The Drone View V2 feature enables you to create a video with a duration of your choice from a drone's perspective by providing a description or prompt for a scene. While the drone is set to autopilot mode, you can modify its movements and responses to obstacles inside the DroneArgs class. See the playlist below for examples of video outputs.

Just a heads up, the art style in the videos are, for the most part, determined by the text-to-image model you choose, and is not influenced by Astro Stable Diffusion methods.

Text-to-video generation for Drone View V3, Virtual Reality, Panorama Photography, and Pan Shot are currently being developed.

Download the required packages and repositories.

pip install -r requirements.txt
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git

Download and save a Stable Diffusion model to the ./stable-diffusion-webui/models/Stable-diffusion folder. Lastly, launch webui-user.bat in ./stable-diffusion-webui before running Astro Stable Diffusion plugins.

This repository is similar to Deforum Stable Diffusion in that both are based on the image-to-image and text-to-image methods of Stable Diffusion. However, Astro Stable Diffusion differs in that it uses non-interpolation methods to create videos. In the coming months, AMLA will introduce and release new open-source models that are not based on diffusion models or derivative work. We believe that generating videos that are well-structured, clear, and logical, such as short action films and music videos, requires models that are capable of learning the relationship between vectors in videos and music, rather than relying on diffusion or interpolation techniques.

Earlier work has been moved to the previous folder, but it still provides useful AI Art helpers.

@article{Forsgren_Martiros_2022,
  author = {Forsgren, Seth* and Martiros, Hayk*},
  title = {{Riffusion - Stable diffusion for real-time music generation}},
  url = {https://riffusion.com/about},
  year = {2022}
}

@article{DBLP:journals/corr/abs-2005-12872,
  author    = {Nicolas Carion and
               Francisco Massa and
               Gabriel Synnaeve and
               Nicolas Usunier and
               Alexander Kirillov and
               Sergey Zagoruyko},
  title     = {End-to-End Object Detection with Transformers},
  journal   = {CoRR},
  volume    = {abs/2005.12872},
  year      = {2020},
  url       = {https://arxiv.org/abs/2005.12872},
  archivePrefix = {arXiv},
  eprint    = {2005.12872},
  timestamp = {Thu, 28 May 2020 17:38:09 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}