AI Models
    Prism Upscaler Max

    OlaversePrism Upscaler Max

    Prism family · open weights on Hugging Face.

    Super-resolution upscaler delivering maximum-quality image enlargement with sharp, artefact-free detail.

    Image To Image
    Super Resolution
    PyTorch
    Prism
    Licence: apache-2.0
    0
    Likes
    34
    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 Upscaler Max, straight from its model card.

    example.py
    from the model card
    from huggingface_hub import hf_hub_download
    import torch, importlib.util, json
    from PIL import Image
    import torchvision.transforms.functional as TF
    model_file = hf_hub_download(repo_id="olaverse/prism-upscaler-max", filename="model.py")
    ckpt_file = hf_hub_download(repo_id="olaverse/prism-upscaler-max", filename="pytorch_model.pt")
    config_file = hf_hub_download(repo_id="olaverse/prism-upscaler-max", 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))
    model = model_module.LIIF(**config)
    model.load_state_dict(torch.load(ckpt_file, map_location="cpu"))
    model.eval()
    img = Image.open("input.jpg").convert("RGB")
    lr_tensor = TF.to_tensor(img)
    out_h, out_w = 1024, 1024 # any target resolution you want
    with torch.no_grad():
    feat = model.gen_feat(lr_tensor.unsqueeze(0))
    coord = model_module.make_coord((out_h, out_w), device="cpu").view(1, -1, 2)
    cell = torch.tensor([2.0 / out_h, 2.0 / out_w]).view(1, 1, 2).repeat(1, coord.shape[1], 1)
    pred = model.query_rgb(feat, coord, cell) # chunk this loop for very large outputs
    output = pred.view(1, out_h, out_w, 3).permute(0, 3, 1, 2).clamp(0, 1)
    TF.to_pil_image(output[0]).save("output.jpg")
    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

    Ready to try Prism Upscaler Max?

    Super-resolution upscaler delivering maximum-quality image enlargement with sharp, artefact-free detail.

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