Comment by ttul
2 days ago
This is super cool work. I’ve built some new sampling techniques for flow matching models that encourage the model to take a “second look” by rewinding sampling to a midpoint and then running the clock forward again. This worked really well with diffusion models (pre-DiT models like SDXL) and I was curious whether it would work with flow matching models like Qwen Image. Yes, it does, but the design is different because flow matching models aren’t de-noising pixels so much as they are simply following a vector field at each step like a ship being pushed by the wind.
Neat! Is that published anywhere?
It seems conceptually related to ddpm/ancestral sampling, no? Except they're just adding noise to the intermediate latent to simulate a "trajectory jump". How does your method compare?