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NAVA inference engine wrapper.
Encapsulates pipeline init, checkpoint loading, SP patching, and single-sample generation.
"""
import os
import math
import importlib
import torch
import torch.distributed as dist
import yaml
import torchaudio
from torchvision.io import write_video
from nava_src.utils.common import set_seed
from nava_src.models.nava.utils.model_loading_utils import load_fusion_checkpoint
def _to01(x):
return torch.clamp((x.float() + 1.0) / 2.0, 0.0, 1.0)
def _toWav(x):
peak = x.abs().max().clamp(min=1e-12)
x = x * (0.95 / peak)
return x.clamp(-1.0, 1.0)
def _convert_backbone_to_sp(backbone):
from nava_src.models.nava.modules.model_mm import (
WanAttentionBlock,
WanDoubleStreamAttentionBlock,
)
from nava_src.models.nava.modules.model_mm_sp import (
WanDoubleStreamSelfAttentionSP,
WanSelfAttentionSP,
_swap_self_attn,
)
for blk in list(backbone.double_blocks) + list(backbone.double_final_blocks):
_swap_self_attn(blk, WanDoubleStreamSelfAttentionSP)
for blk in backbone.single_blocks:
_swap_self_attn(blk, WanSelfAttentionSP)
class NAVAEngine:
def __init__(self, config_path: str, ckpt_path: str, device: torch.device,
rank: int, world_size: int, use_sp: bool = True):
self.rank = rank
self.world_size = world_size
self.device = device
self.use_sp = use_sp
# Load config
self.cfg = yaml.safe_load(open(config_path, "r"))
self.modality = self.cfg.get("modality", "audio_video")
set_seed(self.cfg.get("seed", 42))
# SP init
if use_sp:
from nava_src.models.nava.distributed_comms.parallel_states import (
initialize_sequence_parallel_state,
)
initialize_sequence_parallel_state(world_size)
if rank == 0:
print(f"[SP] Sequence parallel enabled, sp_size={world_size}")
# Load pipeline
module_path, class_name = self.cfg["pipeline"].rsplit(".", 1)
PipelineClass = getattr(importlib.import_module(module_path), class_name)
if "video" in self.modality and "audio" in self.modality:
self.cfg["init_from_meta"] = True
self.pipe = PipelineClass.create(
model_id=self.cfg["model_id"],
use_bf16=self.cfg["use_bf16"],
audio_latent_ch=self.cfg["audio_latent_ch"],
video_latent_ch=self.cfg["video_latent_ch"],
lambda_ddpm=self.cfg["lambda_ddpm"],
cfg=self.cfg,
device=device,
)
# Load checkpoint — prefer .safetensors, fall back to .ckpt
if not os.path.exists(ckpt_path):
ckpt_fallback = os.path.splitext(ckpt_path)[0] + ".ckpt"
if os.path.exists(ckpt_fallback):
if rank == 0:
print(f"[Engine] {ckpt_path} not found, falling back to {ckpt_fallback}")
ckpt_path = ckpt_fallback
else:
raise FileNotFoundError(f"Checkpoint not found: {ckpt_path} (also tried {ckpt_fallback})")
if "video" in self.modality and "audio" in self.modality and not self.cfg.get("use_mmdit_model", False):
load_fusion_checkpoint(self.pipe.model, checkpoint_path=ckpt_path, from_meta=True)
else:
if ckpt_path.endswith(".safetensors"):
from safetensors.torch import load_file as _sf_load
state_dict = _sf_load(ckpt_path, device="cpu")
else:
state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"]
missing, unexpected = self.pipe.model.load_state_dict(state_dict, strict=False)
if rank == 0:
print(f"[Engine] missing: {missing}, unexpected: {unexpected}")
self.pipe = self.pipe.to(device)
self.pipe.model.eval()
self.pipe.model.backbone.set_rope_params()
# SP patching
if use_sp:
_convert_backbone_to_sp(self.pipe.model.backbone)
if rank == 0:
print(f"[SP] Patched backbone blocks to SP-aware self-attn.")
# Inference params from config
self.fps = self.cfg["data"].get("video_fps", 24)
self.audio_tokens_per_sec = self.cfg["data"].get("audio_tokens_per_sec", 25)
self.video_latent_ch = self.cfg["video_latent_ch"]
self.height = self.cfg.get("log_height", 480)
self.width = self.cfg.get("log_width", 832)
self.frames = self.cfg["data"].get("video_tgt_frames", 121)
self.patch_size = self.cfg.get("patch_size", 2)
self.dtype = torch.bfloat16 if self.cfg["use_bf16"] else torch.float16
if rank == 0:
print(f"[Engine] Ready. modality={self.modality}, "
f"resolution={self.width}x{self.height}, frames={self.frames}")
def _build_batch(self, prompt: str, image_path: str = None, spk_wav_paths: list = None):
"""Build a single-sample batch dict from raw inputs."""
# Compute latent dimensions
h = self.height // self.patch_size
w = self.width // self.patch_size
frames = self.frames
# Audio length based on video duration
video_duration = ((frames - 1) * 4 + 1) / self.fps
audio_len = math.ceil(video_duration * self.audio_tokens_per_sec)
batch = {
"captions": [prompt],
"video_latents": torch.randn(1, frames * h * w, self.video_latent_ch),
"audio_latents": [torch.randn(audio_len, self.video_latent_ch)],
"t_h_w_list": [(frames, h, w)],
"first_frames": None,
"spk_embs": None,
"save_path": ["gradio_output.mp4"],
}
# Handle i2v (first frame image)
if image_path and os.path.exists(image_path):
from torchvision import transforms
from PIL import Image
img = Image.open(image_path).convert("RGB")
img = img.resize((self.width, self.height))
img_tensor = transforms.ToTensor()(img).unsqueeze(0) # [1, 3, H, W]
# Encode through video VAE
img_tensor = img_tensor.to(self.device, dtype=self.dtype)
with torch.no_grad():
first_frame_latent = self.pipe.video_vae.encode(img_tensor)
batch["first_frames"] = first_frame_latent
# Handle speaker embeddings
if spk_wav_paths:
spk_embs_list = []
for wav_path in spk_wav_paths:
if os.path.exists(wav_path):
waveform, sr = torchaudio.load(wav_path)
with torch.no_grad():
spk_emb = self.pipe.audio_vae.get_speaker_embedding(
waveform.to(self.device), sr
)
spk_embs_list.append(spk_emb)
if spk_embs_list:
batch["spk_embs"] = [spk_embs_list]
return batch
@torch.no_grad()
def generate(self, prompt: str, image_path: str = None, spk_wav_paths: list = None,
steps: int = 25, output_dir: str = "/tmp/nava_outputs",
is_i2v: bool = False) -> str:
"""
Run single inference. All ranks must call this together in SP mode.
Returns: output video path (only meaningful on rank 0).
"""
os.makedirs(output_dir, exist_ok=True)
batch = self._build_batch(prompt, image_path, spk_wav_paths)
batch = {k: (v.to(self.device) if isinstance(v, torch.Tensor) else v)
for k, v in batch.items()}
amp_ctx = torch.autocast(device_type="cuda", dtype=self.dtype)
with amp_ctx:
gen_vid_out, gen_aud_out = self.pipe.sample(
batch,
num_steps=steps,
audio_guidance_scale=self.cfg.get("audio_guidance_scale", 2.0),
video_guidance_scale=self.cfg.get("video_guidance_scale", 3.0),
align_3d_cfg=self.cfg.get("align_3d_cfg", True),
audio_align_guidance_scale=self.cfg.get("audio_align_guidance_scale", 2.0),
video_align_guidance_scale=self.cfg.get("video_align_guidance_scale", 3.0),
save_vid_latent=False,
is_i2v=is_i2v,
timbre_cfg=self.cfg.get("timbre_cfg", False),
timbre_align_guidance_scale=self.cfg.get("timbre_align_guidance_scale", 3.0),
)
# Only rank 0 saves
if self.rank != 0:
return ""
# Post-process: merge video + audio → mp4
import time
timestamp = int(time.time() * 1000)
output_path = os.path.join(output_dir, f"output_{timestamp}.mp4")
gen_vids = _to01(gen_vid_out).float()
video_tensor = (gen_vids[0] * 255).clamp(0, 255).to(torch.uint8)
video_tensor = video_tensor.permute(0, 2, 3, 1) # [T, C, H, W] -> [T, H, W, C]
aud = gen_aud_out[0]
waveform = _toWav(aud["waveform"])
if waveform.dim() == 1:
waveform = waveform.unsqueeze(0)
sample_rate = aud["sample_rate"]
write_video(
output_path,
video_tensor,
fps=self.fps,
video_codec="h264",
audio_array=waveform.cpu().float().contiguous(),
audio_fps=sample_rate,
audio_codec="aac",
options={"crf": "18"},
)
print(f"[Engine] Saved: {output_path}")
return output_path
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