--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- Bf16 safetensors versions of the DynamiCrafter models by Doubiiu: https://huggingface.co/Doubiiu __________________________________________________________ This is a video diffusion model that takes in a single or two still images as a conditioning
image and text prompt describing dynamics, and generates looping videos or interpolation from them. ## Model Details ### Model Description DynamiCrafter, a (Text-)Image-to-Video/Image Animation approach, aims to generate
short video clips (~2 seconds) from a conditioning image and text prompt. This model was trained to generate 16 video frames at a resolution of 320x512
given a context frame of the same resolution. - **Developed by:** CUHK & Tencent AI Lab - **Funded by:** CUHK & Tencent AI Lab - **Model type:** Generative frame interpolation and looping video generation - **Finetuned from model:** VideoCrafter1 (320x512) ### Model Sources For research purpose, we recommend our Github repository (https://github.com/Doubiiu/DynamiCrafter),
which includes the detailed implementations. - **Repository:** https://github.com/Doubiiu/DynamiCrafter - **Paper:** https://arxiv.org/abs/2310.12190 ## Uses ### Direct Use We develop this repository for RESEARCH purposes, so it can only be used for personal/research/non-commercial purposes. ## Limitations - The generated videos are relatively short (2 seconds, FPS=8). - The model cannot render legible text. - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy, resulting in slight flickering artifacts. ## How to Get Started with the Model Check out https://github.com/Doubiiu/DynamiCrafter