Update README.md

README.md

@@ -41,17 +41,17 @@ Use the model with [UniDiffuser codebase](https://github.com/thu-ml/unidiffuser)
 ## Model Details
 - **Model type:** Diffusion-based multi-modal generation model
 - **Language(s):** English
-- **License:** MIT
+- **License:** agpl-3.0
 - **Model Description:** This is a model that can perform image, text, text-to-image, image-to-text, and image-text pair generation. Its main component is a [U-ViT](https://github.com/baofff/U-ViT), which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from [Stable Diffusion](https://github.com/CompVis/stable-diffusion), a pretrained [image ViT-B/32 CLIP encoder](https://github.com/openai/CLIP), a pretrained [text ViT-L CLIP encoder](https://huggingface.co/openai/clip-vit-large-patch14), and a [GPT-2](https://github.com/openai/gpt-2) text decoder finetuned by ourselves.
 - **Resources for more information:** [GitHub Repository](https://github.com/thu-ml/unidiffuser), [Paper]().
 
 
 ## Direct Use 
 
-_Note: This section is taken from the [Stable Diffusion model card](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), but applies in the same way to UniDiffuser_.
+_Note: Most of this section is taken from the [Stable Diffusion model card](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), but applies in the same way to UniDiffuser_.
 
 
-The model is intended for research purposes only. Possible research areas and tasks include
+The model should be used following the agpl-3.0 license. Possible usage includes
 
 - Safe deployment of models which have the potential to generate harmful content.
 - Probing and understanding the limitations and biases of generative models.