video.py already knew the hazard and wrote it down: accelerate's device_map
only dispatches components it LOADS, so one handed to from_pretrained keeps
whatever device it is on with no offload hook, and the forward pass hits
"index on cuda:0, weights on cpu". It acted on that only for pipeline-cache
injections, while a GGUF component arrives exactly the same way.

LTX-2.5 therefore failed under every strategy it tried -- auto, balanced,
model and pure-disk, three restarts and half an hour each -- always with

    Expected all tensors to be on the same device, but got index is on
    cuda:0, different from other tensors on cpu

and video_resident_experts, the flag that exists for precisely this, was
never consulted because the gate in front of it was false.

An explicit offload_strategy is still respected; only the strategies that let
accelerate place components itself are overridden.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014T3rZnLhydX9QqUcfBbqLu