Function load_weight_norm_conv_transpose1d

src/models/ace_step/vae_decoder.cpp:272–306

    int dilation,
    assets::TensorStorageType storage_type,
    bool use_bias) {
    const auto g = source.require_f32(prefix + ".weight_g", {in_channels, 1, 1});
    const auto v = source.require_f32(prefix + ".weight_v", {in_channels, out_channels, kernel});
    const auto graph_storage_type =
        compatible_conv_transpose_storage_type(source, prefix + ".weight_v", storage_type);
    WeightNormConvTranspose1dWeights out = {};
    out.stride = stride;
    out.padding = padding;
    out.dilation = dilation;
    auto fused_weight = apply_weight_norm(g, v, in_channels, out_channels * kernel);
    auto col_weight = conv_transpose_col_weight(fused_weight, in_channels, out_channels, kernel);
    out.conv.weight = store.make_from_f32(
        core::TensorShape::from_dims({in_channels, out_channels, kernel}),
        graph_storage_type,
        std::move(fused_weight));
    out.col_weight = store.make_from_f32(
        core::TensorShape::from_dims({out_channels * kernel, in_channels}),
        graph_storage_type,
        std::move(col_weight));
    if (use_bias) {
        out.conv.bias = store.load_f32_tensor(source, prefix + ".bias", {out_channels});
    }
    return out;
}

ResidualUnitWeights load_residual_unit(
    core::BackendWeightStore & store,
    const assets::TensorSource & source,
    const std::string & prefix,
    int64_t channels,
    assets::TensorStorageType storage_type) {
    ResidualUnitWeights out = {};
    out.snake1 = load_snake_exact(store, source, prefix + ".snake1", channels);
    out.conv1 = load_weight_norm_conv1d(store, source, prefix + ".conv1", channels, channels, 7, 1, 3, 1, storage_type, true);
    out.snake2 = load_snake_exact(store, source, prefix + ".snake2", channels);
    out.conv2 = load_weight_norm_conv1d(store, source, prefix + ".conv2", channels, channels, 1, 1, 0, 1, storage_type, true);
    return out;
}