Update ADVANCED_TWEAKING.md

fix doc about zero terminal snr

doc/ADVANCED_TWEAKING.md

@@ -201,10 +201,6 @@ Test results: https://huggingface.co/panopstor/ff7r-stable-diffusion/blob/main/z
 
 Very tentatively, I suggest closer to 0.10 for short term training, and lower values of around 0.02 to 0.03 for longer runs (50k+ steps).  Early indications seem to suggest values like 0.10 can cause divergence over time. 
 
-## Zero terminal SNR
-
-Set `zero_frequency_noise_ratio` to -1.
-
 ## Keeping images together (custom batching)
 
 If you have a subset of your dataset that expresses the same style or concept, training quality may be improved by putting all of these images through the trainer together in the same batch or batches, instead of the default behaviour (which is to shuffle them randomly throughout the entire dataset).
@@ -338,4 +334,4 @@ Pyramid noise can be used to improve dynamic range in short finetunes of < 2000
 
 The `attn_type` arg allows you to select `xformers`, `sdp`, or `slice`.  Xformers uses the [xformers package](https://github.com/facebookresearch/xformers).  SDP uses the scaled dot product mechanism [built into  Pytorch](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) as of recent Pytorch updates. Slice uses head splitting.  `sdp` is the default and suggested value as it seems to save a small amount of VRAM while also being approximately 5% faster than xformers.  There is likely little reason to use slice or xformers but are kept for the time being for experimentation or consistency with prior experiments.
 
-[Experimental results](https://discord.com/channels/1026983422431862825/1178007113151287306) (Discord link)
+[Experimental results](https://discord.com/channels/1026983422431862825/1178007113151287306) (Discord link)