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name: turbo-diffusion description: "Use TurboDiffusion for accelerated Wan video generation, interactive serving, checkpoint conversion, CUDA acceleration backends, and TurboT2AV extension planning." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TurboDiffusion

Use this repo skill when a task involves TurboDiffusion, TurboWan, Wan2.1/Wan2.2 accelerated video generation, SLA/SageSLA attention, rCM/SLA checkpoint conversion or training, the turbodiffusion-serve TUI, or TurboT2AV audio-video generation.

TurboDiffusion is a CUDA-first package. Most real generation workflows need a compatible NVIDIA GPU, model checkpoints, Wan VAE/text encoder files, and source-layout imports. Do not start downloads, long generation, or training unless the user explicitly supplied the needed assets and budget.

First checks

1. Read repo provenance when deciding whether this skill is current for a checkout.
2. Read installation and backend notes before installing, compiling, or diagnosing CUDA/SageSLA/custom-op problems.
3. Run scripts/check_turbodiffusion_env.py when a user asks whether their current environment can import TurboDiffusion, see CUDA, load custom ops, or use optional SageSLA.
4. Read model and asset catalog before building commands that mention VAE/text encoder/DiT checkpoints, high/low I2V checkpoints, prompt files, or TurboT2AV assets.
5. Use cross-cutting troubleshooting for install/import, source-layout, missing checkpoint, CUDA, and optional dependency failures.

Sub-skill routing

User request	Read
Build a one-shot text-to-video or image-to-video command; choose Wan2.1/Wan2.2 model flags; validate prompt/image/checkpoint arguments; avoid running models while planning.	video-inference
Launch or debug the interactive terminal server; decide T2V vs I2V TUI flags; understand slash commands and runtime-adjustable parameters.	interactive-serving
Prepare SLA/rCM training commands, checkpoint conversion, checkpoint merging, safetensors-to-pth conversion, quantized checkpoint export, or data/checkpoint layout checks.	training-and-checkpoints
Diagnose CUDA extension build/import, turbo_diffusion_ops, INT8/FastNorm, attention_type=sla vs sagesla, SpargeAttn, flash-attn, or source-layout import quirks.	acceleration-backends
Plan TurboT2AV text-to-audio-video generation, LTX-2/Pixi setup, HF/Gemma checkpoint requirements, or TileLang/SageSLA acceleration choices.	turbot2av-extension

Operating rules

• Prefer the bundled command builders in sub-skill scripts/ for command construction. They render commands and validate obvious mistakes without downloading weights, starting training, or running generation.
• For source checkouts, TurboDiffusion scripts and even the installed TUI entry point may require adding the package source directory to PYTHONPATH so top-level imports such as imaginaire, rcm, serve, and modify_model resolve. Capture this as a public source-layout requirement, not as a local path.
• Treat attention_type=sagesla as optional-backend dependent: install SpargeAttn first or fall back to sla/original when SageSLA is not available.
• Match --quant_linear to quantized checkpoints. Do not add it to unquantized checkpoints on large-memory GPUs unless the user explicitly wants the quantized path.
• Do not run full T2V/I2V/TurboT2AV generation unless the user provides the checkpoints, VAE/text encoder assets, output location, GPU budget, and permission to spend time/GPU memory.
• Do not run full rCM/SLA training unless the user provides checkpoint/data roots, distributed/GPU budget, logging policy, and any credentials such as WandB or Hugging Face tokens.

Minimal environment smoke

Use this public smoke from an installed/source-layout environment before deeper work:

python - <<'PY'
import torch
print('torch', torch.__version__, torch.version.cuda, torch.cuda.is_available())
try:
    import turbo_diffusion_ops
    print('custom ops: ok')
except Exception as exc:
    print('custom ops:', type(exc).__name__, exc)
PY

For a fuller check, run the bundled environment diagnostic. If it reports missing imaginaire or rcm, use the source-layout PYTHONPATH guidance in installation and backend notes.