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Author: zRzRzRzRzRzRzR

README.md

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 ## Introduction
 
-**CogKit** is an open-source initiative by Zhipu AI that provides a user-friendly interface, enabling researchers and developers to access and manipulate the Cog family of models.
-You can check [here](docs/05-Model%20Card.md) to view support models. The project aims to streamline the application of Cog models across multimodal generation tasks such as **text-to-image (t2i)**, **text-to-video (t2v)**, **image-to-video (i2v)**. 
-It should be noted that utilization of CogKit and associated Cog models must adhere to relevant legal frameworks and ethical guidelines to ensure responsible and ethical implementation.
+**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.
+
+Visit our [**Docs**](https://thudum.github.io/CogKit) to start.
 
 ## Features
 
-- Multiple models: CogVideoX, CogVideoX1.5, CogView4.
-- Ensemble methods: (incremental) pre-training, (multimodal) instruction.
-- Multiple precisions: 16-bit full parameter fine-tuning, frozen fine-tuning, LoRA fine-tuning.
-- Fine-tuning methods: single machine single card, single machine multiple cards, multiple machines multiple cards.
-- Wide range of tasks: multi-round dialogue, image generation, video generation, etc.
-- Extreme reasoning: based on OpenAI style API, browser interface and command line interface.
-- Embed Cache: Reduce GPU memory usage.
+- **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), a **GUI**, and a **command-line interface** for seamless model deployment.
+- **Embed Cache**: Optimizes GPU memory usage to enhance efficiency during inference.
 
 ## Roadmap
 
 - [ ] Add support for CogView4 ControlNet model
-- [ ] Docker Image for easy deployment
+- [ ] Docker for easy deployment
 
 ## License
 
-This project is licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for more details.
+This project is licensed under the [Apache 2.0 License](LICENSE).