Use Alignment wording

README.md

@@ -4,14 +4,14 @@ Decoder weights of FDDA. FDDA post-trains the tokenizer decoder of a frozen late
 with a Fréchet distance loss on generated samples. Code, installation and evaluation:
 [github.com/sunset-clouds/Frechet-Distributional-Decoder-Alignment](https://github.com/sunset-clouds/Frechet-Distributional-Decoder-Alignment).
 
-Each checkpoint stores the tokenizer state dict with the adapted decoder (`{"model", "epoch"}`) and
+Each checkpoint stores the tokenizer state dict with the aligned decoder (`{"model", "epoch"}`) and
 is used with the generator in `--generator_name`. ImageNet 256x256, 50,000 generated samples.
 gFD<sub>r6</sub> is the mean over six representation spaces (Inception-v3, ConvNeXt-v2, DINOv2, MAE,
 SigLIP2, CLIP) of FD divided by the FD of the ImageNet validation set.
 
 ## Before generator-side FD post-training
 
-`before_generator_side/<family>/`: decoders adapted to the pretrained generators.
+`before_generator_side/<family>/`: decoders aligned to the pretrained generators.
 
 Model | `--generator_name` | gFID | IS | gFD<sub>r6</sub> | checkpoint
 --- | --- |:---:|:---:|:---:| ---
@@ -29,7 +29,7 @@ iMF-XL | `imf-XL_256` | 1.14 | 288.3 | 5.03 | [`imf-XL_256.pt`](https://huggingf
 
 ## After generator-side FD post-training
 
-`after_generator_side/<family>/`: decoders adapted to the FD post-trained generators (FDAR for
+`after_generator_side/<family>/`: decoders aligned to the FD post-trained generators (FDAR for
 LlamaGen, GigaTok, TiTok and VAR; FD-SIM for iMF).
 
 Model | `--generator_name` | gFID | IS | gFD<sub>r6</sub> | checkpoint