Class LTXICLoRALoaderModelOnly

iclora.py:470–523

@comfy_node(name="LTXICLoRALoaderModelOnly")
class LTXICLoRALoaderModelOnly(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="LTXICLoRALoaderModelOnly",
            display_name=NODES_DISPLAY_NAME_PREFIX + " IC-LoRA Loader Model Only",
            category="Lightricks/IC-LoRA",
            description="Loads a LoRA model and extracts the latent_downscale_factor from the safetensors metadata.",
            inputs=[
                io.Model.Input("model"),
                io.Combo.Input(
                    "lora_name",
                    options=folder_paths.get_filename_list("loras"),
                ),
                io.Float.Input(
                    "strength_model",
                    default=1.0,
                    min=-100.0,
                    max=100.0,
                    step=0.01,
                ),
            ],
            outputs=[
                io.Model.Output("model"),
                io.Float.Output("latent_downscale_factor"),
            ],
        )

    @classmethod
    def execute(cls, model, lora_name, strength_model) -> io.NodeOutput:
        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)

        # Load lora and extract metadata
        lora, metadata = comfy.utils.load_torch_file(
            lora_path, safe_load=True, return_metadata=True
        )

        # Extract latent_downscale_factor from metadata
        try:
            latent_downscale_factor = float(metadata["reference_downscale_factor"])
        except (KeyError, ValueError, TypeError):
            latent_downscale_factor = 1.0
            logging.warning(
                "Failed to extract reference_downscale_factor from metadata for %s, using 1.0",
                lora_path,
            )

        if strength_model == 0:
            return io.NodeOutput(model, latent_downscale_factor)

        model_lora, _ = comfy.sd.load_lora_for_models(
            model, None, lora, strength_model, 0
        )
        return io.NodeOutput(model_lora, latent_downscale_factor)


def _patchify_audio_latent(latent):