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Support SD3.5M multi resolutional training

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Kohya S
2024-10-31 19:58:22 +09:00
parent 70a179e446
commit 1434d8506f
8 changed files with 215 additions and 10 deletions
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13
sd3_train_network.py
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@@ -26,8 +26,8 @@ class Sd3NetworkTrainer(train_network.NetworkTrainer):
super().__init__()
self.sample_prompts_te_outputs = None
def assert_extra_args(self, args, train_dataset_group):
super().assert_extra_args(args, train_dataset_group)
def assert_extra_args(self, args, train_dataset_group: train_util.DatasetGroup):
# super().assert_extra_args(args, train_dataset_group)
# sdxl_train_util.verify_sdxl_training_args(args)
if args.fp8_base_unet:
@@ -53,6 +53,9 @@ class Sd3NetworkTrainer(train_network.NetworkTrainer):
train_dataset_group.verify_bucket_reso_steps(32) # TODO check this
# enumerate resolutions from dataset for positional embeddings
self.resolutions = train_dataset_group.get_resolutions()
def load_target_model(self, args, weight_dtype, accelerator):
# currently offload to cpu for some models
@@ -67,6 +70,12 @@ class Sd3NetworkTrainer(train_network.NetworkTrainer):
self.model_type = mmdit.model_type
mmdit.set_pos_emb_random_crop_rate(args.pos_emb_random_crop_rate)
# set resolutions for positional embeddings
if args.enable_scaled_pos_embed:
latent_sizes = [round(math.sqrt(res[0] * res[1])) // 8 for res in self.resolutions] # 8 is stride for latent
logger.info(f"Prepare scaled positional embeddings for resolutions: {self.resolutions}, sizes: {latent_sizes}")
mmdit.enable_scaled_pos_embed(True, latent_sizes)
if args.fp8_base:
# check dtype of model
if mmdit.dtype == torch.float8_e4m3fnuz or mmdit.dtype == torch.float8_e5m2 or mmdit.dtype == torch.float8_e5m2fnuz: