File size: 9,595 Bytes
3c11df8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | import torch, os, argparse, accelerate
from diffsynth.core import UnifiedDataset
from diffsynth.core.data.operators import LoadAudioWithTorchaudio, ToAbsolutePath, RouteByType, SequencialProcess
from diffsynth.pipelines.mova_audio_video import MovaAudioVideoPipeline, ModelConfig
from diffsynth.diffusion import *
os.environ["TOKENIZERS_PARALLELISM"] = "false"
class MOVATrainingModule(DiffusionTrainingModule):
def __init__(
self,
model_paths=None, model_id_with_origin_paths=None,
tokenizer_path=None,
trainable_models=None,
lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
preset_lora_path=None, preset_lora_model=None,
use_gradient_checkpointing=True,
use_gradient_checkpointing_offload=False,
extra_inputs=None,
fp8_models=None,
offload_models=None,
quant_options=None,
resume_from_checkpoint=None, remove_prefix_in_ckpt=None,
device="cpu",
task="sft",
max_timestep_boundary=1.0,
min_timestep_boundary=0.0,
):
super().__init__()
# Load models
model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, quant_options=quant_options, device=device)
tokenizer_config = ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized") if tokenizer_path is None else ModelConfig(tokenizer_path)
self.pipe = MovaAudioVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config)
self.pipe = self.split_pipeline_units(
task, self.pipe, trainable_models, lora_base_model,
remove_unnecessary_params=True,
force_remove_params_shared=("audio_latents", "video_latents"),
force_remove_params_nega=("audio_context", "video_context")
)
self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt)
# Training mode
self.switch_pipe_to_training_mode(
self.pipe, trainable_models,
lora_base_model, lora_target_modules, lora_rank, lora_checkpoint,
preset_lora_path, preset_lora_model,
task=task,
)
# Store other configs
self.use_gradient_checkpointing = use_gradient_checkpointing
self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
self.fp8_models = fp8_models
self.task = task
self.task_to_loss = {
"sft:data_process": lambda pipe, *args: args,
"sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTAudioVideoLoss(pipe, **inputs_shared, **inputs_posi),
"sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTAudioVideoLoss(pipe, **inputs_shared, **inputs_posi),
}
self.max_timestep_boundary = max_timestep_boundary
self.min_timestep_boundary = min_timestep_boundary
def parse_extra_inputs(self, data, extra_inputs, inputs_shared):
for extra_input in extra_inputs:
if extra_input == "input_image":
inputs_shared["input_image"] = data["video"][0]
else:
inputs_shared[extra_input] = data[extra_input]
return inputs_shared
def get_pipeline_inputs(self, data):
inputs_posi = {"prompt": data["prompt"]}
inputs_nega = {}
inputs_shared = {
# Assume you are using this pipeline for inference,
# please fill in the input parameters.
"input_video": data["video"],
"height": data["video"][0].size[1],
"width": data["video"][0].size[0],
"num_frames": len(data["video"]),
"frame_rate": data.get("frame_rate", 24),
# Please do not modify the following parameters
# unless you clearly know what this will cause.
"cfg_scale": 1,
"tiled": False,
"rand_device": self.pipe.device,
"use_gradient_checkpointing": self.use_gradient_checkpointing,
"use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
"max_timestep_boundary": self.max_timestep_boundary,
"min_timestep_boundary": self.min_timestep_boundary,
}
inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
return inputs_shared, inputs_posi, inputs_nega
def forward(self, data, inputs=None):
if inputs is None: inputs = self.get_pipeline_inputs(data)
inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
for unit in self.pipe.units:
inputs = self.pipe.unit_runner(unit, self.pipe, *inputs)
loss = self.task_to_loss[self.task](self.pipe, *inputs)
return loss
def ltx2_parser():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser = add_general_config(parser)
parser = add_video_size_config(parser)
parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.")
parser.add_argument("--frame_rate", type=float, default=24, help="Frame rate of the training videos. Mova is trained with a frame rate of 24, so it's recommended to use the same frame rate.")
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("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
return parser
if __name__ == "__main__":
parser = ltx2_parser()
args = parser.parse_args()
accelerator = accelerate.Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)],
)
model = MOVATrainingModule(
model_paths=args.model_paths,
model_id_with_origin_paths=args.model_id_with_origin_paths,
tokenizer_path=args.tokenizer_path,
trainable_models=args.trainable_models,
lora_base_model=args.lora_base_model,
lora_target_modules=args.lora_target_modules,
lora_rank=args.lora_rank,
lora_checkpoint=args.lora_checkpoint,
preset_lora_path=args.preset_lora_path,
preset_lora_model=args.preset_lora_model,
use_gradient_checkpointing=args.use_gradient_checkpointing,
use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
extra_inputs=args.extra_inputs,
fp8_models=args.fp8_models,
offload_models=args.offload_models,
quant_options=args.quant_options,
resume_from_checkpoint=args.resume_from_checkpoint,
remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
task=args.task,
device="cpu" if (args.initialize_model_on_cpu or args.enable_model_cpu_offload) else accelerator.device,
max_timestep_boundary=args.max_timestep_boundary,
min_timestep_boundary=args.min_timestep_boundary,
)
video_processor = UnifiedDataset.default_video_operator(
base_path=args.dataset_base_path,
max_pixels=args.max_pixels,
height=args.height,
width=args.width,
height_division_factor=16,
width_division_factor=16,
num_frames=args.num_frames,
time_division_factor=4,
time_division_remainder=1,
frame_rate=args.frame_rate,
fix_frame_rate=True,
)
dataset = UnifiedDataset(
base_path=args.dataset_base_path,
metadata_path=args.dataset_metadata_path,
repeat=args.dataset_repeat,
data_file_keys=args.data_file_keys.split(","),
main_data_operator=video_processor,
special_operator_map={
"input_audio":
ToAbsolutePath(args.dataset_base_path) >> LoadAudioWithTorchaudio(
num_frames=args.num_frames,
time_division_factor=4,
time_division_remainder=1,
frame_rate=args.frame_rate,
),
"in_context_videos":
RouteByType(operator_map=[
(str, video_processor),
(list, SequencialProcess(video_processor)),
]),
},
)
model_logger = ModelLogger(
args.output_path,
remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
enable_tensorboard_log=args.enable_tensorboard_log,
enable_swanlab_log=args.enable_swanlab_log,
swanlab_project=args.swanlab_project,
enable_wandb_log=args.enable_wandb_log,
wandb_project=args.wandb_project,
enable_csv_log=args.enable_csv_log,
)
launcher_map = {
"sft:data_process": launch_data_process_task,
"direct_distill:data_process": launch_data_process_task,
"sft": launch_training_task,
"sft:train": launch_training_task,
"direct_distill": launch_training_task,
"direct_distill:train": launch_training_task,
}
launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)
|