Unverified50% confidenceSolutionExact time
Calling gc.collect() after processing each asset forces Python's garbage collection to trigger, promptly freeing objects no longer in use and effectively lowering peak memory.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCalling gc.collect() once after processing each asset can actively reclaim circular references and avoid memory buildup; on a 2GB machine, actively reclaiming once per processed item can turn the memory curve from a sawtooth buildup into a flat staircase80% similarUnverifiedOn a 1 vCPU / 2 GB instance, not explicitly calling gc.collect() may cause Python's cyclic-reference garbage collection to lag, with peak memory exceeding 2 GB and being terminated by the OOM Killer.71% similarUnverifiedComfyUI provides a /free endpoint that can actively unload loaded model weights, clear cached tensors in VRAM, and trigger Python garbage collection.71% similarUnverifiedOn a machine with 2GB of memory, running multiple parallel processes for image tasks reduces efficiency due to resource contention; calling gc.collect() after processing each asset helps free memory between assets.69% similarUnverifiedFrequent or large memory allocations trigger garbage collection (GC), and in lightweight environments this overhead is amplified manifold.68% similar
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