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-05-port-encoder/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-05-port-encoder)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-05-port-encoder"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-05-port-encoder/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-05-port-encoder"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-05-port-encoder.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 | $0.00040 | $0.01117 |
| Opus 5 | $0.00020 | $0.00558 |
| Sonnet 5 | $0.00008 | $0.00223 |
| Haiku 4.5 | $0.00004 | $0.00112 |
Grade A, and why
add-model-05-port-encoder 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Model Port Encoder
Goal
Prototype or parity-debug one encoder or encoder-like conditioner in FastVideo-native code. Use this for text encoders, image encoders, audio encoders, and compound conditioners that fit the encoder config/loader bucket.
Inputs
Follow ../add-model/shared/component_skill_common.md and require the complete
packet from ../add-model/contracts/component_context.md.
Encoder-specific packet fields:
component: encoder or encoder-like conditioner name.parity_test:tests/local_tests/encoders/test_<family>_<component>_parity.py.weights: converted encoder dir, HF subfolder, or external HF id.target_files:fastvideo/models/encoders/<arch_or_family>.pyandfastvideo/configs/models/encoders/<arch_or_family>.py.
Modes
Use the common prototype and parity-debug modes from
../add-model/shared/component_skill_common.md.
Encoder-specific prototype concerns include tokenizer kwargs, hidden-state extraction, output packing, connector order, and external/passthrough weight needs.
Reuse Proof
Apply the shared reuse proof. Encoder-specific comparison must include tokenizer contracts, hidden-state extraction, masks, positional IDs, output packing, connector/projection ordering, passthrough paths, and returned dataclass shape.
Existing FastVideo Patterns
- Base classes:
TextEncoderandImageEncoderinfastvideo/models/encoders/base.py. - Output type:
BaseEncoderOutput. - Config bases:
TextEncoderConfig,ImageEncoderConfig,TextEncoderArchConfig, andImageEncoderArchConfiginfastvideo/configs/models/encoders/base.py. - Use the matching encoder config bucket. Wrong bucket inheritance can typecheck but fail during pipeline wiring.
- Config export: add the config to
fastvideo/configs/models/encoders/__init__.py. - Registry discovery: set
EntryClass = <ClassName>or a list of class names in the model file. - Reference examples: native
t5.py,clip.py,siglip.py,llama.py,qwen2_5.py,gemma.py, and compoundstable_audio_conditioner.py. - 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 · 120 lines · 40 tokens per session scan A 48f9b65c7c2e
add-model-05-port-encoder 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 40 tokens to every session and 1,117 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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sglang-diffusion-add-model
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sglang-diffusion-modelopt-quant
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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.