2.0 KiB
FunPack Save Refinement Latent
This node saves a latent tensor bundle under a refinement key so FunPack Video Refiner can use it as an optional latent refinement reference on later runs.
Parameters
latent: Plain video latent tensor bundle to save. In LTX audio/video workflows, connect the video output from LTXVSeparateAVLatent, not the audio latent or combined AV latent.
refinement_key: Name of the refinement session. Use the same key in FunPack Video Refiner.
mode: Tokenizer mode namespace. Use the same mode as FunPack Video Refiner.
Outputs
latent: Passthrough latent.
status: Save status with the stored tensor shape.
Notes
The saved latent lives in FunPack's local refinements folder as a PyTorch tensor file. The refiner only uses it when both the key and mode match and the refined_latent output is connected. If the refiner receives a latent and no saved reference exists, it saves the incoming latent automatically. Zero-valued latent positions are treated as intentional and are not refined.
Audio latents and LTX audio/video combined NestedTensor latents are intentionally rejected. Use only the video latent side, then reconnect the refined result to LTXVConcatAVLatent as video_latent. If a separated LTX video latent still reports type: audio, it is accepted when its samples tensor has the 5D video latent shape.
Unintended Usage Boundaries
- Do not connect the sampler's combined AV latent directly to this node.
- Do not connect the audio output from
LTXVSeparateAVLatent. - Do not connect the output of
LTXVConcatAVLatentback into this node. - Do not expect a saved latent to apply across different
refinement_keyormodevalues. - Do not expect an old saved latent shape to resize a new generation. If the current latent shape changes,
FunPack Video Refinerrewrites the reference and passes the current latent through unchanged. - Do not use this node as a general latent cache. It stores the reference used by the Video Refiner and may be replaced by reset/session behavior.