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unified logger

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Vladimir Mandic
2026-02-19 09:46:42 +01:00
parent bfe014f5da
commit a3074baf8b
315 changed files with 2507 additions and 2116 deletions
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modules/processing_args.py
+9 -8
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@@ -7,6 +7,7 @@ import torch
import numpy as np
from PIL import Image
from modules import shared, sd_models, processing, processing_vae, processing_helpers, sd_hijack_hypertile, extra_networks, sd_vae
from modules import logger
from modules.processing_callbacks import diffusers_callback_legacy, diffusers_callback, set_callbacks_p
from modules.processing_helpers import get_generator, apply_circular # pylint: disable=unused-import
from modules.processing_prompt import set_prompt
@@ -14,7 +15,7 @@ from modules.api import helpers
debug_enabled = os.environ.get('SD_DIFFUSERS_DEBUG', None)
debug_log = shared.log.trace if debug_enabled else lambda *args, **kwargs: None
debug_log = logger.log.trace if debug_enabled else lambda *args, **kwargs: None
disable_pbar = os.environ.get('SD_DISABLE_PBAR', None) is not None
@@ -160,7 +161,7 @@ def task_specific_kwargs(p, model):
task_args['image'] = p.init_images
if 'BlipDiffusionPipeline' in model_cls:
if len(p.init_images) == 0:
shared.log.error('BLiP diffusion requires init image')
logger.log.error('BLiP diffusion requires init image')
return task_args
task_args = {
'reference_image': p.init_images[0],
@@ -235,14 +236,14 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:l
p.timesteps = [int(x) for x in timesteps if x.isdigit()]
p.steps = len(timesteps)
args['timesteps'] = p.timesteps
shared.log.debug(f'Sampler: steps={len(p.timesteps)} timesteps={p.timesteps}')
logger.log.debug(f'Sampler: steps={len(p.timesteps)} timesteps={p.timesteps}')
elif ('sigmas' in possible) and hasattr(model.scheduler, 'set_timesteps') and ("sigmas" in set(inspect.signature(model.scheduler.set_timesteps).parameters.keys())):
p.timesteps = [float(x)/1000.0 for x in timesteps if x.isdigit()]
p.steps = len(p.timesteps)
args['sigmas'] = p.timesteps
shared.log.debug(f'Sampler: steps={len(p.timesteps)} sigmas={p.timesteps}')
logger.log.debug(f'Sampler: steps={len(p.timesteps)} sigmas={p.timesteps}')
else:
shared.log.warning(f'Sampler: cls={model.scheduler.__class__.__name__} timesteps not supported')
logger.log.warning(f'Sampler: cls={model.scheduler.__class__.__name__} timesteps not supported')
if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
model.scheduler.noise_sampler = None # noise needs to be reset instead of using cached values
@@ -426,13 +427,13 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:l
del clean[k]
clean['prompt'] = 'embeds'
task = str(sd_models.get_diffusers_task(model)).replace('DiffusersTaskType.', '')
shared.log.info(f'{desc}: pipeline={model.__class__.__name__} task={task} batch={p.iteration + 1}/{p.n_iter}x{p.batch_size} set={clean}')
logger.log.info(f'{desc}: pipeline={model.__class__.__name__} task={task} batch={p.iteration + 1}/{p.n_iter}x{p.batch_size} set={clean}')
if p.hdr_clamp or p.hdr_maximize or p.hdr_brightness != 0 or p.hdr_color != 0 or p.hdr_sharpen != 0:
shared.log.debug(f'HDR: clamp={p.hdr_clamp} maximize={p.hdr_maximize} brightness={p.hdr_brightness} color={p.hdr_color} sharpen={p.hdr_sharpen} threshold={p.hdr_threshold} boundary={p.hdr_boundary} max={p.hdr_max_boundary} center={p.hdr_max_center}')
logger.log.debug(f'HDR: clamp={p.hdr_clamp} maximize={p.hdr_maximize} brightness={p.hdr_brightness} color={p.hdr_color} sharpen={p.hdr_sharpen} threshold={p.hdr_threshold} boundary={p.hdr_boundary} max={p.hdr_max_boundary} center={p.hdr_max_center}')
if shared.cmd_opts.profile:
t1 = time.time()
shared.log.debug(f'Profile: pipeline args: {t1-t0:.2f}')
logger.log.debug(f'Profile: pipeline args: {t1-t0:.2f}')
if debug_enabled:
debug_log(f'Process pipeline args: {args}')