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support redirected tensor path

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Artiprocher
2025-03-18 19:24:27 +08:00
parent 8da0d183a2
commit a8ce9fef33
1 changed files with 25 additions and 3 deletions
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28
examples/wanvideo/train_wan_t2v.py
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@@ -126,7 +126,7 @@ class TextVideoDataset(torch.utils.data.Dataset):
class LightningModelForDataProcess(pl.LightningModule): class LightningModelForDataProcess(pl.LightningModule):
def __init__(self, text_encoder_path, vae_path, image_encoder_path=None, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)): def __init__(self, text_encoder_path, vae_path, image_encoder_path=None, tiled=False, tile_size=(34, 34), tile_stride=(18, 16), redirected_tensor_path=None):
super().__init__() super().__init__()
model_path = [text_encoder_path, vae_path] model_path = [text_encoder_path, vae_path]
if image_encoder_path is not None: if image_encoder_path is not None:
@@ -136,6 +136,7 @@ class LightningModelForDataProcess(pl.LightningModule):
self.pipe = WanVideoPipeline.from_model_manager(model_manager) self.pipe = WanVideoPipeline.from_model_manager(model_manager)
self.tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} self.tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
self.redirected_tensor_path = redirected_tensor_path
def test_step(self, batch, batch_idx): def test_step(self, batch, batch_idx):
data = batch[0] data = batch[0]
@@ -158,20 +159,33 @@ class LightningModelForDataProcess(pl.LightningModule):
else: else:
image_emb = {} image_emb = {}
data = {"latents": latents, "prompt_emb": prompt_emb, "image_emb": image_emb} data = {"latents": latents, "prompt_emb": prompt_emb, "image_emb": image_emb}
if self.redirected_tensor_path is not None:
path = path.replace("/", "_").replace("\\", "_")
path = os.path.join(self.redirected_tensor_path, path)
torch.save(data, path + ".tensors.pth") torch.save(data, path + ".tensors.pth")
class TensorDataset(torch.utils.data.Dataset): class TensorDataset(torch.utils.data.Dataset):
def __init__(self, base_path, metadata_path, steps_per_epoch): def __init__(self, base_path, metadata_path, steps_per_epoch, redirected_tensor_path=None):
metadata = pd.read_csv(metadata_path) metadata = pd.read_csv(metadata_path)
self.path = [os.path.join(base_path, "train", file_name) for file_name in metadata["file_name"]] self.path = [os.path.join(base_path, "train", file_name) for file_name in metadata["file_name"]]
print(len(self.path), "videos in metadata.") print(len(self.path), "videos in metadata.")
self.path = [i + ".tensors.pth" for i in self.path if os.path.exists(i + ".tensors.pth")] if redirected_tensor_path is None:
self.path = [i + ".tensors.pth" for i in self.path if os.path.exists(i + ".tensors.pth")]
else:
cached_path = []
for path in self.path:
path = path.replace("/", "_").replace("\\", "_")
path = os.path.join(redirected_tensor_path, path)
if os.path.exists(path + ".tensors.pth"):
cached_path.append(path + ".tensors.pth")
self.path = cached_path
print(len(self.path), "tensors cached in metadata.") print(len(self.path), "tensors cached in metadata.")
assert len(self.path) > 0 assert len(self.path) > 0
self.steps_per_epoch = steps_per_epoch self.steps_per_epoch = steps_per_epoch
self.redirected_tensor_path = redirected_tensor_path
def __getitem__(self, index): def __getitem__(self, index):
@@ -337,6 +351,12 @@ def parse_args():
default="./", default="./",
help="Path to save the model.", help="Path to save the model.",
) )
parser.add_argument(
"--redirected_tensor_path",
type=str,
default=None,
help="Path to save cached tensors.",
)
parser.add_argument( parser.add_argument(
"--text_encoder_path", "--text_encoder_path",
type=str, type=str,
@@ -542,6 +562,7 @@ def data_process(args):
tiled=args.tiled, tiled=args.tiled,
tile_size=(args.tile_size_height, args.tile_size_width), tile_size=(args.tile_size_height, args.tile_size_width),
tile_stride=(args.tile_stride_height, args.tile_stride_width), tile_stride=(args.tile_stride_height, args.tile_stride_width),
redirected_tensor_path=args.redirected_tensor_path,
) )
trainer = pl.Trainer( trainer = pl.Trainer(
accelerator="gpu", accelerator="gpu",
@@ -556,6 +577,7 @@ def train(args):
args.dataset_path, args.dataset_path,
os.path.join(args.dataset_path, "metadata.csv"), os.path.join(args.dataset_path, "metadata.csv"),
steps_per_epoch=args.steps_per_epoch, steps_per_epoch=args.steps_per_epoch,
redirected_tensor_path=args.redirected_tensor_path,
) )
dataloader = torch.utils.data.DataLoader( dataloader = torch.utils.data.DataLoader(
dataset, dataset,