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chore(ltx): silent=True on run-internal offload walks

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Modules already inventoried at load time; repeating the six-line dump at
each upsample or refine boundary is redundant. silent=True suppresses
the per-module DEBUG lines; op=init and Model class= INFO stay intact.
This commit is contained in:
CalamitousFelicitousness
2026-04-21 02:14:52 +01:00
parent b58f0ae282
commit f72f89c993
1 changed files with 14 additions and 10 deletions
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modules/ltx/ltx_process.py
+14 -10
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@@ -298,7 +298,7 @@ def run_ltx(task_id,
video_overrides.set_overrides(p, selected)
t0 = time.time()
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True)
t1 = time.time()
samplejob = shared.state.begin('Sample')
@@ -346,7 +346,11 @@ def run_ltx(task_id,
return
t2 = time.time()
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
# silent=True everywhere in run_ltx: per-module stats were already dumped during the
# load-time balanced_offload pass. Upsample/refine boundaries force a rebuild because
# the global offload_hook_instance is keyed on checkpoint_name (sd_offload.py:488),
# but re-logging the same inventory adds noise without information.
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True)
devices.torch_gc(force=True, reason='ltx:base')
t3 = time.time()
timer.process.add('offload', t1 - t0)
@@ -367,7 +371,7 @@ def run_ltx(task_id,
if caps.family == '0.9':
global upsample_pipe # pylint: disable=global-statement
upsample_pipe = load_upsample(upsample_pipe, upsample_repo_id_09)
upsample_pipe = sd_models.apply_balanced_offload(upsample_pipe, exclude=upsample_exclude)
upsample_pipe = sd_models.apply_balanced_offload(upsample_pipe, exclude=upsample_exclude, silent=True)
up_args = {
'width': final_w,
'height': final_h,
@@ -379,12 +383,12 @@ def run_ltx(task_id,
log.debug(f'Video: op=upsample family=0.9 latents={latents.shape} {up_args}')
yield None, 'LTX: Upsample in progress...'
latents = upsample_pipe(latents=latents, **up_args).frames[0]
upsample_pipe = sd_models.apply_balanced_offload(upsample_pipe, exclude=upsample_exclude)
upsample_pipe = sd_models.apply_balanced_offload(upsample_pipe, exclude=upsample_exclude, silent=True)
else:
global upsample_pipe_2x # pylint: disable=global-statement
upsample_repo = upsample_repo_id_23 if caps.variant == '2.3' else upsample_repo_id_20
upsample_pipe_2x = load_upsample_2x(upsample_pipe_2x, upsample_repo)
upsample_pipe_2x = sd_models.apply_balanced_offload(upsample_pipe_2x, exclude=upsample_exclude)
upsample_pipe_2x = sd_models.apply_balanced_offload(upsample_pipe_2x, exclude=upsample_exclude, silent=True)
# 2.x base pass returns denormalized latents; latents_normalized=False tells the
# upsampler "already raw, do not denormalize again".
up_args = {
@@ -400,7 +404,7 @@ def run_ltx(task_id,
log.debug(f'Video: op=upsample family=2.x latents={latents.shape} auto={auto_refine_upsample} {up_args}')
yield None, 'LTX: Upsample in progress...'
latents = upsample_pipe_2x(latents=latents, **up_args).frames[0]
upsample_pipe_2x = sd_models.apply_balanced_offload(upsample_pipe_2x, exclude=upsample_exclude)
upsample_pipe_2x = sd_models.apply_balanced_offload(upsample_pipe_2x, exclude=upsample_exclude, silent=True)
except AssertionError as e:
yield from abort(e, ok=True, p=p)
return
@@ -414,7 +418,7 @@ def run_ltx(task_id,
if refine_enable and latents is not None:
t7 = time.time()
refinejob = shared.state.begin('Refine')
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True)
devices.torch_gc(force=True, reason='ltx:refine')
# Refine is terminal: let the pipe decode internally so the final VAE pass runs inside
# the same offload/cudnn context as a normal generation (matches Generic Video tab).
@@ -512,7 +516,7 @@ def run_ltx(task_id,
shared.sd_model.scheduler = saved_scheduler_stage2
log.debug('LTX: canonical Stage 2 cleanup done (LoRA unloaded, scheduler restored)')
t8 = time.time()
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True)
t9 = time.time()
timer.process.add('refine', t8 - t7)
timer.process.add('offload', t9 - t8)
@@ -523,7 +527,7 @@ def run_ltx(task_id,
if needs_latent_path and latents is not None:
# Only reached on upsample-without-refine; refine decodes through the pipe and nulls latents.
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, exclude=['vae'], force=True)
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, exclude=['vae'], force=True, silent=True)
devices.torch_gc(force=True, reason='ltx:vae')
yield None, 'LTX: VAE decode in progress...'
try:
@@ -540,7 +544,7 @@ def run_ltx(task_id,
return
pixels = frames_out
t10 = time.time()
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True)
t11 = time.time()
timer.process.add('offload', t11 - t10)