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I managed to shave off inference timings for SD2.1 by a few seconds for 512x512 (50 steps) and 768x768 (50 Steps).
512x512
768x768
Using just few additions:
torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True pipe = StableDiffusionPipeline.from_pretrained( MODEL_ID, cache_dir=MODEL_CACHE, local_files_only=True, ) pipe = pipe.to("cuda") pipe.enable_xformers_memory_efficient_attention() pipe.enable_vae_slicing()
Overall output didn't suffer coz of this. Getting crisp images. Wanted to know how do I create a PR to add these? And are there any tests around this?
Here are the inferences:
The text was updated successfully, but these errors were encountered:
Model in question: https://replicate.com/pratos/stable-diffusion-2-1-512
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I managed to shave off inference timings for SD2.1 by a few seconds for
512x512
(50 steps) and768x768
(50 Steps).Using just few additions:
Overall output didn't suffer coz of this. Getting crisp images. Wanted to know how do I create a PR to add these? And are there any tests around this?
Here are the inferences:
The text was updated successfully, but these errors were encountered: