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fix(ltx): pin guidance and cross-timestep on every 2.x call path

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Some paths left both to pipeline defaults, which track the current upstream
model: distilled picked up guidance it already bakes in, and 2.0 could land on
2.3's joint-sigma path.
This commit is contained in:
CalamitousFelicitousness
2026-08-12 01:44:53 +01:00
parent 5c495c29cd
commit 5735b8f47d
1 changed files with 26 additions and 14 deletions
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modules/ltx/ltx_process.py
+26 -14
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@@ -32,11 +32,28 @@ def _prompt_tensors_to_device(*tensors):
return tuple(t.to(device=devices.device) if torch.is_tensor(t) else t for t in tensors)
def identity_ltx2_guidance() -> dict:
# Named rather than omitted: pipeline defaults track the current upstream model, so a missing
# term guides a schedule that already bakes it in.
return {
'stg_scale': 0.0,
'modality_scale': 1.0,
'guidance_rescale': 0.0,
'spatio_temporal_guidance_blocks': None,
'audio_guidance_scale': 1.0,
'audio_stg_scale': 0.0,
'audio_modality_scale': 1.0,
'audio_guidance_rescale': 0.0,
}
def _canonical_ltx2_guidance(caps) -> dict:
# Four-way composition (cfg + stg + modality + rescale) from huggingface/diffusers#13217.
# Distilled bakes these into its sigma schedule; skip or we double-apply.
if caps.family != '2.x' or caps.is_distilled:
# Distilled bakes these into its sigma schedule and runs at identity.
if caps.family != '2.x':
return {}
if caps.is_distilled:
return identity_ltx2_guidance()
return {
'stg_scale': caps.stg_default_scale,
'modality_scale': caps.modality_default_scale,
@@ -58,14 +75,7 @@ def _canonical_stage2_kwargs() -> dict:
'sigmas': list(STAGE_2_DISTILLED_SIGMA_VALUES),
'noise_scale': float(STAGE_2_DISTILLED_SIGMA_VALUES[0]),
'guidance_scale': 1.0,
'stg_scale': 0.0,
'modality_scale': 1.0,
'guidance_rescale': 0.0,
'audio_guidance_scale': 1.0,
'audio_stg_scale': 0.0,
'audio_modality_scale': 1.0,
'audio_guidance_rescale': 0.0,
'spatio_temporal_guidance_blocks': None,
**identity_ltx2_guidance(),
}
@@ -104,8 +114,8 @@ def _latent_pass(caps, prompt_embeds, prompt_attention_mask, negative_prompt_emb
base_args['sigmas'] = list(DISTILLED_SIGMA_VALUES)
base_args.pop('num_inference_steps', None)
base_args.update(_canonical_ltx2_guidance(caps))
if caps.use_cross_timestep:
base_args['use_cross_timestep'] = True
if caps.family == '2.x':
base_args['use_cross_timestep'] = caps.use_cross_timestep
log.debug(f'Video: cls={shared.sd_model.__class__.__name__} op=latent_pass args_keys={list(base_args.keys())}')
result = shared.sd_model(**base_args)
latents = result.frames[0] if hasattr(result, 'frames') else None
@@ -316,6 +326,8 @@ def run_ltx(task_id,
p.task_args['sigmas'] = list(DISTILLED_SIGMA_VALUES)
p.task_args.pop('num_inference_steps', None)
p.task_args.update(_canonical_ltx2_guidance(caps))
if caps.family == '2.x':
p.task_args['use_cross_timestep'] = caps.use_cross_timestep
framewise = caps.family == '0.9'
set_vae_params(p, framewise=framewise)
@@ -499,8 +511,8 @@ def run_ltx(task_id,
# Thread Stage-1 I2V init image through Stage 2 so first-frame identity survives refine.
if caps.is_i2v and caps.repo_cls_name in ('LTXImageToVideoPipeline', 'LTX2ImageToVideoPipeline') and p.task_args.get('image') is not None:
refine_args['image'] = p.task_args['image']
if caps.family == '2.x' and caps.use_cross_timestep:
refine_args['use_cross_timestep'] = True
if caps.family == '2.x':
refine_args['use_cross_timestep'] = caps.use_cross_timestep
# output_type='latent' skips the post-loop audio_vae + vocoder pass when audio
# is unwanted; per-step audio cross-attention still runs for video conditioning.
# Internal video decode is also skipped; vae_decode below picks it up.