31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
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"""Direct prompt-only MiniMax H3 T2V shape and latent helpers."""
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import torch
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FPS = 24
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AUDIO_LATENT_FPS = 40
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def align_frame_count(frames: int) -> int:
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"""Snap to H3's valid 17k+5 temporal grid."""
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frames = max(5, frames)
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return frames + (5 - frames) % 17
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def temporal_shape(frames: int) -> tuple[int, int, int]:
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"""Return output frames, video-latent frames, and joint audio-latent steps."""
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frames = align_frame_count(frames)
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video_steps = 2 if frames <= 5 else ((frames - 5) // 17) * 5 + 2
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return frames, video_steps, round(frames / FPS * AUDIO_LATENT_FPS)
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def empty_av_latents(width: int, height: int, frames: int, *, device: torch.device | str = "cuda") -> tuple[torch.Tensor, torch.Tensor, int]:
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"""Allocate H3's joint video/audio sampling state without ComfyUI objects."""
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if width % 32 or height % 32:
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raise ValueError("H3 T2V dimensions must be multiples of 32.")
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frames, video_steps, audio_steps = temporal_shape(frames)
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video = torch.zeros((1, 24, video_steps, height // 16, width // 16), device=device)
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audio = torch.zeros((1, 32, 2, audio_steps), device=device)
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return video, audio, frames
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