Update README.md

README.md

@@ -1,11 +1,68 @@
 ---
 license: creativeml-openrail-m
 library_name: diffusers
+inference: true
 tags:
 - text-to-video
+- text-to-image
 ---
+# ControlNet Canny Avatar Model Card
 
+[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: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](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 is used for our [huggingface demo](https://huggingface.co/spaces/PAIR/Text2Video-Zero) of our work  [text-to-video zero](https://github.com/Picsart-AI-Research/Text2Video-Zero).
+This model provides [DreamBooth](https://arxiv.org/abs/2208.12242) weights for the `Avatar style` to be used with edge guidance (using [ControlNet](https://arxiv.org/abs/2302.05543)) in text2video zero.
 
-The model is from [CIVITAI](https://civitai.com). The license from CIVITAI thus applies.
+
+## 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 avatar style.
+It can be used also with ControlNet in a text-to-image setup with edge guidance.  
+- **DreamBoth Keyword:** avatar 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/9968/avatar-style).
+- **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 Avatar style were taken from [CIVITAI](https://civitai.com/models/9968/avatar-style).
+
+### 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 avatar style, based on text prompts.
+- **DreamBoth Keyword:** avatar style
+- **Resources for more information:** [CIVITAI](https://civitai.com/models/9968/avatar-style).
+
+
+## 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}
+      }