Update README.md with some more NaFlexVit details

Author: rwightman

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)