Three more example folders alongside inference/, all CPU/numpy, self-contained
(sys.path bootstrap), public API only, every script verified to run green.
training/ (7) — one per method, all via the uniform method.train_step seam:
01 finetune · 02 dmd2 distillation · 03 diffusion_nft (likelihood-free RL,
samples from the old policy, feature-cache reuse) · 04 self-forcing (causal
chunk_rollout) · 05 joint LM+generator RL (UniRL; joint + prompt-only) ·
06 N-way joint RL (per_expert vs shared credit) · 07 end-to-end workflow RL
(T2I+I2V from one final-video reward).
omni/ (4) — 01 Cosmos3 (reason→joint denoise, shared MoT) · 02 BAGEL
(text→image, shared MoT; scheduler prices both WorkUnit kinds) · 03 Qwen-Omni
(thinker→talker→vocoder, three separate experts, text+audio) · 04 interleave
parity across AR + diffusion loop types.
workflows/ (2) — 01 cross-model T2I→I2V workflow (image provably conditions the
video) · 02 workflow as a first-class servable (requires/validate, address by
id, register_workflows catalog, WorkflowRegistry).
Each folder has a README indexing its scripts.
Five self-contained, runnable scripts (CPU/numpy) for the Wan2.1-1.3B card on the
v2 runtime, each bootstrapping the repo onto sys.path so they run from anywhere:
- 01_basic_t2v.py minimal path: build engine → T2V request → run → video
- 02_params_and_reproducibility.py DiffusionParams knobs + seeded bit-identical reproducibility
- 03_streaming.py per-denoise-step preview chunks (OutputSpec stream)
- 04_concurrent_interleaved.py step-interleaved batching + interleave parity gate + cache reuse
- 05_async_serving.py AsyncEngine: concurrent generate, event stream, step-boundary cancel
+ README.md indexing them. All five run green; use only the public API.
Peiyuan Zhang