Introduce tiled_scale_multidim_multigpu in comfy/utils.py: a tile scheduler
that dispatches per-device tile functions through the existing
MultiGPUThreadPool and merges per-device CPU output buffers in deterministic
key order. The worker only catches BaseException at the thread boundary to
funnel errors to the main thread; bare torch.cuda.set_device and
torch.cuda.synchronize calls inside the worker fail loud if the device is
not CUDA, which is part of the primitive's contract.
Add UPSCALE_MODEL input on the MultiGPU CFG Split node and an upscale-model
descriptor deepclone helper in comfy/multigpu.py. Clones stay CPU-resident
until execute time and are returned to CPU afterward.
ImageUpscaleWithModel dispatches through tiled_scale_multidim_multigpu when
a multigpu descriptor is attached; the single-device path runs unchanged
when no clones are present.
create_multigpu_deepclones cloned the existing 'multigpu' additional_models list verbatim and never pruned entries beyond limit_extra_devices. If a workflow was previously prepared for more GPUs, reducing max_gpus would leave stale clones attached and eligible for later scheduling. Replace the TODO block with a real prune that keeps only clones whose load_device is either the model's load_device or in limit_extra_devices, and re-match clones if anything was removed.
Amp-Thread-ID: https://ampcode.com/threads/T-019e43b8-8258-70fd-ab3a-53e4c97f85d5
Co-authored-by: Amp <amp@ampcode.com>
Replace per-step thread create/destroy in _calc_cond_batch_multigpu with a
persistent MultiGPUThreadPool. Each worker thread calls torch.cuda.set_device()
once at startup, preserving compiled kernel caches across diffusion steps.
- Add MultiGPUThreadPool class in comfy/multigpu.py
- Create pool in CFGGuider.outer_sample(), shut down in finally block
- Main thread handles its own device batch directly for zero overhead
- Falls back to sequential execution if no pool is available
