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…o gguf * introduce `model_file` to read key-value data from gguf files * conditionally set `cwhn` flag based on gguf tensor data layout
migan: can now run in cwhn and whcn mode (but cwhn remains faster in all cases)
* convert weights on CPU after model load
* whcn is slower on both cpu or vulkan * whcn is more correct on vulkan, likely there is a bug in cwhn version of conv2d/deform
…meter * even though almost all tests use cwhn right now, whcn is the default in both ggml and pytorch.
* it's only available for WHCN layout for now, but faster than anything else * WHCN for model weights * WHCN for Vulkan * CWHN for CPU (converted at model load)
* probably CWHN version of birefnet is still somewhat broken, but since WHCN doesn't have the issue and is way faster there's not much incentive atm to fix
* times old -> new / new with coopmat2 * birefnet: 268ms -> 315ms / 243ms * birefnet-lite: 109ms -> 119 / 87ms * deform conv2d is a bit slower without coopmat2 support, but also requires much less vram, so still worth it
…memory was uninitialized
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…s, OpenBLAS slower) Documents an honest negative result for the zero-quality-loss CPU speedup attempt: F16-weights-on-CPU segfaults at load (cwhn layout + CPU ops assume F32), OpenBLAS measures ~2% slower than ggml's AVX2 MMQ on Zen3, and the 2x encoder / CONT levers have no lossless redundancy to remove. CPU floor stays ~9.9s @ YAVG 180.974. No source change kept; ggml untouched (af69870c).
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