Reproduce “NitroFusion2: Bringing Adversarial Loss to the Text Stream of Multimodal Diffusion Transformers” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Dar-Yen Chen, Yi-Zhe Song
NitroFusion2 uses image-informed text-token states in a multimodal diffusion transformer to judge generated images, alongside image-token features. Its cue-based discriminator can emphasize rendered text. At one network evaluation, text-stream discrimination alone beats image-stream discrimination on all five reported benchmark scores, and combining streams improves four; results do not establish parity with multi-step models, which retain an advantage in text rendering.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.