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-license: other
-license_name: license
-license_link: LICENSE
+# 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 (256x256) (text-)Image-to-Video/Image Animation Model Card
+![row01](DynamiCrafter-256.webp)
+<!-- Provide a quick summary of what the model is/does. -->
+
+DynamiCrafter (256x256) (Text-)Image-to-Video is a video diffusion model that <br> takes in a still image as a conditioning image and text prompt describing dynamics,<br> and generates videos from it.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+DynamiCrafter, a (Text-)Image-to-Video/Image Animation approach, aims to generate <br>
+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 256x256 <br>
+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:** VideoCrafter1 (256x256)
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+For research purpose, we recommend our Github repository (https://github.com/Doubiiu/DynamiCrafter), <br>
+which includes the detailed implementations.
+- **Repository:** https://github.com/Doubiiu/DynamiCrafter
+- **Paper:** https://arxiv.org/abs/2310.12190
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+We develop this repository for RESEARCH purposes, so it can only be used for personal/research/non-commercial purposes.
+
+
+
+## Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical 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