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name: local-inference description: "Guides LTX-Video local CLI and Python inference workflows, conditioning media, prompt handling, and safe command construction." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LTX-Video local inference

Use this sub-skill when a task asks to run or reason about LTX-Video generation through the repository CLI inference.py or the Python API infer(InferenceConfig).

Route here for

• Building an inference.py command for text-to-video, image-to-video, video extension, video-to-video, or multiple conditioning items.
• Translating a user request into InferenceConfig fields for infer(config).
• Diagnosing conditioning_media_paths, conditioning_start_frames, conditioning_strengths, input_media_path, offload_to_cpu, output-path, padding, or prompt-enhancement behavior.
• Checking whether a proposed command is safe before a heavy run that may download checkpoints or prompt-enhancer models.

Before running generation

1. Choose or inspect the YAML pipeline config with model-configs. This sub-skill can pass a config path, but it does not own model-family or YAML-field selection.

2. Confirm runtime readiness: Python package imports, [inference] extras for media I/O, a practical CUDA/MPS/CPU backend, and Hugging Face cache/network expectations.

3. Use the bundled command builder before shelling out:

   python scripts/build_inference_command.py \
     --prompt "A precise cinematic prompt" \
     --pipeline-config PATH/TO/ltxv-13b-0.9.8-distilled.yaml \
     --height 704 --width 1216 --num-frames 121 \
     --output-path outputs/ltx-run
   

   The script validates static argument rules and prints a shell-safe python inference.py ... command. It never imports LTX-Video, runs inference, or downloads models.

4. Treat full generation as expensive: checkpoint, text-encoder, spatial-upscaler, and prompt-enhancer downloads may occur when paths in the config are not local.

Main workflows

• For CLI and Python recipes, see references/workflows.md.
• For verified InferenceConfig fields and key function behavior, see references/api-reference.md.
• For failure modes and repairs, see references/troubleshooting.md.

Boundary routing

• YAML/model selection, FP8-vs-bfloat16 config choice, base-vs-multi-scale config details, and config editing route to model-configs.
• Direct LTXVideoPipeline, LTXMultiScalePipeline, scheduler, VAE, transformer, latent, or prompt-enhancer internals route to pipeline-components.
• Training, ComfyUI workflow editing, Diffusers documentation, LTX-2, and external control/trainer repositories are non-goals for this local CLI/API workflow.