Python scripts to download JWST observations and their associated metadata. For novelty research purposes, we create free animations or videos from deep neural networks.
The following parameters produced 5 second videos on an A100 GPU after pre-editing the photos for a 576 H x 1024 W pixel image.
- Frames: 50
- Frames per second: 10
- Number of Steps: 60
- Seed: Random
- Number of frames decoded at a time: 1
- Motion bucket id: 200
- Condition augmentation factor: 0.02
The current notebook supports frames that are in multiples of 25. Length in time is Frames / Frames per second (5 seconds = 50 frames / 10 fps). Increase in Condition augmentation factor increases how much can change in the output video from changing it for the same input as Motion bucket id is the rate of change.