🎥Goku
Saiyan-World/goku · homepage ↗
[CVPR2025 Highlight] Video Generation Foundation Models: https://saiyan-world.github.io/goku/
⭐ Popular: 3k stars
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momentum
durability
since we covered it
why it's a big deal
- Goku is a joint image-and-video generation foundation model covering text-to-video, image-to-video, and text-to-image in one family, so teams can target several generation tasks from a single approach.
- The authors report competitive public benchmark scores, including GenEval 0.76 and DPG-Bench 83 for text-to-image and VBench 84 for text-to-video, giving practitioners a documented reference point rather than only claims.
- The repository accompanies a CVPR 2025 paper from HKU and ByteDance, so it mainly serves researchers tracking rectified flow methods rather than users wanting a ready-to-run tool, since checkpoints and download links are not documented in the README.
under the hood
- The model is built on rectified flow Transformers, applying a rectified flow formulation to the interaction between video and image tokens.
- The codebase is entirely Python and is organized into configs, a goku module, and tools, with a requirements.txt for dependencies.
- The authors attribute results partly to fine-grained image and video data curation alongside the flow-based architecture, though specific model parameter sizes are not stated in the README.
Radar summary, generated from the project's public sources
star history
- PR#2 2k 2025-02-19
- now 3k + 805 since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Better known than its recent output, coasting a little on attention.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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Flow Based Video Generative Foundation Models
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