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name: core-components description: "Helps agents build custom LTX-2 core component code with ltx_core APIs, model loaders, diffusion schedulers/guiders, conditioning, LoRA, quantization, media, and shape troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Core Components

Use this sub-skill when a user wants custom Python code against LTX-2 internals rather than a ready-made command line pipeline. It covers ltx_core building blocks and the package utilities that wire them together: model path contracts, model builders, state-dict operations, schedulers, guiders, noisers, patchifiers, conditioning items, generated keyframe slots, LoRA fusion, quantization policies, text/audio/video component classes, and safe media/HDR helpers.

Route here for

• Writing or reviewing custom denoising loops, diffusion stages, model builders, or component wiring.
• Inspecting exact class/function names and import paths for ltx_core and relevant ltx_pipelines.utils modules.
• Debugging wrong API names such as LinearQuadraticSchedule instead of LinearQuadraticScheduler, or ClassifierFreeGuidance instead of CFGGuider.
• Explaining latent/video/audio/text tensor shapes, frame alignment, VAE scale factors, patchification, Modality, and LatentState layouts.
• Using ModelPaths, SingleGPUModelBuilder, SDOps, LoRA maps, QuantizationPolicy, FP8 policy factories, or block streaming builders without loading real checkpoints in examples.
• Diagnosing generated keyframe slot errors and checkpoint requirements.

Route elsewhere

• Complete runnable inference recipes, CLI flags, output writing, prompt/HDR pipeline workflows, or model asset selection: read ../inference-pipelines/SKILL.md.
• Dataset preparation, captioning, manifests, preprocessing, references, masks, and precomputed latents for training: read ../data-preparation/SKILL.md.
• Training/fine-tuning configs, launch/resume, validation, optimizer, W&B, or custom strategies: read ../training-workflows/SKILL.md.
• CUDA installation, optional kernels, NATTEN/FlashAttention, NVFP4 hardware, multi-GPU, compile/offload performance tuning: read ../performance-backends/SKILL.md.

Read first

• API reference for verified imports, exact signatures, and selected pipeline constructor surfaces.
• Model and data shapes for latent layouts, patchification, frame/spatial alignment, text/audio context shapes, and generated keyframe slot constraints.
• Loading and LoRAs for ModelPaths, SingleGPUModelBuilder, SDOps, safetensors metadata, LoRA fusion, block streaming, and quantization policy wiring.
• Troubleshooting for wrong names, missing component paths, shape errors, LoRA metadata/key mismatches, keyframe-capable checkpoint requirements, and optional backend routing.

Safe helper

Run the bundled inspector in any Python environment where LTX-2 packages are installed. It performs imports, signature inspection, and optional tiny CPU shape checks only; it does not download models or load checkpoints.

python sub-skills/core-components/scripts/inspect_core_api.py --help
python sub-skills/core-components/scripts/inspect_core_api.py --json --tiny-shapes
python sub-skills/core-components/scripts/inspect_core_api.py --include-pipelines --object ltx_pipelines.distilled:DistilledPipeline

Core coding rules

1. Treat docs and examples as conceptual only unless the exact import and signature appear in API reference or are re-inspected with the helper.
2. Use placeholders for checkpoint paths in examples and state clearly when real .safetensors files are required. Do not write examples that silently load missing model assets.
3. Build path contracts with ModelPaths.from_monolith(...) or ModelPaths.from_split(...); do not mix monolith checkpoint/Gemma flags with split component flags.
4. For LoRA loading, pair each adapter with a matching SDOps map and a strength; preserve CPU LoRA staging unless the user explicitly accepts higher GPU memory use.
5. For FP8, create a QuantizationPolicy first and pass its sd_ops, module_ops, and fuse_rule into the builder. Route NVFP4 and compiled-kernel constraints to performance-backends.
6. For generated keyframe slots, require a checkpoint whose transformer config sets use_keyframes_abs_pos_embedding; otherwise recommend DFR/generated-keyframe-capable assets or route to the inference pipeline owner.