| | |
| | import argparse |
| | import logging |
| | import os |
| | import sys |
| | import warnings |
| |
|
| | warnings.filterwarnings('ignore') |
| | import imageio |
| | from einops import rearrange |
| | import numpy as np |
| |
|
| | import torchvision |
| | import torch |
| | import torch.distributed as dist |
| | import random |
| |
|
| | import models.wan as wan |
| | from models.wan.configs import WAN_CONFIGS, SIZE_CONFIGS, SUPPORTED_SIZES |
| | from models.wan.utils.utils import str2bool |
| |
|
| |
|
| | EXAMPLE_PROMPT = { |
| | "t2v-1.3B": { |
| | "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", |
| | }, |
| | "t2v-14B": { |
| | "prompt": "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", |
| | }, |
| | "t2i-14B": { |
| | "prompt": "一个朴素端庄的美人", |
| | }, |
| | "i2v-14B": { |
| | "prompt": |
| | "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.", |
| | "image": |
| | "examples/i2v_input.JPG", |
| | }, |
| | } |
| |
|
| |
|
| | def _validate_args(args): |
| | |
| | assert args.ckpt_dir is not None, "Please specify the checkpoint directory." |
| | assert args.task in WAN_CONFIGS, f"Unsupport task: {args.task}" |
| | assert args.task in EXAMPLE_PROMPT, f"Unsupport task: {args.task}" |
| |
|
| | |
| | if args.sample_steps is None: |
| | args.sample_steps = 40 if "i2v" in args.task else 50 |
| |
|
| | if args.sample_shift is None: |
| | args.sample_shift = 5.0 |
| | if "i2v" in args.task and args.size in ["832*480", "480*832"]: |
| | args.sample_shift = 3.0 |
| |
|
| | |
| | if args.frame_num is None: |
| | args.frame_num = 1 if "t2i" in args.task else 81 |
| |
|
| | |
| | if "t2i" in args.task: |
| | assert args.frame_num == 1, f"Unsupport frame_num {args.frame_num} for task {args.task}" |
| |
|
| | args.base_seed = args.base_seed if args.base_seed >= 0 else random.randint( |
| | 0, sys.maxsize) |
| | |
| | assert args.size in SUPPORTED_SIZES[ |
| | args. |
| | task], f"Unsupport size {args.size} for task {args.task}, supported sizes are: {', '.join(SUPPORTED_SIZES[args.task])}" |
| |
|
| |
|
| | def _parse_args(): |
| | parser = argparse.ArgumentParser( |
| | description="Generate a image or video from a text prompt or image using Wan" |
| | ) |
| |
|
| | parser.add_argument( |
| | "--task", |
| | type=str, |
| | default="t2v-14B", |
| | choices=list(WAN_CONFIGS.keys()), |
| | help="The task to run.") |
| | parser.add_argument( |
| | "--size", |
| | type=str, |
| | default="1280*720", |
| | choices=list(SIZE_CONFIGS.keys()), |
| | help="The area (width*height) of the generated video. For the I2V task, the aspect ratio of the output video will follow that of the input image." |
| | ) |
| | parser.add_argument( |
| | "--frame_num", |
| | type=int, |
| | default=None, |
| | help="How many frames to sample from a image or video. The number should be 4n+1" |
| | ) |
| | parser.add_argument( |
| | "--ckpt_dir", |
| | type=str, |
| | default=None, |
| | help="The path to the checkpoint directory.") |
| | parser.add_argument( |
| | "--offload_model", |
| | type=str2bool, |
| | default=None, |
| | help="Whether to offload the model to CPU after each model forward, reducing GPU memory usage." |
| | ) |
| | parser.add_argument( |
| | "--ulysses_size", |
| | type=int, |
| | default=1, |
| | help="The size of the ulysses parallelism in DiT.") |
| | parser.add_argument( |
| | "--ring_size", |
| | type=int, |
| | default=1, |
| | help="The size of the ring attention parallelism in DiT.") |
| | parser.add_argument( |
| | "--t5_fsdp", |
| | action="store_true", |
| | default=False, |
| | help="Whether to use FSDP for T5.") |
| | parser.add_argument( |
| | "--t5_cpu", |
| | action="store_true", |
| | default=False, |
| | help="Whether to place T5 model on CPU.") |
| | parser.add_argument( |
| | "--dit_fsdp", |
| | action="store_true", |
| | default=False, |
| | help="Whether to use FSDP for DiT.") |
| | parser.add_argument( |
| | "--save_file", |
| | type=str, |
| | default=None, |
| | help="The file to save the generated image or video to.") |
| | parser.add_argument( |
| | "--save_dir", |
| | type=str, |
| | default=None, |
| | help="The directory to save the generated image or video to.") |
| | parser.add_argument( |
| | "--prompt", |
| | type=str, |
| | default=None, |
| | help="The prompt to generate the image or video from.") |
| | parser.add_argument( |
| | "--use_prompt_extend", |
| | action="store_true", |
| | default=False, |
| | help="Whether to use prompt extend.") |
| | parser.add_argument( |
| | "--prompt_extend_method", |
| | type=str, |
| | default="local_qwen", |
| | choices=["dashscope", "local_qwen"], |
| | help="The prompt extend method to use.") |
| | parser.add_argument( |
| | "--prompt_extend_model", |
| | type=str, |
| | default=None, |
| | help="The prompt extend model to use.") |
| | parser.add_argument( |
| | "--prompt_extend_target_lang", |
| | type=str, |
| | default="zh", |
| | choices=["zh", "en"], |
| | help="The target language of prompt extend.") |
| | parser.add_argument( |
| | "--base_seed", |
| | type=int, |
| | default=-1, |
| | help="The seed to use for generating the image or video.") |
| | parser.add_argument( |
| | "--image", |
| | type=str, |
| | default=None, |
| | help="The image to generate the video from.") |
| | parser.add_argument( |
| | "--sample_solver", |
| | type=str, |
| | default='unipc', |
| | choices=['unipc', 'dpm++', 'euler'], |
| | help="The solver used to sample.") |
| | parser.add_argument( |
| | "--sample_steps", type=int, default=None, help="The sampling steps.") |
| | parser.add_argument( |
| | "--sample_shift", |
| | type=float, |
| | default=None, |
| | help="Sampling shift factor for flow matching schedulers.") |
| | parser.add_argument( |
| | "--sample_guide_scale", |
| | type=float, |
| | default=5.0, |
| | help="Classifier free guidance scale.") |
| | parser.add_argument( |
| | "--dit_ckpt_path", |
| | type=str, |
| | default=None, |
| | help="Finetune checkpoint.") |
| |
|
| | args = parser.parse_args() |
| |
|
| | _validate_args(args) |
| |
|
| | return args |
| |
|
| |
|
| | def _init_logging(rank): |
| | |
| | if rank == 0: |
| | |
| | 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 generate(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 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}.") |
| |
|
| | 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." |
| |
|
| | cfg = WAN_CONFIGS[args.task] |
| |
|
| | 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] |
| |
|
| | logging.info("Creating WanT2V pipeline.") |
| | wan_t2v = wan.WanT2V( |
| | config=cfg, |
| | checkpoint_dir=args.ckpt_dir, |
| | device_id=device, |
| | rank=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, |
| | dit_path=args.dit_ckpt_path, |
| | ) |
| |
|
| |
|
| | with open("assets/prompt.txt") as f: |
| | prompts = f.readlines() |
| |
|
| | import time |
| |
|
| | os.makedirs(args.save_dir, exist_ok=True) |
| | for idx, prompt in enumerate(prompts): |
| | save_file = os.path.join(args.save_dir, f"{idx}.mp4") |
| | if os.path.exists(save_file): continue |
| | logging.info( |
| | f"Generating {'image' if 't2i' in args.task else 'video'} ...") |
| | start_time = time.time() |
| | videos, context = wan_t2v.generate( |
| | prompt, |
| | size=SIZE_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=1024, |
| | offload_model=False, |
| | few_step=True, |
| | no_cfg=True) |
| |
|
| | print('generation time:', time.time() - start_time) |
| | if rank == 0: |
| | videos = videos.unsqueeze(0) |
| | videos = rearrange(videos, "b c t h w -> t b c h w") |
| | videos = (videos.cpu() + 1) / 2 |
| | outputs = [] |
| | for x in videos: |
| | x = torchvision.utils.make_grid(x, nrow=6) |
| | x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) |
| | outputs.append((x * 255).numpy().astype(np.uint8)) |
| | imageio.mimsave( |
| | save_file, outputs, fps=16 |
| | ) |
| |
|
| | logging.info("Finished.") |
| |
|
| |
|
| | if __name__ == "__main__": |
| | args = _parse_args() |
| | generate(args) |
| |
|