Don't convert Nan to zero.

Converting Nan to zero is a bad idea because it makes it hard to tell when
something went wrong.

comfy/samplers.py

@@ -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