# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import argparse import json import time from pathlib import Path from typing import Any import numpy as np import torch from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args from vllm_omni.entrypoints.omni import Omni from vllm_omni.inputs.data import OmniDiffusionSamplingParams from vllm_omni.lora.request import LoRARequest from vllm_omni.lora.utils import stable_lora_int_id from vllm_omni.model_extras import get_extra_body_params, get_model_class_name, get_output_tensor_range from vllm_omni.outputs import OmniRequestOutput from vllm_omni.platforms import current_omni_platform _MODEL_PRESETS = { "vace": { "height": 480, "width": 832, "num_frames": 81, "num_inference_steps": 30, "guidance_scale": 5.0, "fps": 16, "output": "vace_t2v_output.mp4", }, "wan": { "height": 720, "width": 1280, "num_frames": 81, "num_inference_steps": 40, "guidance_scale": 4.0, "fps": 24, "output": "wan22_output.mp4", }, "hunyuan": { "height": 480, "width": 832, "num_frames": 121, "num_inference_steps": 50, "guidance_scale": 6.0, "fps": 24, "output": "hunyuan_video_15_output.mp4", }, "cosmos": { "height": 720, "width": 1280, "num_frames": 189, "num_inference_steps": 35, "guidance_scale": 6.0, "fps": 24, "flow_shift": 10.0, "output": "cosmos3_t2v_output.mp4", }, # Cosmos3-Edge is a compact checkpoint with its own native resolution / guidance / # flow_shift. It must NOT inherit the Nano/Super "cosmos" numbers above (720p / gs 6.0 / # flow_shift 10.0), which produce degenerate output on Edge. "cosmos3_edge": { "height": 480, "width": 832, "num_frames": 189, "num_inference_steps": 35, "guidance_scale": 5.0, "fps": 24, "flow_shift": 3.0, "output": "cosmos3_edge_t2v_output.mp4", }, "helios": { "height": 384, "width": 640, "num_frames": 99, "num_inference_steps": 50, "guidance_scale": 5.0, "fps": 16, "output": "helios_output.mp4", }, "lingbot": { "model_class_name": "LingBotVideoPipeline", "height": 192, "width": 320, "num_frames": 9, "num_inference_steps": 2, "guidance_scale": 3.0, "fps": 24, "flow_shift": 3.0, "output": "lingbot_video_output.mp4", }, "ltx2": { "height": 512, "width": 768, "num_frames": 121, "num_inference_steps": 40, "fps": 24, "output": "ltx2_output.mp4", }, "ltx2_distilled": { "height": 1024, "width": 1536, "num_frames": 121, "num_inference_steps": 8, "fps": 24, "output": "ltx2_distilled_output.mp4", }, "ltx23": { "height": 512, "width": 768, "num_frames": 121, "num_inference_steps": 30, "fps": 24, "output": "ltx23_output.mp4", }, } def _detect_preset(model: str, model_class_name: str | None = None) -> dict: model_lower = model.lower() class_lower = (model_class_name or "").lower() if "lingbot" in model_lower or "lingbotvideo" in class_lower: return _MODEL_PRESETS["lingbot"] if "ltx" in class_lower or "ltx" in model_lower: if "distilled" in class_lower or "distilled" in model_lower: return _MODEL_PRESETS["ltx2_distilled"] if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower: return _MODEL_PRESETS["ltx23"] return _MODEL_PRESETS["ltx2"] if "vace" in model_lower or "vace" in class_lower: return _MODEL_PRESETS["vace"] # Edge must be matched before the generic cosmos branch (its "cosmos" substring would # otherwise pick up the Nano/Super 720p / gs 6.0 / flow_shift 10.0 preset). if ("cosmos" in model_lower or "cosmos" in class_lower) and ("edge" in model_lower or "edge" in class_lower): return _MODEL_PRESETS["cosmos3_edge"] if "cosmos" in model_lower or "cosmos" in class_lower: return _MODEL_PRESETS["cosmos"] if "hunyuan" in model_lower or "hunyuan" in class_lower: return _MODEL_PRESETS["hunyuan"] if "helios" in model_lower or "helios" in class_lower: return _MODEL_PRESETS["helios"] return _MODEL_PRESETS["wan"] def build_text_to_video_prompt(prompt: str, negative_prompt: str | None) -> dict[str, Any]: """Build the canonical request envelope for the shared T2V example.""" result: dict[str, Any] = { "prompt": prompt, "modalities": ["video"], } if negative_prompt is not None: result["negative_prompt"] = negative_prompt return result def _normalize_float_tensor(tensor: torch.Tensor, source_range: str) -> torch.Tensor: """Normalize decoded frames according to the pipeline's output contract.""" if not tensor.is_floating_point(): return tensor if source_range == "negative_one_to_one": return tensor.clamp(-1, 1) * 0.5 + 0.5 if source_range == "zero_to_one": return tensor.clamp(0, 1) raise ValueError(f"Unsupported floating-point tensor range: {source_range!r}") def parse_profiler_config(value: str) -> dict[str, Any]: try: config = json.loads(value) except json.JSONDecodeError as e: raise argparse.ArgumentTypeError(f"--profiler-config must be valid JSON: {e}") from e if not isinstance(config, dict): raise argparse.ArgumentTypeError("--profiler-config must be a JSON object") return config def parse_extra_body(value: str) -> dict[str, Any]: """Parse a JSON object of model-specific extra_body params. Pipeline-declared knobs (see vllm_omni/model_extras/) are merged into OmniDiffusionSamplingParams.extra_args, so a single generic example can drive model-specific behaviour without bespoke per-model flags. """ try: body = json.loads(value) except json.JSONDecodeError as e: raise argparse.ArgumentTypeError(f"--extra-body must be valid JSON: {e}") from e if not isinstance(body, dict): raise argparse.ArgumentTypeError("--extra-body must be a JSON object") return body def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description="Generate a video from a text prompt. " "Supports Wan2.2, HunyuanVideo-1.5, Helios, LingBot-Video, and other text-to-video models." ) parser.add_argument( "--model", default="Wan-AI/Wan2.2-T2V-A14B-Diffusers", help="Diffusers model ID or local path. " "Examples: Wan-AI/Wan2.2-T2V-A14B-Diffusers, " "hunyuanvideo-community/HunyuanVideo-1.5-480p_t2v", ) parser.add_argument( "--model-class-name", default=None, help="Override model class name (e.g., LTX2Pipeline).", ) parser.add_argument( "--deploy-config", default=None, help="Optional deploy config YAML to use for pipeline-backed runs.", ) parser.add_argument("--prompt", default="A serene lakeside sunrise with mist over the water.", help="Text prompt.") parser.add_argument("--negative-prompt", default=None, help="Negative prompt. Default: model-specific.") parser.add_argument( "--extra-body", type=parse_extra_body, default=None, help="JSON dict of model-specific extra_body params (declared in vllm_omni/model_extras/), " "merged into sampling extra_args. Unknown keys for the chosen model are dropped. " 'Cosmos3 example: \'{"flow_shift": 10.0, "max_sequence_length": 4096, "guardrails": false, ' '"use_resolution_template": false, "use_duration_template": false}\' ' '(add "generate_sound": true and optional "sound_duration" for synchronized audio). ' 'Helios-Distilled example: \'{"is_enable_stage2": true, ' '"pyramid_num_inference_steps_list": [2, 2, 2], "is_amplify_first_chunk": true}\'.', ) parser.add_argument("--seed", type=int, default=42, help="Random seed.") parser.add_argument("--guidance-scale", type=float, default=None, help="CFG scale. Default: model-specific.") parser.add_argument( "--guidance-scale-high", type=float, default=None, help="Separate CFG for high-noise stage (Wan2.2 only)." ) parser.add_argument("--height", type=int, default=None, help="Video height. Default: model-specific.") parser.add_argument("--width", type=int, default=None, help="Video width. Default: model-specific.") parser.add_argument("--num-frames", type=int, default=None, help="Number of frames. Default: model-specific.") parser.add_argument( "--frame-rate", type=float, default=None, help="Optional generation frame rate (used by models like LTX2). Defaults to --fps.", ) parser.add_argument( "--num-inference-steps", type=int, default=None, help="Sampling steps. Default: model-specific." ) parser.add_argument( "--boundary-ratio", type=float, default=None, help="(Wan2.2) Boundary split ratio for low/high DiT. Default 0.875.", ) parser.add_argument( "--flow-shift", type=float, default=None, help="Scheduler flow_shift. Wan2.2: 5.0(720p)/12.0(480p). HunyuanVideo-1.5: 5.0(480p)/9.0(720p).", ) parser.add_argument( "--cache-backend", type=str, default=None, choices=["cache_dit"], help="Cache backend for acceleration (Wan2.2). Default: None.", ) parser.add_argument( "--enable-cache-dit-summary", action="store_true", help="Enable cache-dit summary logging after diffusion forward passes.", ) parser.add_argument("--output", type=str, default=None, help="Output path (mp4). Default: model-specific.") parser.add_argument("--fps", type=int, default=None, help="Frames per second for the output video.") parser.add_argument( "--vae-use-slicing", action="store_true", help="Enable VAE slicing for memory optimization.", ) parser.add_argument( "--vae-use-tiling", action="store_true", help="Enable VAE tiling for memory optimization.", ) parser.add_argument( "--enforce-eager", action="store_true", help="Disable torch.compile and force eager execution.", ) parser.add_argument( "--enable-cpu-offload", action="store_true", help="Enable CPU offloading for diffusion models.", ) parser.add_argument( "--enable-layerwise-offload", action="store_true", help="Enable layerwise (blockwise) offloading on DiT modules.", ) parser.add_argument( "--audio-sample-rate", type=int, default=24000, help="Sample rate for audio output when saved (default: 24000).", ) parser.add_argument( "--enable-diffusion-pipeline-profiler", action="store_true", help="Enable diffusion pipeline profiler to display stage durations.", ) parser.add_argument( "--profiler-config", type=parse_profiler_config, default=None, help='JSON profiler config for torch/cuda profiling, e.g. \'{"profiler":"torch","torch_profiler_dir":"./perf"}\'.', ) parser.add_argument( "--quantization", type=str, default=None, choices=["fp8", "mxfp8", "mxfp4", "mxfp4_dualscale", "int8"], help="Quantization method for the transformer. mxfp8: W8A8 MXFP8 (NPU). mxfp4: W4A4 MXFP4 (NPU). mxfp4_dualscale: W4A4 MXFP4 dual-scale + BF16 fallback mixed (NPU). fp8: online FP8 (GPU).", ) # Distributed and parallel execution parser.add_argument( "--ulysses-mode", type=str, default="strict", choices=["strict", "advanced_uaa"], help="Ulysses sequence-parallel mode: 'strict' (divisibility required) or 'advanced_uaa' (UAA).", ) parser.add_argument( "--ulysses-degree", type=int, default=1, help="Number of GPUs used for ulysses sequence parallelism.", ) parser.add_argument( "--ring-degree", type=int, default=1, help="Number of GPUs used for ring sequence parallelism.", ) parser.add_argument( "--cfg-parallel-size", type=int, default=1, choices=[1, 2], help="Number of GPUs used for classifier free guidance parallel size.", ) parser.add_argument( "--tensor-parallel-size", type=int, default=1, help="Number of GPUs used for tensor parallelism (TP) inside the DiT.", ) parser.add_argument( "--vae-patch-parallel-size", type=int, default=1, help="Number of GPUs used for VAE patch/tile parallelism (decode).", ) parser.add_argument( "--pipeline-parallel-size", type=int, default=1, help="Number of pipeline parallel stages.", ) parser.add_argument( "--enable-expert-parallel", action="store_true", help="Enable expert parallelism for MoE layers.", ) parser.add_argument( "--lora-path", type=str, nargs="+", default=None, help="Path to LoRA adapter folder (PEFT format) or concrete LoRA checkpoint files. Loaded at initialization and used for generation." "Note: for Wan2.2 MoE models, two checkpoints positionally mapped to high-noise and low-noise modules.", ) parser.add_argument( "--lora-scale", type=float, default=1.0, help="Scale factor for LoRA weights (default: 1.0).", ) parser.add_argument( "--lora-backend", type=str, default="peft", choices=["peft", "distill"], help="LoRA backend for loading LoRA adapters. Default: peft" "'peft' loads a PEFT-format adapter folder, used e.g. for RL" "'distill' fuses one or more concrete LoRA checkpoint files, used e.g. for distilled few-step LoRAs", ) parser.add_argument( "--use-hsdp", action="store_true", help="Enable HSDP (Hybrid Sharded Data Parallel) for diffusion models.", ) parser.add_argument( "--hsdp-shard-size", type=int, default=1, help="Number of GPUs to shard weights across for HSDP.", ) parser.add_argument( "--hsdp-replicate-size", type=int, default=1, help="Number of HSDP replica groups.", ) return parser.parse_args() def _extract_peak_memory_mb(result: Any) -> float: """Pull worker-reported peak VRAM (MiB) generation result. Mirrors vllm_omni/entrypoints/openai/serving_video.py:_extract_peak_memory_mb. """ if isinstance(result, list): result = result[0] if result else None if result is None: return 0.0 val = getattr(result, "peak_memory_mb", 0.0) if not val: inner = result if isinstance(inner, list): inner = inner[0] if inner else None val = getattr(inner, "peak_memory_mb", 0.0) try: return float(val or 0.0) except (TypeError, ValueError): return 0.0 def main(): args = parse_args() model_class_name = args.model_class_name preset = _detect_preset(args.model, model_class_name) for key, default_val in preset.items(): if getattr(args, key.replace("-", "_"), None) is None: setattr(args, key.replace("-", "_"), default_val) model_class_name = args.model_class_name generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed) # Cache-dit config (Wan2.2 only) cache_config = None if args.cache_backend == "cache_dit": cache_config = { "Fn_compute_blocks": 1, "Bn_compute_blocks": 0, "max_warmup_steps": 4, "max_cached_steps": 20, "residual_diff_threshold": 0.24, "max_continuous_cached_steps": 3, "enable_taylorseer": False, "taylorseer_order": 1, "scm_steps_mask_policy": None, "scm_steps_policy": "dynamic", } profiler_enabled = args.profiler_config is not None omni_kwargs = dict( model=args.model, enable_layerwise_offload=args.enable_layerwise_offload, vae_use_slicing=args.vae_use_slicing, vae_use_tiling=args.vae_use_tiling, enable_cpu_offload=args.enable_cpu_offload, ulysses_degree=args.ulysses_degree, ring_degree=args.ring_degree, cfg_parallel_size=args.cfg_parallel_size, tensor_parallel_size=args.tensor_parallel_size, vae_patch_parallel_size=args.vae_patch_parallel_size, pipeline_parallel_size=args.pipeline_parallel_size, enable_expert_parallel=args.enable_expert_parallel, enforce_eager=args.enforce_eager, model_class_name=model_class_name, cache_backend=args.cache_backend, cache_config=cache_config, enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler, profiler_config=args.profiler_config, ) if args.deploy_config: omni_kwargs["deploy_config"] = args.deploy_config if args.boundary_ratio is not None: omni_kwargs["boundary_ratio"] = args.boundary_ratio if args.flow_shift is not None: omni_kwargs["flow_shift"] = args.flow_shift if args.quantization is not None: omni_kwargs["quantization"] = args.quantization if args.cache_backend is not None: omni_kwargs["cache_backend"] = args.cache_backend omni_kwargs["cache_config"] = cache_config omni_kwargs["enable_cache_dit_summary"] = args.enable_cache_dit_summary if args.lora_path is not None: lora_path = args.lora_path if len(lora_path) == 1: lora_path = lora_path[0] omni_kwargs["lora_path"] = lora_path omni_kwargs["lora_backend"] = args.lora_backend # Cosmos3 loads its (gated) guardrail models at build time, so the guardrails # gate is an engine-level config (offline analog of the server's --no-guardrails). if args.extra_body and "guardrails" in args.extra_body: omni_kwargs["model_config"] = {"guardrails": bool(args.extra_body["guardrails"])} omni = Omni(**omni_kwargs) model_class_name = get_model_class_name(omni) or model_class_name declared_extra_body_params = get_extra_body_params(model_class_name) output_tensor_range = get_output_tensor_range(model_class_name) if profiler_enabled: print("[Profiler] Starting profiling...") omni.start_profile() # Print generation configuration print(f"\n{'=' * 60}") print("Generation Configuration:") print(f" Model: {args.model}") print(f" Inference steps: {args.num_inference_steps}") print(f" Frames: {args.num_frames}") print( f" Parallel configuration: ulysses_degree={args.ulysses_degree}, ring_degree={args.ring_degree}," f" cfg_parallel_size={args.cfg_parallel_size}, tensor_parallel_size={args.tensor_parallel_size}," f" vae_patch_parallel_size={args.vae_patch_parallel_size}, pipeline_parallel_size={args.pipeline_parallel_size}," f" enable_expert_parallel={args.enable_expert_parallel}" ) print(f" Video size: {args.width}x{args.height}") print(f"{'=' * 60}\n") lora_request = None if args.lora_path and args.lora_backend == "peft": if len(args.lora_path) != 1: raise ValueError("Only one LoRA path is expected for PEFT backend.") lora_path = args.lora_path[0] lora_request_id = stable_lora_int_id(lora_path) lora_request = LoRARequest( lora_name=Path(lora_path).stem, lora_int_id=lora_request_id, lora_path=lora_path, ) negative_prompt = args.negative_prompt if negative_prompt is None and all(preset is not _MODEL_PRESETS[name] for name in ("lingbot", "ltx2", "ltx23")): # Preserve the historical empty-prompt behavior for non-LTX examples. negative_prompt = "" prompt_dict = build_text_to_video_prompt(args.prompt, negative_prompt) extra_args = {} if lora_request: extra_args["lora_request"] = lora_request extra_args["lora_scale"] = args.lora_scale sampling_kwargs = dict( height=args.height, width=args.width, generator=generator, guidance_scale=args.guidance_scale, num_inference_steps=args.num_inference_steps, num_frames=args.num_frames, extra_args=extra_args, ) if args.frame_rate is not None: sampling_kwargs["frame_rate"] = args.frame_rate if args.guidance_scale_high is not None: sampling_kwargs["guidance_scale_2"] = args.guidance_scale_high sampling_params = OmniDiffusionSamplingParams(**sampling_kwargs) # Route model-specific knobs through extra_body, filtered against the model's # declared extra_body_params. Models without a declaration only forward explicit # --extra-body JSON (preserving the generic flags' legacy behavior). extra_body = dict(args.extra_body or {}) if declared_extra_body_params: apply_declared_extra_args(sampling_params, declared_extra_body_params, extra_body) elif extra_body: sampling_params.extra_args.update({k: v for k, v in extra_body.items() if v is not None}) generation_start = time.perf_counter() frames = omni.generate( prompt_dict, sampling_params, ) generation_end = time.perf_counter() generation_time = generation_end - generation_start # Print profiling results print(f"Total generation time: {generation_time:.4f} seconds ({generation_time * 1000:.2f} ms)") peak_mb = _extract_peak_memory_mb(frames) if peak_mb: print(f"Worker peak GPU memory (reserved): {peak_mb:.2f} MiB ({peak_mb / 1024:.2f} GiB)") audio = None audio_sample_rate = args.audio_sample_rate if isinstance(frames, list): frames = frames[0] if frames else None if isinstance(frames, OmniRequestOutput): if frames.final_output_type != "image": raise ValueError( f"Unexpected output type '{frames.final_output_type}', expected 'image' for video generation." ) if frames.multimodal_output and "audio" in frames.multimodal_output: audio = frames.multimodal_output["audio"] audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate) if frames.is_pipeline_output and frames is not None: inner_output = frames if isinstance(inner_output, OmniRequestOutput): if inner_output.multimodal_output and "audio" in inner_output.multimodal_output: audio = inner_output.multimodal_output["audio"] audio_sample_rate = inner_output.multimodal_output.get("audio_sample_rate", audio_sample_rate) frames = inner_output if isinstance(frames, OmniRequestOutput): if frames.images: if len(frames.images) == 1 and isinstance(frames.images[0], tuple) and len(frames.images[0]) == 2: frames, audio = frames.images[0] elif len(frames.images) == 1 and isinstance(frames.images[0], dict): audio = frames.images[0].get("audio") audio_sample_rate = frames.images[0].get("audio_sample_rate", audio_sample_rate) frames = frames.images[0].get("frames") or frames.images[0].get("video") else: frames = frames.images else: raise ValueError("No video frames found in OmniRequestOutput.") if isinstance(frames, list) and frames: first_item = frames[0] if isinstance(first_item, tuple) and len(first_item) == 2: frames, audio = first_item elif isinstance(first_item, dict): audio = first_item.get("audio") audio_sample_rate = first_item.get("audio_sample_rate", audio_sample_rate) frames = first_item.get("frames") or first_item.get("video") elif isinstance(first_item, list): frames = first_item if isinstance(frames, tuple) and len(frames) == 2: frames, audio = frames elif isinstance(frames, dict): audio = frames.get("audio") audio_sample_rate = frames.get("audio_sample_rate", audio_sample_rate) frames = frames.get("frames") or frames.get("video") if frames is None: raise ValueError("No video frames found in output.") output_path = Path(args.output) output_path.parent.mkdir(parents=True, exist_ok=True) try: from diffusers.utils import export_to_video except ImportError: raise ImportError("diffusers is required for export_to_video.") def _normalize_frame(frame): if isinstance(frame, torch.Tensor): frame_tensor = frame.detach().cpu() if frame_tensor.dim() == 4 and frame_tensor.shape[0] == 1: frame_tensor = frame_tensor[0] if frame_tensor.dim() == 3 and frame_tensor.shape[0] in (3, 4): frame_tensor = frame_tensor.permute(1, 2, 0) frame_tensor = _normalize_float_tensor(frame_tensor, output_tensor_range) return frame_tensor.float().numpy() if isinstance(frame, np.ndarray): frame_array = frame if frame_array.ndim == 4 and frame_array.shape[0] == 1: frame_array = frame_array[0] if np.issubdtype(frame_array.dtype, np.integer): frame_array = frame_array.astype(np.float32) / 255.0 return frame_array try: from PIL import Image except ImportError: Image = None if Image is not None and isinstance(frame, Image.Image): return np.asarray(frame).astype(np.float32) / 255.0 return frame def _ensure_frame_list(video_array): if isinstance(video_array, list): if len(video_array) == 0: return video_array first_item = video_array[0] if isinstance(first_item, np.ndarray): if first_item.ndim == 5: return list(first_item[0]) if first_item.ndim == 4: if len(video_array) == 1: return list(first_item) return list(first_item) if first_item.ndim == 3: return video_array return video_array if isinstance(video_array, np.ndarray): if video_array.ndim == 5: return list(video_array[0]) if video_array.ndim == 4: return list(video_array) if video_array.ndim == 3: return [video_array] return video_array # frames may be np.ndarray, torch.Tensor, or list of tensors/arrays/images # export_to_video expects a list of frames with values in [0, 1] if isinstance(frames, torch.Tensor): video_tensor = frames.detach().cpu() if video_tensor.dim() == 5: if video_tensor.shape[1] in (3, 4): video_tensor = video_tensor[0].permute(1, 2, 3, 0) else: video_tensor = video_tensor[0] elif video_tensor.dim() == 4 and video_tensor.shape[0] in (3, 4): video_tensor = video_tensor.permute(1, 2, 3, 0) video_tensor = _normalize_float_tensor(video_tensor, output_tensor_range) video_array = video_tensor.float().numpy() elif isinstance(frames, np.ndarray): video_array = frames if video_array.ndim == 5: video_array = video_array[0] if np.issubdtype(video_array.dtype, np.integer): video_array = video_array.astype(np.float32) / 255.0 elif isinstance(frames, list): if len(frames) == 0: raise ValueError("No video frames found in output.") video_array = [_normalize_frame(frame) for frame in frames] else: video_array = frames video_array = _ensure_frame_list(video_array) if audio is not None: from vllm_omni.diffusion.utils.media_utils import mux_video_audio_bytes if isinstance(video_array, list): frames_np = np.stack(video_array, axis=0) elif isinstance(video_array, np.ndarray): frames_np = video_array else: frames_np = np.asarray(video_array) frames_u8 = (np.clip(frames_np, 0.0, 1.0) * 255).round().clip(0, 255).astype("uint8") audio_np = audio if isinstance(audio_np, list): audio_np = audio_np[0] if audio_np else None if isinstance(audio_np, torch.Tensor): audio_np = audio_np.detach().cpu().float().numpy() if isinstance(audio_np, np.ndarray): audio_np = np.squeeze(audio_np).astype(np.float32) video_bytes = mux_video_audio_bytes( frames_u8, audio_np, fps=float(args.fps), audio_sample_rate=audio_sample_rate, ) with open(str(output_path), "wb") as f: f.write(video_bytes) else: export_to_video(video_array, str(output_path), fps=args.fps) print(f"Saved generated video to {output_path}") if profiler_enabled: print("\n[Profiler] Stopping profiler and collecting results...") profile_results = omni.stop_profile() if profile_results and isinstance(profile_results, dict): traces = profile_results.get("traces", []) print("\n" + "=" * 60) print("PROFILING RESULTS:") for rank, trace in enumerate(traces): print(f"\nRank {rank}:") if trace: print(f" • Trace: {trace}") if not traces: print(" No traces collected.") print("=" * 60) else: print("[Profiler] No valid profiling data returned.") if __name__ == "__main__": main()