VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-08 22:35:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

Replace print with logger if they are logs (#905)

Browse Source 
* Add get_my_logger()

* Use logger instead of print

* Fix log level

* Removed line-breaks for readability

* Use setup_logging()

* Add rich to requirements.txt

* Make simple

* Use logger instead of print

---------

Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>
This commit is contained in:
Yuta Hayashibe
2024-02-04 16:14:34 +07:00
committed by GitHub
parent 7f948db158
commit 5f6bf29e52
62 changed files with 1195 additions and 961 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
library/custom_train_functions.py
View File

@@ -3,7 +3,10 @@ import argparse
import random
import re
from typing import List, Optional, Union
from .utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
def prepare_scheduler_for_custom_training(noise_scheduler, device):
if hasattr(noise_scheduler, "all_snr"):
@@ -21,7 +24,7 @@ def prepare_scheduler_for_custom_training(noise_scheduler, device):
def fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler):
# fix beta: zero terminal SNR
print(f"fix noise scheduler betas: https://arxiv.org/abs/2305.08891")
logger.info(f"fix noise scheduler betas: https://arxiv.org/abs/2305.08891")
def enforce_zero_terminal_snr(betas):
# Convert betas to alphas_bar_sqrt
@@ -49,8 +52,8 @@ def fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler):
alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
# print("original:", noise_scheduler.betas)
# print("fixed:", betas)
# logger.info(f"original: {noise_scheduler.betas}")
# logger.info(f"fixed: {betas}")
noise_scheduler.betas = betas
noise_scheduler.alphas = alphas
@@ -79,13 +82,13 @@ def get_snr_scale(timesteps, noise_scheduler):
snr_t = torch.minimum(snr_t, torch.ones_like(snr_t) * 1000) # if timestep is 0, snr_t is inf, so limit it to 1000
scale = snr_t / (snr_t + 1)
# # show debug info
# print(f"timesteps: {timesteps}, snr_t: {snr_t}, scale: {scale}")
# logger.info(f"timesteps: {timesteps}, snr_t: {snr_t}, scale: {scale}")
return scale
def add_v_prediction_like_loss(loss, timesteps, noise_scheduler, v_pred_like_loss):
scale = get_snr_scale(timesteps, noise_scheduler)
# print(f"add v-prediction like loss: {v_pred_like_loss}, scale: {scale}, loss: {loss}, time: {timesteps}")
# logger.info(f"add v-prediction like loss: {v_pred_like_loss}, scale: {scale}, loss: {loss}, time: {timesteps}")
loss = loss + loss / scale * v_pred_like_loss
return loss
@@ -268,7 +271,7 @@ def get_prompts_with_weights(tokenizer, prompt: List[str], max_length: int):
tokens.append(text_token)
weights.append(text_weight)
if truncated:
print("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
return tokens, weights