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Replace print with logging (#6138)

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* Replace print with logging

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This commit is contained in:
Chenlei Hu
2024-12-20 13:24:55 -08:00
committed by GitHub
parent bddb02660c
commit d7969cb070
22 changed files with 49 additions and 45 deletions
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1
comfy/ldm/aura/mmdit.py
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@@ -381,7 +381,6 @@ class MMDiT(nn.Module):
pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
self.h_max, self.w_max = target_dim
print("PE extended to", target_dim)
def pe_selection_index_based_on_dim(self, h, w):
h_p, w_p = h // self.patch_size, w // self.patch_size

7
comfy/ldm/modules/diffusionmodules/util.py
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@@ -9,6 +9,7 @@
import math
import logging
import torch
import torch.nn as nn
import numpy as np
@@ -130,7 +131,7 @@ def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timestep
# add one to get the final alpha values right (the ones from first scale to data during sampling)
steps_out = ddim_timesteps + 1
if verbose:
print(f'Selected timesteps for ddim sampler: {steps_out}')
logging.info(f'Selected timesteps for ddim sampler: {steps_out}')
return steps_out
@@ -142,8 +143,8 @@ def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
# according the the formula provided in https://arxiv.org/abs/2010.02502
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
if verbose:
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
print(f'For the chosen value of eta, which is {eta}, '
logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
logging.info(f'For the chosen value of eta, which is {eta}, '
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
return sigmas, alphas, alphas_prev

5
comfy/ldm/util.py
View File

@@ -1,4 +1,5 @@
import importlib
import logging
import torch
from torch import optim
@@ -23,7 +24,7 @@ def log_txt_as_img(wh, xc, size=10):
try:
draw.text((0, 0), lines, fill="black", font=font)
except UnicodeEncodeError:
print("Cant encode string for logging. Skipping.")
logging.warning("Cant encode string for logging. Skipping.")
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
txts.append(txt)
@@ -65,7 +66,7 @@ def mean_flat(tensor):
def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
logging.info(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
return total_params