lyraDiff: An Out-of-the-box Acceleration Engine for Diffusion and DiT Models

lyraDiff introduces a recompilation-free inference engine for Diffusion and DiT models, achieving state-of-the-art speed, extensive model support, and pixel-level image consistency.

Highlights

State-of-the-art Inference Speed: lyraDiff utilizes multiple techniques to achieve up to 6.1x speedup of the model inference, including Quantization, Fused GEMM Kernels, Flash Attention, and NHWC & Fused GroupNorm.
Memory Efficiency: lyraDiff utilizes buffer-based DRAM reuse strategy and multiple types of quantizations (FP8/INT8/INT4) to save 10-40% of DRAM usage.
Extensive Model Support: lyraDiff supports a wide range of top Generative/SR models such as SD1.5, SDXL, FLUX, S3Diff, etc., and those most commonly used plugins such as LoRA, ControlNet and Ip-Adapter.
Zero Compilation Deployment: Unlike TensorRT or AITemplate, which takes minutes to compile, lyraDiff eliminates runtime recompilation overhead even with model inputs of dynamic shapes.
Image Gen Consistency: The outputs of lyraDiff are aligned with the ones of HF diffusers at the pixel level, even under LoRA switch in quantization mode.
Fast Plugin Hot-swap: lyraDiff provides Super Fast Model Hot-swap for ControlNet and LoRA which can hugely benefit a real-time image gen service.

Usage

lyraDiff-IP-Adapters is converted from the standard IP-Adapter weights using this script to be compatiable with lyraDiff, and contains both SD1.5 and SDXL version of converted IP-Adapter

We provide a reference implementation of lyraDiff version of SD1.5/SDXL, as well as sampling code, in a dedicated github repository.

Example

We provide minimal script for running SDXL models + IP-Adapter with lyraDiff as follows:

import torch
import time
import sys, os
from diffusers import StableDiffusionXLPipeline
from lyradiff.lyradiff_model.module.lyradiff_ip_adapter import LyraIPAdapter
from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
from lyradiff.lyradiff_model.lyradiff_unet_model import LyraDiffUNet2DConditionModel
from lyradiff.lyradiff_model.lyradiff_vae_model import LyraDiffVaeModel
from diffusers import EulerAncestralDiscreteScheduler
from PIL import Image
from diffusers.utils import load_image
import GPUtil

model_path = "/path/to/sdxl/model/"
vae_model_path = "/path/to/sdxl/sdxl-vae-fp16-fix"

text_encoder = CLIPTextModel.from_pretrained(model_path, subfolder="text_encoder").to(torch.float16).to(torch.device("cuda"))
text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(model_path, subfolder="text_encoder_2").to(torch.float16).to(torch.device("cuda"))
tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
tokenizer_2 = CLIPTokenizer.from_pretrained( model_path, subfolder="tokenizer_2")

unet = LyraDiffUNet2DConditionModel(is_sdxl=True)
vae = LyraDiffVaeModel(scaling_factor=0.13025, is_upcast=False)

unet.load_from_diffusers_model(os.path.join(model_path, "unet"))
vae.load_from_diffusers_model(vae_model_path)

scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler", timestep_spacing="linspace")

pipe = StableDiffusionXLPipeline(
    vae=vae,
    unet=unet,
    text_encoder=text_encoder,
    text_encoder_2=text_encoder_2,
    tokenizer=tokenizer,
    tokenizer_2=tokenizer_2,
    scheduler=scheduler
)

ip_ckpt = "/path/to/sdxl/ip_ckpt/ip-adapter-plus_sdxl_vit-h.bin"
image_encoder_path = "/path/to/sdxl/ip_ckpt/image_encoder"

# Create LyraIPAdapter
ip_adapter = LyraIPAdapter(unet_model=unet.model, sdxl=True, device=torch.device("cuda"), ip_ckpt=ip_ckpt, ip_plus=True, image_encoder_path=image_encoder_path, num_ip_tokens=16, ip_projection_dim=1024)

# load ip_adapter image
ip_image = load_image("https://cdn-uploads.huggingface.co/production/uploads/6461b412846a6c8c8305319d/8U6yNHTPLaOC3gIWJZWGL.png")
ip_scale = 0.5

# get ip image embedding and pass it to the pipeline
ip_image_embedding = [ip_adapter.get_image_embeds_lyradiff(ip_image)['ip_hidden_states']]
# unet set ip adapter scale in unet model obj, since we cannot set ip_adapter_scale through diffusers pipeline
unet.set_ip_adapter_scale(ip_scale)

for i in range(3):
    generator = torch.Generator("cuda").manual_seed(123)
    start = time.perf_counter()
    images = pipe(prompt="a beautiful girl, cartoon style",
                   height=1024,
                   width=1024,
                   num_inference_steps=20,
                   num_images_per_prompt=1,
                   guidance_scale=7.5,
                   negative_prompt="NSFW",
                   generator=torch.Generator("cuda").manual_seed(123),
                   ip_adapter_image_embeds=ip_image_embedding
                   )[0]
    images[0].save(f"sdxl_ip_{i}.png")

Citation

@Misc{lyraDiff_2025,
  author =       {Kangjian Wu, Zhengtao Wang, Yibo Lu, Haoxiong Su, Sa Xiao, Qiwen Mao, Mian Peng, Bin Wu, Wenjiang Zhou},
  title =        {lyraDiff: Accelerating Diffusion Models with best flexibility},
  howpublished = {\url{https://github.com/TMElyralab/lyraDiff}},
  year =         {2025}
}