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How to reduce condition graph computing time? #433
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flux employs t5xxl, which is relatively heavy. And it is only implemented to run on cpu right now. |
@hxgqh Set the numbers of threads with the -t argument. |
You're right @hxgqh , it uses the number of physical cores of the CPU by default. (so 12 in my case) Then I guess either your CPU is too slow, or you're running out of system memory and it's using swap (if that's the case, maybe using a quantized version of t5xxxl could help). |
@Green-Sky Is there any plan to run t5xxl on GPU? |
it seems smth is off with it, that same t5xxl step on ryzen 5600x the python cpu implementation (from diffusers lib) running 67 seconds faster (237%) than CPP implementation and that's actually the best of sd-cpp measurements, with 6 threads, when setting to 12 (nproc) - it becomes 22 seconds even slower
offtopic: |
i found the solution for my problem with high condition graph computing time, mb would be useful for someone else: because i was building the package on old xeon server, but running on a new ryzen workstation - apparently some cpu optimizations were disabled during compilation so after re-compiling it on the workstation itself - condition graph computes now in 18 seconds (34 seconds faster than diffusers-cpu and 100 seconds faster than before, without those optimizations) in the same testcase |
it consumes 34 seconds, while sampling only consumes 5.5s/it
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