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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import logging
import os
import sys
import warnings
warnings.filterwarnings('ignore')
import torch
import torch.distributed as dist
from easydict import EasyDict
from torchvision import transforms
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from diffusers_lite import wan
from diffusers_lite.wan.configs import WAN_CONFIGS, MAX_AREA_CONFIGS, SIZE_CONFIGS
from diffusers_lite.wan.utils.utils import cache_video
from diffusers_lite.arguments import args_wan_init
from diffusers_lite.datasets.image2video_dataset import Image2VideoEvalDataset
def _init_logging(rank):
if rank == 0:
# set format
logging.basicConfig(
level=logging.INFO,
format="[%(asctime)s] %(levelname)s: %(message)s",
handlers=[logging.StreamHandler(stream=sys.stdout)])
else:
logging.basicConfig(level=logging.ERROR)
def basic_init(args):
rank = int(os.getenv("RANK", 0))
world_size = int(os.getenv("WORLD_SIZE", 1))
local_rank = int(os.getenv("LOCAL_RANK", 0))
device = local_rank
_init_logging(rank)
if rank == 0:
os.makedirs(args.save_folder, exist_ok=True)
logging.info(f"Creating save directory: {args.save_folder}")
if args.offload_model is None:
args.offload_model = False if world_size > 1 else True
logging.info(
f"offload_model is not specified, set to {args.offload_model}.")
if args.ulysses_size == 1 and args.ring_size == 1:
args.ddp_mode = True
# args.t5_fsdp = False
# args.dit_fsdp = False
logging.info(f"DDP mode enabled.")
if world_size > 1:
torch.cuda.set_device(local_rank)
dist.init_process_group(
backend="nccl",
init_method="env://",
rank=rank,
world_size=world_size)
else:
assert not (
args.t5_fsdp or args.dit_fsdp
), f"t5_fsdp and dit_fsdp are not supported in non-distributed environments."
assert not (
args.ulysses_size > 1 or args.ring_size > 1
), f"context parallel are not supported in non-distributed environments."
if args.ulysses_size > 1 or args.ring_size > 1:
assert args.ulysses_size * args.ring_size == world_size, f"The number of ulysses_size and ring_size should be equal to the world size."
from xfuser.core.distributed import (initialize_model_parallel,
init_distributed_environment)
init_distributed_environment(
rank=dist.get_rank(), world_size=dist.get_world_size())
initialize_model_parallel(
sequence_parallel_degree=dist.get_world_size(),
ring_degree=args.ring_size,
ulysses_degree=args.ulysses_size,
)
cfg = WAN_CONFIGS[args.task]
if args.ulysses_size > 1:
assert cfg.num_heads % args.ulysses_size == 0, f"`num_heads` must be divisible by `ulysses_size`."
logging.info(f"Generation job args: {args}")
logging.info(f"Generation model config: {cfg}")
if dist.is_initialized():
base_seed = [args.base_seed] if rank == 0 else [None]
dist.broadcast_object_list(base_seed, src=0)
args.base_seed = base_seed[0]
basic_kwargs = EasyDict({
"rank": rank,
"local_rank": local_rank,
"world_size": world_size,
"device": device,
"cfg": cfg,
})
return basic_kwargs
def dataset_init(args, basic_kwargs):
dataset = Image2VideoEvalDataset(
args.dataset_path,
do_scale=True,
resolution=SIZE_CONFIGS[args.size]
)
logging.info(f"Dataset length: {len(dataset)}")
if args.ddp_mode:
sampler = DistributedSampler(
dataset,
num_replicas=basic_kwargs.world_size,
rank=basic_kwargs.rank,
shuffle=False,
drop_last=False,
)
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
sampler=sampler,
drop_last=False
)
dataset = dataloader
return dataset
def pipeline_t2v_init(args, basic_kwargs):
logging.info("Creating WanT2V pipeline.")
wan_t2v = wan.WanT2V(
config=basic_kwargs.cfg,
checkpoint_dir=args.ckpt_dir,
transformer_path=args.transformer_path,
lora_path=args.lora_path,
lora_alpha=args.lora_alpha,
distill_lora_path=args.distill_lora_path,
distill_lora_alpha=args.distill_lora_alpha,
device_id=basic_kwargs.device,
rank=basic_kwargs.rank,
t5_fsdp=args.t5_fsdp,
dit_fsdp=args.dit_fsdp,
use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
t5_cpu=args.t5_cpu,
teacache_thresh=args.teacache_thresh,
sample_steps=args.sample_steps,
ckpt_dir=args.ckpt_dir,
)
return wan_t2v
def pipeline_i2v_init(args, basic_kwargs):
logging.info("Creating WanI2V pipeline.")
wan_i2v = wan.WanI2V(
config=basic_kwargs.cfg,
checkpoint_dir=args.ckpt_dir,
transformer_path=args.transformer_path,
lora_path=args.lora_path,
lora_alpha=args.lora_alpha,
distill_lora_path=args.distill_lora_path,
distill_lora_alpha=args.distill_lora_alpha,
device_id=basic_kwargs.device,
rank=basic_kwargs.rank,
t5_fsdp=args.t5_fsdp,
dit_fsdp=args.dit_fsdp,
use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
t5_cpu=args.t5_cpu,
teacache_thresh=args.teacache_thresh,
sample_steps=args.sample_steps,
ckpt_dir=args.ckpt_dir,
)
return wan_i2v
def pipeline_flf2v_init(args, basic_kwargs):
logging.info("Creating WanFLF2V pipeline.")
wan_flf2v = wan.WanFLF2V(
config=basic_kwargs.cfg,
checkpoint_dir=args.ckpt_dir,
transformer_path=args.transformer_path,
lora_path=args.lora_path,
lora_alpha=args.lora_alpha,
distill_lora_path=args.distill_lora_path,
distill_lora_alpha=args.distill_lora_alpha,
device_id=basic_kwargs.device,
rank=basic_kwargs.rank,
t5_fsdp=args.t5_fsdp,
dit_fsdp=args.dit_fsdp,
use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
t5_cpu=args.t5_cpu,
teacache_thresh=args.teacache_thresh,
sample_steps=args.sample_steps,
ckpt_dir=args.ckpt_dir,
)
return wan_flf2v
def inference_t2v_loop(args, pipeline, batch):
if args.ddp_mode:
prompt = batch["prompt"][0]
image_id = batch["image_id"][0]
else:
prompt = batch["prompt"]
image_id = batch["image_id"]
# image_id = prompt[:200]
info_str = f"""
height: {args.resolution[1]}
width: {args.resolution[0]}
video_length: {args.frame_num}
prompt: {prompt}
neg_prompt: {args.negative_prompt}
seed: {int(batch["seed"])}
infer_steps: {args.sample_steps}
guidance_scale: {args.sample_guide_scale}
flow_shift: {args.sample_shift}"""
logging.info(info_str)
video = pipeline.generate(
prompt,
n_prompt=args.negative_prompt,
size=args.resolution,
frame_num=args.frame_num,
shift=args.sample_shift,
sample_solver=args.sample_solver,
sampling_steps=args.sample_steps,
guide_scale=args.sample_guide_scale,
seed=int(batch["seed"]),
# seed=args.base_seed,
offload_model=args.offload_model,
ddp_mode=args.ddp_mode,
)
return video, image_id
def inference_i2v_loop(args, pipeline, batch):
if args.ddp_mode:
prompt = batch["prompt"][0]
image_id = batch["image_id"][0]
cond_image = transforms.ToPILImage()(batch["image"][0])
else:
prompt = batch["prompt"]
image_id = batch["image_id"]
cond_image = transforms.ToPILImage()(batch["image"])
width, height = cond_image.size[0], cond_image.size[1]
info_str = f"""
height: {height}
width: {width}
current_araa: {height} * {width}
max_area: {MAX_AREA_CONFIGS[args.size]}
video_length: {args.frame_num}
prompt: {prompt}
neg_prompt: {args.negative_prompt}
seed: {int(batch["seed"])}
infer_steps: {args.sample_steps}
guidance_scale: {args.sample_guide_scale}
flow_shift: {args.sample_shift}"""
logging.info(info_str)
video = pipeline.generate(
prompt,
cond_image,
n_prompt=args.negative_prompt,
max_area=MAX_AREA_CONFIGS[args.size],
frame_num=args.frame_num,
shift=args.sample_shift,
sample_solver=args.sample_solver,
sampling_steps=args.sample_steps,
guide_scale=args.sample_guide_scale,
# seed=args.base_seed,
seed=int(batch["seed"]),
offload_model=args.offload_model,
ddp_mode=args.ddp_mode,
)
return video, image_id
def inference_flf2v_loop(args, pipeline, batch):
if args.ddp_mode:
prompt = batch["prompt"][0]
image_id = batch["image_id"][0]
cond_image = transforms.ToPILImage()(batch["image"][0])
last_image = transforms.ToPILImage()(batch["last_image"][0])
else:
prompt = batch["prompt"]
image_id = batch["image_id"]
cond_image = transforms.ToPILImage()(batch["image"])
last_image = transforms.ToPILImage()(batch["last_image"])
width, height = cond_image.size[0], cond_image.size[1]
info_str = f"""
height: {height}
width: {width}
max_area: {MAX_AREA_CONFIGS[args.size]}
video_length: {args.frame_num}
prompt: {prompt}
neg_prompt: {args.negative_prompt}
seed: {args.base_seed}
infer_steps: {args.sample_steps}
guidance_scale: {args.sample_guide_scale}
flow_shift: {args.sample_shift}"""
logging.info(info_str)
video = pipeline.generate(
prompt,
cond_image,
last_image,
n_prompt=args.negative_prompt,
max_area=MAX_AREA_CONFIGS[args.size],
frame_num=args.frame_num,
shift=args.sample_shift,
sample_solver=args.sample_solver,
sampling_steps=args.sample_steps,
guide_scale=args.sample_guide_scale,
seed=args.base_seed,
offload_model=args.offload_model,
ddp_mode=args.ddp_mode,
)
return video, image_id
def main(args):
basic_kwargs = basic_init(args)
dataset = dataset_init(args, basic_kwargs)
if "t2v" in args.task:
pipeline = pipeline_t2v_init(args, basic_kwargs)
elif "i2v" in args.task:
pipeline = pipeline_i2v_init(args, basic_kwargs)
elif "flf2v" in args.task:
pipeline = pipeline_flf2v_init(args, basic_kwargs)
for i, batch in enumerate(dataset):
image_id = batch["image_id"][0]
save_path = os.path.join(args.save_folder, f"{image_id}.mp4")
if os.path.exists(save_path):
continue
else:
if "t2v" in args.task:
video, image_id = inference_t2v_loop(
args, pipeline, batch
)
elif "i2v" in args.task:
video, image_id = inference_i2v_loop(
args, pipeline, batch
)
elif "flf2v" in args.task:
video, image_id = inference_flf2v_loop(
args, pipeline, batch
)
if basic_kwargs.rank == 0 or args.ddp_mode:
save_path = os.path.join(args.save_folder, f"{image_id}.mp4")
cache_video(
tensor=video[None],
save_file=save_path,
fps=basic_kwargs.cfg.sample_fps,
nrow=1,
normalize=True,
value_range=(-1, 1)
)
logging.info(f"Saving generated video to {save_path}")
logging.info("Finished.")
if __name__ == "__main__":
args = args_wan_init()
main(args)
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