2xParagonSR_Nano_gan
This is a perceptual model I had trained on my ParagonSR network that I had released. This is the nano variant, so the fastest.
This model was trained on downsampled content only (different downsampling as to not overfit to a specific one like bicubic) on a phtographic cc0 dataset, meaning it is meant for good quality inputs, as can be seen in the visual examples.
Being the fastest option, it is also the least capable. This one is meant for speed. I attach some photo visuals at the bottom to visually showcase its capability and at the same time its limits. I tried to tune my losses in a way that the output is (mostly) artifact-free while being tuned for perceptual results (sharper losses can lead to ringing pretty fast with such a small option, i attached my training config). While other variants should give better quality, this one is more focussed a bit on speed, you should be able to try it out with trt or directml, though i only tested it with onnxruntime so far.
The models released beneath are the release models as envisioned with my network. The op18_fp16 is the recommended version. But for completeness, i also provide the fp32 model, and for compatibility, in case of your onnx version not being able to handle mish (this is why opset 18 had been chosen, because of mish) I also provide a no_mish fp16 checkpoint, opset 17, for maximum compatibility.
I think ill also provide the training config and the fused checkpoint, just for completeness, maybe also the unfused "raw" training checkpoint.
Though I am excited to maybe show once what i am working on right now. Or rather i am training (or did multiple tries already) a model based on a different version of this network, with its own discriminator, r3gan instead of vanilla gan for more stability, slight clip-based contrastive loss additionally, replaced perceptualloss which uses that legacy imagenet vgg with a convnext-tiny loss (more modern, i basically disliked the waterpainterly gan look and also gan artifacts and try to counteract them), and so forth, just optimizing multiple different things for my sisr training currently for future models. But thats a slight other topic or model than this release.
Here some visual examples for this one
