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@@ -41,17 +41,17 @@ Use the model with [UniDiffuser codebase](https://github.com/thu-ml/unidiffuser)
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  ## Model Details
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  - **Model type:** Diffusion-based multi-modal generation model
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  - **Language(s):** English
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- - **License:** MIT
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  - **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.
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  - **Resources for more information:** [GitHub Repository](https://github.com/thu-ml/unidiffuser), [Paper]().
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  ## Direct Use
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- _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_.
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- The model is intended for research purposes only. Possible research areas and tasks include
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  - Safe deployment of models which have the potential to generate harmful content.
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  - Probing and understanding the limitations and biases of generative models.
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  ## Model Details
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  - **Model type:** Diffusion-based multi-modal generation model
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  - **Language(s):** English
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+ - **License:** agpl-3.0
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  - **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.
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  - **Resources for more information:** [GitHub Repository](https://github.com/thu-ml/unidiffuser), [Paper]().
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  ## Direct Use
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+ _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_.
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+ The model should be used following the agpl-3.0 license. Possible usage includes
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  - Safe deployment of models which have the potential to generate harmful content.
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  - Probing and understanding the limitations and biases of generative models.