prompter: What is LAION.ai?

assistant: The Large-scale Artificial Intelligence Open Network (LAION) is a German non-profit with a stated goal "to make large-scale machine learning models, datasets and related code available to the general public". It is best known for releasing a number of large datasets of images and captions scraped from the web which have been used to train a number of high-profile text-to-image models, including Stable Diffusion and Imagen

prompter: Are the models and datasets that they produce release under an open-source license?

assistant: Yes, every product is licensed under the MIT License, a short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.

You can read about this here: https://github.com/LAION-AI/laion-datasets/blob/main/LICENSE

prompter: What is Laion working on right now?

assistant: Yes, LAION.ai create open-source models and datasets.  Another example of a project made by LAION.ai is Open-Assistant, this is licensed under the Apache-2.0 licence which allows anyone to make modifications to the source code and redistribute it without needing to provide the source themselves.

assistant: LAION is an acronym for Large-scale Artificial Intelligence Open Network, a not-for-profit AI research team that prides themselves on enabling truly accessible and liberated AI development, by providing datasets and machine learning models for others to use.

prompter: Who is the founder(s) of LAION.ai?

assistant: The founders of LAION.ai are: 
Christoph Schuhmann - Organizational Lead / Founder, 
Jenia Jitsev - Scientific Lead / Founder, 
Richard Vencu - Engineering Lead / Founder,
Robert Kaczmarczyk - Community / Medical Lead / Founder.

assistant: The founder(s) of LAION.ai is/are not publicly disclosed.

assistant: Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While
diffusion models have been successfully applied for image
editing, very few works have done so for video editing. We
present the first diffusion-based method that is able to perform text-based motion and appearance editing of general
videos. Our approach uses a video diffusion model to combine, at inference time, the low-resolution spatio-temporal
information from the original video with new, high resolution information that it synthesized to align with the guiding
text prompt. As obtaining high-fidelity to the original video
requires retaining some of its high-resolution information,
we add a preliminary stage of finetuning the model on the
original video, significantly boosting fidelity. We propose to
improve motion editability by a new, mixed objective that
jointly finetunes with full temporal attention and with temporal attention masking. We further introduce a new framework for image animation. We first transform the image into
a coarse video by simple image processing operations such
as replication and perspective geometric projections, and
then use our general video editor to animate it.

explain the above paragraph in details

prompter: This is not what I was asking. Please write a fitting response to my earlier request.