| | --- |
| | license: creativeml-openrail-m |
| | library_name: diffusers |
| | inference: true |
| | pipeline_tag: text-to-video |
| | tags: |
| | - text-to-video |
| | - text-to-image |
| | --- |
| | # Text2Video-Zero Model Card - ControlNet Canny Anime Style |
| |
|
| |
|
| | [Text2Video-Zero](https://arxiv.org/abs/2303.13439) is a zero-shot text to video generator. It can perform `zero-shot text-to-video generation`, `Video Instruct Pix2Pix` (instruction-guided video editing), |
| | `text and pose conditional video generation`, `text and canny-edge conditional video generation`, and |
| | `text, canny-edge and dreambooth conditional video generation`. For more information about this work, |
| | please have a look at our [paper](https://arxiv.org/abs/2303.13439) and our demo: [](https://huggingface.co/spaces/PAIR/Text2Video-Zero) |
| | Our [code](https://github.com/Picsart-AI-Research/Text2Video-Zero) works with any StableDiffusion base model. |
| |
|
| | This model provides [DreamBooth](https://arxiv.org/abs/2208.12242) weights for the `Anime style` to be used with edge guidance (using [ControlNet](https://arxiv.org/abs/2302.05543)) in text2video zero. |
| |
|
| |
|
| | ## Weights for Text2Video-Zero |
| | We converted the original weights into diffusers and made them usable for [ControlNet](https://arxiv.org/abs/2302.05543) with edge guidance using: https://github.com/lllyasviel/ControlNet/discussions/12. |
| |
|
| |
|
| | ### Model Details |
| | - **Developed by:** Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan and Humphrey Shi |
| | - **Model type:** Dreambooth text-to-image and text-to-video generation model with edge control for text2video zero |
| | - **Language(s):** English |
| | - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license). |
| | - **Model Description:** This is a model for [text2video zero](https://github.com/Picsart-AI-Research/Text2Video-Zero) with edge guidance and anime style. |
| | It can be used also with ControlNet in a text-to-image setup with edge guidance. |
| | - **DreamBoth Keyword:** anime style |
| | - **Resources for more information:** [GitHub](https://github.com/Picsart-AI-Research/Text2Video-Zero), [Paper](https://arxiv.org/abs/2303.13439), [CIVITAI](https://civitai.com/models/8740/superanime-viper). |
| | - **Cite as:** |
| | |
| | @article{text2video-zero, |
| | title={Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators}, |
| | author={Khachatryan, Levon and Movsisyan, Andranik and Tadevosyan, Vahram and Henschel, Roberto and Wang, Zhangyang and Navasardyan, Shant and Shi, Humphrey}, |
| | journal={arXiv preprint arXiv:2303.13439}, |
| | year={2023} |
| | } |
| | |
| |
|
| |
|
| |
|
| | ## Original Weights |
| | The Dreambooth weights for the Anime style were taken from [CIVITAI](https://civitai.com/models/8740/superanime-viper). |
| |
|
| | ### Model Details |
| | - **Developed by:** Quiet_Joker (Username listed on CIVITAI) |
| | - **Model type:** Dreambooth text-to-image generation model |
| | - **Language(s):** English |
| | - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license). |
| | - **Model Description:** This is a model that was created using [DreamBooth](https://arxiv.org/abs/2208.12242) to generate images with Anime style, based on text prompts. |
| | - **DreamBoth Keyword:** anime style |
| | - **Resources for more information:** [CIVITAI](https://civitai.com/models/8740/superanime-viper). |
| | |
| | |
| | ## Biases content acknowledgement: |
| | Beware that Text2Video-Zero may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography, and violence. Text2Video-Zero in this demo is meant only for research purposes. |
| | |
| | |
| | # Citation |
| | @article{text2video-zero, |
| | title={Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators}, |
| | author={Khachatryan, Levon and Movsisyan, Andranik and Tadevosyan, Vahram and Henschel, Roberto and Wang, Zhangyang and Navasardyan, Shant and Shi, Humphrey}, |
| | journal={arXiv preprint arXiv:2303.13439}, |
| | year={2023} |
| | } |
| | |