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This video is from the following sermon: Higher Ground Baptist Church 6_23_24 Minister: Pastor Morris Holmes Titile Of Sermon: What Is Being Rich? Scripture: Proverbs 10:22

Pastor Morris Holmes talks about true riches, the type of riches that can only come as the blessing of the Lord. He grew up in poverty and all he wanted was to have money. His testimony is that he answered the call of God as a young man and became a minister of the Gospel. After he surrendered his life to the Lord the blessing of the Lord on his life has been much better than anything that he could have brought about himself.

Visit the Higher Ground Baptist Church Facebook page for the entire sermon.

Proverbs 10:15, 22 NKJV 15 The rich man’s wealth is his strong city; The destruction of the poor is their poverty.

22 The blessing of the Lord makes one rich, And He adds no sorrow with it.

22 MSG God’s blessing makes life rich…

22 AMP The blessing of the Lord brings [true] riches, And He adds no sorrow to it [

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Crossposted from: https://www.reddit.com/r/StableDiffusion/comments/1ag5h5s/the_vae_used_for_stable_diffusion_1x2x_and_other/

I am not the OP!

Short summary for those who are technically inclined:

CompVis fucked up the KL divergence loss on the KL-F8 VAE that is used by SD1.x, SD2.x, SVD, DALL-E 3, and probably other models. As a result, the latent space created by it has a massive KL divergence and is smuggling global information about the image through a few pixels. If you are thinking of using it for training a new, trained-from-scratch foundation model, don't! (for the less technically inclined this does not mean switch out your VAE for your LoRAs or finetunes, you absolutely do not have the compute power to change the model to a whole new latent space, that would require effectively a full retrain's worth of training.) SDXL is not subject to this issue because it has its own VAE, which as far as I can tell is trained correctly and does not exhibit the

I know what I am asking is rather niche, but it has been bugging me for quite a while. Suppose I have the following function:

  python

    
def foo(return_more: bool):
   ....
    if return_more:
        return data, more_data
   return data

  

You can imagine it is a function that may return more data if given a flag.

How should I typehint this function? When I use the function in both ways

 undefined

    
data = foo(False)

data, more_data = foo(True)

  

either the first or the 2nd statement would say that the function cannot be assigned due to wrong size of return tuple.

Is having variable signature an anti-pattern? Is Python's typehinting mechanism not powerful enough and thus I am forced to ignore this error?

Edit: Thanks for all the suggestions. I was enlightened by this suggestion about the existence of overload and this solution fit my requirements perfectly

 undefined

    
from typing import overload, Literal

@overload
def foo(return_more: Literal[False