T-GATE
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 torch 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.
Accelerate `PixArtAlphaPipeline` with T-GATE:
```py
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(
pipe,
gate_step=gate_step,
num_inference_steps=inference_step,
).to("cuda")
image = pipe.tgate(
"An alpaca made of colorful building blocks, cyberpunk.",
gate_step=gate_step,
num_inference_steps=inference_step,
).images[0]
```
Accelerate `StableDiffusionXLPipeline` with T-GATE:
```py
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import DPMSolverMultistepScheduler
from tgate import TgateSDXLLoader
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
gate_step = 10
inference_step = 25
pipe = TgateSDXLLoader(
pipe,
gate_step=gate_step,
num_inference_steps=inference_step,
).to("cuda")
image = pipe.tgate(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
gate_step=gate_step,
num_inference_steps=inference_step
).images[0]
```
Accelerate `StableDiffusionXLPipeline` with [DeepCache](https://github.com/horseee/DeepCache) and T-GATE:
```py
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import DPMSolverMultistepScheduler
from tgate import TgateSDXLDeepCacheLoader
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
gate_step = 10
inference_step = 25
pipe = TgateSDXLDeepCacheLoader(
pipe,
cache_interval=3,
cache_branch_id=0,
).to("cuda")
image = pipe.tgate(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
gate_step=gate_step,
num_inference_steps=inference_step
).images[0]
```
Accelerate `latent-consistency/lcm-sdxl` with T-GATE:
```py
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import UNet2DConditionModel, LCMScheduler
from diffusers import DPMSolverMultistepScheduler
from tgate import TgateSDXLLoader
unet = UNet2DConditionModel.from_pretrained(
"latent-consistency/lcm-sdxl",
torch_dtype=torch.float16,
variant="fp16",
)
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
unet=unet,
torch_dtype=torch.float16,
variant="fp16",
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
gate_step = 1
inference_step = 4
pipe = TgateSDXLLoader(
pipe,
gate_step=gate_step,
num_inference_steps=inference_step,
lcm=True
).to("cuda")
image = pipe.tgate(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
gate_step=gate_step,
num_inference_steps=inference_step
).images[0]
```
T-GATE also supports [StableDiffusionPipeline] and PixArt-alpha/PixArt-LCM-XL-2-1024-MS.
Benchmarks
| 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.