| # Checkpoint in ongoing research |
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| This is not a release. I am still trying to improve this. |
| but it is currently better than sd or sdxl vae. |
| As it SHOULD be for f8c32 instead of f8c4 |
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| But it should be better |
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| See https://github.com/ppbrown/sd15_vae-f8c32 |
| for tools I used to train it. |
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| Results from utility "calculate_loss.py" |
| (Smaller is better) |
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| image l1 rawvgg edge lap |
| P1 step_010000/vae_sample.webp 0.2104 7.4103 0.2934 0.0729 |
| P1 step_070000/vae_sample.webp 0.0153 1.1987 0.0686 0.0385 |
| (LR 1e-5, lpips weight 0.1 lap 0.02 [NO RAWVGG!!] edge_l1_weight 0.1) |
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| P2 step_960000/vae_sample.webp 0.0121 0.7109 0.0535 0.0355 |
| (LR 8e-6, lpips weight 0.04 lap 0.02 rawvgg hires_tiling) |
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| P3 step_950000/vae_sample.webp 0.0116 0.6232 0.0492 0.0342 |
| (LR 4e-6, lpips weight 0.04 lap 0.02 rawvgg hires_tiling) |
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| P4 step_230000/vae_sample.webp 0.0111 0.6042 0.0488 0.0339 |
| (LR 2e-6, lpips weight 0.04 lap 0.02 rawvgg hires_tiling) |
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| As a comparison: |
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| sampleimg.img_sdxl.webp 0.0174 0.9795 0.0710 0.0439 |
| sampleimg.img_flux2.webp 0.0075 0.2425 0.0283 0.0281 |
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