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Turns out, you can cut critical errors in VLM-generated image editing instructions in half with a clever two-stage training pipeline, leading to SOTA editing performance.
Flow-matching transformers with latent multi-modal conditioning and self-reference can leapfrog existing virtual try-on methods in both visual fidelity and inference speed.
