diff --git a/README.md b/README.md
index c56469c..560a87c 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,10 @@ library_name: diffusers
 
 
 # Key Feature 
-Allegro is capable of producing high-quality, 6-second videos at 30 frames per second and 720p resolution from simple text prompts.
+- **High-Quality Output**: Generate detailed 6-second videos at 15 FPS with 720x1280 resolution, which can be interpolated to 30 FPS with EMA-VFI.
+- **Small and Efficient**: Features a 175M parameter VAE and a 2.8B parameter DiT model. Supports multiple precisions (FP32, BF16, FP16) and uses 9.3 GB of GPU memory in BF16 mode with CPU offloading.
+- **Extensive Context Length**: Handles up to 79.2k tokens, providing rich and comprehensive text-to-video generation capabilities.
+- **Versatile Content Creation**: Capable of generating a wide range of content, from close-ups of humans and animals to diverse dynamic scenes.
 
 
 # Model info 
@@ -29,7 +32,7 @@ Allegro is capable of producing high-quality, 6-second videos at 30 frames per s
   </tr>
   <tr>
     <th>Description</th>
-    <td>Text-to-Video Diffusion Transformer</td>
+    <td>Text-to-Video Generation Model</td>
   </tr>
   <tr>
     <th>Download</th>
@@ -76,17 +79,14 @@ Allegro is capable of producing high-quality, 6-second videos at 30 frames per s
 You can quickly get started with Allegro using the Hugging Face Diffusers library.
 For more tutorials, see Allegro GitHub (link-tbd).
 
-Install necessary requirements:
-```python
-pip install diffusers transformers imageio
-```
-Inference on single gpu:
+1. Install necessary requirements. Please refer to [requirements.txt](https://github.com/rhymes-ai) on Allegro GitHub.
+2. Perform inference on a single GPU.
 ```python
 from diffusers import DiffusionPipeline
 import torch
 
 allegro_pipeline = DiffusionPipeline.from_pretrained(
-    "rhythms-ai/allegro", trust_remote_code=True, torch_dtype=torch.bfloat16
+"rhymes-ai/Allegro", trust_remote_code=True, torch_dtype=torch.bfloat16
 ).to("cuda")
 
 allegro_pipeline.vae = allegro_pipeline.vae.to(torch.float32)
@@ -121,8 +121,10 @@ out_video = allegro_pipeline(
 ).video[0]
 
 imageio.mimwrite("test_video.mp4", out_video, fps=15, quality=8)
-
 ```
+Tip: 
+- It is highly recommended to use a video frame interpolation model (such as EMA-VFI) to enhance the result to 30 FPS.
+- For more tutorials, see [Allegro GitHub](https://github.com/rhymes-ai).
 
 # License
 This repo is released under the Apache 2.0 License.