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Don't convert Nan to zero.

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Converting Nan to zero is a bad idea because it makes it hard to tell when
something went wrong.
This commit is contained in:
comfyanonymous 2023-11-03 13:11:16 -04:00
parent ee74ef5c9e
commit ae2acfc21b
1 changed files with 2 additions and 5 deletions
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7
comfy/samplers.py
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@ -136,10 +136,10 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, model_options):
out_cond = torch.zeros_like(x_in)
out_count = torch.zeros_like(x_in)
out_count = torch.ones_like(x_in) * 1e-37
out_uncond = torch.zeros_like(x_in)
out_uncond_count = torch.zeros_like(x_in)
out_uncond_count = torch.ones_like(x_in) * 1e-37
COND = 0
UNCOND = 1
@ -239,9 +239,6 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
del out_count
out_uncond /= out_uncond_count
del out_uncond_count
torch.nan_to_num(out_cond, nan=0.0, posinf=0.0, neginf=0.0, out=out_cond) #in case out_count or out_uncond_count had some zeros
torch.nan_to_num(out_uncond, nan=0.0, posinf=0.0, neginf=0.0, out=out_uncond)
return out_cond, out_uncond