Function load_loras

tools/convert/converter.py:445–471

Load and apply LoRA weights to model weights using the LoRALoader class. Args: lora_path: Path to LoRA safetensors file weight_dict: Model weights dictionary (will be modified in place) alpha: Global alpha scaling factor key_mapping_rules: Optional list of (

(lora_path, weight_dict, alpha, key_mapping_rules=None, strength=1.0)

    logger.info(f"Total final model size: {total_final_size_mb:.2f} MB")
    logger.info(f"Size reduction in quantized tensors: {size_reduction_mb:.2f} MB ({size_reduction_mb / original_size_mb * 100:.1f}%)")

    if comfyui_mode:
        weights["scaled_fp8"] = torch.zeros(2, dtype=torch.float8_e4m3fn)

    return weights


def _validate_lora_merge(lora_path, weight_dict, lora_weights, lora_pairs, lora_diffs, alpha, require_alpha):
    """Validate a LoRA completely before modifying any base-model tensors."""
    if not lora_pairs and not lora_diffs:
        raise ValueError(f"No supported LoRA weights found in: {lora_path}")

    consumed_keys = set()
    for pair_info in lora_pairs.values():
        consumed_keys.update((pair_info["up_key"], pair_info["down_key"]))
        if pair_info["mid_key"] is not None:
            consumed_keys.add(pair_info["mid_key"])
    consumed_keys.update(diff_info["diff_key"] for diff_info in lora_diffs.values())

    tensor_keys = {key for key in lora_weights if not key.endswith(".alpha")}
    unsupported_keys = sorted(tensor_keys - consumed_keys)
    missing_model_keys = sorted((set(lora_pairs) | set(lora_diffs)) - set(weight_dict))
    missing_alpha_keys = sorted(model_key for model_key, pair_info in lora_pairs.items() if pair_info["alpha"] is None)

    shape_mismatches = []
    for model_key, pair_info in lora_pairs.items():
        if model_key not in weight_dict:
            continue
        param = weight_dict[model_key]
        lora_up = lora_weights[pair_info["up_key"]]