motion Generation',
     description="1. Upload your audio.  <br/>\
         2. Then, sit back and wait for the rendering to happen! This may take a while (e.g. 2-5 minutes) <br/>\
-        (The reason of running time so long is that provided GPU have an limitation in GPU running time, we must use CPU to handle some GPU tasks)\
+        (The reason of running time so long is that provided GPU have an limitation in GPU running time, we must use CPU to handle some GPU tasks)<br/>\
         3. After, you can view the videos.  <br/>\
         4. Notice that we use a fix face animation, our method only produce body motion. <br/>\
         5. Use DDPM sample strategy will generate a better result, while it will take more inference time.  \