Update README.md

README.md

@@ -52,8 +52,8 @@ The model is zero-terminal-SNR with V-prediction. Use the ModelSamplingDiscrete
 Experimental textual inversion embeddings in a similar vein to the [Boring Embeddings](https://huggingface.co/FoodDesert/Boring_Embeddings) are provided above.
 They're intended to improve quality while not drastically altering image content. They should be used as part of a negative prompt, although using them in the positive prompt can be fun too.
 
-- The "lite" version is 6 tokens wide and is initialized on the values of ``by <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>``, which is very close to a "blank slate".
-- The "plus" version is trained on the same dataset, is 8 tokens wide, and is initialized on an average vector of 100 low-scoring artists. Currently, the "lite" version is recommended.
+- The "lite" version is 6 tokens wide and is initialized on the values of ``by <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>``, which is very close to a "blank slate". Current, this version is recommended.
+- The "plus" version is trained on the same dataset, is 8 tokens wide, and is initialized on an average vector of 100 low-scoring artists.
 
 ## Training Details
 - Adaptive timestep weighting: Timesteps are weighted using a similar method to what the EDM2 paper used, according to the homoscedastic uncertainty of MSE loss on each timestep, thereby equalizing the contribution of each timestep.  Loss weight was also conditioned on resolution in order to equalize the contribution of each resolution group.  The overall effect of this is that the model is now very good at both high- and low-frequency details, and is not as biased towards blurry backgrounds.