NeuroBrix 0.3.0 — The Video Release

NeuroBrix 0.3.0 — The Video Release

The video generation family is complete: ten video models — text-to-video and image-to-video — validated in all four execution modes (PyTorch sequential/compiled, Triton sequential/compiled), plus Allegro-TI2V at native 720×1280×88-frame resolution. All eleven are published on the NeuroBrix Hub and install with one command:

neurobrix import Wan-AI/Wan2.1-T2V-1.3B
neurobrix run --model Wan2.1-T2V-1.3B --prompt "a red fox walking in a snowy forest" --num-frames 49

Highlights

• Video family (11 hub models): Wan2.1-T2V-1.3B, CogVideoX-2b, SANA-Video-2B-720p, CogVideoX-5b-I2V, Mochi-1-preview, Wan2.1-I2V-14B-480P, Wan2.2-I2V-A14B (dual-denoiser, 28B), Wan2.1-VACE-1.3B, Open-Sora-v2, Allegro, Allegro-TI2V. Image-to-video conditioning via --input-image, classifier-free guidance, deterministic seeded sampling.
• 5D spatio-temporal VAE tiling — native-resolution and long-clip video encode AND decode on a single 32 GB GPU (a 156 GiB encode plans as 24 tiles of ~10.6 GiB), with seamless blending and byte-identical untiled paths.
• Multi-GPU component placement for 14B+ models, including dual-expert lifecycle with one-way expert eviction (Wan2.2's 2×14B denoisers).
• New Triton kernels & engine hardening — max_pool3d, conv3d temporal chunk-streaming under memory pressure, complex-dtype rotary support, bool-op broadcasting, per-branch numerical parity for guided models (0.9999+ correlation vs the PyTorch reference at native scale).
• Scheduler parity — Euler-family float-timestep spacing bit-identical to the reference pipelines; runtime-pure UniPC including flow matching; CogVideoX DDIM variant; inverted-sigma flow support.
• Accuracy & memory fixes across all four execution modes — see the CHANGELOG for the full list.

The audio (11 models), LLM, image, multimodal, and upscaler families from 0.2.1 are unchanged and re-verified.

Install: pip install neurobrix==0.3.0 · Docs: https://neurobrix.es/docs · Hub: https://neurobrix.es