Comments (6)
leejet
commented on June 11, 2024
Can you give a detailed log?
from stable-diffusion.cpp.
engineer1109
commented on June 11, 2024
I found RTX4090 and GTX1660S is the same speed of sampling，and they are costing CPU a lot performance.
Then I search the code of the conv2d, and found that conv2d is only for CPU impl.
https://github.com/FSSRepo/ggml/blob/70474c6890c015b53dc10a2300ae35246cc73589/src/ggml-cuda.cu
There is no GPU impl about conv2d.
https://github.com/FSSRepo/ggml/blob/70474c6890c015b53dc10a2300ae35246cc73589/src/ggml.c
Here is the CPU conv2d.
from stable-diffusion.cpp.
engineer1109
commented on June 11, 2024
Let me check again with latest version tomorrow.
from stable-diffusion.cpp.
engineer1109
commented on June 11, 2024
Consider add clblast conv func to support OpenCL?
from stable-diffusion.cpp.
leejet
commented on June 11, 2024
The conv_2d operation is divided into two steps: im2col and mul_mat. Hence, you won't find the implementation related to conv2d directly in ggml-cuda.cu.
from stable-diffusion.cpp.
engineer1109
commented on June 11, 2024
forgot add -DSD_CUBLAS. GGML_CUBLAS is no use.
from stable-diffusion.cpp.
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from stable-diffusion.cpp.