What Are Sigma Schedules and Step Counts per Pipeline in LTX‑2?

Sigma schedules in LTX‑2 are 1‑D tensors of noise levels (σ values) that guide the diffusion process from noisy to clean latent representations, with step counts determining how many denoising iterations each pipeline performs.

In the Lightricks/LTX‑2 video generation framework, understanding sigma schedules and step counts is essential for tuning inference quality and speed. The codebase implements multiple schedule types tailored to different pipelines, from the adaptive LTX2Scheduler to handcrafted distilled schedules for accelerated generation.

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How Sigma Schedules Work in LTX‑2

A sigma schedule defines the noise level at each step of the diffusion process. LTX‑2 uses σ (sigma) to represent the standard deviation of the noise added to latents. Higher σ means more noise; lower σ approaches a clean sample.

The framework provides three core schedule implementations as defined in ltx_core/components/schedulers.py:

• LTX2Scheduler – Linear-to-exponential schedule that adapts to token count
• LinearQuadraticScheduler – Alternative interpolation-based schedule
• BetaScheduler – Beta-distributed noise scheduling

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Default LTX2 Schedule: Adaptive Noise Injection

The LTX2Scheduler.execute() method in ltx_core/components/schedulers.py generates the primary schedule used by most pipelines.

Key characteristics:

• Computes a linear-to-exponential decay curve
• Adapts schedule length based on latent tensor token count
• Optionally stretches final σ to match a configurable terminal value (default 0.1)

from ltx_core.components.schedulers import LTX2Scheduler

scheduler = LTX2Scheduler()
sigmas = scheduler.execute(
    steps=30,           # num_inference_steps

    terminal_sigma=0.1  # final noise level

)

# Returns: 1-D tensor of 30+1 sigma values (including σ=0)

When no custom sigmas argument is provided, pipelines automatically invoke self._scheduler.execute(steps=num_inference_steps).

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Distilled Sigma Schedules: Fixed 9-Step Acceleration

For distilled LoRA models, LTX‑2 provides hand-tuned sigma sequences in ltx_pipelines/utils/constants.py. These schedules trade some quality for dramatically faster inference.

Available distilled schedules

Schedule	Source constant	Step count	Use case
Full distilled schedule	DISTILLED_SIGMAS	9	Standard distilled inference
Stage‑2 distilled schedule	STAGE_2_DISTILLED_SIGMAS	Subset of 9	Two-stage pipeline refinement
TDP distilled schedule	TDP_DISTILLED_SIGMAS	3	Multi-GPU tiled parallel inference

# From ltx_pipelines/utils/constants.py

DISTILLED_SIGMAS = torch.tensor([
    1.0,      # Step 0: maximum noise

    0.99375,
    0.9875,
    0.98125,
    0.975,
    0.909375,
    0.725,
    0.421875,
    0.0       # Step 8: no noise (clean sample)

])

These values were empirically tuned for the distilled model checkpoints and typically override user-supplied step counts.

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Step Count Defaults by Pipeline Configuration

The num_inference_steps parameter controls schedule length. LTX‑2 defines version-specific defaults in ltx_pipelines/utils/constants.py:

Configuration	Default steps	Applies to
PipelineParams (LTX‑2.0)	40	Legacy checkpoints
LTX_2_3_PARAMS	30	LTX‑2.3 generation
LTX_2_3_HQ_PARAMS	15	High-quality preset
Distilled pipelines	9 (fixed schedule)	When distilled=True

from dataclasses import dataclass, replace
from ltx_pipelines.utils.constants import PipelineParams, LTX_2_3_PARAMS

@dataclass(frozen=True)
class PipelineParams:
    num_inference_steps: int = 40
    # ... other params

# LTX-2.3 reduces steps for faster inference

LTX_2_3_PARAMS = replace(LTX_2_PARAMS, num_inference_steps=30)

# HQ preset uses even fewer steps

LTX_2_3_HQ_PARAMS = PipelineParams(num_inference_steps=15, ...)

Users override via --num_inference_steps CLI argument processed through resolve_cli_params().

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Sigma Schedule Selection per Pipeline

Each pipeline in packages/ltx-pipelines/src/ltx_pipelines/ implements specific schedule selection logic:

TI2VidOneStagePipeline (single-stage text-to-video)

File: ltx_pipelines/ti2vid_one_stage.py

# Default behavior: LTX2Scheduler with user-specified steps

pipeline = TI2VidOneStagePipeline(model_paths=..., device='cuda')
video = pipeline(
    prompt="A mountain landscape",
    num_inference_steps=30,  # Uses LTX2Scheduler.execute(steps=30)

)

# Override with custom schedule

video = pipeline(
    prompt="A mountain landscape",
    sigmas=torch.linspace(1.0, 0.0, 50),  # Custom 50-step linear schedule

)

TI2VidTwoStagesPipeline (coarse-then-refine)

File: ltx_pipelines/ti2vid_two_stages.py

Two-stage pipelines allow independent schedule control:

Stage	Default schedule	Override parameter
Stage 1 (coarse generation)	LTX2Scheduler.execute(steps=num_inference_steps)	stage_1_sigmas
Stage 2 (distilled refinement)	STAGE_2_DISTILLED_SIGMAS	stage_2_sigmas

from ltx_pipelines.ti2vid_two_stages import TI2VidTwoStagesPipeline
from ltx_pipelines.utils.constants import DISTILLED_SIGMAS

pipeline = TI2VidTwoStagesPipeline(
    model_paths=...,
    distilled_lora=...,  # Enables stage-2 distillation

    device='cuda'
)

video = pipeline(
    prompt="A futuristic robot",
    num_inference_steps=40,           # Stage 1: 40 steps with LTX2Scheduler

    stage_1_sigmas=None,              # Use default scheduler

    stage_2_sigmas=DISTILLED_SIGMAS,  # Stage 2: 9-step distilled schedule

)

Other Pipelines

• RetakePipeline, A2VidTwoStagePipeline, KeyframeInterpolationPipeline, HDRICLoraPipeline, DistilledPipeline – All follow the same pattern: use LTX2Scheduler by default or switch to DISTILLED_SIGMAS when distilled=True or custom sigmas provided.

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Complete Usage Examples

Standard LTX‑2.3 generation with default schedule

from ltx_pipelines.ti2vid_one_stage import TI2VidOneStagePipeline
from ltx_pipelines.utils.constants import LTX_2_3_PARAMS

pipeline = TI2VidOneStagePipeline(
    model_paths="/path/to/ltx-2.3",
    loras=[],
    device="cuda"
)

video, audio, tiling = pipeline(
    prompt="A serene lake at dawn",
    negative_prompt="blur, low quality",
    seed=42,
    height=720,
    width=1280,
    frame_rate=24.0,
    num_inference_steps=LTX_2_3_PARAMS.num_inference_steps,  # 30 steps

    # No sigmas argument: uses LTX2Scheduler.execute(steps=30)

    images=[],
)

Maximum quality with HQ preset (15 steps)

from ltx_pipelines.utils.constants import LTX_2_3_HQ_PARAMS

video = pipeline(
    prompt="Crystal-clear ocean waves",
    num_inference_steps=LTX_2_3_HQ_PARAMS.num_inference_steps,  # 15 steps

    # Optimized sigmas for quality at reduced step count

)

Two-stage with distilled acceleration

from ltx_pipelines.ti2vid_two_stages import TI2VidTwoStagesPipeline
from ltx_pipelines.utils.constants import DISTILLED_SIGMAS

pipeline = TI2VidTwoStagesPipeline(
    model_paths=...,
    distilled_lora="/path/to/distilled_lora.safetensors",
    spatial_upsampler_path=...,
    device="cuda"
)

video, audio, _, tiling = pipeline(
    prompt="A cyberpunk street scene",
    num_inference_steps=40,              # Stage 1 steps

    stage_2_sigmas=DISTILLED_SIGMAS,     # Fixed 9-step stage 2

    height=1080,
    width=1920,
)

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Key Source Files

File	Purpose
ltx_core/components/schedulers.py	LTX2Scheduler, LinearQuadraticScheduler, BetaScheduler implementations
ltx_pipelines/utils/constants.py	DISTILLED_SIGMAS, STAGE_2_DISTILLED_SIGMAS, TDP_DISTILLED_SIGMAS, PipelineParams defaults
ltx_pipelines/ti2vid_one_stage.py	Single-stage pipeline schedule selection
ltx_pipelines/ti2vid_two_stages.py	Two-stage pipeline with independent stage schedules

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Summary

• Sigma schedules are 1-D tensors defining noise levels (σ) across diffusion steps, implemented in ltx_core/components/schedulers.py
• LTX2Scheduler provides adaptive linear-to-exponential schedules; distilled schedules in ltx_pipelines/utils/constants.py offer fixed 9-step acceleration
• Step counts default to 40 (LTX‑2.0), 30 (LTX‑2.3), or 15 (HQ preset), overridable via num_inference_steps
• Single-stage pipelines use LTX2Scheduler unless custom sigmas provided
• Two-stage pipelines support independent schedules: adaptive for stage 1, distilled for stage 2

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Frequently Asked Questions

How do I use the 9-step distilled schedule in LTX‑2?

Pass DISTILLED_SIGMAS from ltx_pipelines/utils/constants.py to the sigmas or stage_2_sigmas parameter. For single-stage pipelines: pipeline(prompt=..., sigmas=DISTILLED_SIGMAS). For two-stage pipelines, this applies to stage 2 via stage_2_sigmas=DISTILLED_SIGMAS.

What happens if I set num_inference_steps=50 with a distilled model?

The distilled schedule (DISTILLED_SIGMAS) contains exactly 9 values. If you request 50 steps, LTX‑2 either stretches the 9-step schedule to 50 points or ignores num_inference_steps depending on the pipeline implementation. For true 50-step generation, omit distilled flags and use LTX2Scheduler with custom num_inference_steps.

Where does the terminal_sigma parameter affect generation?

terminal_sigma (default 0.1) controls the final noise level in LTX2Scheduler.execute(). It appears in ltx_core/components/schedulers.py and ensures the schedule terminates at the desired cleanliness rather than exactly zero, providing subtle control over generation fidelity.

Can I mix different schedules across pipeline stages?

Yes. TI2VidTwoStagesPipeline explicitly supports this via stage_1_sigmas and stage_2_sigmas parameters. Common configurations use the full LTX2Scheduler for coarse generation (stage 1) and STAGE_2_DISTILLED_SIGMAS or DISTILLED_SIGMAS for fast refinement (stage 2).

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