AI Models
    Prism Steganography

    OlaversePrism Steganography

    Prism family · open weights on Hugging Face.

    Neural steganography model that invisibly hides data inside images and recovers it losslessly.

    Image To Image
    Steganography
    PyTorch
    Prism
    Licence: apache-2.0
    0
    Likes
    85
    Downloads
    Jul 2026
    Created

    Model Card

    Built for production use.

    Open-weights repository on Hugging Face

    Integrated ecosystem protocol tier.

    Compatible with Transformers library

    Integrated ecosystem protocol tier.

    Optimised for low-latency inference

    Integrated ecosystem protocol tier.

    Community engagement: 0 likes

    Integrated ecosystem protocol tier.

    Quick Start

    Use Prism Steganography, straight from its model card.

    example.py
    from the model card
    from huggingface_hub import hf_hub_download
    import torch, importlib.util, json
    model_file = hf_hub_download(repo_id="olaverse/prism-steganography", filename="model.py")
    ckpt_file = hf_hub_download(repo_id="olaverse/prism-steganography", filename="pytorch_model.pt")
    config_file = hf_hub_download(repo_id="olaverse/prism-steganography", filename="config.json")
    spec = importlib.util.spec_from_file_location("model", model_file)
    model_module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(model_module)
    config = json.load(open(config_file))
    checkpoint = torch.load(ckpt_file, map_location="cpu")
    encoder = model_module.StegEncoder(**config)
    encoder.load_state_dict(checkpoint["encoder"])
    encoder.eval()
    decoder = model_module.StegDecoder(**config)
    decoder.load_state_dict(checkpoint["decoder"])
    decoder.eval()
    Model Card

    Official Repository README

    Dynamically loaded from Hugging Face

    Model card metadata is available on Hugging Face.

    Built with

    Transformers PyTorch Python
    Built by Olaverse Labs

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    Neural steganography model that invisibly hides data inside images and recovers it losslessly.

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