[chore] fix: retain memory utility.

examples/cogvideo/train_cogvideox_lora.py

@@ -38,10 +38,7 @@
 from diffusers.models.embeddings import get_3d_rotary_pos_embed
 from diffusers.optimization import get_scheduler
 from diffusers.pipelines.cogvideo.pipeline_cogvideox import get_resize_crop_region_for_grid
-from diffusers.training_utils import (
-    cast_training_params,
-    clear_objs_and_retain_memory,
-)
+from diffusers.training_utils import cast_training_params, retain_memory
 from diffusers.utils import check_min_version, convert_unet_state_dict_to_peft, export_to_video, is_wandb_available
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.torch_utils import is_compiled_module
@@ -726,7 +723,8 @@ def log_validation(
                 }
             )
 
-    clear_objs_and_retain_memory([pipe])
+    del pipe
+    retain_memory()
 
     return videos
 

examples/controlnet/train_controlnet_flux.py

@@ -54,7 +54,7 @@
 from diffusers.models.controlnet_flux import FluxControlNetModel
 from diffusers.optimization import get_scheduler
 from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
-from diffusers.training_utils import clear_objs_and_retain_memory, compute_density_for_timestep_sampling
+from diffusers.training_utils import compute_density_for_timestep_sampling, retain_memory
 from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available
@@ -193,7 +193,8 @@ def log_validation(
         else:
             logger.warning(f"image logging not implemented for {tracker.name}")
 
-        clear_objs_and_retain_memory([pipeline])
+        del pipeline
+        retain_memory()
         return image_logs
 
 
@@ -1103,7 +1104,8 @@ def compute_embeddings(batch, proportion_empty_prompts, flux_controlnet_pipeline
             compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=50
         )
 
-    clear_objs_and_retain_memory([text_encoders, tokenizers])
+    del text_encoders, tokenizers, text_encoder_one, text_encoder_two, tokenizer_one, tokenizer_two
+    retain_memory()
 
     # Then get the training dataset ready to be passed to the dataloader.
     train_dataset = prepare_train_dataset(train_dataset, accelerator)

examples/controlnet/train_controlnet_sd3.py

@@ -50,9 +50,9 @@
 )
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import (
-    clear_objs_and_retain_memory,
     compute_density_for_timestep_sampling,
     compute_loss_weighting_for_sd3,
+    retain_memory,
 )
 from diffusers.utils import check_min_version, is_wandb_available
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
@@ -174,7 +174,8 @@ def log_validation(controlnet, args, accelerator, weight_dtype, step, is_final_v
         else:
             logger.warning(f"image logging not implemented for {tracker.name}")
 
-        clear_objs_and_retain_memory(pipeline)
+        del pipeline
+        retain_memory()
 
         if not is_final_validation:
             controlnet.to(accelerator.device)
@@ -1131,7 +1132,9 @@ def compute_text_embeddings(batch, text_encoders, tokenizers):
         new_fingerprint = Hasher.hash(args)
         train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
 
-    clear_objs_and_retain_memory(text_encoders + tokenizers)
+    del text_encoder_one, text_encoder_two, text_encoder_three
+    del tokenizer_one, tokenizer_two, tokenizer_three
+    retain_memory()
 
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset,

examples/dreambooth/train_dreambooth_lora_flux.py

@@ -55,9 +55,9 @@
 from diffusers.training_utils import (
     _set_state_dict_into_text_encoder,
     cast_training_params,
-    clear_objs_and_retain_memory,
     compute_density_for_timestep_sampling,
     compute_loss_weighting_for_sd3,
+    retain_memory,
 )
 from diffusers.utils import (
     check_min_version,
@@ -1437,7 +1437,8 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
 
     # Clear the memory here
     if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
-        clear_objs_and_retain_memory([tokenizers, text_encoders, text_encoder_one, text_encoder_two])
+        del text_encoder_one, text_encoder_two, tokenizer_one, tokenizer_two
+        retain_memory()
 
     # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
     # pack the statically computed variables appropriately here. This is so that we don't
@@ -1480,7 +1481,8 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
                 latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
 
         if args.validation_prompt is None:
-            clear_objs_and_retain_memory([vae])
+            del vae
+            retain_memory()
 
     # Scheduler and math around the number of training steps.
     overrode_max_train_steps = False
@@ -1817,7 +1819,8 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     torch_dtype=weight_dtype,
                 )
                 if not args.train_text_encoder:
-                    clear_objs_and_retain_memory([text_encoder_one, text_encoder_two])
+                    del text_encoder_one, text_encoder_two
+                    retain_memory()
 
     # Save the lora layers
     accelerator.wait_for_everyone()

examples/dreambooth/train_dreambooth_lora_sd3.py

@@ -55,9 +55,9 @@
 from diffusers.training_utils import (
     _set_state_dict_into_text_encoder,
     cast_training_params,
-    clear_objs_and_retain_memory,
     compute_density_for_timestep_sampling,
     compute_loss_weighting_for_sd3,
+    retain_memory,
 )
 from diffusers.utils import (
     check_min_version,
@@ -211,7 +211,8 @@ def log_validation(
                 }
             )
 
-    clear_objs_and_retain_memory(objs=[pipeline])
+    del pipeline
+    retain_memory()
 
     return images
 
@@ -1106,7 +1107,8 @@ def main(args):
                     image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
                     image.save(image_filename)
 
-            clear_objs_and_retain_memory(objs=[pipeline])
+            del pipeline
+            retain_memory()
 
     # Handle the repository creation
     if accelerator.is_main_process:
@@ -1453,9 +1455,9 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
     # Clear the memory here
     if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
         # Explicitly delete the objects as well, otherwise only the lists are deleted and the original references remain, preventing garbage collection
-        clear_objs_and_retain_memory(
-            objs=[tokenizers, text_encoders, text_encoder_one, text_encoder_two, text_encoder_three]
-        )
+        del tokenizers, text_encoders
+        del text_encoder_one, text_encoder_two, text_encoder_three
+        retain_memory()
 
     # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
     # pack the statically computed variables appropriately here. This is so that we don't
@@ -1791,11 +1793,9 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     epoch=epoch,
                     torch_dtype=weight_dtype,
                 )
-                objs = []
-                if not args.train_text_encoder:
-                    objs.extend([text_encoder_one, text_encoder_two, text_encoder_three])
 
-                clear_objs_and_retain_memory(objs=objs)
+                del text_encoder_one, text_encoder_two, text_encoder_three
+                retain_memory()
 
     # Save the lora layers
     accelerator.wait_for_everyone()

src/diffusers/training_utils.py

@@ -260,12 +260,8 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
     return weighting
 
 
-def clear_objs_and_retain_memory(objs: List[Any]):
-    """Deletes `objs` and runs garbage collection. Then clears the cache of the available accelerator."""
-    if len(objs) >= 1:
-        for obj in objs:
-            del obj
-
+def retain_memory():
+    """Runs garbage collection. Then clears the cache of the available accelerator."""
     gc.collect()
 
     if torch.cuda.is_available():