AI Models
    Prism Upscaler Max

    Prism Upscaler Max

    Prism family Β· open weights on Hugging Face.

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

    Image To ImageSuper ResolutionPyTorchPrismLicence: apache-2.0
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    Likes
    42
    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.pyfrom 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

    Ready to try Prism Upscaler Max?

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

    / Model ecosystem

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