FastVideo is a framework for training and running accelerated video-generation models, including real-time inference and post-training workflows. It is for researchers and developers building or deploying diffusion-based systems that generate video.
Borrowing it
Nothing to install: this file belongs to hao-ai-lab/FastVideo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/hao-ai-lab/FastVideo/main/.agents/skills/add-model-06-port-generic/SKILL.mdgit clone --depth 1 https://github.com/hao-ai-lab/FastVideoWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-06-port-generic)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-06-port-generic"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-06-port-generic/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-06-port-generic"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-06-port-generic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.00989 |
| Opus 5 | $0.00021 | $0.00495 |
| Sonnet 5 | $0.00008 | $0.00198 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
add-model-06-port-generic scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Model Port Generic
Goal
Prototype or parity-debug one scheduler, conditioner, upsampler, vocoder, adapter, preprocessor, or unknown component in FastVideo-native code.
Inputs
Follow ../add-model/shared/component_skill_common.md and require the complete
packet from ../add-model/contracts/component_context.md.
Generic-component packet fields:
component: component name.component_type: scheduler, conditioner, upsampler, vocoder, adapter, preprocessor, or unknown.parity_test:tests/local_tests/<bucket>/test_<family>_<component>_parity.py.weights: converted component dir, HF subfolder, or none.target_files: matchingfastvideo/models/andfastvideo/configs/models/bucket files when applicable.
Modes
Use the common prototype and parity-debug modes from
../add-model/shared/component_skill_common.md.
Generic-component prototype concerns include stateless/stateful ambiguity, missing loader buckets, source prefixes, mutable scheduler state, and output container shape.
Reuse Proof
Apply the shared reuse proof. Generic-component comparison must include mutable state, scaling constants, scheduler/conditioner semantics, output containers, and whether the component owns state or is stateless.
Existing FastVideo Patterns
- Schedulers live under
fastvideo/models/schedulers/and exposeEntryClass. - Upsamplers use
fastvideo/models/upsamplers/plus configs underfastvideo/configs/models/upsamplers/; seehunyuan15.py. - Vocoders and audio-specific modules can live under
fastvideo/models/audio/with configs underfastvideo/configs/models/audio/; seeltx2_audio_vae.py. - Compound conditioners may fit the encoder bucket when the pipeline loader uses
ConditionerLoader; seestable_audio_conditioner.py. - Registry discovery uses
EntryClass; config bucket exports are required when pipeline configs import them by bucket. - Use the narrowest matching config bucket. Wrong bucket inheritance can typecheck but fail during pipeline wiring.
- Layer guidance:
fastvideo/layers/AGENTS.md.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 112 lines · 42 tokens per session scan A 7058f28a1178
add-model-06-port-generic is a skill published in the GitHub repository hao-ai-lab/FastVideo (4,368 stars, last pushed 2d ago), licensed Apache-2.0. It adds 42 tokens to every session and 989 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.