Optimizations - linwownil/stable-diffusion-webui GitHub Wiki

A number of optimization can be enabled by commandline arguments:

commandline argument	explanation
--xformers	Use xformers library. Great improvement to memory consumption and speed. Windows version installs binaries mainained by C43H66N12O12S2. Will only be enabled on small subset of configuration because that's what we have binaries for. Documentation
--force-enable-xformers	Enables xformers above regardless of whether the program thinks you can run it or not. Do not report bugs you get running this.
--opt-split-attention	Cross attention layer optimization significantly reducing memory use for almost no cost (some report improved performance with it). Black magic. On by default for torch.cuda, which includes both NVidia and AMD cards.
--disable-opt-split-attention	Disables the optimization above.
--opt-sub-quad-attention	Sub-quadratic attention, a memory efficient Cross Attention layer optimization that can significantly reduce required memory, sometimes at a slight performance cost. Recommended if getting poor performance or failed generations with a hardware/software configuration that xformers doesn't work for. On macOS, this will also allow for generation of larger images.
--opt-split-attention-v1	Uses an older version of the optimization above that is not as memory hungry (it will use less VRAM, but will be more limiting in the maximum size of pictures you can make).
--medvram	Makes the Stable Diffusion model consume less VRAM by splitting it into three parts - cond (for transforming text into numerical representation), first_stage (for converting a picture into latent space and back), and unet (for actual denoising of latent space) and making it so that only one is in VRAM at all times, sending others to CPU RAM. Lowers performance, but only by a bit - except if live previews are enabled.
--lowvram	An even more thorough optimization of the above, splitting unet into many modules, and only one module is kept in VRAM. Devastating for performance.
*do-not-batch-cond-uncond	Prevents batching of positive and negative prompts during sampling, which essentially lets you run at 0.5 batch size, saving a lot of memory. Decreases performance. Not a command line option, but an optimization implicitly enabled by using --medvram or --lowvram.
--always-batch-cond-uncond	Disables the optimization above. Only makes sense together with --medvram or --lowvram
--opt-channelslast	Changes torch memory type for stable diffusion to channels last. Effects not closely studied.

Extra tips (Windows):

• https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/3889 Disable Hardware GPU scheduling.
• disable browser hardware acceleration
• Go in nvidia control panel, 3d parameters, and change power profile to "maximum performance"

This is an example test using specific hardware and configuration, your mileage may vary
Tested using nVidia RTX3060 and CUDA 11.7

Cross-attention	Peak Memory at Batch size 1/2/4/8/16	Initial It/s	Peak It/s	Note
None	4.1 / 6.2 / OOM / OOM / OOM	4.2	4.6	slow and early out-of-memory
v1	2.8 / 2.8 / 2.8 / 3.1 / 4.1	4.1	4.7	slow but lowest memory usage and does not require sometimes problematic xformers
InvokeAI	3.1 / 4.2 / 6.3 / 6.6 / 7.0	5.5	6.6	almost identical to default optimizer
Doggetx	3.1 / 4.2 / 6.3 / 6.6 / 7.1	5.4	6.6	default
Doggetx	2.2 / 2.7 / 3.8 / 5.9 / 6.2	4.1	6.3	using medvram preset result in decent memory savings without huge performance hit
Doggetx	0.9 / 1.1 / 2.2 / 4.3 / 6.4	1.0	6.3	using lowvram preset is extremely slow due to constant swapping
Xformers	2.8 / 2.8 / 2.8 / 3.1 / 4.1	6.5	7.5	fastest and low memory
Xformers	2.9 / 2.9 / 2.9 / 3.6 / 4.1	6.4	7.6	with cuda_alloc_conf and opt-channelslast

Notes:

• Performance at batch-size 1 is around ~70% of peak performance
• Peak performance is typically around batch size 8
  After that it grows by few percent if you have extra VRAM before it starts to drop due to GC kicking in
• Performance with lowvram preset is very low below batch size 8 and by then memory savings are not that big

Other possible optimizations:

• PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0.9,max_split_size_mb:512
  No performance impact and increases initial memory footprint a bit but reduces memory fragmentation in long runs
• opt-channelslast
  Hit-and-miss: seems like additional slight performance increase with higher batch sizes and slower with small sizes, but differences are within margin-of-error