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update wan training

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Artiprocher
2025-06-16 15:46:20 +08:00
parent c0706e3fbd
commit 040e7fda49
2 changed files with 104 additions and 41 deletions
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131
diffsynth/trainers/utils.py
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@@ -11,8 +11,9 @@ class VideoDataset(torch.utils.data.Dataset):
def __init__(
self,
base_path=None, metadata_path=None,
frame_interval=1, num_frames=81,
dynamic_resolution=True, max_pixels=1920*1080, height=None, width=None,
num_frames=81,
time_division_factor=4, time_division_remainder=1,
max_pixels=1920*1080, height=None, width=None,
height_division_factor=16, width_division_factor=16,
data_file_keys=("video",),
image_file_extension=("jpg", "jpeg", "png", "webp"),
@@ -25,17 +26,15 @@ class VideoDataset(torch.utils.data.Dataset):
metadata_path = args.dataset_metadata_path
height = args.height
width = args.width
max_pixels = args.max_pixels
num_frames = args.num_frames
data_file_keys = args.data_file_keys.split(",")
repeat = args.dataset_repeat
metadata = pd.read_csv(metadata_path)
self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
self.base_path = base_path
self.frame_interval = frame_interval
self.num_frames = num_frames
self.dynamic_resolution = dynamic_resolution
self.time_division_factor = time_division_factor
self.time_division_remainder = time_division_remainder
self.max_pixels = max_pixels
self.height = height
self.width = width
@@ -46,9 +45,43 @@ class VideoDataset(torch.utils.data.Dataset):
self.video_file_extension = video_file_extension
self.repeat = repeat
if height is not None and width is not None and dynamic_resolution == True:
if height is not None and width is not None:
print("Height and width are fixed. Setting `dynamic_resolution` to False.")
self.dynamic_resolution = False
elif height is None and width is None:
print("Height and width are none. Setting `dynamic_resolution` to True.")
self.dynamic_resolution = True
if metadata_path is None:
print("No metadata. Trying to generate it.")
metadata = self.generate_metadata(base_path)
print(f"{len(metadata)} lines in metadata.")
else:
metadata = pd.read_csv(metadata_path)
self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
def generate_metadata(self, folder):
video_list, prompt_list = [], []
file_set = set(os.listdir(folder))
for file_name in file_set:
if "." not in file_name:
continue
file_ext_name = file_name.split(".")[-1].lower()
file_base_name = file_name[:-len(file_ext_name)-1]
if file_ext_name not in self.image_file_extension and file_ext_name not in self.video_file_extension:
continue
prompt_file_name = file_base_name + ".txt"
if prompt_file_name not in file_set:
continue
with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f:
prompt = f.read().strip()
video_list.append(file_name)
prompt_list.append(prompt)
metadata = pd.DataFrame()
metadata["video"] = video_list
metadata["prompt"] = prompt_list
return metadata
def crop_and_resize(self, image, target_height, target_width):
@@ -75,15 +108,22 @@ class VideoDataset(torch.utils.data.Dataset):
height, width = self.height, self.width
return height, width
def get_num_frames(self, reader):
num_frames = self.num_frames
if int(reader.count_frames()) < num_frames:
num_frames = int(reader.count_frames())
while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
num_frames -= 1
return num_frames
def load_frames_using_imageio(self, file_path, start_frame_id, interval, num_frames):
def load_video(self, file_path):
reader = imageio.get_reader(file_path)
if reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval:
reader.close()
return None
num_frames = self.get_num_frames(reader)
frames = []
for frame_id in range(num_frames):
frame = reader.get_data(start_frame_id + frame_id * interval)
frame = reader.get_data(frame_id)
frame = Image.fromarray(frame)
frame = self.crop_and_resize(frame, *self.get_height_width(frame))
frames.append(frame)
@@ -95,11 +135,6 @@ class VideoDataset(torch.utils.data.Dataset):
image = Image.open(file_path).convert("RGB")
image = self.crop_and_resize(image, *self.get_height_width(image))
return image
def load_video(self, file_path):
frames = self.load_frames_using_imageio(file_path, 0, self.frame_interval, self.num_frames)
return frames
def is_image(self, file_path):
@@ -182,34 +217,50 @@ class DiffusionTrainingModule(torch.nn.Module):
def launch_training_task(model: DiffusionTrainingModule, dataset, learning_rate=1e-4, num_epochs=1, output_path="./models", remove_prefix_in_ckpt=None, args=None):
if args is not None:
learning_rate = args.learning_rate
num_epochs = args.num_epochs
output_path = args.output_path
remove_prefix_in_ckpt = args.remove_prefix_in_ckpt
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0])
optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
class ModelLogger:
def __init__(self, output_path, remove_prefix_in_ckpt=None):
self.output_path = output_path
self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
accelerator = Accelerator(gradient_accumulation_steps=1)
def on_step_end(self, loss):
pass
def on_epoch_end(self, accelerator, model, epoch_id):
accelerator.wait_for_everyone()
if accelerator.is_main_process:
state_dict = accelerator.get_state_dict(model)
state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
os.makedirs(self.output_path, exist_ok=True)
path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors")
accelerator.save(state_dict, path, safe_serialization=True)
def launch_training_task(
dataset: torch.utils.data.Dataset,
model: DiffusionTrainingModule,
model_logger: ModelLogger,
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler.LRScheduler,
num_epochs: int = 1,
gradient_accumulation_steps: int = 1,
):
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0])
accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps)
model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
for epoch in range(num_epochs):
for epoch_id in range(num_epochs):
for data in tqdm(dataloader):
with accelerator.accumulate(model):
optimizer.zero_grad()
loss = model(data)
accelerator.backward(loss)
optimizer.step()
scheduler.step()
accelerator.wait_for_everyone()
if accelerator.is_main_process:
state_dict = accelerator.get_state_dict(model)
state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=remove_prefix_in_ckpt)
os.makedirs(output_path, exist_ok=True)
path = os.path.join(output_path, f"epoch-{epoch}.safetensors")
accelerator.save(state_dict, path, safe_serialization=True)
model_logger.on_step_end(loss)
scheduler.step()
model_logger.on_epoch_end(accelerator, model, epoch_id)
@@ -228,8 +279,9 @@ def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_pat
def wan_parser():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument("--dataset_base_path", type=str, default="", help="Base path of the dataset.")
parser.add_argument("--dataset_metadata_path", type=str, default="", required=True, help="Path to the metadata file of the dataset.")
parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.")
parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.")
parser.add_argument("--max_pixels", type=int, default=1280*720, help="Maximum number of pixels per frame, used for dynamic resolution..")
parser.add_argument("--height", type=int, default=None, help="Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution.")
parser.add_argument("--width", type=int, default=None, help="Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution.")
parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.")
@@ -247,5 +299,6 @@ def wan_parser():
parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
return parser