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import imageio
from einops import rearrange
import torchvision
import numpy as np
from pathlib import Path
import argparse
import os
from models.hunyuan.inference import HunyuanVideoSampler
def main(args):
print(args)
models_root_path = Path(args.model_path)
if not models_root_path.exists():
raise ValueError(f"`models_root` not exists: {models_root_path}")
# Create save folder to save the samples
save_path = args.output_path
os.makedirs(save_path, exist_ok=True)
with open(args.prompt_file) as f:
prompts = f.readlines()
# Load models
hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(
models_root_path, args=args
)
# Get the updated args
args = hunyuan_video_sampler.args
for idx, prompt in enumerate(prompts):
seed = args.seed
outputs = hunyuan_video_sampler.predict(
prompt=prompt,
height=args.height,
width=args.width,
video_length=args.num_frames,
seed=seed,
negative_prompt=args.neg_prompt,
infer_steps=args.num_inference_steps,
guidance_scale=args.guidance_scale,
num_videos_per_prompt=args.num_videos,
flow_shift=args.flow_shift,
batch_size=args.batch_size,
embedded_guidance_scale=args.embedded_cfg_scale,
few_step=True
)
if 'LOCAL_RANK' not in os.environ or int(os.environ['LOCAL_RANK']) == 0:
videos = rearrange(outputs["samples"], "b c t h w -> t b c h w")
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))
os.makedirs(args.output_path, exist_ok=True)
imageio.mimsave(
os.path.join(args.output_path, f"{idx}.mp4"), outputs, fps=args.fps
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Basic parameters
parser.add_argument("--prompt_file", type=str, default="./assets/prompt.txt", help="prompt file for inference")
parser.add_argument("--num_frames", type=int, default=16)
parser.add_argument("--height", type=int, default=256)
parser.add_argument("--width", type=int, default=256)
parser.add_argument("--num_inference_steps", type=int, default=50)
parser.add_argument("--model_path", type=str, default="./ckpts")
parser.add_argument("--output_path", type=str, default="./outputs/accvideo-5-steps")
parser.add_argument("--fps", type=int, default=24)
# Additional parameters
parser.add_argument(
"--denoise-type",
type=str,
default="flow",
help="Denoise type for noised inputs.",
)
parser.add_argument("--seed", type=int, default=None, help="Seed for evaluation.")
parser.add_argument(
"--neg_prompt", type=str, default=None, help="Negative prompt for sampling."
)
parser.add_argument(
"--guidance_scale",
type=float,
default=1.0,
help="Classifier free guidance scale.",
)
parser.add_argument(
"--embedded_cfg_scale",
type=float,
default=6.0,
help="Embedded classifier free guidance scale.",
)
parser.add_argument(
"--flow_shift", type=int, default=7, help="Flow shift parameter."
)
parser.add_argument(
"--batch_size", type=int, default=1, help="Batch size for inference."
)
parser.add_argument(
"--num_videos",
type=int,
default=1,
help="Number of videos to generate per prompt.",
)
parser.add_argument(
"--load-key",
type=str,
default="module",
help="Key to load the model states. 'module' for the main model, 'ema' for the EMA model.",
)
parser.add_argument(
"--use-cpu-offload",
action="store_true",
help="Use CPU offload for the model load.",
)
parser.add_argument(
"--dit-weight",
type=str,
default="data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt",
)
parser.add_argument(
"--reproduce",
action="store_true",
help="Enable reproducibility by setting random seeds and deterministic algorithms.",
)
parser.add_argument(
"--disable-autocast",
action="store_true",
help="Disable autocast for denoising loop and vae decoding in pipeline sampling.",
)
# Flow Matching
parser.add_argument(
"--flow-reverse",
action="store_true",
help="If reverse, learning/sampling from t=1 -> t=0.",
)
parser.add_argument(
"--flow-solver", type=str, default="euler", help="Solver for flow matching."
)
parser.add_argument(
"--use-linear-quadratic-schedule",
action="store_true",
help="Use linear quadratic schedule for flow matching. Following MovieGen (https://ai.meta.com/static-resource/movie-gen-research-paper)",
)
parser.add_argument(
"--linear-schedule-end",
type=int,
default=25,
help="End step for linear quadratic schedule for flow matching.",
)
# Model parameters
parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill")
parser.add_argument("--latent-channels", type=int, default=16)
parser.add_argument(
"--precision", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]
)
parser.add_argument(
"--rope-theta", type=int, default=256, help="Theta used in RoPE."
)
parser.add_argument("--vae", type=str, default="884-16c-hy")
parser.add_argument(
"--vae-precision", type=str, default="fp16", choices=["fp32", "fp16", "bf16"]
)
parser.add_argument("--vae-tiling", action="store_true", default=True)
parser.add_argument("--text-encoder", type=str, default="llm")
parser.add_argument(
"--text-encoder-precision",
type=str,
default="fp16",
choices=["fp32", "fp16", "bf16"],
)
parser.add_argument("--text-states-dim", type=int, default=4096)
parser.add_argument("--text-len", type=int, default=256)
parser.add_argument("--tokenizer", type=str, default="llm")
parser.add_argument("--prompt-template", type=str, default="dit-llm-encode")
parser.add_argument(
"--prompt-template-video", type=str, default="dit-llm-encode-video"
)
parser.add_argument("--hidden-state-skip-layer", type=int, default=2)
parser.add_argument("--apply-final-norm", action="store_true")
parser.add_argument("--text-encoder-2", type=str, default="clipL")
parser.add_argument(
"--text-encoder-precision-2",
type=str,
default="fp16",
choices=["fp32", "fp16", "bf16"],
)
parser.add_argument("--text-states-dim-2", type=int, default=768)
parser.add_argument("--tokenizer-2", type=str, default="clipL")
parser.add_argument("--text-len-2", type=int, default=77)
# ======================== Model loads ========================
parser.add_argument(
"--ulysses-degree",
type=int,
default=1,
help="Ulysses degree.",
)
parser.add_argument(
"--ring-degree",
type=int,
default=1,
help="Ulysses degree.",
)
parser.add_argument(
"--use-fp8",
action="store_true",
help="Enable use fp8 for inference acceleration."
)
args = parser.parse_args()
main(args)
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