Diffusion for World Modeling is an experimental reinforcement learning system that trains intelligent agents inside a simulated environment generated by a diffusion-based world model. The project introduces the idea of using diffusion models, commonly used for image generation, to simulate the dynamics of an environment and predict future states based on previous observations and actions. Instead of interacting directly with a real environment, the reinforcement learning agent learns within...
...These methods could contain side effects of the state change. You may also take a look at the Django-fsm-admin project containing a mixin and template tags to integrate Django-fsm state transitions into the Django admin. FSM really helps to structure the code, especially when a new developer comes to the project. FSM is most effective when you use it for some sequential steps. Transition logging support could be achieved with help of django-fsm-log package.
Code and data for paper "Deep Photo Style Transfer"
...It relies on semantic segmentation masks to guide style transfer (so that e.g. sky maps to sky, building maps to building), and uses a matting Laplacian regularization term to ensure smooth transitions. The repository provides code in Torch (Lua), MATLAB / Octave scripts for computing the Laplacian, and pre-trained models. Pretrained models and example scripts for ease of use. Compatibility with MATLAB / Octave for Laplacian computations.