cosmos3-codebase-nav

작성자: nvidia

Cosmos3 패키지 코드베이스를 탐색하여 매개변수, 구성, 기본값, 스크립트 및 문서가 어디에 있는지 찾습니다. 사용자가 "X는 어디에 있나요..."라고 물을 때 사용합니다.

npx skills add https://github.com/nvidia/cosmos-framework --skill cosmos3-codebase-nav

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Cosmos3 Codebase Navigation

When to use this skill

Use this skill when an agent is navigating the Cosmos3 package
Use this skill to answer "where is X", "how do I find the config for Y", or any file-location question
Use this skill when the user opens or edits cosmos3 files and needs orientation

Path convention

All paths below are relative to this file's location (.agents/skills/cosmos3-codebase-nav/). The repo is laid out as:

cosmos_framework/ — main training package (data, model, trainer, callbacks, checkpoint, utils, …).
cosmos_framework/configs/base/experiment/ — vfm (generator) experiment SKUs referenced by [train.train_policy].experiment in the recipe TOMLs.
cosmos_framework/configs/base/reasoner/experiment/ — vlm (reasoner) experiment SKUs.
cosmos_framework/inference/ — inference subpackage (args, model, inference engine, defaults, Ray serving, common helpers).
cosmos_framework/scripts/ — top-level entry-point scripts (train, inference, eval, export_model, convert_model_to_dcp, upsample_prompts, caption_from_video, captions_to_sft_jsonl, action_policy_server, …). Invoked as python -m cosmos_framework.scripts.<name>.
examples/toml/sft_config/<recipe>.toml + examples/launch_sft_<recipe>.sh — paired SFT recipes (training entry-point input). The shell sources examples/_sft_launcher_common.sh, which forwards into cosmos_framework.scripts.train --sft-toml=....
cosmos_framework/configs/toml_config/ — pydantic schemas (sft_config.py) and helpers that validate the recipe TOML at load time.

Quick Reference

Where parameters and defaults live

What you're looking for	File
Sampling params (num_steps, guidance, shift, fps, etc.)	`../../../cosmos_framework/inference/args.py` → `SamplingArgs`, `SamplingOverrides`
Per-modality default values	`../../../cosmos_framework/inference/defaults/<mode>/sample_args.json`
Setup params (parallelism, checkpoints, model path)	`../../../cosmos_framework/inference/args.py` → `OmniSetupArgs`, `OmniSetupOverrides`
Common args base classes	`../../../cosmos_framework/inference/common/args.py` → `ArgsBase`, `OverridesBase`
Ray serving parallelism presets	`../../../cosmos_framework/inference/ray/configs/latency.yaml`, `../../../cosmos_framework/inference/ray/configs/throughput.yaml`
Feature flags	`../../../cosmos_framework/utils/flags.py`
Prompt upsampler system prompt	`../../../cosmos_framework/inference/defaults/prompt_upsampler.txt`
Video captioner system prompt	`../../../cosmos_framework/inference/defaults/video_captioner.txt`
SFT recipe TOMLs (paired with `examples/launch_sft_*.sh`)	`../../../examples/toml/sft_config/<recipe>.toml`
SFT pydantic schema (validates the recipe TOML)	`../../../cosmos_framework/configs/toml_config/sft_config.py`
Training experiment SKUs (vfm)	`../../../cosmos_framework/configs/base/experiment/`
Training experiment SKUs (vlm / reasoner)	`../../../cosmos_framework/configs/base/reasoner/experiment/`
Example inputs	`../../../inputs/omni/t2i.json`, `../../../inputs/omni/t2v.json`, `../../../inputs/omni/i2v.json`, …

Available modality modes for defaults: text2image, text2video, image2video, image2image, video2video, forward_dynamics, inverse_dynamics, wam.

Config defaults resolution chain

When a user runs inference, default parameter values are resolved in this order:

cosmos_framework/inference/defaults/<mode>/sample_args.json     # 1. Per-modality JSON defaults (num_steps, guidance, shift, fps, etc.)
        ↓
_load_modality_defaults() in cosmos_framework/inference/args.py # 2. Loaded and cached at import time
        ↓
SamplingArgs / SamplingOverrides                      # 3. Pydantic models with field-level validation
        ↓
OmniSampleOverrides.build_sample()                    # 4. Merges user overrides → final resolved args
        ↓
_RESOLUTION_SHIFT_DEFAULTS[model_size, resolution]    # 5. Model+resolution shift override (if user didn't set shift)
        ↓
CLI flags (--guidance, --shift, etc.)                 # 6. User overrides from command line

The _RESOLUTION_SHIFT_DEFAULTS table in ../../../cosmos_framework/inference/args.py (on OmniSampleOverrides) overrides the default shift based on model size and resolution, unless the user explicitly specified --shift.

Mode	Default file	Key defaults
`text2image`	`../../../cosmos_framework/inference/defaults/text2image/sample_args.json`	`num_frames=1`, `guidance=6.0`, `shift=10.0`
`text2video`	`../../../cosmos_framework/inference/defaults/text2video/sample_args.json`	`num_frames=189`, `guidance=6.0`, `shift=10.0`
`image2video`	`../../../cosmos_framework/inference/defaults/image2video/sample_args.json`	`num_frames=189`, `guidance=6.0`, `shift=10.0`

Action and video2video modes also have defaults under cosmos_framework/inference/defaults/{image2image,video2video,forward_dynamics,inverse_dynamics,policy}/sample_args.json.

Users can also supply a custom defaults file per-request via the defaults_file field in sample arguments (see ../../../docs/inference.md).

Where to make changes

Task	Edit
Change a built-in default value	`../../../cosmos_framework/inference/defaults/<mode>/sample_args.json`
Add a new CLI parameter	`SamplingArgs` + `SamplingOverrides` in `../../../cosmos_framework/inference/args.py`, then add to each `sample_args.json`
Change parallelism presets	`../../../cosmos_framework/inference/ray/configs/latency.yaml` or `throughput.yaml`
Add a new script	`../../../cosmos_framework/scripts/` — follow `inference.py` as the pattern

Key entry points

Entry point	How to run
Batch inference	`python -m cosmos_framework.scripts.inference`
Training	`python -m cosmos_framework.scripts.train --sft-toml=examples/toml/sft_config/<recipe>.toml`
Online serving (Ray)	`python -m cosmos_framework.inference.ray.serve`
Submit to Ray server	`python -m cosmos_framework.inference.ray.submit`
Gradio UI	`python -m cosmos_framework.inference.ray.gradio`
Prompt upsampling	`python -m cosmos_framework.scripts.upsample_prompts`
Model export (HF)	`python -m cosmos_framework.scripts.export_model`
DCP conversion	`python -m cosmos_framework.scripts.convert_model_to_dcp`
Diffusers conversion	`python -m cosmos_framework.scripts.convert_model_to_diffusers`
Video captioning	`python -m cosmos_framework.scripts.caption_from_video`
Captions → SFT JSONL	`python -m cosmos_framework.scripts.captions_to_sft_jsonl`
Action policy server (LIBERO HTTP)	`python -m cosmos_framework.scripts.action_policy_server_libero`
Action policy server (RoboLab WS)	`python -m cosmos_framework.scripts.action_policy_server_robolab`

Documentation

Doc	Covers
`../../../AGENTS.md`	Commands, rules, key file locations (read this first)
`../../../README.md`	Overview, quickstart, examples
`../../../docs/setup.md`	Installation, environment, checkpoints
`../../../docs/code_structure.md`	Repo layout and per-subpackage tour of `cosmos_framework/`
`../../../docs/inference.md`	Sample args, default values, custom defaults
`../../../docs/training.md`	SFT / post-training workflow
`../../../docs/faq.md`	FAQ, tips, and troubleshooting