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name: turbot2av-extension description: "Plan TurboT2AV text-to-audio-video extension setup, checkpoints, Pixi/LTX environment separation, inference commands, and TurboDiffusion-style acceleration choices." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TurboT2AV Extension

Use this sub-skill when the task is about the TurboT2AV text-to-audio-video extension rather than the core Wan T2V/I2V TurboDiffusion workflows.

Read these bundled references before giving commands:

• TurboT2AV setup and inference for environment separation, required weights, prompt files, student/teacher inference planning, and CLI options.
• Acceleration notes for SageSLA, TileLang W8A8, FastNorm, topk=0.3, trim_text_context, and how TurboT2AV acceleration differs from core TurboDiffusion.
• Troubleshooting for Pixi/LTX isolation, Gemma gated access, missing checkpoints, CUDA/TileLang/SageAttention/SpargeAttn failures, and interpretation pitfalls.

Use the bundled command renderer instead of hand-assembling long inference commands:

• scripts/build_turbot2av_command.py renders a student or teacher ltx_distillation.tools.run_av_inference_eval command from user-supplied paths and options. It validates that required paths are supplied, prints only commands, and never downloads weights or runs inference.

Route elsewhere:

• Core Wan text-to-video or image-to-video inference belongs to the sibling video-inference guidance, not this sub-skill.
• TurboDiffusion CUDA operator build and Wan-specific quant_linear troubleshooting belongs to the acceleration-backends guidance; this sub-skill only explains how those ideas are adapted for LTX/TurboT2AV.
• Full LTX-2 internals, training, and Pixi installation script maintenance are reference-only; do not duplicate or rewrite the LTX-2 project here.