JoshuaPeddle/keepsake-models

Yesteryear models

On-device ExecuTorch builds of the models used by Yesteryear, a private photo restoration app. Photos are processed on the phone; these files are downloaded by the app the first time a feature is used.

Each model is a re-export of a published open model, converted for ExecuTorch 1.4 with no retraining. *_xnnpack.pte files run on the CPU (XNNPACK, float32) on Android and iOS; *_coreml.pte files run on iOS (Core ML, float16). LaMa and DDColor ship as XNNPACK only: their float16 Core ML builds were not accurate enough, so iOS uses the XNNPACK files for them.

File	Model	Purpose	Upstream license
realesrgan_x4v3_t256_{xnnpack,coreml}.pte	Real-ESRGAN general x4v3	4x upscaling, 256 px tiles	BSD-3-Clause
bopbtl_scratch_t384_{xnnpack,coreml}.pte	Scratch detection from Bringing Old Photos Back to Life	Finds scratches and tears, 384 px	MIT
lama_big_t512_xnnpack.pte	LaMa (big-lama)	Inpaints the damage, 512 px tiles	Apache-2.0
ddcolor_paper_tiny_t512_xnnpack.pte	DDColor tiny	Colorization, 512 px	Apache-2.0

Full license texts and copyright notices are in licenses/. See LICENSE.md for details and changes made.

Changes from upstream

The networks and weights are unchanged. To run under ExecuTorch, some operations are rewritten as mathematically equivalent forms (verified against the upstream PyTorch models):

• LaMa: the Fourier transform is computed as matrix multiplications against fixed DFT tables.
• DDColor: layer norm, attention and pooling are written with operations XNNPACK supports; spectral normalization is folded into the weights.
• All: reflection padding is built from slices; inputs and outputs are wrapped for the app (for example, LaMa takes RGB plus a mask channel and composites its own output).

Notes

• The LaMa and DDColor weights were trained on Places365 and ImageNet respectively; those datasets have their own terms of use for the images themselves.
• The Bringing Old Photos Back to Life repository states that its code and pretrained models are MIT licensed.