MiniMax H3 cfg-distilled full denoise loop.
Per step, the positive presentation is forwarded exactly once. Video and audio target rows chain through the selected solver while visual and audio condition rows stay pinned to their noised step-0 anchors.
Static per-branch state: packed layout + fixed forward kwargs.
packed is a minimax_h3_packed_sequence(...) result (or equivalent layout dict); text_embeddings is the branch's [text_len, 5120] hidden states; token_tags must already carry any fl2va vision-span overrides.
instance-attribute ¶
instance-attribute ¶
instance-attribute ¶
audio_x_base = torch.zeros(
1,
seq_len,
MINIMAX_H3_AUDIO_ROW_WIDTH,
dtype=torch.float32,
device=device,
)
instance-attribute ¶
instance-attribute ¶
static_kwargs: dict[str, Any] = {
"img_position_ids": self.img_position_ids_dev[None],
"update_mask": self.update_mask_dev,
"token_tags": self.token_tags_dev,
"skip_mask_out_condition": False,
"prompt_embeds": self.text_embeddings_dev,
"img_pos_info": {"position_ids": self.img_pos_dev},
"audio_pos_info": {"position_ids": self.audio_pos_dev},
"text_pos_info": {"position_ids": self.text_pos_dev},
"img_pos_for_infer_output_info": {
"position_ids": self.img_pos_dev
},
"packed_seq_params": {
"cu_seqlens_q": cu.to(device),
"max_seqlen_q": self.used_len,
"num_requests": 1,
},
"refiner_packed_seq_params": {
"cu_seqlens_q": torch.tensor(
[0, text_len, text_len],
dtype=torch.int32,
device=device,
),
"max_seqlen_q": text_len,
},
}
instance-attribute ¶
instance-attribute ¶
instance-attribute ¶
x_base = torch.zeros(
1,
seq_len,
MINIMAX_H3_VIDEO_ROW_WIDTH,
dtype=torch.float32,
device=device,
)
fill_timesteps(
timesteps: Tensor,
*,
t_video: float,
t_audio: float,
imgvid_cond_timestep: float,
audio_ref_cond_timestep: float,
video_target_timesteps: Tensor | None = None,
audio_target_timesteps: Tensor | None = None,
) -> None
Fill one request's packed row timesteps in an existing tensor.
forward_kwargs(
*,
video_rows: Tensor,
audio_rows: Tensor,
t_video: float,
t_audio: float,
imgvid_cond_timestep: float,
audio_ref_cond_timestep: float,
video_target_timesteps: Tensor | None = None,
audio_target_timesteps: Tensor | None = None,
) -> dict[str, Any]
prepare_rope_table(model: Any) -> None
Materialize the branch-local DiT RoPE table once per denoise run.
img_position_ids is immutable for this branch while latents and timesteps change every scheduler step. Keeping the table in static_kwargs makes every model call reuse the exact same BF16 tensor without extending its lifetime beyond this request branch.
minimax_h3_denoise_loop(
*,
model: Any,
positive: MiniMaxH3DenoiseBranch,
initial_video_rows: Tensor,
initial_audio_rows: Tensor,
keyframe_cond_rows: Tensor | None,
audio_ref_rows: Tensor | None = None,
video_edit: MiniMaxH3LatentEdit | None = None,
audio_edit: MiniMaxH3LatentEdit | None = None,
sigmas_video: list[float],
sigmas_audio: list[float],
device: device,
imgvid_cond_noise_aug_for_inference: float = MINIMAX_H3_IMGVID_COND_TIMESTEP,
audio_cond_noise_aug_for_inference: float = MINIMAX_H3_AUDIO_REF_COND_TIMESTEP,
on_step: Callable[[int, Tensor, Tensor], None]
| None = None,
step_profiler: Callable[[int], AbstractContextManager]
| None = None,
sampler: str = "euler",
) -> tuple[Tensor, Tensor]
Run the full denoise loop; returns final (video_rows, audio_rows).
initial_video_rows covers all image rows of the positive layout. For a conditional task, pass keyframe_cond_rows and/or audio_ref_rows to pin those rows across every step. The model's raw positive velocity is the update signal; MiniMax H3 only supports cfg-distilled checkpoints.
minimax_h3_prepare_denoise_rows(
*,
positive: MiniMaxH3DenoiseBranch,
initial_video_rows: Tensor,
initial_audio_rows: Tensor,
keyframe_cond_rows: Tensor | None,
audio_ref_rows: Tensor | None,
device: device,
) -> tuple[Tensor, Tensor, Tensor | None, Tensor | None]
Validate the initial rows against the layout and pin the condition rows.
Returns (video_rows, audio_rows, cond_anchor, audio_anchor) with the anchors already written into their rows, which is the state both the request-mode loop and step-mode prepare_encode() start from.
minimax_h3_publish_denoise_progress(
step: int | None,
sigma_video: float | None,
total_steps: int | None = None,
) -> None
Publish denoise progress for step-gated attention features.
Both execution modes must publish the same trio: the step index drives the dense warmup of RAINFUSION_ATTN, the normalized descending timestep drives the TRTLLM_ATTN skip gate (which stays dense while it is unset), and the total step count enables the end_step tail fallback.