simplyfy code by updating accelerate to 0.30.0

* init

* use no schedule

* fix typo

* update for eval()

* fix typo

fine_tune.py

@@ -250,23 +250,32 @@ def train(args):
         unet.to(weight_dtype)
         text_encoder.to(weight_dtype)
 
+    use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
     if args.deepspeed:
         if args.train_text_encoder:
             ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet, text_encoder=text_encoder)
         else:
             ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet)
-        ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            ds_model, optimizer, train_dataloader, lr_scheduler
-        )
+        ds_model, optimizer, train_dataloader = accelerator.prepare(ds_model, optimizer, train_dataloader)
+        if not use_schedule_free_optimizer:
+            lr_scheduler = accelerator.prepare(lr_scheduler)
         training_models = [ds_model]
     else:
         # acceleratorがなんかよろしくやってくれるらしい
         if args.train_text_encoder:
-            unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                unet, text_encoder, optimizer, train_dataloader, lr_scheduler
-            )
+            unet, text_encoder, optimizer, train_dataloader = accelerator.prepare(unet, text_encoder, optimizer, train_dataloader)
         else:
-            unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
+            unet, optimizer, train_dataloader = accelerator.prepare(unet, optimizer, train_dataloader)
+        if not use_schedule_free_optimizer:
+            lr_scheduler = accelerator.prepare(lr_scheduler)
+
+    # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+    if use_schedule_free_optimizer:
+        optimizer_train_if_needed = lambda: optimizer.train()
+        optimizer_eval_if_needed = lambda: optimizer.eval()
+    else:
+        optimizer_train_if_needed = lambda: None
+        optimizer_eval_if_needed = lambda: None
 
     # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
     if args.full_fp16:
@@ -324,6 +333,7 @@ def train(args):
             m.train()
 
         for step, batch in enumerate(train_dataloader):
+            optimizer_train_if_needed()
             current_step.value = global_step
             with accelerator.accumulate(*training_models):
                 with torch.no_grad():
@@ -354,7 +364,9 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    args, noise_scheduler, latents
+                )
 
                 # Predict the noise residual
                 with accelerator.autocast():
@@ -368,7 +380,9 @@ def train(args):
 
                 if args.min_snr_gamma or args.scale_v_pred_loss_like_noise_pred or args.debiased_estimation_loss:
                     # do not mean over batch dimension for snr weight or scale v-pred loss
-                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                    loss = train_util.conditional_loss(
+                        noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                    )
                     loss = loss.mean([1, 2, 3])
 
                     if args.min_snr_gamma:
@@ -380,7 +394,9 @@ def train(args):
 
                     loss = loss.mean()  # mean over batch dimension
                 else:
-                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c)
+                    loss = train_util.conditional_loss(
+                        noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c
+                    )
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients and args.max_grad_norm != 0.0:
@@ -390,9 +406,11 @@ def train(args):
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                 optimizer.step()
-                lr_scheduler.step()
+                lr_scheduler.step()  # if schedule-free optimizer is used, this is a no-op
                 optimizer.zero_grad(set_to_none=True)
 
+            optimizer_eval_if_needed()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)
@@ -471,7 +489,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if is_main_process and (args.save_state or args.save_state_on_train_end):        
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     del accelerator  # この後メモリを使うのでこれは消す

library/train_util.py

@@ -3087,7 +3087,7 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
     )
     parser.add_argument("--seed", type=int, default=None, help="random seed for training / 学習時の乱数のseed")
     parser.add_argument(
-        "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / grandient checkpointingを有効にする"
+        "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / gradient checkpointingを有効にする"
     )
     parser.add_argument(
         "--gradient_accumulation_steps",
@@ -4088,6 +4088,21 @@ def get_optimizer(args, trainable_params):
         optimizer_class = torch.optim.AdamW
         optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
+    elif optimizer_type.endswith("schedulefree".lower()):
+        try:
+            import schedulefree as sf
+        except ImportError:
+            raise ImportError("No schedulefree / schedulefreeがインストールされていないようです")
+        if optimizer_type == "AdamWScheduleFree".lower():
+            optimizer_class = sf.AdamWScheduleFree
+            logger.info(f"use AdamWScheduleFree optimizer | {optimizer_kwargs}")
+        elif optimizer_type == "SGDScheduleFree".lower():
+            optimizer_class = sf.SGDScheduleFree
+            logger.info(f"use SGDScheduleFree optimizer | {optimizer_kwargs}")
+        else:
+            raise ValueError(f"Unknown optimizer type: {optimizer_type}")
+        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
+
     if optimizer is None:
         # 任意のoptimizerを使う
         optimizer_type = args.optimizer_type  # lowerでないやつ（微妙）
@@ -4116,6 +4131,14 @@ def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int):
     """
     Unified API to get any scheduler from its name.
     """
+    # supports schedule free optimizer
+    if args.optimizer_type.lower().endswith("schedulefree"):
+        # return dummy scheduler: it has 'step' method but does nothing
+        logger.info("use dummy scheduler for schedule free optimizer / schedule free optimizer用のダミースケジューラを使用します")
+        lr_scheduler = TYPE_TO_SCHEDULER_FUNCTION[SchedulerType.CONSTANT](optimizer)
+        lr_scheduler.step = lambda: None
+        return lr_scheduler
+
     name = args.lr_scheduler
     num_warmup_steps: Optional[int] = args.lr_warmup_steps
     num_training_steps = args.max_train_steps * num_processes  # * args.gradient_accumulation_steps
@@ -4250,7 +4273,7 @@ def load_tokenizer(args: argparse.Namespace):
     return tokenizer
 
 
-def prepare_accelerator(args: argparse.Namespace):
+def prepare_accelerator(args: argparse.Namespace) -> Accelerator:
     """
     this function also prepares deepspeed plugin
     """

requirements.txt

@@ -1,4 +1,4 @@
-accelerate==0.25.0
+accelerate==0.30.0
 transformers==4.36.2
 diffusers[torch]==0.25.0
 ftfy==6.1.1
@@ -9,6 +9,7 @@ pytorch-lightning==1.9.0
 bitsandbytes==0.43.0
 prodigyopt==1.0
 lion-pytorch==0.0.6
+schedulefree==1.2.5
 tensorboard
 safetensors==0.4.2
 # gradio==3.16.2

sdxl_train.py

@@ -407,6 +407,7 @@ def train(args):
         text_encoder1.text_model.encoder.layers[-1].requires_grad_(False)
         text_encoder1.text_model.final_layer_norm.requires_grad_(False)
 
+    use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
     if args.deepspeed:
         ds_model = deepspeed_utils.prepare_deepspeed_model(
             args,
@@ -415,9 +416,9 @@ def train(args):
             text_encoder2=text_encoder2 if train_text_encoder2 else None,
         )
         # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007
-        ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            ds_model, optimizer, train_dataloader, lr_scheduler
-        )
+        ds_model, optimizer, train_dataloader = accelerator.prepare(ds_model, optimizer, train_dataloader)
+        if not use_schedule_free_optimizer:
+            lr_scheduler = accelerator.prepare(lr_scheduler)
         training_models = [ds_model]
 
     else:
@@ -428,7 +429,17 @@ def train(args):
             text_encoder1 = accelerator.prepare(text_encoder1)
         if train_text_encoder2:
             text_encoder2 = accelerator.prepare(text_encoder2)
-        optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
+        if not use_schedule_free_optimizer:
+            lr_scheduler = accelerator.prepare(lr_scheduler)
+        optimizer, train_dataloader = accelerator.prepare(optimizer, train_dataloader)
+
+    # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+    if use_schedule_free_optimizer:
+        optimizer_train_if_needed = lambda: optimizer.train()
+        optimizer_eval_if_needed = lambda: optimizer.eval()
+    else:
+        optimizer_train_if_needed = lambda: None
+        optimizer_eval_if_needed = lambda: None
 
     # TextEncoderの出力をキャッシュするときにはCPUへ移動する
     if args.cache_text_encoder_outputs:
@@ -503,6 +514,7 @@ def train(args):
             m.train()
 
         for step, batch in enumerate(train_dataloader):
+            optimizer_train_if_needed()
             current_step.value = global_step
             with accelerator.accumulate(*training_models):
                 if "latents" in batch and batch["latents"] is not None:
@@ -582,7 +594,9 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    args, noise_scheduler, latents
+                )
 
                 noisy_latents = noisy_latents.to(weight_dtype)  # TODO check why noisy_latents is not weight_dtype
 
@@ -600,7 +614,9 @@ def train(args):
                     or args.masked_loss
                 ):
                     # do not mean over batch dimension for snr weight or scale v-pred loss
-                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                    loss = train_util.conditional_loss(
+                        noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                    )
                     if args.masked_loss:
                         loss = apply_masked_loss(loss, batch)
                     loss = loss.mean([1, 2, 3])
@@ -616,7 +632,9 @@ def train(args):
 
                     loss = loss.mean()  # mean over batch dimension
                 else:
-                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c)
+                    loss = train_util.conditional_loss(
+                        noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c
+                    )
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients and args.max_grad_norm != 0.0:
@@ -626,9 +644,11 @@ def train(args):
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                 optimizer.step()
-                lr_scheduler.step()
+                lr_scheduler.step()  # if schedule-free optimizer is used, this is a no-op
                 optimizer.zero_grad(set_to_none=True)
 
+            optimizer_eval_if_needed()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)
@@ -736,7 +756,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if args.save_state or args.save_state_on_train_end:        
+    if args.save_state or args.save_state_on_train_end:
         train_util.save_state_on_train_end(args, accelerator)
 
     del accelerator  # この後メモリを使うのでこれは消す

sdxl_train_control_net_lllite.py

@@ -15,6 +15,7 @@ from tqdm import tqdm
 
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
+
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -286,7 +287,18 @@ def train(args):
     unet.to(weight_dtype)
 
     # acceleratorがなんかよろしくやってくれるらしい
-    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
+    use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
+    unet, optimizer, train_dataloader = accelerator.prepare(unet, optimizer, train_dataloader)
+    if not use_schedule_free_optimizer:
+        lr_scheduler = accelerator.prepare(lr_scheduler)
+
+    # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+    if use_schedule_free_optimizer:
+        optimizer_train_if_needed = lambda: optimizer.train()
+        optimizer_eval_if_needed = lambda: optimizer.eval()
+    else:
+        optimizer_train_if_needed = lambda: None
+        optimizer_eval_if_needed = lambda: None
 
     if args.gradient_checkpointing:
         unet.train()  # according to TI example in Diffusers, train is required -> これオリジナルのU-Netしたので本当は外せる
@@ -390,6 +402,7 @@ def train(args):
         current_epoch.value = epoch + 1
 
         for step, batch in enumerate(train_dataloader):
+            optimizer_train_if_needed()
             current_step.value = global_step
             with accelerator.accumulate(unet):
                 with torch.no_grad():
@@ -439,7 +452,9 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    args, noise_scheduler, latents
+                )
 
                 noisy_latents = noisy_latents.to(weight_dtype)  # TODO check why noisy_latents is not weight_dtype
 
@@ -458,7 +473,9 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                loss = train_util.conditional_loss(
+                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                )
                 loss = loss.mean([1, 2, 3])
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
@@ -484,6 +501,8 @@ def train(args):
                 lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            optimizer_eval_if_needed()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

sdxl_train_control_net_lllite_old.py

@@ -12,6 +12,7 @@ from tqdm import tqdm
 
 import torch
 from library.device_utils import init_ipex, clean_memory_on_device
+
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -254,9 +255,19 @@ def train(args):
         network.to(weight_dtype)
 
     # acceleratorがなんかよろしくやってくれるらしい
-    unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet, network, optimizer, train_dataloader, lr_scheduler
-    )
+    use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
+    unet, network, optimizer, train_dataloader = accelerator.prepare(unet, network, optimizer, train_dataloader)
+    if not use_schedule_free_optimizer:
+        lr_scheduler = accelerator.prepare(lr_scheduler)
+
+    # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+    if use_schedule_free_optimizer:
+        optimizer_train_if_needed = lambda: optimizer.train()
+        optimizer_eval_if_needed = lambda: optimizer.eval()
+    else:
+        optimizer_train_if_needed = lambda: None
+        optimizer_eval_if_needed = lambda: None
+
     network: control_net_lllite.ControlNetLLLite
 
     if args.gradient_checkpointing:
@@ -357,6 +368,7 @@ def train(args):
         network.on_epoch_start()  # train()
 
         for step, batch in enumerate(train_dataloader):
+            optimizer_train_if_needed()
             current_step.value = global_step
             with accelerator.accumulate(network):
                 with torch.no_grad():
@@ -406,7 +418,9 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    args, noise_scheduler, latents
+                )
 
                 noisy_latents = noisy_latents.to(weight_dtype)  # TODO check why noisy_latents is not weight_dtype
 
@@ -426,7 +440,9 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                loss = train_util.conditional_loss(
+                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                )
                 loss = loss.mean([1, 2, 3])
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
@@ -452,6 +468,8 @@ def train(args):
                 lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            optimizer_eval_if_needed()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

train_controlnet.py

@@ -13,6 +13,7 @@ from tqdm import tqdm
 import torch
 from library import deepspeed_utils
 from library.device_utils import init_ipex, clean_memory_on_device
+
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -226,7 +227,7 @@ def train(args):
             )
         vae.to("cpu")
         clean_memory_on_device(accelerator.device)
-        
+
         accelerator.wait_for_everyone()
 
     if args.gradient_checkpointing:
@@ -276,9 +277,18 @@ def train(args):
         controlnet.to(weight_dtype)
 
     # acceleratorがなんかよろしくやってくれるらしい
-    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        controlnet, optimizer, train_dataloader, lr_scheduler
-    )
+    use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
+    controlnet, optimizer, train_dataloader = accelerator.prepare(controlnet, optimizer, train_dataloader)
+    if not use_schedule_free_optimizer:
+        lr_scheduler = accelerator.prepare(lr_scheduler)
+
+    # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+    if use_schedule_free_optimizer:
+        optimizer_train_if_needed = lambda: optimizer.train()
+        optimizer_eval_if_needed = lambda: optimizer.eval()
+    else:
+        optimizer_train_if_needed = lambda: None
+        optimizer_eval_if_needed = lambda: None
 
     unet.requires_grad_(False)
     text_encoder.requires_grad_(False)
@@ -393,6 +403,7 @@ def train(args):
         current_epoch.value = epoch + 1
 
         for step, batch in enumerate(train_dataloader):
+            optimizer_train_if_needed()
             current_step.value = global_step
             with accelerator.accumulate(controlnet):
                 with torch.no_grad():
@@ -420,7 +431,9 @@ def train(args):
                     )
 
                 # Sample a random timestep for each image
-                timesteps, huber_c = train_util.get_timesteps_and_huber_c(args, 0, noise_scheduler.config.num_train_timesteps, noise_scheduler, b_size, latents.device)
+                timesteps, huber_c = train_util.get_timesteps_and_huber_c(
+                    args, 0, noise_scheduler.config.num_train_timesteps, noise_scheduler, b_size, latents.device
+                )
 
                 # Add noise to the latents according to the noise magnitude at each timestep
                 # (this is the forward diffusion process)
@@ -452,7 +465,9 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                loss = train_util.conditional_loss(
+                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                )
                 loss = loss.mean([1, 2, 3])
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
@@ -472,6 +487,8 @@ def train(args):
                 lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            optimizer_eval_if_needed()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

train_db.py

@@ -224,25 +224,34 @@ def train(args):
         text_encoder.to(weight_dtype)
 
     # acceleratorがなんかよろしくやってくれるらしい
+    use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
     if args.deepspeed:
         if args.train_text_encoder:
             ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet, text_encoder=text_encoder)
         else:
             ds_model = deepspeed_utils.prepare_deepspeed_model(args, unet=unet)
-        ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            ds_model, optimizer, train_dataloader, lr_scheduler
-        )
+        ds_model, optimizer, train_dataloader = accelerator.prepare(ds_model, optimizer, train_dataloader)
+        if not use_schedule_free_optimizer:
+            lr_scheduler = accelerator.prepare(lr_scheduler)
         training_models = [ds_model]
 
     else:
         if train_text_encoder:
-            unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                unet, text_encoder, optimizer, train_dataloader, lr_scheduler
-            )
+            unet, text_encoder, optimizer, train_dataloader = accelerator.prepare(unet, text_encoder, optimizer, train_dataloader)
             training_models = [unet, text_encoder]
         else:
-            unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
+            unet, optimizer, train_dataloader = accelerator.prepare(unet, optimizer, train_dataloader)
             training_models = [unet]
+        if not use_schedule_free_optimizer:
+            lr_scheduler = accelerator.prepare(lr_scheduler)
+
+    # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+    if use_schedule_free_optimizer:
+        optimizer_train_if_needed = lambda: optimizer.train()
+        optimizer_eval_if_needed = lambda: optimizer.eval()
+    else:
+        optimizer_train_if_needed = lambda: None
+        optimizer_eval_if_needed = lambda: None
 
     if not train_text_encoder:
         text_encoder.to(accelerator.device, dtype=weight_dtype)  # to avoid 'cpu' vs 'cuda' error
@@ -307,6 +316,7 @@ def train(args):
             text_encoder.train()
 
         for step, batch in enumerate(train_dataloader):
+            optimizer_train_if_needed()
             current_step.value = global_step
             # 指定したステップ数でText Encoderの学習を止める
             if global_step == args.stop_text_encoder_training:
@@ -346,7 +356,9 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    args, noise_scheduler, latents
+                )
 
                 # Predict the noise residual
                 with accelerator.autocast():
@@ -358,7 +370,9 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                loss = train_util.conditional_loss(
+                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                )
                 if args.masked_loss:
                     loss = apply_masked_loss(loss, batch)
                 loss = loss.mean([1, 2, 3])
@@ -387,6 +401,8 @@ def train(args):
                 lr_scheduler.step()
                 optimizer.zero_grad(set_to_none=True)
 
+            optimizer_eval_if_needed()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)

train_network.py

@@ -412,6 +412,7 @@ class NetworkTrainer:
                 t_enc.text_model.embeddings.to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype))
 
         # acceleratorがなんかよろしくやってくれるらしい / accelerator will do something good
+        use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
         if args.deepspeed:
             ds_model = deepspeed_utils.prepare_deepspeed_model(
                 args,
@@ -420,9 +421,9 @@ class NetworkTrainer:
                 text_encoder2=text_encoders[1] if train_text_encoder and len(text_encoders) > 1 else None,
                 network=network,
             )
-            ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                ds_model, optimizer, train_dataloader, lr_scheduler
-            )
+            ds_model, optimizer, train_dataloader = accelerator.prepare(ds_model, optimizer, train_dataloader)
+            if not use_schedule_free_optimizer:
+                lr_scheduler = accelerator.prepare(lr_scheduler)
             training_model = ds_model
         else:
             if train_unet:
@@ -438,14 +439,23 @@ class NetworkTrainer:
             else:
                 pass  # if text_encoder is not trained, no need to prepare. and device and dtype are already set
 
-            network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                network, optimizer, train_dataloader, lr_scheduler
-            )
+            network, optimizer, train_dataloader = accelerator.prepare(network, optimizer, train_dataloader)
+            if not use_schedule_free_optimizer:
+                lr_scheduler = accelerator.prepare(lr_scheduler)
             training_model = network
 
+        # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+        if use_schedule_free_optimizer:
+            optimizer_train_if_needed = lambda: (optimizer.optimizer if hasattr(optimizer, "optimizer") else optimizer).train()
+            optimizer_eval_if_needed = lambda: (optimizer.optimizer if hasattr(optimizer, "optimizer") else optimizer).eval()
+        else:
+            optimizer_train_if_needed = lambda: None
+            optimizer_eval_if_needed = lambda: None
+
         if args.gradient_checkpointing:
             # according to TI example in Diffusers, train is required
             unet.train()
+
             for t_enc in text_encoders:
                 t_enc.train()
 
@@ -804,6 +814,7 @@ class NetworkTrainer:
             accelerator.unwrap_model(network).on_epoch_start(text_encoder, unet)
 
             for step, batch in enumerate(train_dataloader):
+                optimizer_train_if_needed()
                 current_step.value = global_step
                 with accelerator.accumulate(training_model):
                     on_step_start(text_encoder, unet)
@@ -920,6 +931,8 @@ class NetworkTrainer:
                 else:
                     keys_scaled, mean_norm, maximum_norm = None, None, None
 
+                optimizer_eval_if_needed()
+
                 # Checks if the accelerator has performed an optimization step behind the scenes
                 if accelerator.sync_gradients:
                     progress_bar.update(1)

train_textual_inversion.py

@@ -415,20 +415,28 @@ class TextualInversionTrainer:
         lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
 
         # acceleratorがなんかよろしくやってくれるらしい
+        use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
         if len(text_encoders) == 1:
-            text_encoder_or_list, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                text_encoder_or_list, optimizer, train_dataloader, lr_scheduler
+            text_encoder_or_list, optimizer, train_dataloader = accelerator.preparet(
+                text_encoder_or_list, optimizer, train_dataloader
             )
-
         elif len(text_encoders) == 2:
-            text_encoder1, text_encoder2, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-                text_encoders[0], text_encoders[1], optimizer, train_dataloader, lr_scheduler
+            text_encoder1, text_encoder2, optimizer, train_dataloader = accelerator.prepare(
+                text_encoders[0], text_encoders[1], optimizer, train_dataloader
             )
-
             text_encoder_or_list = text_encoders = [text_encoder1, text_encoder2]
-
         else:
             raise NotImplementedError()
+        if not use_schedule_free_optimizer:
+            optimizer, lr_scheduler = accelerator.prepare(optimizer, lr_scheduler)
+
+        # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+        if use_schedule_free_optimizer:
+            optimizer_train_if_needed = lambda: (optimizer.optimizer if hasattr(optimizer, "optimizer") else optimizer).train()
+            optimizer_eval_if_needed = lambda: (optimizer.optimizer if hasattr(optimizer, "optimizer") else optimizer).eval()
+        else:
+            optimizer_train_if_needed = lambda: None
+            optimizer_eval_if_needed = lambda: None
 
         index_no_updates_list = []
         orig_embeds_params_list = []
@@ -557,6 +565,7 @@ class TextualInversionTrainer:
             loss_total = 0
 
             for step, batch in enumerate(train_dataloader):
+                optimizer_train_if_needed()
                 current_step.value = global_step
                 with accelerator.accumulate(text_encoders[0]):
                     with torch.no_grad():
@@ -588,7 +597,9 @@ class TextualInversionTrainer:
                     else:
                         target = noise
 
-                    loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                    loss = train_util.conditional_loss(
+                        noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                    )
                     if args.masked_loss:
                         loss = apply_masked_loss(loss, batch)
                     loss = loss.mean([1, 2, 3])
@@ -627,6 +638,8 @@ class TextualInversionTrainer:
                                 index_no_updates
                             ]
 
+                optimizer_eval_if_needed()
+
                 # Checks if the accelerator has performed an optimization step behind the scenes
                 if accelerator.sync_gradients:
                     progress_bar.update(1)

train_textual_inversion_XTI.py

@@ -335,9 +335,18 @@ def train(args):
     lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
 
     # acceleratorがなんかよろしくやってくれるらしい
-    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        text_encoder, optimizer, train_dataloader, lr_scheduler
-    )
+    use_schedule_free_optimizer = args.optimizer_type.lower().endswith("schedulefree")
+    text_encoder, optimizer, train_dataloader = accelerator.prepare(text_encoder, optimizer, train_dataloader)
+    if not use_schedule_free_optimizer:
+        lr_scheduler = accelerator.prepare(lr_scheduler)
+
+    # make lambda function for calling optimizer.train() and optimizer.eval() if schedule-free optimizer is used
+    if use_schedule_free_optimizer:
+        optimizer_train_if_needed = lambda: optimizer.train()
+        optimizer_eval_if_needed = lambda: optimizer.eval()
+    else:
+        optimizer_train_if_needed = lambda: None
+        optimizer_eval_if_needed = lambda: None
 
     index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0]
     # logger.info(len(index_no_updates), torch.sum(index_no_updates))
@@ -438,6 +447,7 @@ def train(args):
         loss_total = 0
 
         for step, batch in enumerate(train_dataloader):
+            optimizer_train_if_needed()
             current_step.value = global_step
             with accelerator.accumulate(text_encoder):
                 with torch.no_grad():
@@ -461,7 +471,9 @@ def train(args):
 
                 # Sample noise, sample a random timestep for each image, and add noise to the latents,
                 # with noise offset and/or multires noise if specified
-                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
+                noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
+                    args, noise_scheduler, latents
+                )
 
                 # Predict the noise residual
                 with accelerator.autocast():
@@ -473,7 +485,9 @@ def train(args):
                 else:
                     target = noise
 
-                loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
+                loss = train_util.conditional_loss(
+                    noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
+                )
                 if args.masked_loss:
                     loss = apply_masked_loss(loss, batch)
                 loss = loss.mean([1, 2, 3])
@@ -505,6 +519,8 @@ def train(args):
                         index_no_updates
                     ]
 
+            optimizer_eval_if_needed()
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)