DynamicVRAM's on-demand model loading/offloading conflicted with process isolation in three ways: RPC tensor transport stalls from mid-call GPU offload, race conditions between model lifecycle and active RPC operations, and false positive memory leak detection from changed finalizer patterns.
- Marshal CUDA tensors to CPU before RPC transport for dynamic models
- Add operation state tracking + quiescence waits at workflow boundaries
- Distinguish proxy reference release from actual leaks in cleanup_models_gc
- Fix init order: DynamicVRAM must initialize before isolation proxies
- Add RPC timeouts to prevent indefinite hangs on model unavailability
- Prevent proxy-of-proxy chains from DynamicVRAM model reload cycles
- Add torch.device/torch.dtype serializers for new DynamicVRAM RPC paths
- Guard isolation overhead so non-isolated workflows are unaffected
- Migrate env var to PYISOLATE_CHILD
* Add MaHiRo (improved CFG)
long explanation of what it is is [here](https://huggingface.co/spaces/yoinked/blue-arxiv) (2024-1208.1)
note: if the node name has encoding issues (utf 8/whatever), id suggest to replace the face at the end with `(>w<)`
* add it to nodes.py, add description, and make it a post_cfg function
* fix
* revert the sampler_cfg_function thing
* switch cfg to args["denoised"]
