[training] fixes to the quantization training script and add AdEMAMix optimizer as an option

examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py

@@ -349,14 +349,19 @@ def parse_args(input_args=None):
         "--optimizer",
         type=str,
         default="AdamW",
-        help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'),
+        choices=["AdamW", "Prodigy", "AdEMAMix"],
     )
 
     parser.add_argument(
         "--use_8bit_adam",
         action="store_true",
         help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
     )
+    parser.add_argument(
+        "--use_8bit_ademamix",
+        action="store_true",
+        help="Whether or not to use 8-bit AdEMAMix from bitsandbytes.",
+    )
 
     parser.add_argument(
         "--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
@@ -820,16 +825,15 @@ def load_model_hook(models, input_dir):
     params_to_optimize = [transformer_parameters_with_lr]
 
     # Optimizer creation
-    if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
+    if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
         logger.warning(
-            f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]."
-            "Defaulting to adamW"
+            f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
+            f"set to {args.optimizer.lower()}"
         )
-        args.optimizer = "adamw"
 
-    if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
+    if args.use_8bit_ademamix and not args.optimizer.lower() == "ademamix":
         logger.warning(
-            f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
+            f"use_8bit_ademamix is ignored when optimizer is not set to 'AdEMAMix'. Optimizer was "
             f"set to {args.optimizer.lower()}"
         )
 
@@ -853,6 +857,20 @@ def load_model_hook(models, input_dir):
             eps=args.adam_epsilon,
         )
 
+    elif args.optimizer.lower() == "ademamix":
+        try:
+            import bitsandbytes as bnb
+        except ImportError:
+            raise ImportError(
+                "To use AdEMAMix (or its 8bit variant), please install the bitsandbytes library: `pip install -U bitsandbytes`."
+            )
+        if args.use_8bit_ademamix:
+            optimizer_class = bnb.optim.AdEMAMix8bit
+        else:
+            optimizer_class = bnb.optim.AdEMAMix
+
+        optimizer = optimizer_class(params_to_optimize)
+
     if args.optimizer.lower() == "prodigy":
         try:
             import prodigyopt
@@ -868,7 +886,6 @@ def load_model_hook(models, input_dir):
 
         optimizer = optimizer_class(
             params_to_optimize,
-            lr=args.learning_rate,
             betas=(args.adam_beta1, args.adam_beta2),
             beta3=args.prodigy_beta3,
             weight_decay=args.adam_weight_decay,
@@ -1020,12 +1037,12 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                 model_input = (model_input - vae_config_shift_factor) * vae_config_scaling_factor
                 model_input = model_input.to(dtype=weight_dtype)
 
-                vae_scale_factor = 2 ** (len(vae_config_block_out_channels))
+                vae_scale_factor = 2 ** (len(vae_config_block_out_channels) - 1)
 
                 latent_image_ids = FluxPipeline._prepare_latent_image_ids(
                     model_input.shape[0],
-                    model_input.shape[2],
-                    model_input.shape[3],
+                    model_input.shape[2] // 2,
+                    model_input.shape[3] // 2,
                     accelerator.device,
                     weight_dtype,
                 )
@@ -1059,7 +1076,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                 )
 
                 # handle guidance
-                if transformer.config.guidance_embeds:
+                if unwrap_model(transformer).config.guidance_embeds:
                     guidance = torch.tensor([args.guidance_scale], device=accelerator.device)
                     guidance = guidance.expand(model_input.shape[0])
                 else:
@@ -1082,8 +1099,8 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                 )[0]
                 model_pred = FluxPipeline._unpack_latents(
                     model_pred,
-                    height=int(model_input.shape[2] * vae_scale_factor / 2),
-                    width=int(model_input.shape[3] * vae_scale_factor / 2),
+                    height=model_input.shape[2] * vae_scale_factor,
+                    width=model_input.shape[3] * vae_scale_factor,
                     vae_scale_factor=vae_scale_factor,
                 )