diff --git a/.cog/tmp/build2821786764/cog-0.0.1.dev-py3-none-any.whl b/.cog/tmp/build2821786764/cog-0.0.1.dev-py3-none-any.whl
new file mode 100644
index 0000000..d05a3ee
Binary files /dev/null and b/.cog/tmp/build2821786764/cog-0.0.1.dev-py3-none-any.whl differ
diff --git a/predict.py b/predict.py
index 71a0c83..a31578e 100644
--- a/predict.py
+++ b/predict.py
@@ -27,6 +27,8 @@ from SwissArmyTransformer.resources import auto_create
 from models.cogvideo_cache_model import CogVideoCacheModel
 from coglm_strategy import CoglmStrategy
 
+sys.path.append('./Image-Local-Attention')
+
 
 def get_masks_and_position_ids_stage1(data, textlen, framelen):
     # Extract batch size and sequence length.
@@ -415,8 +417,7 @@ class InferenceModel_Interpolate(CogVideoCacheModel):
 class Predictor(BasePredictor):
     def setup(self):
         logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.DEBUG, datefmt='%Y-%m-%d %H:%M:%S')
-        subprocess.call("python setup.py develop", cwd="/src/Image-Local-Attention", shell=True)
-        sys.path.append('./Image-Local-Attention')
+        subprocess.call("python setup.py develop", cwd="/src/Image-Local-Attention", shell=True)        
         os.environ["SAT_HOME"] = "/sharefs/cogview-new"
         args = get_args([
             "--batch-size", "1",                                               
@@ -482,7 +483,7 @@ class Predictor(BasePredictor):
         both_stages: bool = Input(
             description="Run both stages (uncheck to run more quickly and output only a few frames)", default=True
         ),
-        use_guidance: bool = Input(description="Use stage 1 guidance (recommended)", default=True),
+        use_guidance: bool = Input(description="Use stage 1 guidance (recommended)", default=True)        
     ) -> typing.Iterator[Path]:
         if translate:
             prompt = self.translator.translate(prompt.strip())
@@ -632,7 +633,7 @@ class Predictor(BasePredictor):
             for clip_i in range(len(imgs)):
                 # os.makedirs(output_dir_full_paths[clip_i], exist_ok=True)
                 my_save_multiple_images(imgs[clip_i], outputdir, subdir=f"frames/{clip_i}", debug=False)
-                os.system(f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{outputdir}/{clip_i}.gif' -d 0.25")
+                subprocess.call(f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{outputdir}/{clip_i}.gif' -d 0.25", shell=True)
             torch.save(save_tokens, os.path.join(outputdir, 'frame_tokens.pt'))
         
         logging.info("CogVideo Stage1 completed. Taken time {:.2f}\n".format(time.time() - process_start_time))
@@ -763,7 +764,7 @@ class Predictor(BasePredictor):
 
             for sample_i in range(sample_num):
                 my_save_multiple_images(decoded_sr_videos[sample_i], outputdir,subdir=f"frames/{sample_i+sample_num*gpu_rank}", debug=False)
-                os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125")
+                subprocess.call(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125", shell=True)
             
             logging.info("Direct super-resolution completed. Taken time {:.2f}\n".format(time.time() - dsr_starttime))
         else:            
@@ -782,7 +783,7 @@ class Predictor(BasePredictor):
 
             for sample_i in range(sample_num):
                 my_save_multiple_images(decoded_videos[sample_i], outputdir,subdir=f"frames/{sample_i+sample_num*gpu_rank}", debug=False)
-                os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125")
+                subprocess.call(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125", shell=True)                
 
             
             #imgs = []