FlowBender Trains Conditional Flow Models to Self-Correct Against Their Own Constraints

FlowBender trains conditional diffusion and flow models using feedback from the forward operator (e.g., a depth predictor) during training, so the model learns to self-correct constraint violations rather than treating conditioning signals as static cues. This closes the alignment gap where depth-conditioned models produce outputs whose re-extracted depth disagrees with the input.