cstr/scunet-GGUF

SCUNet Color Denoising (GGUF)

Swin-Conv-UNet for color image denoising (CVPR 2022). 18M params, 69 MB F32. U-Net with hybrid ConvTransBlocks: Swin window attention + residual Conv. Trained on SIDD real-world noise dataset.

Parity: cos=1.000000 vs Python reference (all encoder/decoder stages). Source: cszn/SCUNet (Apache-2.0).

Provenance and EU AI Act Art. 53 note

• Upstream model: cszn/SCUNet.
• Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.