sgl-project/sglang · error · ValueError

SANA-WM forward requires timestep.

Error message

SANA-WM forward requires timestep.

What it means

SANA-WM's forward requires the diffusion timestep; timestep is mandatory and None raises immediately since the model always operates on noised latents.

Source

Thrown at python/sglang/multimodal_gen/runtime/models/dits/sana_wm.py:622

        if not torch.is_grad_enabled():
            self._plucker_emb_cache = (key, chunk_plucker, plucker_emb)
        return plucker_emb

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        timestep: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        camera_conditions: Optional[torch.Tensor] = None,
        chunk_plucker: Optional[torch.Tensor] = None,
        guidance: Optional[torch.Tensor] = None,  # kept for compat
        **kwargs,
    ) -> torch.Tensor:
        if encoder_hidden_states is None:
            raise ValueError("SANA-WM forward requires encoder_hidden_states.")
        if timestep is None:
            raise ValueError("SANA-WM forward requires timestep.")

        B, C, T_raw, H_raw, W_raw = hidden_states.shape
        p_t, p_h, p_w = self.patch_size
        T = T_raw // p_t
        H = H_raw // p_h
        W = W_raw // p_w
        chunk_size = kwargs.get("chunk_size", self.chunk_size)
        chunk_split_strategy = kwargs.get(
            "chunk_split_strategy", self.chunk_split_strategy
        )
        chunk_index = kwargs.get("chunk_index", None)

        # Patch embed: (B, C, T, H, W) -> (B, T*H*W, D)
        x = self.x_embedder(hidden_states.to(dtype=self.x_embedder.proj.weight.dtype))

        # Timestep AdaLN-single. SANA-WM's LTX sampler passes per-frame
        # timesteps shaped (B, 1, T) so the clean first-frame condition can stay
        # at timestep 0 while remaining latent frames denoise. Keep the scalar

View on GitHub (pinned to 0132848349)

Solutions

1. Pass the scheduler's current timestep tensor (shape (B,) or broadcastable) to forward
2. Verify your sampler loop forwards t each step

Example fix

# before
out = model(h, encoder_hidden_states=ehs)
# after
out = model(h, timestep=t, encoder_hidden_states=ehs)

assert timestep is not None and timestep.numel() == B

def valid_timestep(t) -> bool: return isinstance(t, torch.Tensor) and t.numel() >= 1

When it happens

Trigger: Calling forward without timestep, or with timestep=None; e.g. running a single clean pass or a wrapper that only supplies hidden_states and text embeddings.

Common situations: Adapters from other DiT APIs where timestep is optional (e.g. some inference wrappers default t=0); forgetting to pass scheduler timesteps in a sampling loop.

Understand the failure class

Background: "missing required argument" and "the following required arguments were not provided": what required-argument errors mean and how to fix them — this error's family across 20 libraries.

Related errors

• SANA-WM forward_long requires timestep.
• SANA-WM forward requires encoder_hidden_states.
• SANA-WM forward_long requires encoder_hidden_states.
• Either chunk_index or chunk_size must be provided.
• num_frames/height/width are required when hidden_states is p

AI-assisted analysis of sgl-project/sglang@0132848349 (2026-08-28). Data as JSON: /api/errors/6d6923fdd706d065. Report an issue: GitHub.