Speed up inference

There are several ways to optimize Diffusers for inference speed, such as reducing the computational burden by lowering the data precision or using a lightweight distilled model. There are also memory-efficient attention implementations, xFormers and scaled dot product attention in PyTorch 2.0, that reduce memory usage which also indirectly speeds up inference. Different speed optimizations can be stacked together to get the fastest inference times.

The inference times below are obtained from generating a single 512x512 image from the prompt “a photo of an astronaut riding a horse on mars” with 50 DDIM steps on a NVIDIA A100.

TensorFloat-32

On Ampere and later CUDA devices, matrix multiplications and convolutions can use the TensorFloat-32 (tf32) mode for faster, but slightly less accurate computations. By default, PyTorch enables tf32 mode for convolutions but not matrix multiplications. Unless your network requires full float32 precision, we recommend enabling tf32 for matrix multiplications. It can significantly speed up computations with typically negligible loss in numerical accuracy.

import torch

torch.backends.cuda.matmul.allow_tf32 = True

Learn more about tf32 in the Mixed precision training guide.

Half-precision weights

To save GPU memory and get more speed, set torch_dtype=torch.float16 to load and run the model weights directly with half-precision weights.

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    torch_dtype=torch.float16,
    use_safetensors=True,
)
pipe = pipe.to("cuda")

Distilled model

You could also use a distilled Stable Diffusion model and autoencoder to speed up inference. During distillation, many of the UNet’s residual and attention blocks are shed to reduce the model size by 51% and improve latency on CPU/GPU by 43%. The distilled model is faster and uses less memory while generating images of comparable quality to the full Stable Diffusion model.

The inference times below are obtained from generating 4 images from the prompt “a photo of an astronaut riding a horse on mars” with 25 PNDM steps on a NVIDIA A100. Each generation is repeated 3 times with the distilled Stable Diffusion v1.4 model by Nota AI.

Let’s load the distilled Stable Diffusion model and compare it against the original Stable Diffusion model.

from diffusers import StableDiffusionPipeline
import torch

distilled = StableDiffusionPipeline.from_pretrained(
    "nota-ai/bk-sdm-small", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
prompt = "a golden vase with different flowers"
generator = torch.manual_seed(2023)
image = distilled("a golden vase with different flowers", num_inference_steps=25, generator=generator).images[0]
image

Tiny AutoEncoder

To speed inference up even more, replace the autoencoder with a distilled version of it.

import torch
from diffusers import AutoencoderTiny, StableDiffusionPipeline

distilled = StableDiffusionPipeline.from_pretrained(
    "nota-ai/bk-sdm-small", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
distilled.vae = AutoencoderTiny.from_pretrained(
    "sayakpaul/taesd-diffusers", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")

prompt = "a golden vase with different flowers"
generator = torch.manual_seed(2023)
image = distilled("a golden vase with different flowers", num_inference_steps=25, generator=generator).images[0]
image

More tiny autoencoder models for other Stable Diffusion models, like Stable Diffusion 3, are available from madebyollin.