Method define_schema

iclora.py:270–345

(cls)

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LTXAddVideoICLoRAGuideAdvanced",
            display_name=NODES_DISPLAY_NAME_PREFIX
            + " Add Video IC-LoRA Guide Advanced",
            category="Lightricks/IC-LoRA",
            description=(
                "Adds IC-LoRA guide conditioning with per-guide attention strength control. "
                "Same as LTXAddVideoICLoRAGuide, but allows controlling how strongly this "
                "guide influences generation via self-attention, optionally with a spatial mask."
            ),
            inputs=[
                io.Conditioning.Input("positive"),
                io.Conditioning.Input("negative"),
                io.Vae.Input("vae"),
                io.Latent.Input(
                    "latent",
                    tooltip="Video-only latent to condition. Must be a 5D video latent.",
                ),
                io.Image.Input("image"),
                io.Int.Input(
                    "frame_idx",
                    default=0,
                    min=-9999,
                    max=9999,
                    tooltip=(
                        "Frame index to start the conditioning at. The value is rounded to the "
                        "nearest frame and wrapped modulo the number of video frames. Negative "
                        "values are counted from the end of the video before wrapping."
                    ),
                ),
                io.Float.Input("strength", default=1.0, min=0.0, max=1.0, step=0.01),
                io.Float.Input(
                    "latent_downscale_factor",
                    default=1.0,
                    min=1.0,
                    max=10.0,
                    step=1.0,
                    tooltip="For IC-LoRA on small grid. 1 = original size, 2 = half, etc.",
                ),
                io.Combo.Input(
                    "crop",
                    options=["disabled", "center"],
                    default="disabled",
                ),
                io.Boolean.Input("use_tiled_encode", default=False),
                io.Int.Input("tile_size", default=256, min=64, max=512, step=32),
                io.Int.Input("tile_overlap", default=64, min=16, max=256, step=16),
                io.Float.Input(
                    "attention_strength",
                    default=1.0,
                    min=0.0,
                    max=1.0,
                    step=0.01,
                    tooltip=(
                        "Controls how strongly this guide influences generation via "
                        "self-attention. 1.0 = full conditioning (default), 0.0 = ignore. "
                        "When an attention_mask is also provided, this multiplies the mask values."