Open comfyanonymous opened 1 year ago
@comfyanonymous I can confirm nn.Upsampling2d
can be made to work with explicit output size rather than scale factor. I will bring the changes here soon :)
@comfyanonymous I can confirm
nn.Upsampling2d
can be made to work with explicit output size rather than scale factor. I will bring the changes here soon :)
Hey, I'm also working on this; how did you guys compile the Windows AIT modules?
@Shaistrong #841 is not merged yet, you can use a local branch with it applied. VS2022, x64 Native Tools Command Prompt for VS 2022
, set AIT_USE_CMAKE_COMPILATION=1
, then run compilation scripts. Comment on the PR if you have any issues related to MSVC, I may already know a fix.
@Shaistrong #841 is not merged yet, you can use a local branch with it applied. VS2022,
x64 Native Tools Command Prompt for VS 2022
,set AIT_USE_CMAKE_COMPILATION=1
, then run compilation scripts. Comment on the PR if you have any issues related to MSVC, I may already know a fix.
fatal error C1083: Cannot open include file: 'dlfcn.h': No such file or directory
any idea what this means?
@Shaistrong #841 is not merged yet, you can use a local branch with it applied. VS2022,
x64 Native Tools Command Prompt for VS 2022
,set AIT_USE_CMAKE_COMPILATION=1
, then run compilation scripts. Comment on the PR if you have any issues related to MSVC, I may already know a fix.
also; spits at me a huge paragraph of errors of my directories, then it shows that error I quoted on my last update
The original stable diffusion implementation for SD1.x only supports latent sizes that are multiples of 8 however there is a trick (here it is implemented in diffusers) to upsample the latent in the upsample block to the exact size so that the later concat doesn't fail. This makes it work with any latent resolution.
I would like to implement that here: https://github.com/facebookincubator/AITemplate/blob/main/examples/05_stable_diffusion/src/modeling/resnet.py#L64
If it was possible to slice the tensor like this after the upsampling it would work:
However ops.dynamic_slice does not work when the size parameters are ranges. Is there a way I can do this that will work with dynamic sizes?
Another way would be to make the nn.Upsampling2d op take the output size as input instead of the scale factor: The kernel seems to take an output size instead of a scale factor so maybe that would be easy to add.
What's the best way to solve this problem?