def __init__()

in consistencydecoder/__init__.py [0:0]

• 22 lines of code
• 1 McCabe index (conditional complexity)

    def __init__(self, device="cuda:0", download_root=os.path.expanduser("~/.cache/clip")):
        self.n_distilled_steps = 64
        download_target = _download("https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt", download_root)
        self.ckpt = torch.jit.load(download_target).to(device)
        self.device = device
        sigma_data = 0.5
        betas = betas_for_alpha_bar(
            1024, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
        ).to(device)
        alphas = 1.0 - betas
        alphas_cumprod = torch.cumprod(alphas, dim=0)
        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
        sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
        sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)
        self.c_skip = (
            sqrt_recip_alphas_cumprod
            * sigma_data**2
            / (sigmas**2 + sigma_data**2)
        )
        self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5
        self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5