| 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}") |
|
|
| |
| save_path = args.output_path |
| os.makedirs(save_path, exist_ok=True) |
|
|
| with open(args.prompt_file) as f: |
| prompts = f.readlines() |
|
|
|
|
| |
| hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained( |
| models_root_path, args=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() |
|
|
| |
| 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) |
|
|
| |
| 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.", |
| ) |
|
|
| |
| 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.", |
| ) |
|
|
| |
| 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) |
|
|
|
|
| |
| 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) |
|
|
