cpu_offload_checkpointing

flux_train_comfy.py

@@ -276,7 +276,7 @@ class FluxTrainer:
         flux = flux_utils.load_flow_model(name, args.pretrained_model_name_or_path, weight_dtype, "cpu")
 
         if args.gradient_checkpointing:
-            flux.enable_gradient_checkpointing(args.cpu_offload_checkpointing)
+            flux.enable_gradient_checkpointing(cpu_offload=args.cpu_offload_checkpointing)
 
         flux.requires_grad_(True)
 
@@ -680,7 +680,7 @@ class FluxTrainer:
                     else:
                         with torch.no_grad():
                             # encode images to latents. images are [-1, 1]
-                            latents = ae.encode(batch["images"])
+                            latents = ae.encode(batch["images"].to(ae.dtype)).to(accelerator.device, dtype=weight_dtype)
 
                         # NaNが含まれていれば警告を表示し0に置き換える
                         if torch.any(torch.isnan(latents)):

flux_train_network_comfy.py

@@ -43,6 +43,10 @@ class FluxNetworkTrainer(NetworkTrainer):
         if args.max_token_length is not None:
             logger.warning("max_token_length is not used in Flux training")
 
+        assert not args.split_mode or not args.cpu_offload_checkpointing, (
+            "split_mode and cpu_offload_checkpointing cannot be used together"
+        )
+
         train_dataset_group.verify_bucket_reso_steps(32)  # TODO check this
 
     def get_flux_model_name(self, args):

train_network.py

@@ -457,7 +457,11 @@ class NetworkTrainer:
             accelerator.print(f"load network weights from {args.network_weights}: {info}")
 
         if args.gradient_checkpointing:
-            unet.enable_gradient_checkpointing()
+            if args.cpu_offload_checkpointing:
+                unet.enable_gradient_checkpointing(cpu_offload=True)
+            else:
+                unet.enable_gradient_checkpointing()
+
             for t_enc, flag in zip(text_encoders, self.get_text_encoders_train_flags(args, text_encoders)):
                 if flag:
                     if t_enc.supports_gradient_checkpointing:
@@ -1383,6 +1387,11 @@ def setup_parser() -> argparse.ArgumentParser:
         help="initial step number including all epochs, 0 means first step (same as not specifying). overwrites initial_epoch."
         + " / 初期ステップ数、全エポックを含むステップ数、0で最初のステップ（未指定時と同じ）。initial_epochを上書きする",
     )
+    parser.add_argument(
+        "--cpu_offload_checkpointing",
+        action="store_true",
+        help="[EXPERIMENTAL] enable offloading of tensors to CPU during checkpointing for U-Net or DiT, if supported",
+    )
     # parser.add_argument("--loraplus_lr_ratio", default=None, type=float, help="LoRA+ learning rate ratio")
     # parser.add_argument("--loraplus_unet_lr_ratio", default=None, type=float, help="LoRA+ UNet learning rate ratio")
     # parser.add_argument("--loraplus_text_encoder_lr_ratio", default=None, type=float, help="LoRA+ text encoder learning rate ratio")