Add curator-to-SFT JSONL converter#62
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Add cosmos_framework.scripts.curator_to_sft_jsonl, which converts cosmos-curator splitting-pipeline metas_jsonl output directly into the SFT training JSONL format, applying the same hard filters sft_dataset.py applies silently at train time (duration > 61.0s, per-window frames < 61, optional short-edge) so dataset counts match what training consumes. Emits a sidecar <output>.summary.json with per-reason drop counts and rewrites vision_path relative to the JSONL so datasets stay portable across mounts. Document the path as a new "Create Dataset from a Cosmos-Curator output directory" section in docs/dataset_jsonl.md. Ported from imaginaire4 MR 9217: cosmos3.scripts -> cosmos_framework.scripts, OSS SPDX header, and stale sft_dataset.py line refs corrected to 548-550. Verified: 24/24 tests pass, ruff check/format clean, CLI --help imports. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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…ge (#68) ## Summary Ports the control-input CFG feature (from i4 commit `f11349b`) into the transfer inference path, reconciling with logic already synced into this repo, and adds CD smoke-test coverage for transfer inference. - **`omni_mot_model.py`** already carries the `velocity_postprocess_builder` hook — no model change needed. - **`transfer.py`**: add `_build_no_control_inference_state` and `build_control_cfg_postprocess`, wired through `generate_samples_from_batch` via `velocity_postprocess_builder`. Previously `transfer.py` passed `control_guidance`/`control_guidance_interval` directly, where they were silently dropped by `**kwargs` (control-CFG was a no-op). - **`args.py`**: add `emphasize_control_in_prompt` (`TransferDataArgs`/`Overrides` + `_TRANSFER_SAMPLE_DEFAULTS`) to match the ported prompt-emphasis logic. ## Test coverage Extends `tests/nano_inference_smoke_test.py` (the `generator-inference-smoke` CD job) to also run a `video2video` edge transfer with `control_guidance=1.5` in the same Nano inference call: - Spec is built inline (`_TRANSFER_SPEC`, written to a temp file — not committed under `inputs/`), pulling the control video from the public `NVIDIA/cosmos` GitHub raw URL (same file the cookbook `edge.json` uses), downscaled for a fast smoke run (480p / 10 steps / single 29-frame chunk). - Validates transfer-specific attributes (edge `control_path`, `control_guidance > 1`, `guidance > 1`) and a non-degenerate output clip via a new `_assert_video_has_content` helper (frame count + pixel variation). ## Verification Verified end-to-end on a GB200 node with this repo's `.venv`: - README Nano edge transfer (`control_guidance=1.5`) → valid 121-frame 720p video; `emphasize_control_in_prompt` and the control-CFG path both run. - Inline smoke spec → `status: success`, `control_guidance=1.5`, `guidance=3.0`, non-degenerate output (`frames=29`, pixel std ≈ 68). > Note: the branch name predates this work; after rebase it carries only the transfer commit (the curator-to-SFT converter already merged as #62). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Add cosmos_framework.scripts.curator_to_sft_jsonl, which converts cosmos-curator splitting-pipeline metas_jsonl output directly into the SFT training JSONL format, applying the same hard filters sft_dataset.py applies silently at train time (duration > 61.0s, per-window frames < 61, optional short-edge) so dataset counts match what training consumes. Emits a sidecar .summary.json with per-reason drop counts and rewrites vision_path relative to the JSONL so datasets stay portable across mounts.
Document the path as a new "Create Dataset from a Cosmos-Curator output directory" section in docs/dataset_jsonl.md.
Ported from imaginaire4 MR 9217: cosmos3.scripts -> cosmos_framework.scripts, OSS SPDX header, and stale sft_dataset.py line refs corrected to 548-550.
Verified: 24/24 tests pass, ruff check/format clean, CLI --help imports.
from MR 9217