Update README.md

README.md

@@ -34,7 +34,6 @@ license: apache-2.0
 - **Mask-free shifted-window self-attention (MFSWA).** Each spatial window is **pre-gathered into a dense tensor**, so every attention call reduces to a single standard scaled-dot-product (SDPA) call — *no attention mask, cyclic shift, or padding ever enters the graph*. This gives a **1.62× throughput gain over its full-attention teacher** at essentially identical quality, with **no dedicated sparse kernel**.
 - **Restoration-aware Autoencoder (ReAE).** A lightweight encoder–decoder jointly fine-tuned with the DiT in pixel space removes the heavy-3D-VAE / tiled-decoding bottleneck.
 - **Causal chunk-wise streaming.** A minimal causal protocol (no rolling KV cache, no overlapped DiT inference) bounds the temporal axis, confining the residual \(\mathcal{O}(N^2)\) cost to the spatial axes.
-- **Kernel-agnostic & portable.** The same checkpoint runs **bit-identically** across PyTorch SDPA, FlashAttention-2/3, SageAttention, and xFormers — no retraining, weight conversion, or kernel rewrite.
 
 ---
 ## 📊 Results