SolitaryThinker
455cd83bed
The first real-weights Cosmos3-Nano T2V run produced an all-black video.
Root cause: the checkpoint's diffusers-style scheduler_config.json sets
use_karras_sigmas=true, and FastVideo's vendored UniPC checks karras
*before* use_flow_sigmas, so it built diffusion (beta) sigmas instead of
flow-matching sigmas -> scheduler.step diverged to NaN latents -> black
frames. The DiT/CFG velocity itself was clean.
The framework samples with FlowUniPCMultistepScheduler (pure flow:
shift + num_train_timesteps only). FastVideo's vendored UniPC is the same
algorithm, so the fix is config, not a port:
- Coerce the loaded scheduler to the flow setup in
Cosmos3OmniDiffusersPipeline.initialize_pipeline (kill karras/exp/beta
sigmas, force use_flow_sigmas + flow_prediction + final_sigmas_type=zero)
and rebuild the runtime scheduler from it.
- Use FastVideo's native UniPC (not diffusers) in the pipeline and tests,
honoring the "no diffusers at runtime" constraint.
Parity:
- Add test_cosmos3_scheduler_parity.py: FastVideo vendored UniPC (flow
config) vs the framework FlowUniPCMultistepScheduler. timesteps bit-exact,
sigmas within fp32 epsilon (~1e-8), full multi-step trajectory < ~1e-6
across shift in {10 (video), 3 (t2i)} and steps in {4,10,35}.
- Fix test_cosmos3_denoise_cfg_parity to use the framework scheduler as the
oracle (it previously compared diffusers-vs-diffusers, so the scheduler
was never actually checked against the framework).
Integration fixes to run end-to-end on real weights:
- registry: map checkpoint DiT class Cosmos3OmniTransformer ->
Cosmos3VFMTransformer.
- component_loader: route the "text_tokenizer" module to TokenizerLoader;
filter scheduler config to the class's __init__ params (robust to
diffusers schema drift like shift_terminal/sigma_min/sigma_max).
- DiT: materialize_non_persistent_buffers (recompute rotary_emb.inv_freq
after meta-device FSDP load) + cast vision latents / timestep embeds to
the compute dtype (no-ops in the fp32 parity tests).
- sequence_packing: move packed tensors to the model device in to_dit_kwargs.
- config: drop the unused identity text-preprocess (Cosmos3 has no text
encoder; the DiT tokenizes in the denoise stage).
Adds examples/inference/basic/basic_cosmos3_new_api.py (new VideoGenerator
API). Verified: 1280x704, 29 frames, 35 steps on B200 -> coherent video
matching the prompt (no NaNs, real pixel variance). Full cosmos3 parity
suite green.
Local Tests
Local-only parity, smoke, and component tests for FastVideo model ports. They compare FastVideo against the official reference implementations and are skipped in CI; run them locally on a single GPU (or CPU where noted).
For the CI-backed test suite, see fastvideo/tests/.
Layout
Tests are organized by model family, one directory per port. Each family
directory follows the layout produced by the
add-model-prep skill:
tests/local_tests/<family>/
├── README.md # reference assets, setup, test inventory
├── __init__.py
└── test_<family>_<component>_*.py
Families
| Family | Workload | Dir |
|---|---|---|
| Flux2 | T2I | flux2/ |
| Hunyuan GameCraft | T2V / I2V | gamecraft/ |
| GEN3C | T2V | gen3c/ |
| Kandinsky-5 | T2V | kandinsky5/ |
| LTX-2 | T2V (+ audio) | ltx2/ |
| Stable Diffusion 3.5 | T2I | sd35/ |
| Stable Audio Open 1.0 | T2A | stable_audio/ |
| Wan2.2 | I2V | wan22/ |
Running a family
pytest tests/local_tests/<family>/ -v -s
See each family's README.md for setup (clones, weights, env vars, gated
tokens) and the per-component parity coverage.
Adding a new family
Run the add-model-prep
skill from the FastVideo repo root. It scaffolds the family directory and
README.md and stages reference clones + weights. (In-progress ports may also
add a PORT_STATUS.md from the same skill template; remove it once the port
lands.)