diff --git a/README.md b/README.md
index c399ed9..b9725d1 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 <div align="center">
   
 ## StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation  [![Paper page](https://huggingface.co/datasets/huggingface/badges/resolve/main/paper-page-md-dark.svg)]()
-[[Paper](https://arxiv.org/abs/2405.01434)] &emsp; [[Project Page](https://storydiffusion.github.io/)] &emsp;  [[🤗 Comic Generation Demo ](https://huggingface.co/spaces/YupengZhou/StoryDiffusion)] <br>
+[[Paper](https://arxiv.org/abs/2405.01434)] &emsp; [[Project Page](https://storydiffusion.github.io/)] &emsp;  [[🤗 Comic Generation Demo ](https://huggingface.co/spaces/YupengZhou/StoryDiffusion)] [![Replicate](https://replicate.com/cjwbw/StoryDiffusion/badge)](https://replicate.com/cjwbw/StoryDiffusion) <br>
 
 
 </div>
diff --git a/predict.py b/predict.py
index 3c7d667..0308dce 100644
--- a/predict.py
+++ b/predict.py
@@ -457,6 +457,7 @@ class Predictor(BasePredictor):
         self.pipe_realvision.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
         self.pipe_realvision.fuse_lora()
 
+    @torch.inference_mode()
     def predict(
         self,
         sd_model: str = Input(
@@ -559,7 +560,7 @@ class Predictor(BasePredictor):
             default=768,
         ),
         num_steps: int = Input(
-            description="Number of sample steps", ge=25, le=50, default=50
+            description="Number of sample steps", ge=20, le=50, default=25
         ),
         guidance_scale: float = Input(
             description="Scale for classifier-free guidance", ge=0.1, le=10, default=5
@@ -569,20 +570,19 @@ class Predictor(BasePredictor):
         ),
         sa32_setting: float = Input(
             description="The degree of Paired Attention at 32 x 32 self-attention layers",
-            default=0.7,
+            default=0.5,
             ge=0,
             le=1.0,
         ),
         sa64_setting: float = Input(
             description="The degree of Paired Attention at 64 x 64 self-attention layers",
-            default=0.7,
+            default=0.5,
             ge=0,
             le=1.0,
         ),
         num_ids: int = Input(
             description="Number of id images in total images. This should not exceed total number of line-separated prompts",
             default=3,
-            choices=[2, 3, 4],
         ),
         output_format: str = Input(
             description="Format of the output images",
@@ -776,4 +776,6 @@ class Predictor(BasePredictor):
             sample.save(output_filename, **save_params)
             output_paths.append(Path(output_filename))
 
+        del pipe
+
         return ModelOutput(comic=Path(comic_out), individual_images=output_paths)