diff --git a/doc/OPTIMIZER.md b/doc/OPTIMIZER.md
index 1ceffae..2efa76c 100644
--- a/doc/OPTIMIZER.md
+++ b/doc/OPTIMIZER.md
@@ -51,7 +51,9 @@ There are no known recommendations for the CLIP text encoder.  Using an even lar
 
 #### D-Adaption optimizers
 
-[Dadaptation](https://arxiv.org/abs/2301.07733) [version](https://github.com/facebookresearch/dadaptation) of various optimizers.  These require drastically different hyperparameters.  Early indications seem to point to LR of 0.1 to 1.0 and weight decay of 0.8 may work well for these.  There is a `decouple` parameter that appears to need to be set to `true` for dadaptation to work and is defaulted. Another `d0` parameter is defaulted to 1e-6 as suggested and, according to the paper authors, does not need to be tuned, but is optional.  See `optimizer_dadapt.json` for an example of a fully configured `dadapt_adam` training. 
+[Dadaptation](https://arxiv.org/abs/2301.07733) [version](https://github.com/facebookresearch/dadaptation) of various optimizers.  
+
+These require drastically different hyperparameters.  Early indications seem to point to LR of 0.1 to 1.0 and weight decay of 0.8 may work well.  There is a `decouple` parameter that appears to need to be set to `true` for dadaptation to work and is defaulted. Another `d0` parameter is defaulted to 1e-6 as suggested and, according to the paper authors, does not need to be tuned, but is optional.  See `optimizer_dadapt.json` for an example of a fully configured `dadapt_adam` training. 
 
 These are not memory efficient.  You should use gradient checkpointing even with 24GB GPU.
 
diff --git a/optimizer/optimizers.py b/optimizer/optimizers.py
index b854eaa..94269d8 100644
--- a/optimizer/optimizers.py
+++ b/optimizer/optimizers.py
@@ -313,23 +313,39 @@ class EveryDreamOptimizer():
 
                 if optimizer_name == "dadapt_adam":
                     opt_class = dadaptation.DAdaptAdam
+                    optimizer = opt_class(
+                        itertools.chain(parameters),
+                        lr=curr_lr,
+                        betas=(betas[0], betas[1]),
+                        weight_decay=weight_decay,
+                        eps=epsilon, #unused for lion
+                        d0=d0,
+                        log_every=args.log_step,
+                        growth_rate=1e5,
+                        decouple=decouple,
+                    )
                 elif optimizer_name == "dadapt_lion":
                     opt_class = dadaptation.DAdaptLion
+                    optimizer = opt_class(
+                        itertools.chain(parameters),
+                        lr=curr_lr,
+                        betas=(betas[0], betas[1]),
+                        weight_decay=weight_decay,
+                        d0=d0,
+                        log_every=args.log_step,
+                    )
                 elif optimizer_name == "dadapt_sgd":
                     opt_class = dadaptation.DAdaptSGD
+                    optimizer = opt_class(
+                        itertools.chain(parameters),
+                        lr=curr_lr,
+                        momentum=momentum,
+                        weight_decay=weight_decay,
+                        d0=d0,
+                        log_every=args.log_step,
+                        growth_rate=float("inf"),
+                    )
 
-                optimizer = opt_class(
-                    itertools.chain(parameters),
-                    lr=curr_lr,
-                    betas=(betas[0], betas[1]),
-                    weight_decay=weight_decay,
-                    eps=epsilon, #unused for lion
-                    d0=d0,
-                    log_every=args.log_step,
-                    growth_rate=1e5,
-                    decouple=decouple,
-                    momentum=momentum,
-                )
             else:
                 import bitsandbytes as bnb
                 opt_class = bnb.optim.AdamW8bit
diff --git a/train.py b/train.py
index 5e8dcf2..deea4e3 100644
--- a/train.py
+++ b/train.py
@@ -451,15 +451,16 @@ def main(args):
             vae = pipe.vae
             unet = pipe.unet
             del pipe
-
-        reference_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler")
         
         if args.zero_frequency_noise_ratio == -1.0:
+            # use zero terminal SNR, currently backdoor way to enable it by setting ZFN to -1, still in testing
             from utils.unet_utils import enforce_zero_terminal_snr
-            temp_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", prediction_type="v_prediction")
+            temp_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler")
             trained_betas = enforce_zero_terminal_snr(temp_scheduler.betas).numpy().tolist()
-            noise_scheduler = DDPMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", prediction_type="v_prediction", trained_betas=trained_betas)
+            reference_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", trained_betas=trained_betas)
+            noise_scheduler = DDPMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", trained_betas=trained_betas)
         else:
+            reference_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler")
             noise_scheduler = DDPMScheduler.from_pretrained(model_root_folder, subfolder="scheduler")
 
         tokenizer = CLIPTokenizer.from_pretrained(model_root_folder, subfolder="tokenizer", use_fast=False)