fix: Resolve first-run CUDA OOM and optimize VRAM usage during multi-GPU cloning

Critical memory optimization for Parallel Anything node to prevent CUDA out-of-memory
errors on first execution and improve stability across multi-GPU setups.

Problem:

• First-run OOM errors occurred due to CUDA memory fragmentation and holding
  duplicate model copies (CPU + GPU) simultaneously during cloning
• No handling for devices that failed during cloning due to insufficient VRAM
• Original model restoration caused temporary 2x VRAM usage spikes

Solution:

• Skip cloning when target device matches source device (use reference)
• Incremental state_dict loading to minimize peak memory usage
• Aggressive ComfyUI model cache unloading before cloning operations
• Progressive cleanup between device clones with synchronized CUDA operations
• Graceful degradation: skip OOM devices and redistribute workload

Technical Changes:

• Added aggressive_cleanup() helper for forced GC and CUDA cache clearing
• Modified safe_model_clone() with incremental parameter loading and
  early-exit if model already on target device
• Updated setup_parallel() to use reference counting for original device
• Implemented successful_devices tracking for OOM fallback handling
• Added periodic torch.cuda.empty_cache() during large model transfers
• Enhanced error handling with device-level try/except blocks
• Fixed state dict parsing for nested modules in incremental loading

Memory optimizations:

• Unload ComfyUI models (unload_all_models) before cloning
• Delete state_dict entries immediately after transfer
• Force CPU transition only when necessary (not when reusing original)
• Synchronize CUDA devices before/after memory operations

Breaking Changes: None
Backwards Compatible: Yes