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OdaxAI/
betaclarity-betasr

Name: betaclarity-betasr
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BETACLARITY — BetaSR v1

Medical image super-resolution and denoising powered by latent diffusion.

🔥 New: Edge-optimized version available! A quantized ONNX bundle (UNet INT8, 3.97x smaller, no PyTorch required at runtime) is now published at OdaxAI/betaclarity-betasr-onnx. It runs natively on Apple Silicon via the CoreML Execution Provider. Quantized with our open-source OdaxAI SDK.

Research use only. This model is not intended for clinical decision-making. See the Disclaimer below.

Model description

BetaSR is the core model of BETACLARITY — a latent diffusion pipeline fine-tuned on synthetic degradations (noise + downscaling) applied to multi-modality medical images.

It wraps CompVis/ldm-super-resolution-4x-openimages and fine-tunes the UNet using:

Wavelet-domain regularization — selective masking of high-frequency wavelet coefficients
Dual masking strategy — latent random masking + wavelet coefficient masking
DDIM controllable inference — 5–50 steps for quality/speed trade-off

Supported modalities: chest X-ray, brain MRI, cardiac MRI, breast MRI, knee MRI, CT, ultrasound.

Downloads

huggingface-cli download OdaxAI/betaclarity-betasr model.pth --local-dir ./weights

python

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="OdaxAI/betaclarity-betasr",
    filename="model.pth",
    local_dir="./weights",
)

Usage

python

from betaclarity.core.models import EnhancedLatentDiffusionModel
from diffusers import LDMSuperResolutionPipeline
import torch

pipeline = LDMSuperResolutionPipeline.from_pretrained(
    "CompVis/ldm-super-resolution-4x-openimages"
)
model = EnhancedLatentDiffusionModel(pipeline=pipeline)

checkpoint = torch.load("./weights/model.pth", map_location="cpu")
model.unet.load_state_dict(checkpoint["model_state_dict"])
model.eval()

enhanced = model.reconstruct(degraded_tensor, ddim_steps=10)

Full documentation and training instructions: github.com/odaxai/BETACLARITY

Model card

Property	Value
Base model	`CompVis/ldm-super-resolution-4x-openimages`
Task	Medical image super-resolution and denoising
Scale factor	4×
Input	128×128 low-quality image
Output	512×512 enhanced image
Framework	PyTorch + Hugging Face Diffusers
License	Apache License 2.0

Disclaimer

The model described in this repository is provided for research and development use only.

The model is not intended for use in clinical decision-making or for any other clinical use, and the performance of the model for clinical use has not been established. You bear sole responsibility for any use of this model, including incorporation into any product intended for clinical use.

This software does not constitute a medical device. It has not been cleared or approved by any regulatory agency (FDA, EMA, etc.).

License

Apache License 2.0 — see LICENSE.