Method generate

latents.py:434–495

(
        self, vae, positive, negative, latent, guiding_latent, latent_idx, strength
    )

    DESCRIPTION = "Adds a keyframe or a video segment at a specific frame index."

    def generate(
        self, vae, positive, negative, latent, guiding_latent, latent_idx, strength
    ):
        noise_mask = nodes_lt.get_noise_mask(latent)
        latent = latent["samples"]
        guide = guiding_latent["samples"]

        # Record original (pre-dilation) guide latent shape for spatial mask downsampling
        guide_orig_shape = list(guide.shape[2:])  # [F, H_small, W_small]

        assert (
            latent.shape[4] % guide.shape[4] == 0
            and latent.shape[3] % guide.shape[3] == 0
        ), "The ratio of the height and width of the latents and optional_guiding_latents must be an integer"

        guiding_latent = LTXVDilateLatent().dilate_latent(
            guiding_latent,
            horizontal_scale=latent.shape[4] // guide.shape[4],
            vertical_scale=latent.shape[3] // guide.shape[3],
        )[0]

        guide = guiding_latent["samples"]
        guide_mask = guiding_latent.get("noise_mask", None)

        # Pre-filter token count = product of dilated spatial dims
        # (before grid_mask filtering removes padding positions)
        iclora_tokens_added = guide.shape[2] * guide.shape[3] * guide.shape[4]

        scale_factors = vae.downscale_index_formula

        if latent_idx <= 0:
            frame_idx = latent_idx * scale_factors[0]
        else:
            frame_idx = 1 + (latent_idx - 1) * scale_factors[0]

        positive, negative, latent, noise_mask = nodes_lt.LTXVAddGuide.append_keyframe(
            positive=positive,
            negative=negative,
            frame_idx=frame_idx,
            latent_image=latent,
            noise_mask=noise_mask,
            guiding_latent=guide,
            strength=strength,
            scale_factors=scale_factors,
            guide_mask=guide_mask,
        )

        # Track this guide in guide_attention_entries for per-reference attention control.
        from .iclora_attention import append_guide_attention_entry

        positive = append_guide_attention_entry(
            positive, iclora_tokens_added, guide_orig_shape
        )
        negative = append_guide_attention_entry(
            negative, iclora_tokens_added, guide_orig_shape
        )

        return (