pipeline-components

Pipeline Components

Use this sub-skill when the task is about direct Python use or debugging of LTX-Video components rather than end-user command construction.

Use when

• The user says: "use LTXVideoPipeline directly", "ConditioningItem", "scheduler timestep", "RectifiedFlowScheduler", "VAE downscale factor", "load safetensors checkpoint", "multi-scale class", "skip_layer_strategy", "tone_map_latents", or "component smoke".
• The user already has package components or checkpoints and needs API signatures, shapes, validation rules, or safe diagnostics.
• The user needs a no-download component smoke for scheduler/VAE configuration behavior.

Route away

• For CLI commands, InferenceConfig, infer(...), media file loading, output writing, or normal local generation workflows, route to ../local-inference/SKILL.md.
• For YAML pipeline config choice, model-family selection, FP8-vs-bfloat16 config choice, or multi-scale config fields, route to ../model-configs/SKILL.md.
• For full checkpoint inference quality, prompt quality, or performance benchmarking, do not use component smokes as proof; use the inference route and an explicitly authorized generation run.

Read order

1. references/component-api.md for direct pipeline, conditioning, prompt enhancement, skip-layer, multi-scale, and shape contracts.
2. references/scheduler-and-vae.md for scheduler math, VAE/transformer/latent-upsampler loading, and safe component-check facts.
3. references/troubleshooting.md when a direct component call fails.
4. scripts/check_components.py --help for no-download diagnostics.

Operating rules

• Prefer verified signatures and runtime facts over source-code guesses.
• Keep direct pipeline snippets explicit about tensor ranks, latent shapes, dtype/device, and checkpoint-loading assumptions.
• Never claim that scripts/check_components.py, scheduler tests, or demo VAE config checks verify full LTX checkpoint generation.
• Avoid downloading checkpoints or loading prompt-enhancer models during component diagnosis unless the user explicitly authorizes that heavier workflow.