[Training Utils] create a utility for casting the lora params during training.

examples/consistency_distillation/train_lcm_distill_lora_sdxl.py

@@ -51,7 +51,7 @@
     UNet2DConditionModel,
 )
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import resolve_interpolation_mode
+from diffusers.training_utils import cast_training_params, resolve_interpolation_mode
 from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
 
@@ -860,10 +860,8 @@ def main(args):
 
     # Make sure the trainable params are in float32.
     if args.mixed_precision == "fp16":
-        for param in unet.parameters():
-            # only upcast trainable parameters (LoRA) into fp32
-            if param.requires_grad:
-                param.data = param.to(torch.float32)
+        # only upcast trainable parameters (LoRA) into fp32
+        cast_training_params(unet, dtype=torch.float32)
 
     # Also move the alpha and sigma noise schedules to accelerator.device.
     alpha_schedule = alpha_schedule.to(accelerator.device)

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -53,7 +53,7 @@
 )
 from diffusers.loaders import LoraLoaderMixin
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import _set_state_dict_into_text_encoder, compute_snr
+from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr
 from diffusers.utils import (
     check_min_version,
     convert_state_dict_to_diffusers,
@@ -1086,11 +1086,8 @@ def load_model_hook(models, input_dir):
             models = [unet_]
             if args.train_text_encoder:
                 models.extend([text_encoder_one_, text_encoder_two_])
-            for model in models:
-                for param in model.parameters():
-                    # only upcast trainable parameters (LoRA) into fp32
-                    if param.requires_grad:
-                        param.data = param.to(torch.float32)
+                # only upcast trainable parameters (LoRA) into fp32
+                cast_training_params(models)
 
     accelerator.register_save_state_pre_hook(save_model_hook)
     accelerator.register_load_state_pre_hook(load_model_hook)
@@ -1110,11 +1107,9 @@ def load_model_hook(models, input_dir):
         models = [unet]
         if args.train_text_encoder:
             models.extend([text_encoder_one, text_encoder_two])
-        for model in models:
-            for param in model.parameters():
-                # only upcast trainable parameters (LoRA) into fp32
-                if param.requires_grad:
-                    param.data = param.to(torch.float32)
+
+        # only upcast trainable parameters (LoRA) into fp32
+        cast_training_params(models, dtype=torch.float32)
 
     unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters()))
 

examples/text_to_image/train_text_to_image_lora.py

@@ -43,7 +43,7 @@
 import diffusers
 from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionPipeline, UNet2DConditionModel
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import compute_snr
+from diffusers.training_utils import cast_training_params, compute_snr
 from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
 
@@ -466,10 +466,8 @@ def main():
     # Add adapter and make sure the trainable params are in float32.
     unet.add_adapter(unet_lora_config)
     if args.mixed_precision == "fp16":
-        for param in unet.parameters():
-            # only upcast trainable parameters (LoRA) into fp32
-            if param.requires_grad:
-                param.data = param.to(torch.float32)
+        # only upcast trainable parameters (LoRA) into fp32
+        cast_training_params(unet, dtype=torch.float32)
 
     if args.enable_xformers_memory_efficient_attention:
         if is_xformers_available():

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -51,7 +51,7 @@
 )
 from diffusers.loaders import LoraLoaderMixin
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import compute_snr
+from diffusers.training_utils import cast_training_params, compute_snr
 from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
 
@@ -634,11 +634,8 @@ def main(args):
         models = [unet]
         if args.train_text_encoder:
             models.extend([text_encoder_one, text_encoder_two])
-        for model in models:
-            for param in model.parameters():
-                # only upcast trainable parameters (LoRA) into fp32
-                if param.requires_grad:
-                    param.data = param.to(torch.float32)
+            # only upcast trainable parameters (LoRA) into fp32
+            cast_training_params(models, dtype=torch.float32)
 
     # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
     def save_model_hook(models, weights, output_dir):

src/diffusers/training_utils.py

@@ -1,7 +1,7 @@
 import contextlib
 import copy
 import random
-from typing import Any, Dict, Iterable, Optional, Union
+from typing import Any, Dict, Iterable, List, Optional, Union
 
 import numpy as np
 import torch
@@ -121,6 +121,16 @@ def unet_lora_state_dict(unet: UNet2DConditionModel) -> Dict[str, torch.Tensor]:
     return lora_state_dict
 
 
+def cast_training_params(model: Union[torch.nn.Module, List[torch.nn.Module]], dtype=torch.float32):
+    if not isinstance(model, list):
+        model = [model]
+    for m in model:
+        for param in m.parameters():
+            # only upcast trainable parameters into fp32
+            if param.requires_grad:
+                param.data = param.to(dtype)
+
+
 def _set_state_dict_into_text_encoder(
     lora_state_dict: Dict[str, torch.Tensor], prefix: str, text_encoder: torch.nn.Module
 ):