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Add optional grad ckpt and algo selection

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This commit is contained in:
Kohaku-Blueleaf 2025-07-22 16:33:10 +08:00
parent 7aa3184e85
commit 545cb2187c
1 changed files with 18 additions and 6 deletions
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24
comfy_extras/nodes_train.py
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@ -20,7 +20,7 @@ import folder_paths
import node_helpers import node_helpers
from comfy.cli_args import args from comfy.cli_args import args
from comfy.comfy_types.node_typing import IO from comfy.comfy_types.node_typing import IO
from comfy.weight_adapter import adapters from comfy.weight_adapter import adapters, adapter_maps
def make_batch_extra_option_dict(d, indicies, full_size=None): def make_batch_extra_option_dict(d, indicies, full_size=None):
@ -478,6 +478,17 @@ class TrainLoraNode:
["bf16", "fp32"], ["bf16", "fp32"],
{"default": "bf16", "tooltip": "The dtype to use for lora."}, {"default": "bf16", "tooltip": "The dtype to use for lora."},
), ),
"algorithm": (
list(adapter_maps.keys()),
{"default": list(adapter_maps.keys())[0], "tooltip": "The algorithm to use for training."},
),
"gradient_checkpointing": (
IO.BOOLEAN,
{
"default": True,
"tooltip": "Use gradient checkpointing for training.",
}
),
"existing_lora": ( "existing_lora": (
folder_paths.get_filename_list("loras") + ["[None]"], folder_paths.get_filename_list("loras") + ["[None]"],
{ {
@ -508,6 +519,8 @@ class TrainLoraNode:
seed, seed,
training_dtype, training_dtype,
lora_dtype, lora_dtype,
algorithm,
gradient_checkpointing,
existing_lora, existing_lora,
): ):
mp = model.clone() mp = model.clone()
@ -558,10 +571,8 @@ class TrainLoraNode:
if existing_adapter is not None: if existing_adapter is not None:
break break
else: else:
# If no existing adapter found, use LoRA
# We will add algo option in the future
existing_adapter = None existing_adapter = None
adapter_cls = adapters[0] adapter_cls = adapter_maps[algorithm]
if existing_adapter is not None: if existing_adapter is not None:
train_adapter = existing_adapter.to_train().to(lora_dtype) train_adapter = existing_adapter.to_train().to(lora_dtype)
@ -615,8 +626,9 @@ class TrainLoraNode:
criterion = torch.nn.SmoothL1Loss() criterion = torch.nn.SmoothL1Loss()
# setup models # 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) mp.model.requires_grad_(False)
comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True) comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True)