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import imageio, os, torch, warnings, torchvision, argparse, json
from peft import LoraConfig, inject_adapter_in_model
from PIL import Image
import pandas as pd
from tqdm import tqdm
from accelerate import Accelerator
from accelerate.utils import DistributedDataParallelKwargs
class ImageDataset(torch.utils.data.Dataset):
def __init__(
self,
base_path=None, metadata_path=None,
max_pixels=1920*1080, height=None, width=None,
height_division_factor=16, width_division_factor=16,
data_file_keys=("image",),
image_file_extension=("jpg", "jpeg", "png", "webp"),
repeat=1,
args=None,
):
if args is not None:
base_path = args.dataset_base_path
metadata_path = args.dataset_metadata_path
height = args.height
width = args.width
max_pixels = args.max_pixels
data_file_keys = args.data_file_keys.split(",")
repeat = args.dataset_repeat
self.base_path = base_path
self.max_pixels = max_pixels
self.height = height
self.width = width
self.height_division_factor = height_division_factor
self.width_division_factor = width_division_factor
self.data_file_keys = data_file_keys
self.image_file_extension = image_file_extension
self.repeat = repeat
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.")
self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
elif metadata_path.endswith(".json"):
with open(metadata_path, "r") as f:
metadata = json.load(f)
self.data = metadata
elif metadata_path.endswith(".jsonl"):
metadata = []
with open(metadata_path, 'r') as f:
for line in tqdm(f):
metadata.append(json.loads(line.strip()))
self.data = 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):
image_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:
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()
image_list.append(file_name)
prompt_list.append(prompt)
metadata = pd.DataFrame()
metadata["image"] = image_list
metadata["prompt"] = prompt_list
return metadata
def crop_and_resize(self, image, target_height, target_width):
width, height = image.size
scale = max(target_width / width, target_height / height)
image = torchvision.transforms.functional.resize(
image,
(round(height*scale), round(width*scale)),
interpolation=torchvision.transforms.InterpolationMode.BILINEAR
)
image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
return image
def get_height_width(self, image):
if self.dynamic_resolution:
width, height = image.size
if width * height > self.max_pixels:
scale = (width * height / self.max_pixels) ** 0.5
height, width = int(height / scale), int(width / scale)
height = height // self.height_division_factor * self.height_division_factor
width = width // self.width_division_factor * self.width_division_factor
else:
height, width = self.height, self.width
return height, width
def load_image(self, file_path):
image = Image.open(file_path).convert("RGB")
image = self.crop_and_resize(image, *self.get_height_width(image))
return image
def load_data(self, file_path):
return self.load_image(file_path)
def __getitem__(self, data_id):
data = self.data[data_id % len(self.data)].copy()
for key in self.data_file_keys:
if key in data:
if isinstance(data[key], list):
path = [os.path.join(self.base_path, p) for p in data[key]]
data[key] = [self.load_data(p) for p in path]
else:
path = os.path.join(self.base_path, data[key])
data[key] = self.load_data(path)
if data[key] is None:
warnings.warn(f"cannot load file {data[key]}.")
return None
return data
def __len__(self):
return len(self.data) * self.repeat
class VideoDataset(torch.utils.data.Dataset):
def __init__(
self,
base_path=None, metadata_path=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"),
video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"),
repeat=1,
args=None,
):
if args is not None:
base_path = args.dataset_base_path
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
self.base_path = base_path
self.num_frames = num_frames
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
self.height_division_factor = height_division_factor
self.width_division_factor = width_division_factor
self.data_file_keys = data_file_keys
self.image_file_extension = image_file_extension
self.video_file_extension = video_file_extension
self.repeat = repeat
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.")
self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
elif metadata_path.endswith(".json"):
with open(metadata_path, "r") as f:
metadata = json.load(f)
self.data = 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):
width, height = image.size
scale = max(target_width / width, target_height / height)
image = torchvision.transforms.functional.resize(
image,
(round(height*scale), round(width*scale)),
interpolation=torchvision.transforms.InterpolationMode.BILINEAR
)
image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
return image
def get_height_width(self, image):
if self.dynamic_resolution:
width, height = image.size
if width * height > self.max_pixels:
scale = (width * height / self.max_pixels) ** 0.5
height, width = int(height / scale), int(width / scale)
height = height // self.height_division_factor * self.height_division_factor
width = width // self.width_division_factor * self.width_division_factor
else:
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_video(self, file_path):
reader = imageio.get_reader(file_path)
num_frames = self.get_num_frames(reader)
frames = []
for frame_id in range(num_frames):
frame = reader.get_data(frame_id)
frame = Image.fromarray(frame)
frame = self.crop_and_resize(frame, *self.get_height_width(frame))
frames.append(frame)
reader.close()
return frames
def load_image(self, file_path):
image = Image.open(file_path).convert("RGB")
image = self.crop_and_resize(image, *self.get_height_width(image))
frames = [image]
return frames
def is_image(self, file_path):
file_ext_name = file_path.split(".")[-1]
return file_ext_name.lower() in self.image_file_extension
def is_video(self, file_path):
file_ext_name = file_path.split(".")[-1]
return file_ext_name.lower() in self.video_file_extension
def load_data(self, file_path):
if self.is_image(file_path):
return self.load_image(file_path)
elif self.is_video(file_path):
return self.load_video(file_path)
else:
return None
def __getitem__(self, data_id):
data = self.data[data_id % len(self.data)].copy()
for key in self.data_file_keys:
if key in data:
path = os.path.join(self.base_path, data[key])
data[key] = self.load_data(path)
if data[key] is None:
warnings.warn(f"cannot load file {data[key]}.")
return None
return data
def __len__(self):
return len(self.data) * self.repeat
class DiffusionTrainingModule(torch.nn.Module):
def __init__(self):
super().__init__()
def to(self, *args, **kwargs):
for name, model in self.named_children():
model.to(*args, **kwargs)
return self
def trainable_modules(self):
trainable_modules = filter(lambda p: p.requires_grad, self.parameters())
return trainable_modules
def trainable_param_names(self):
trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters()))
trainable_param_names = set([named_param[0] for named_param in trainable_param_names])
return trainable_param_names
def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None, upcast_dtype=None):
if lora_alpha is None:
lora_alpha = lora_rank
lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules)
model = inject_adapter_in_model(lora_config, model)
if upcast_dtype is not None:
for param in model.parameters():
if param.requires_grad:
param.data = param.to(upcast_dtype)
return model
def mapping_lora_state_dict(self, state_dict):
new_state_dict = {}
for key, value in state_dict.items():
if "lora_A.weight" in key or "lora_B.weight" in key:
new_key = key.replace("lora_A.weight", "lora_A.default.weight").replace("lora_B.weight", "lora_B.default.weight")
new_state_dict[new_key] = value
elif "lora_A.default.weight" in key or "lora_B.default.weight" in key:
new_state_dict[key] = value
return new_state_dict
def export_trainable_state_dict(self, state_dict, remove_prefix=None):
trainable_param_names = self.trainable_param_names()
state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names}
if remove_prefix is not None:
state_dict_ = {}
for name, param in state_dict.items():
if name.startswith(remove_prefix):
name = name[len(remove_prefix):]
state_dict_[name] = param
state_dict = state_dict_
return state_dict
class ModelLogger:
def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x):
self.output_path = output_path
self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
self.state_dict_converter = state_dict_converter
self.num_steps = 0
def on_step_end(self, accelerator, model, save_steps=None):
self.num_steps += 1
if save_steps is not None and self.num_steps % save_steps == 0:
self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors")
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)
state_dict = self.state_dict_converter(state_dict)
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 on_training_end(self, accelerator, model, save_steps=None):
if save_steps is not None and self.num_steps % save_steps != 0:
self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors")
def save_model(self, accelerator, model, file_name):
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)
state_dict = self.state_dict_converter(state_dict)
os.makedirs(self.output_path, exist_ok=True)
path = os.path.join(self.output_path, file_name)
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_workers: int = 8,
save_steps: int = None,
num_epochs: int = 1,
gradient_accumulation_steps: int = 1,
find_unused_parameters: bool = False,
):
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
accelerator = Accelerator(
gradient_accumulation_steps=gradient_accumulation_steps,
kwargs_handlers=[DistributedDataParallelKwargs(find_unused_parameters=find_unused_parameters)],
)
model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
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()
model_logger.on_step_end(accelerator, model, save_steps)
scheduler.step()
if save_steps is None:
model_logger.on_epoch_end(accelerator, model, epoch_id)
model_logger.on_training_end(accelerator, model, save_steps)
def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"):
dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0])
accelerator = Accelerator()
model, dataloader = accelerator.prepare(model, dataloader)
os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True)
for data_id, data in enumerate(tqdm(dataloader)):
with torch.no_grad():
inputs = model.forward_preprocess(data)
inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs}
torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth"))
def wan_parser():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
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.")
parser.add_argument("--data_file_keys", type=str, default="image,video", help="Data file keys in the metadata. Comma-separated.")
parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.")
parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.")
parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.")
parser.add_argument("--output_path", type=str, default="./models", help="Output save path.")
parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.")
parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.")
parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.")
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.")
parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).")
parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).")
parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.")
return parser
def flux_parser():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
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=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..")
parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.")
parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.")
parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.")
parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.")
parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.")
parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.")
parser.add_argument("--output_path", type=str, default="./models", help="Output save path.")
parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.")
parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.")
parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.")
parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.")
parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
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.")
parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.")
return parser
def qwen_image_parser():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
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=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..")
parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.")
parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.")
parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.")
parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.")
parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.")
parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.")
parser.add_argument("--tokenizer_path", type=str, default=None, help="Paths to tokenizer.")
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.")
parser.add_argument("--output_path", type=str, default="./models", help="Output save path.")
parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.")
parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.")
parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.")
parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
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.")
parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.")
parser.add_argument("--processor_path", type=str, default=None, help="Path to the processor. If provided, the processor will be used for image editing.")
parser.add_argument("--enable_fp8_training", default=False, action="store_true", help="Whether to enable FP8 training. Only available for LoRA training on a single GPU.")
return parser
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