Instructions to use RockTalk/Lance-3B-Video-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RockTalk/Lance-3B-Video-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Lance-3B-Video-MLX RockTalk/Lance-3B-Video-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
Companion repo link returns 404; usage snippet doesn't run standalone
Hi RockTalk, thanks for shipping the first MLX port of Lance and publishing the bf16 checkpoint β great work.
Two notes for users following the README:
The companion repo link returns HTTP 404. https://github.com/RockTalk/Lance-MLX (and the embedded reference to tools/lance_t2i.py) is unreachable. Is it private, moved, or pending a public release?
Without it, the Usage snippet doesn't run. The imports
from lance_mlx.lance import Lance, LanceConfig
from lance_mlx.vae_wan22 import Wan2_2_VAE
can't resolve (and there's no lance-mlx package on PyPI β pypi.org/pypi/lance-mlx/json 404s). The snippet also references undefined symbols (lance_cfg, text_ids), and a comment in the snippet itself says "see tools/lance_t2i.py in the companion repo for the full builder" β but that file lives in the 404'd repo. Net effect: anyone trying to use the checkpoint from this HF page alone hits an ImportError on line 2.
The same issue affects the image variant at RockTalk/Lance-3B-MLX (its README references the same companion repo).
For context: I'm porting Lance to MLX in parallel at mlx-community/Lance-3B-Video-bf16 (Apache-2.0). I verified your weights are numerically equivalent to ours β remapping your F32 keys into our layout + casting to bf16 produces byte-identical pixel output through our pipeline. So the checkpoint itself is verified β
; it's just hard to load standalone right now.
Your published ocean-wave sample at 256Β² Γ 9f (24 steps, CFG=4) is also reproducible through our pipeline at the same config β looks comparable to your sample. Happy to share that grid if useful.
If the companion repo is intended to be open and just temporarily missing β no rush. If publishing it is on your roadmap, would you consider either:
- publishing a minimal
pip installable package (even a tarball release would help), or - dropping an end-to-end
inference.pydirectly in this HF repo?
Either would unblock community use. Thanks again!