REPA-G: Test-Time Conditioning with Representation-Aligned Visual Features
REPA-G is a method for controlling image generation at inference time using learned visual features from a pre-trained model. It optimizes a similarity objective to steer the denoising process toward representations aligned with conditioning data, offering fine-grained control over texture, shape, and concept synthesis without needing retraining.