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name: helios description: "Route Helios video-generation, data-preparation, and training workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Helios

Use this repo skill for the Helios family of video-generation workflows built around Diffusers-style pipelines and the Helios-Base, Helios-Mid, and Helios-Distilled checkpoints.

Start here

Before choosing a route, read:

• references/overview.md for the supported workflow map and model-family summary.
• references/compatibility.md for the required Python/package/backend shape.
• references/api-reference.md for the verified diffusers/local Helios API surfaces used by the bundled workflows.
• references/troubleshooting.md for cross-cutting install, import, and GPU issues.
• scripts/check_helios_env.py for a quick local smoke check of the installed environment.

Routes

1) Video generation and demos

Use sub-skills/inference/SKILL.md when the user wants to:

• generate text-to-video, image-to-video, or video-to-video clips;
• choose between Base, Mid, or Distilled checkpoints;
• run the public-style Gradio demo flow or local inference;
• use low-VRAM offload, multi-GPU context parallelism, or prompt/video input variants.

2) Data preparation

Use sub-skills/data-preparation/SKILL.md when the user wants to:

• validate Helios dataset metadata or prompt/video layout;
• prepare the metadata, latent, or prompt-embedding inputs that training consumes;
• understand the expected JSON/CSV/file naming conventions before launching distributed preprocessing jobs.

3) Training and fine-tuning

Use sub-skills/training/SKILL.md when the user wants to:

• configure or debug Stage 1, Stage 2, or Stage 3 training;
• choose between DDP and DeepSpeed launch patterns;
• validate YAML config constraints before a launch;
• merge or load checkpoints and LoRA-style weights after training.

What is intentionally out of scope

• The repo's metric-benchmark suite is not a primary runtime route in this generated skill. Treat it as a separate maintenance/evaluation concern unless a later skill refresh explicitly adds it.
• One-off maintainer scripts, benchmark scratch files, and generated outputs are not part of the runtime graph.

Choosing the right sub-skill

• If the task is "make a video" or "run the demo", start with inference.
• If the task is "prepare my data" or "check the metadata format", start with data-preparation.
• If the task is "train", "fine-tune", "resume", or "merge a checkpoint", start with training.

If a request spans more than one route, handle the earliest blocking step first: validate inputs, then prepare data, then train, then generate.

Quick rules

• Prefer the bundled compatibility check before assuming the environment is ready.
• Treat CUDA as the core backend for the generation and training routes.
• Treat optional extras such as DeepSpeed, NPU-specific kernels, or demo-only UI packages as add-ons, not the baseline.
• Keep all runtime guidance inside the bundled skill tree; do not depend on the original repository checkout.