Datou

I’ve developed TARA — Temporally Aligned Reference Attention, an experimental method for MiniMax H3. Research, implementation and video examples: github.com/alisson-anjos/ai-… Native video references can capture an action but drift out of sync with the source. TARA aligns the reference and output timelines inside attention, helping preserve performance and timing while using native reference conditioning, without Add Guide. I’ve also developed optional precompiled kernels for Comfy Kitchen and sol-attn, with TARA’s temporal attention bias fused directly into the attention computation. This avoids the slower per-frame compatibility path. BFSNodes automatically downloads the matching binary on supported systems, compatibility and performance across GPUs are still being validated. Early tests look promising. Interestingly, my guide-latent-trained LoRA currently seems to work better with TARA than the unfinished TARA-trained checkpoint. This isn’t a controlled comparison; I’m training a fresh LoRA from scratch to investigate further. There’s a TARA node in BFSNodes for native ComfyUI workflows. If you’d like to try it, feedback, settings and short comparisons are very welcome, especially from different GPUs!