AI Models
Prism Upscaler 4x
Prism family Β· open weights on Hugging Face.
Super-resolution upscaler delivering 4x image enlargement with sharp, artefact-free detail.
Image To ImageSuper ResolutionPyTorchPrismLicence: apache-2.0
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Jul 2026
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/ 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 4x, 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-4x", filename="model.py")
ckpt_file = hf_hub_download(repo_id="olaverse/prism-upscaler-4x", filename="pytorch_model.pt")
config_file = hf_hub_download(repo_id="olaverse/prism-upscaler-4x", 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.FSRCNN(**config)
model.load_state_dict(torch.load(ckpt_file, map_location="cpu"))
model.eval()
img = Image.open("input.jpg").convert("RGB")
with torch.no_grad():
output = model(TF.to_tensor(img).unsqueeze(0)).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 4x?
Super-resolution upscaler delivering 4x image enlargement with sharp, artefact-free detail.
/ Model ecosystem