diff --git a/README.md b/README.md
index e1746928ba..82ea5d08b6 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,25 @@
 
 ## What's New
 
+## June 5, 2025
+* Initial NaFlexVit model code. NaFlexVit is a Vision Transformer with:
+  1. Encapsulated embedding and position encoding in a single module
+  2. Support for nn.Linear patch embedding on pre-patchified (dictionary) inputs
+  3. Support for NaFlex variable aspect, variable resolution (SigLip-2: https://arxiv.org/abs/2502.14786)
+  4. Support for FlexiViT variable patch size (https://arxiv.org/abs/2212.08013)
+  5. Support for NaViT fractional/factorized position embedding (https://arxiv.org/abs/2307.06304)
+* Existing vit models in `vision_transformer.py` can be loaded into the NaFlexVit model by adding the `use_naflex=True` flag to `create_model`
+  * Some native weights coming soon
+* A full NaFlex data pipeline is available that allows training / fine-tuning / evaluating with variable aspect / size images
+  * To enable in `train.py` and `validate.py` add the `--naflex-loader` arg, must be used with a NaFlexVit
+* To evaluate an existing (classic) ViT loaded in NaFlexVit model w/ NaFlex data pipe:
+  * `python validate.py /imagenet --amp -j 8 --model vit_base_patch16_224 --model-kwargs use_naflex=True --naflex-loader --naflex-max-seq-len 256` 
+* The training has some extra args features worth noting
+  * The `--naflex-train-seq-lens'` argument specifies which sequence lengths to randomly pick from per batch during training
+  * The `--naflex-max-seq-len` argument sets the target sequence length for validation
+  * Adding `--model-kwargs enable_patch_interpolator=True --naflex-patch-sizes 12 16 24` will enable random patch size selection per-batch w/ interpolation
+  * The `--naflex-loss-scale` arg changes loss scaling mode per batch relative to the batch size, `timm` NaFlex loading changes the batch size for each seq len
+
 ## May 28, 2025
 * Add a number of small/fast models thanks to https://github.com/brianhou0208
   * SwiftFormer - [(ICCV2023) SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://github.com/Amshaker/SwiftFormer) 
diff --git a/timm/models/naflexvit.py b/timm/models/naflexvit.py
index e32e94f396..ac1d964c95 100644
--- a/timm/models/naflexvit.py
+++ b/timm/models/naflexvit.py
@@ -1192,27 +1192,16 @@ def _pool(
             patch_valid: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         if self.attn_pool is not None:
-            # For attention pooling, we need to pass the mask for NaFlex models
-            if self.pool_include_prefix:
-                # Include all tokens in attention pooling - create mask for all tokens including prefix
-                attn_mask = create_attention_mask(
-                    patch_valid,
-                    num_prefix_tokens=self.num_prefix_tokens,
-                    symmetric=False,
-                    q_len=1,
-                    dtype=x.dtype,
-                )
-                x = self.attn_pool(x, attn_mask=attn_mask)
-            else:
-                # Exclude prefix tokens from attention pooling (default behavior)
-                attn_mask = create_attention_mask(
-                    patch_valid,
-                    num_prefix_tokens=0,  # No prefix tokens when we slice them off
-                    symmetric=False,
-                    q_len=1,
-                    dtype=x.dtype,
-                )
-                x = self.attn_pool(x[:, self.num_prefix_tokens:], attn_mask=attn_mask)
+            attn_mask = create_attention_mask(
+                patch_valid,
+                num_prefix_tokens=self.num_prefix_tokens if self.pool_include_prefix else 0,
+                symmetric=False,
+                q_len=1,
+                dtype=x.dtype,
+            )
+            if not self.pool_include_prefix:
+                x = x[:, self.num_prefix_tokens:]
+            x = self.attn_pool(x, attn_mask=attn_mask)
             return x
 
         pool_type = self.global_pool if pool_type is None else pool_type