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cleanup logger

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Vladimir Mandic
2026-02-19 11:09:13 +01:00
parent 22aa9eab01
commit e5c494f999
279 changed files with 2898 additions and 2771 deletions
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modules/processing_vae.py
+20 -20
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@@ -3,12 +3,12 @@ import time
import numpy as np
import torch
from modules import shared, devices, sd_models, sd_vae, errors
from modules import logger
from modules.logger import log
from modules.vae import sd_vae_taesd
debug = os.environ.get('SD_VAE_DEBUG', None) is not None
log_debug = logger.log.trace if debug else lambda *args, **kwargs: None
log_debug = log.trace if debug else lambda *args, **kwargs: None
log_debug('Trace: VAE')
@@ -23,7 +23,7 @@ def create_latents(image, p, dtype=None, device=None):
latents = [vae_encode(i, model=shared.sd_model, vae_type=p.vae_type).squeeze(dim=0) for i in image]
latents = torch.stack(latents, dim=0).to(shared.device)
else:
logger.log.warning(f'Latents: input type: {type(image)} {image}')
log.warning(f'Latents: input type: {type(image)} {image}')
return image
noise = p.denoising_strength * create_random_tensors(latents.shape[1:], seeds=p.all_seeds, subseeds=p.all_subseeds, subseed_strength=p.subseed_strength, p=p)
latents = (1 - p.denoising_strength) * latents + noise
@@ -37,7 +37,7 @@ def create_latents(image, p, dtype=None, device=None):
def full_vqgan_decode(latents, model):
t0 = time.time()
if model is None or not hasattr(model, 'vqgan'):
logger.log.error('VQGAN not found in model')
log.error('VQGAN not found in model')
return []
if debug:
devices.torch_gc(force=True)
@@ -63,7 +63,7 @@ def full_vqgan_decode(latents, model):
try:
decoded = model.vqgan.decode(latents).sample.clamp(0, 1)
except Exception as e:
logger.log.error(f'VAE decode: {e}')
log.error(f'VAE decode: {e}')
errors.display(e, 'VAE decode')
decoded = []
@@ -82,7 +82,7 @@ def full_vqgan_decode(latents, model):
if debug:
log_debug(f'VAE memory: {shared.mem_mon.read()}')
vae_name = os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0] if sd_vae.loaded_vae_file is not None else "default"
logger.log.debug(f'VAE decode: vae="{vae_name}" type="vqgan" dtype={model.vqgan.dtype} device={model.vqgan.device} time={round(t1-t0, 3)}')
log.debug(f'VAE decode: vae="{vae_name}" type="vqgan" dtype={model.vqgan.dtype} device={model.vqgan.device} time={round(t1-t0, 3)}')
return decoded
@@ -91,7 +91,7 @@ def full_vae_decode(latents, model):
if not hasattr(model, 'vae') and hasattr(model, 'pipe'):
model = model.pipe
if model is None or not hasattr(model, 'vae'):
logger.log.error('VAE not found in model')
log.error('VAE not found in model')
return []
if debug:
devices.torch_gc(force=True)
@@ -153,7 +153,7 @@ def full_vae_decode(latents, model):
with devices.inference_context():
decoded = model.vae.decode(latents, return_dict=False)[0]
except Exception as e:
logger.log.error(f'VAE decode: {e}')
log.error(f'VAE decode: {e}')
if 'out of memory' not in str(e) and 'no data' not in str(e):
errors.display(e, 'VAE decode')
decoded = []
@@ -177,7 +177,7 @@ def full_vae_decode(latents, model):
log_debug(f'VAE memory: {shared.mem_mon.read()}')
vae_name = os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0] if sd_vae.loaded_vae_file is not None else "default"
vae_scale_factor = sd_vae.get_vae_scale_factor(model)
logger.log.debug(f'Decode: vae="{vae_name}" scale={vae_scale_factor} upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)} latents={list(latents.shape)}:{latents.device} dtype={latents.dtype} time={t1-t0:.3f}')
log.debug(f'Decode: vae="{vae_name}" scale={vae_scale_factor} upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)} latents={list(latents.shape)}:{latents.device} dtype={latents.dtype} time={t1-t0:.3f}')
return decoded
@@ -210,7 +210,7 @@ def full_vae_encode(image, model):
if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False) and hasattr(model, 'unet'):
sd_models.move_model(model.unet, unet_device)
t1 = time.time()
logger.log.debug(f'Encode: vae="{vae_name}" upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)} latents={encoded.shape}:{encoded.device}:{encoded.dtype} time={t1-t0:.3f}')
log.debug(f'Encode: vae="{vae_name}" upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)} latents={encoded.shape}:{encoded.device}:{encoded.dtype} time={t1-t0:.3f}')
return encoded
@@ -225,12 +225,12 @@ def taesd_vae_decode(latents):
else:
decoded = sd_vae_taesd.decode(latents)
t1 = time.time()
logger.log.debug(f'Decode: vae="taesd" latents={latents.shape}:{latents.device} dtype={latents.dtype} time={t1-t0:.3f}')
log.debug(f'Decode: vae="taesd" latents={latents.shape}:{latents.device} dtype={latents.dtype} time={t1-t0:.3f}')
return decoded
def taesd_vae_encode(image):
logger.log.debug(f'Encode: vae="taesd" image={image.shape}')
log.debug(f'Encode: vae="taesd" image={image.shape}')
encoded = sd_vae_taesd.encode(image)
return encoded
@@ -264,7 +264,7 @@ def vae_postprocess(tensor, model, output_type='np'):
else:
images = tensor if isinstance(tensor, list) or isinstance(tensor, np.ndarray) else [tensor]
except Exception as e:
logger.log.error(f'VAE postprocess: {e}')
log.error(f'VAE postprocess: {e}')
errors.display(e, 'VAE')
return images
@@ -278,12 +278,12 @@ def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, he
return latents
if latents.shape[0] == 0:
logger.log.error(f'VAE nothing to decode: {latents.shape}')
log.error(f'VAE nothing to decode: {latents.shape}')
return []
if shared.state.interrupted or shared.state.skipped:
return []
if not hasattr(model, 'vae') and not hasattr(model, 'vqgan'):
logger.log.error('VAE not found in model')
log.error('VAE not found in model')
return []
if vae_type == 'Remote':
@@ -321,13 +321,13 @@ def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, he
elif hasattr(model, "vae"):
decoded = full_vae_decode(latents=latents, model=model)
else:
logger.log.error('VAE not found in model')
log.error('VAE not found in model')
decoded = []
images = vae_postprocess(decoded, model, output_type)
if shared.cmd_opts.profile or debug:
t1 = time.time()
logger.log.debug(f'Profile: VAE decode: {t1-t0:.2f}')
log.debug(f'Profile: VAE decode: {t1-t0:.2f}')
devices.torch_gc()
shared.state.end(jobid)
return images
@@ -341,7 +341,7 @@ def vae_encode(image, model, vae_type='Full'): # pylint: disable=unused-variable
if not hasattr(model, 'vae') and hasattr(model, 'pipe'):
model = model.pipe
if not hasattr(model, 'vae'):
logger.log.error('VAE not found in model')
log.error('VAE not found in model')
return []
tensor = convert.to_tensor(image.convert("RGB")).unsqueeze(0).to(devices.device, devices.dtype_vae)
if vae_type == 'Tiny':
@@ -350,7 +350,7 @@ def vae_encode(image, model, vae_type='Full'): # pylint: disable=unused-variable
tensor = tensor * 2 - 1
latents = full_vae_encode(image=tensor, model=shared.sd_model)
else:
logger.log.error('VAE not found in model')
log.error('VAE not found in model')
latents = []
devices.torch_gc()
shared.state.end(jobid)
@@ -363,7 +363,7 @@ def reprocess(gallery):
latent, index = shared.history.selected
if latent is None or gallery is None:
return None
logger.log.info(f'Reprocessing: latent={latent.shape}')
log.info(f'Reprocessing: latent={latent.shape}')
reprocessed = vae_decode(latent, shared.sd_model, output_type='pil')
outputs = []
for i0, i1 in zip(gallery, reprocessed, strict=False):