Update

Author: OleehyO

README.md

@@ -2,15 +2,17 @@
 
 ## Introduction
 
-CogKit is an open-source project that provides a user-friendly interface for researchers and developers to utilize ZhipuAI's [**CogView**](https://huggingface.co/collections/THUDM/cogview-67ac3f241eefad2af015669b) (image generation) and [**CogVideoX**](https://huggingface.co/collections/THUDM/cogvideo-66c08e62f1685a3ade464cce) (video generation) models. It streamlines multimodal tasks such as **text-to-image (T2I)**, **text-to-video (T2V)**, and **image-to-video (I2V)**. Users must comply with legal and ethical guidelines to ensure responsible implementation.
+CogKit is an open-source project that provides a user-friendly interface for researchers and developers to utilize models from ZhipuAI, currently supports [CogView](https://huggingface.co/collections/THUDM/cogview-67ac3f241eefad2af015669b) (image generation) and [CogVideoX](https://huggingface.co/collections/THUDM/cogvideo-66c08e62f1685a3ade464cce) (video generation) series. Users must comply with legal and ethical guidelines to ensure responsible implementation.
 
-Visit our [**Docs**](https://thudm.github.io/CogKit) to start.
+Visit our [Docs](https://thudm.github.io/CogKit) to start.
 
 ## Features
 
-- **Fine-tuning Methods**: Supports **LoRA** and **full-parameter fine-tuning** across various setups, including **single-machine single-GPU**, **single-machine multi-GPU**, and **multi-machine multi-GPU** configurations.
-- **Inference**: Provides an **OpenAI-style API** (T2I Only) and a **command-line interface** for seamless model deployment.
-- **Embed Cache**: Optimizes GPU memory usage to enhance efficiency during inference.
+- Training Optimization: Includes pre-computation and caching of latents and embeddings, sequence packing, and various memory-efficient strategies to improve training throughput and reduce GPU memory usage.
+
+- Native Resolution Training Support: Seamlessly train models at original image resolutions for improved quality and consistency.
+
+- Easy-to-use Interface: Offers multiple easy-to-use inference options, including a CLI, OpenAI-compatible API server, and interactive Gradio-based UIs for both training and inference.
 
 ## Roadmap