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T-GATE accelerates inference for Stable Diffusion, PixArt, and Latency Consistency Model pipelines by skipping the cross-attention calculation once it converges. This method doesn’t require any additional training and it can speed up inference from 10-50%. T-GATE is also compatible with other optimization methods like DeepCache.

Before you begin, make sure you install T-GATE.

pip install tgate
pip install -U pytorch diffusers transformers accelerate DeepCache

To use T-GATE with a pipeline, you need to use its corresponding loader.

Pipeline T-GATE Loader
PixArt TgatePixArtLoader
Stable Diffusion XL TgateSDXLLoader
Stable Diffusion XL + DeepCache TgateSDXLDeepCacheLoader
Stable Diffusion TgateSDLoader
Stable Diffusion + DeepCache TgateSDDeepCacheLoader

Next, create a TgateLoader with a pipeline, the gate step (the time step to stop calculating the cross attention), and the number of inference steps. Then call the tgate method on the pipeline with a prompt, gate step, and the number of inference steps.

Let’s see how to enable this for several different pipelines.

Stable Diffusion XL
StableDiffusionXL with DeepCache
Latent Consistency Model

Accelerate PixArtAlphaPipeline with T-GATE:

import torch
from diffusers import PixArtAlphaPipeline
from tgate import TgatePixArtLoader

pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)

gate_step = 8
inference_step = 25
pipe = TgatePixArtLoader(

image = pipe.tgate(
       "An alpaca made of colorful building blocks, cyberpunk.",

T-GATE also supports StableDiffusionPipeline and PixArt-alpha/PixArt-LCM-XL-2-1024-MS.


Model MACs Param Latency Zero-shot 10K-FID on MS-COCO
SD-1.5 16.938T 859.520M 7.032s 23.927
SD-1.5 w/ T-GATE 9.875T 815.557M 4.313s 20.789
SD-2.1 38.041T 865.785M 16.121s 22.609
SD-2.1 w/ T-GATE 22.208T 815.433 M 9.878s 19.940
SD-XL 149.438T 2.570B 53.187s 24.628
SD-XL w/ T-GATE 84.438T 2.024B 27.932s 22.738
Pixart-Alpha 107.031T 611.350M 61.502s 38.669
Pixart-Alpha w/ T-GATE 65.318T 462.585M 37.867s 35.825
DeepCache (SD-XL) 57.888T - 19.931s 23.755
DeepCache w/ T-GATE 43.868T - 14.666s 23.999
LCM (SD-XL) 11.955T 2.570B 3.805s 25.044
LCM w/ T-GATE 11.171T 2.024B 3.533s 25.028
LCM (Pixart-Alpha) 8.563T 611.350M 4.733s 36.086
LCM w/ T-GATE 7.623T 462.585M 4.543s 37.048

The latency is tested on an NVIDIA 1080TI, MACs and Params are calculated with calflops, and the FID is calculated with PytorchFID.

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