--- # 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 {} --- # DynamiCrafter (576x1024) (text-)Image-to-Video/Image Animation Model Card ![row01](DynamiCrafter-1024-21.webp) ![row02](DynamiCrafter-10241.webp) DynamiCrafter (576x1024) (Text-)Image-to-Video is a video diffusion model that
takes in a still image as a conditioning image and text prompt describing dynamics,
and generates videos from it. ## 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 576x1024
given a context frame of the same resolution. - **Developed by:** CUHK & Tencent AI Lab - **Funded by:** CUHK & Tencent AI Lab - **Model type:** Generative (text-)image-to-video model - **Finetuned from model:** DynamiCrafter (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 - **Demo1:** https://huggingface.co/spaces/Doubiiu/DynamiCrafter - **Demo2:** https://replicate.com/camenduru/dynami-crafter-576x1024 ## 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