mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 01:04:32 +02:00
unified logger
This commit is contained in:
Vladimir Mandic
+17
-16
@@ -4,6 +4,7 @@ import time
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import numpy as np
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from PIL import Image, ImageOps
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from modules import shared, devices, errors, images, scripts_manager, memstats, script_callbacks, extra_networks, detailer, sd_models, sd_checkpoint, sd_vae, processing_helpers, timer
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from modules import logger
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from modules.sd_hijack_hypertile import context_hypertile_vae, context_hypertile_unet
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from modules.processing_class import ( # pylint: disable=unused-import
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StableDiffusionProcessing,
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@@ -18,7 +19,7 @@ from modules.modeldata import model_data
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opt_C = 4
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opt_f = 8
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debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
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debug = logger.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
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debug('Trace: PROCESS')
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create_binary_mask = processing_helpers.create_binary_mask
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apply_overlay = processing_helpers.apply_overlay
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@@ -139,10 +140,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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timer.process.reset()
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debug(f'Process images: class={p.__class__.__name__} {vars(p)}')
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if not hasattr(p.sd_model, 'sd_checkpoint_info'):
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shared.log.error('Processing: incomplete model')
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logger.log.error('Processing: incomplete model')
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return None
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if p.abort:
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shared.log.debug('Processing: aborted')
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logger.log.debug('Processing: aborted')
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return None
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if p.scripts is not None and isinstance(p.scripts, scripts_manager.ScriptRunner):
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p.scripts.before_process(p)
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@@ -159,11 +160,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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try:
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# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
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if p.override_settings.get('sd_model_checkpoint', None) is not None and sd_checkpoint.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
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shared.log.warning(f"Override not found: checkpoint={p.override_settings.get('sd_model_checkpoint', None)}")
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logger.log.warning(f"Override not found: checkpoint={p.override_settings.get('sd_model_checkpoint', None)}")
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p.override_settings.pop('sd_model_checkpoint', None)
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sd_models.reload_model_weights()
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if p.override_settings.get('sd_model_refiner', None) is not None and sd_checkpoint.checkpoint_aliases.get(p.override_settings.get('sd_model_refiner')) is None:
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shared.log.warning(f"Override not found: refiner={p.override_settings.get('sd_model_refiner', None)}")
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logger.log.warning(f"Override not found: refiner={p.override_settings.get('sd_model_refiner', None)}")
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p.override_settings.pop('sd_model_refiner', None)
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sd_models.reload_model_weights()
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if p.override_settings.get('sd_vae', None) is not None:
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@@ -176,7 +177,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if p.override_settings.get('Hires upscaler', None) is not None:
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p.enable_hr = True
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if len(p.override_settings.keys()) > 0:
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shared.log.debug(f'Override: {p.override_settings}')
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logger.log.debug(f'Override: {p.override_settings}')
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for k, v in p.override_settings.items():
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setattr(shared.opts, k, v)
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if k == 'sd_model_checkpoint':
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@@ -206,7 +207,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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activities.append(torch.profiler.ProfilerActivity.CUDA)
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if devices.has_xpu() and hasattr(torch.profiler.ProfilerActivity, "XPU"):
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activities.append(torch.profiler.ProfilerActivity.XPU)
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shared.log.debug(f'Torch profile: activities={activities}')
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logger.log.debug(f'Torch profile: activities={activities}')
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if shared.profiler is None:
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profile_args = {
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'activities': activities,
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@@ -218,7 +219,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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'record_shapes': os.environ.get('SD_PROFILE_SHAPES', None) is not None,
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'on_trace_ready': torch.profiler.tensorboard_trace_handler(os.environ.get('SD_PROFILE_FOLDER', None)) if os.environ.get('SD_PROFILE_FOLDER', None) is not None else None,
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}
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shared.log.debug(f'Torch profile: {profile_args}')
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logger.log.debug(f'Torch profile: {profile_args}')
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shared.profiler = torch.profiler.profile(**profile_args)
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shared.profiler.start()
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results = process_images_inner(p)
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@@ -295,7 +296,7 @@ def process_samples(p: StableDiffusionProcessing, samples):
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if isinstance(image, list):
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if len(image) > 1:
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shared.log.warning(f'Processing: images={image} contains multiple images using first one only')
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logger.log.warning(f'Processing: images={image} contains multiple images using first one only')
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image = image[0]
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if not shared.state.interrupted and not shared.state.skipped:
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@@ -408,15 +409,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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for n in range(p.n_iter):
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p.init_images = p.iter_init_images
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if p.n_iter > 1:
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shared.log.debug(f'Processing: batch={n+1} total={p.n_iter} progress={(n+1)/p.n_iter:.2f}')
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logger.log.debug(f'Processing: batch={n+1} total={p.n_iter} progress={(n+1)/p.n_iter:.2f}')
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shared.state.batch_no = n + 1
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debug(f'Processing inner: iteration={n+1}/{p.n_iter}')
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p.iteration = n
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if shared.state.interrupted:
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shared.log.debug(f'Process interrupted: {n+1}/{p.n_iter}')
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logger.log.debug(f'Process interrupted: {n+1}/{p.n_iter}')
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break
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if shared.state.skipped:
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shared.log.debug(f'Process skipped: {n+1}/{p.n_iter}')
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logger.log.debug(f'Process skipped: {n+1}/{p.n_iter}')
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shared.state.skipped = False
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continue
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@@ -450,7 +451,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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timer.process.record('process')
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if shared.state.interrupted:
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shared.log.debug(f'Process: batch={n+1}/{p.n_iter} interrupted')
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logger.log.debug(f'Process: batch={n+1}/{p.n_iter} interrupted')
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p.do_not_save_samples = not shared.opts.keep_incomplete
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if shared.state.current_image is not None and isinstance(shared.state.current_image, Image.Image):
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samples = [shared.state.current_image]
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@@ -525,9 +526,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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timer.process.record('post')
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p.ops = list(set(p.ops))
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if not p.disable_extra_networks:
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shared.log.info(f'Processed: images={len(output_images)} its={(p.steps * len(output_images)) / (t1 - t0):.2f} ops={p.ops}')
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shared.log.debug(f'Processed: timers={timer.process.dct()}')
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shared.log.debug(f'Processed: memory={memstats.memory_stats()}')
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logger.log.info(f'Processed: images={len(output_images)} its={(p.steps * len(output_images)) / (t1 - t0):.2f} ops={p.ops}')
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logger.log.debug(f'Processed: timers={timer.process.dct()}')
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logger.log.debug(f'Processed: memory={memstats.memory_stats()}')
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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devices.torch_gc(force=True, reason='final')
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