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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_helpers.py
+25 -24
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@@ -8,11 +8,12 @@ import numpy as np
import cv2
from PIL import Image
from modules import shared, devices, images, sd_models, sd_samplers, sd_vae, sd_hijack_hypertile, processing_vae, timer
from modules import logger
from modules.api import helpers
debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
debug_steps = shared.log.trace if os.environ.get('SD_STEPS_DEBUG', None) is not None else lambda *args, **kwargs: None
debug = logger.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
debug_steps = logger.log.trace if os.environ.get('SD_STEPS_DEBUG', None) is not None else lambda *args, **kwargs: None
debug_steps('Trace: STEPS')
@@ -40,7 +41,7 @@ def apply_color_correction(correction, original_image):
install('blendmodes', quiet=True)
from skimage import exposure
from blendmodes.blend import blendLayers, BlendType
shared.log.debug(f"Applying color correction: correction={correction.shape} image={original_image}")
logger.log.debug(f"Applying color correction: correction={correction.shape} image={original_image}")
np_image = np.asarray(original_image)
np_recolor = cv2.cvtColor(np_image, cv2.COLOR_RGB2LAB)
np_match = exposure.match_histograms(np_recolor, correction, channel_axis=2)
@@ -71,7 +72,7 @@ def apply_overlay(image: Image, paste_loc, index, overlays):
image.alpha_composite(overlay)
image = image.convert('RGB')
except Exception as e:
shared.log.error(f'Apply overlay: {e}')
logger.log.error(f'Apply overlay: {e}')
return image
@@ -105,10 +106,10 @@ def get_sampler_name(sampler_index: int, img: bool = False) -> str:
sampler_name = sd_samplers.samplers[sampler_index].name
else:
sampler_name = "Default"
shared.log.warning(f'Sampler not found: index={sampler_index} available={[s.name for s in sd_samplers.samplers]} fallback={sampler_name}')
logger.log.warning(f'Sampler not found: index={sampler_index} available={[s.name for s in sd_samplers.samplers]} fallback={sampler_name}')
if img and sampler_name == "PLMS":
sampler_name = "Default"
shared.log.warning(f'Sampler not compatible: name=PLMS fallback={sampler_name}')
logger.log.warning(f'Sampler not compatible: name=PLMS fallback={sampler_name}')
return sampler_name
@@ -210,7 +211,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
def decode_first_stage(model, x):
if not shared.opts.keep_incomplete and (shared.state.skipped or shared.state.interrupted):
shared.log.debug(f'Decode VAE: skipped={shared.state.skipped} interrupted={shared.state.interrupted}')
logger.log.debug(f'Decode VAE: skipped={shared.state.skipped} interrupted={shared.state.interrupted}')
x_sample = torch.zeros((len(x), 3, x.shape[2] * 8, x.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
return x_sample
with devices.autocast(disable = x.dtype==devices.dtype_vae):
@@ -222,10 +223,10 @@ def decode_first_stage(model, x):
x_sample = processing_vae.vae_decode(latents=x, model=model, output_type='np')
else:
x_sample = x
shared.log.error('Decode VAE unknown model')
logger.log.error('Decode VAE unknown model')
except Exception as e:
x_sample = x
shared.log.error(f'Decode VAE: {e}')
logger.log.error(f'Decode VAE: {e}')
return x_sample
@@ -273,7 +274,7 @@ def validate_sample(tensor):
elif isinstance(tensor, np.ndarray):
sample = tensor
else:
shared.log.warning(f'Decode: type={type(tensor)} unknown sample')
logger.log.warning(f'Decode: type={type(tensor)} unknown sample')
return tensor
sample = 255.0 * sample
with warnings.catch_warnings(record=True) as w:
@@ -284,10 +285,10 @@ def validate_sample(tensor):
cast = cast.astype(np.uint8)
vae = shared.sd_model.vae.dtype if hasattr(shared.sd_model, 'vae') else None
upcast = getattr(shared.sd_model.vae.config, 'force_upcast', None) if hasattr(shared.sd_model, 'vae') and hasattr(shared.sd_model.vae, 'config') else None
shared.log.error(f'Decode: sample={sample.shape} invalid={nans} dtype={dtype} vae={vae} upcast={upcast} failed to validate')
logger.log.error(f'Decode: sample={sample.shape} invalid={nans} dtype={dtype} vae={vae} upcast={upcast} failed to validate')
if upcast is not None and not upcast:
setattr(shared.sd_model.vae.config, 'force_upcast', True) # noqa: B010
shared.log.info('Decode: set upcast=True and attempt to retry operation')
logger.log.info('Decode: set upcast=True and attempt to retry operation')
t1 = time.time()
timer.process.add('validate', t1 - t0)
return cast
@@ -301,24 +302,24 @@ def decode_images(image):
try:
decoded.append(helpers.decode_base64_to_image(img, quiet=True))
except Exception as e:
shared.log.error(f'Decode image[{i}]: {e}')
logger.log.error(f'Decode image[{i}]: {e}')
elif isinstance(img, Image.Image):
decoded.append(img)
else:
shared.log.error(f'Decode image[{i}]: {type(img)} unknown type')
logger.log.error(f'Decode image[{i}]: {type(img)} unknown type')
return decoded
elif isinstance(image, str):
try:
return helpers.decode_base64_to_image(image, quiet=True)
except Exception as e:
shared.log.error(f'Decode image: {e}')
logger.log.error(f'Decode image: {e}')
# elif isinstance(image, Image.Image):
# return image
# elif torch.is_tensor(image):
# return image
else:
return image
# shared.log.error(f'Decode image: {type(image)} unknown type')
# logger.log.error(f'Decode image: {type(image)} unknown type')
return None
@@ -332,7 +333,7 @@ def resize_init_images(p):
tgt_width = vae_scale_factor * math.ceil(p.init_images[0].width / vae_scale_factor)
tgt_height = vae_scale_factor * math.ceil(p.init_images[0].height / vae_scale_factor)
if p.init_images[0].size != (tgt_width, tgt_height):
shared.log.debug(f'Resizing init images: original={p.init_images[0].width}x{p.init_images[0].height} target={tgt_width}x{tgt_height}')
logger.log.debug(f'Resizing init images: original={p.init_images[0].width}x{p.init_images[0].height} target={tgt_width}x{tgt_height}')
p.init_images = [images.resize_image(1, image, tgt_width, tgt_height, upscaler_name=None) for image in p.init_images]
p.height = tgt_height
p.width = tgt_width
@@ -354,7 +355,7 @@ def resize_init_images(p):
def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler else latent
if (p.hr_upscale_to_x == 0 or p.hr_upscale_to_y == 0) and hasattr(p, 'init_hr'):
shared.log.error('Hires: missing upscaling dimensions')
logger.log.error('Hires: missing upscaling dimensions')
return latents
jobid = shared.state.begin('Resize')
@@ -364,14 +365,14 @@ def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler
try:
for i in range(len(latents)):
if not torch.is_tensor(latents[i]):
shared.log.warning(f'Hires: input[{i}]={type(latents[i])} not tensor')
logger.log.warning(f'Hires: input[{i}]={type(latents[i])} not tensor')
latents[i] = processing_vae.vae_encode(image=latents[i], model=shared.sd_model, vae_type=p.vae_type)
latents = torch.cat(latents, dim=0)
except Exception as e:
shared.log.error(f'Hires: prepare latents: {e}')
logger.log.error(f'Hires: prepare latents: {e}')
resized = latents
elif not torch.is_tensor(latents):
shared.log.warning(f'Hires: input={type(latents)} not tensor')
logger.log.warning(f'Hires: input={type(latents)} not tensor')
resized = images.resize_image(p.hr_resize_mode, latents, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
else:
decoded = processing_vae.vae_decode(latents=latents, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
@@ -457,7 +458,7 @@ def get_generator(p):
devices.randn(p.seeds[0])
generator = [torch.Generator(generator_device).manual_seed(s) for s in p.seeds]
except Exception as e:
shared.log.error(f'Torch generator: seeds={p.seeds} device={generator_device} {e}')
logger.log.error(f'Torch generator: seeds={p.seeds} device={generator_device} {e}')
generator = None
return generator
@@ -492,7 +493,7 @@ def apply_circular(enable: bool, model):
layer.padding_mode = 'circular' if enable else 'zeros'
model.texture_tiling = enable
if current is not None or enable:
shared.log.debug(f'Apply texture tiling: enabled={enable} layers={i} cls={model.__class__.__name__} ')
logger.log.debug(f'Apply texture tiling: enabled={enable} layers={i} cls={model.__class__.__name__} ')
except Exception as e:
debug(f"Diffusers tiling failed: {e}")
@@ -513,7 +514,7 @@ def update_sampler(p, sd_model, second_pass=False):
return
sampler = sd_samplers.find_sampler(sampler_selection)
if sampler is None:
shared.log.warning(f'Sampler: "{sampler_selection}" not found')
logger.log.warning(f'Sampler: "{sampler_selection}" not found')
sampler = sd_samplers.all_samplers_map.get("UniPC")
sampler = sd_samplers.create_sampler(sampler.name, sd_model)
if sampler is None or sampler_selection == 'Default':