Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
- Published
- Source
- arXiv
- Paper number
- 682
- Field
- Computer Vision
- arXiv ID
- 2607.19064
Key points
- At 4B parameters, it matches the performance of competitors in the 6B to 80B range while keeping memory usage down to about 18 GB.
- The Mage-VAE tokenizer reduces compute per pixel by 12 to 22 times compared with conventional VAEs.
- CUDA kernel fusion improves training throughput by about 2.5 times.
- The turbo variant generates a 1024 by 1024 image in 0.59 seconds and performs editing in 1.02 seconds on a single A100.
- Its cost efficiency makes it a practical open baseline for researchers who want to experiment and iterate.
Paper links
External research summaries. These are not HDATF publications or measured product results.