def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
    """
    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

    Args:
        timesteps (`torch.Tensor`):
            generate embedding vectors at these timesteps
        embedding_dim (`int`, *optional*, defaults to 512):
            dimension of the embeddings to generate
        dtype:
            data type of the generated embeddings

    Returns:
        `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
    """
    assert len(w.shape) == 1
    w = w * 1000.0

    half_dim = embedding_dim // 2
    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
    emb = w.to(dtype)[:, None] * emb[None, :]
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
    if embedding_dim % 2 == 1:  # zero pad
        emb = torch.nn.functional.pad(emb, (0, 1))
    assert emb.shape == (w.shape[0], embedding_dim)
    return emb

    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

    Args:
        timesteps (`torch.Tensor`):
            generate embedding vectors at these timesteps
        embedding_dim (`int`, *optional*, defaults to 512):
            dimension of the embeddings to generate
        dtype:
            data type of the generated embeddings

    Returns:
        `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
    
examples/consistency_distillation/train_lcm_distill_sd_wds.py