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-01-prep/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-01-prep)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-01-prep"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-01-prep/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-01-prep"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-01-prep.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 | $0.00056 | $0.01498 |
| Opus 5 | $0.00028 | $0.00749 |
| Sonnet 5 | $0.00011 | $0.00300 |
| Haiku 4.5 | $0.00006 | $0.00150 |
Grade A, and why
add-model-01-prep 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 9d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Model Prep
Goal
Prepare external assets and the shared parity-test environment for a FastVideo model port. Stop before writing conversion scripts, model components, pipeline code, registry entries, or executable parity tests.
Ask First
Ask once, then proceed if the HF token is already exported:
Before prep: (1) official reference repo or Diffusers pipeline URL, (2) HF repo
id or local weights path and whether it has a root model_index.json, (3) target
model_family, (4) workload types, (5) which token env var is exported:
HF_TOKEN, HUGGINGFACE_HUB_TOKEN, or HF_API_KEY, (6) may I stage clone and
weights under the FastVideo repo root, and (7) may I install official reference
dependencies into the current FastVideo conda/env for parity tests?
Useful optional inputs: pipeline_class, reference_dir, hf_revision,
official_revision, reuse_hints, download_scope.
Rules
- Follow
../add-model/shared/common_rules.mdfor token/auth safety, state files, escape hatches, and skip/pass semantics. - Run from the FastVideo repo root.
- Use repo-relative defaults:
<ReferenceDir>/,official_weights/<model_family>/,converted_weights/<model_family>/. - Install official reference deps into the current FastVideo environment, not a new venv/conda env, so parity tests run both implementations with one shared numeric stack.
- If the reference is a Diffusers class/package instead of a cloneable repo, record import path and version instead of cloning.
- Prep may create only the local-test README and
PORT_STATUS.mdskeletons; executable.pyparity tests belong to../add-model-02-parity/SKILL.md.
Escape Hatches
Follow ../add-model/shared/common_rules.md. Prep-specific ask cases include
overwriting an existing clone or weight directory, installing untrusted/private
deps, choosing between incompatible official references, large downloads outside
the agreed scope, or missing gated-repo auth setup by env var name.
Workflow
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 174 lines · 56 tokens per session scan A dbcb7d26a3be
add-model-01-prep is a skill published in the GitHub repository hao-ai-lab/FastVideo (4,363 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 1,498 once invoked, about $0.0003 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.
Other skills, from other repositories
diffusers-ascend-pipeline
A guide for running image and video generation pipelines on Huawei Ascend NPUs with the Diffusers library. Diffusers is a software library for using generative models, and the guide covers model pipelines, memory settings, LoRA adapters, and multi-card inference.
sglang-diffusion-performance
Use when choosing the fastest SGLang Diffusion flags for a model, GPU, and VRAM budget.
sglang-diffusion-add-model
Use when adding a new diffusion model or Diffusers pipeline to SGLang.
sglang-diffusion-modelopt-quant
Use when quantizing a diffusion DiT with NVIDIA ModelOpt and making the resulting FP8 or NVFP4 checkpoint loadable, verifiable, and benchmarkable in SGLang Diffusion.
grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training.
gptq
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.