apply changes from https://github.com/comfyanonymous/ComfyUI/pull/9015

comfy_extras/v3/nodes_train.py

@@ -17,7 +17,7 @@ import comfy.utils
 import comfy_extras.nodes_custom_sampler
 import folder_paths
 import node_helpers
-from comfy.weight_adapter import adapters
+from comfy.weight_adapter import adapter_maps, adapters
 from comfy_api.v3 import io, ui
 
 
@@ -38,12 +38,13 @@ def make_batch_extra_option_dict(d, indicies, full_size=None):
 
 class TrainSampler(comfy.samplers.Sampler):
 
-    def __init__(self, loss_fn, optimizer, loss_callback=None, batch_size=1, total_steps=1, seed=0, training_dtype=torch.bfloat16):
+    def __init__(self, loss_fn, optimizer, loss_callback=None, batch_size=1, grad_acc=1, total_steps=1, seed=0, training_dtype=torch.bfloat16):
         self.loss_fn = loss_fn
         self.optimizer = optimizer
         self.loss_callback = loss_callback
         self.batch_size = batch_size
         self.total_steps = total_steps
+        self.grad_acc = grad_acc
         self.seed = seed
         self.training_dtype = training_dtype
 
@@ -90,8 +91,9 @@ class TrainSampler(comfy.samplers.Sampler):
                 self.loss_callback(loss.item())
             pbar.set_postfix({"loss": f"{loss.item():.4f}"})
 
-            self.optimizer.step()
+            if (i + 1) % self.grad_acc == 0:
-            self.optimizer.zero_grad()
+                self.optimizer.step()
+                self.optimizer.zero_grad()
         torch.cuda.empty_cache()
         return torch.zeros_like(latent_image)
 
@@ -461,6 +463,7 @@ class TrainLoraNode(io.ComfyNode):
                 io.Latent.Input("latents", tooltip="The Latents to use for training, serve as dataset/input of the model."),
                 io.Conditioning.Input("positive", tooltip="The positive conditioning to use for training."),
                 io.Int.Input("batch_size", default=1, min=1, max=10000, step=1, tooltip="The batch size to use for training."),
+                io.Int.Input("grad_accumulation_steps", default=1, min=1, max=1024, step=1, tooltip="The number of gradient accumulation steps to use for training."),
                 io.Int.Input("steps", default=16, min=1, max=100000, tooltip="The number of steps to train the LoRA for."),
                 io.Float.Input("learning_rate", default=0.0005, min=0.0000001, max=1.0, step=0.000001, tooltip="The learning rate to use for training."),
                 io.Int.Input("rank", default=8, min=1, max=128, tooltip="The rank of the LoRA layers."),
@@ -469,6 +472,8 @@ class TrainLoraNode(io.ComfyNode):
                 io.Int.Input("seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF, tooltip="The seed to use for training (used in generator for LoRA weight initialization and noise sampling)"),
                 io.Combo.Input("training_dtype", options=["bf16", "fp32"], default="bf16", tooltip="The dtype to use for training."),
                 io.Combo.Input("lora_dtype", options=["bf16", "fp32"], default="bf16", tooltip="The dtype to use for lora."),
+                io.Combo.Input("algorithm", options=list(adapter_maps.keys()), default=list(adapter_maps.keys())[0], tooltip="The algorithm to use for training."),
+                io.Boolean.Input("gradient_checkpointing", default=True, tooltip="Use gradient checkpointing for training."),
                 io.Combo.Input("existing_lora", options=folder_paths.get_filename_list("loras") + ["[None]"], default="[None]", tooltip="The existing LoRA to append to. Set to None for new LoRA."),
             ],
             outputs=[
@@ -487,6 +492,7 @@ class TrainLoraNode(io.ComfyNode):
         positive,
         batch_size,
         steps,
+        grad_accumulation_steps,
         learning_rate,
         rank,
         optimizer,
@@ -494,6 +500,8 @@ class TrainLoraNode(io.ComfyNode):
         seed,
         training_dtype,
         lora_dtype,
+        algorithm,
+        gradient_checkpointing,
         existing_lora,
     ):
         mp = model.clone()
@@ -544,10 +552,8 @@ class TrainLoraNode(io.ComfyNode):
                                 if existing_adapter is not None:
                                     break
                             else:
-                                # If no existing adapter found, use LoRA
-                                # We will add algo option in the future
                                 existing_adapter = None
-                                adapter_cls = adapters[0]
+                                adapter_cls = adapter_maps[algorithm]
 
                             if existing_adapter is not None:
                                 train_adapter = existing_adapter.to_train().to(lora_dtype)
@@ -601,8 +607,9 @@ class TrainLoraNode(io.ComfyNode):
                 criterion = torch.nn.SmoothL1Loss()
 
             # setup models
-            for m in find_all_highest_child_module_with_forward(mp.model.diffusion_model):
+            if gradient_checkpointing:
-                patch(m)
+                for m in find_all_highest_child_module_with_forward(mp.model.diffusion_model):
+                    patch(m)
             mp.model.requires_grad_(False)
             comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True)
 
@@ -615,7 +622,8 @@ class TrainLoraNode(io.ComfyNode):
                 optimizer,
                 loss_callback=loss_callback,
                 batch_size=batch_size,
-                total_steps=steps,
+                grad_acc=grad_accumulation_steps,
+                total_steps=steps * grad_accumulation_steps,
                 seed=seed,
                 training_dtype=dtype
             )