Memory Allocator

Combination of OS default memory allocator malloc with Python's default memory allocator is pessimistic when it comes to system memory garbage collection and it will sometimes hold on to allocated memory longer than necessary even if GC is triggered explicitly
This appears to user as a memory leak as process memory usage grows over time
This is especially noticeable when frequently loading/unloading large objects such as models or LoRAs

Note

This applies to system memory only and has no impact on GPU memory management

Linux

Tip

For Linux deployments you can switch out memory allocator to tcmalloc, jemalloc or mimalloc
which are more efficient and have better memory management

tcmalloc

sudo apt install google-perftools  
sudo ldconfig  
export LD_PRELOAD=libtcmalloc.so.4  
./webui.sh --debug

jemalloc

sudo apt install libjemalloc2
sudo ldconfig  
export LD_PRELOAD=libjemalloc.so.2  
./webui.sh --debug

mimalloc

sudo apt install libmimalloc2.0
sudo ldconfig  
export LD_PRELOAD=libmimalloc.so.2  
./webui.sh --debug

Windows

Tip

For Windows deployments you can switch out memory allocator to mimalloc
which is more efficient and has better memory management

mimalloc

mimalloc for windows requires custom compiled library
Full instructions can be found here: https://microsoft.github.io/mimalloc/index.html

Compare

Using Ubuntu 24.04 and after executing 10 batches of 1024px images using SDXL model

manager	reserved memory	note
malloc	9345 MB	baseline
tcmaloc	8423 MB	very performant and stable over long runs
jemalloc	5468 MB	best memory savings
mimalloc	6132 MB	new contender

Note

Results will vary based on system and usage pattern
What matters is that memory allocator is can intelligently free up memory that is marked as available by SD.Next over time
while maintaining low memory fragmentation and allowing low-latency allocations
For example, when unloading LoRAs or switching models