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Latent Consistency Model (LCM) LoRA: SSD-1B

Latent Consistency Model (LCM) LoRA was proposed in LCM-LoRA: A universal Stable-Diffusion Acceleration Module by Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.

It is a distilled consistency adapter for segmind/SSD-1B that allows to reduce the number of inference steps to only between 2 - 8 steps.

Model Params / M
lcm-lora-sdv1-5 67.5
lcm-lora-ssd-1b 105
lcm-lora-sdxl 197M


LCM-LoRA is supported in πŸ€— Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first install the latest version of the Diffusers library as well as peft, accelerate and transformers. audio dataset from the Hugging Face Hub:

pip install --upgrade pip
pip install --upgrade diffusers transformers accelerate peft


Let's load the base model segmind/SSD-1B first. Next, the scheduler needs to be changed to LCMScheduler and we can reduce the number of inference steps to just 2 to 8 steps. Please make sure to either disable guidance_scale or use values between 1.0 and 2.0.

import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image

model_id = "segmind/SSD-1B"
adapter_id = "latent-consistency/lcm-lora-ssd-1b"

pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

# load and fuse lcm lora

prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

# disable guidance_scale by passing 0
image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]


Works as well! TODO docs


Works as well! TODO docs


Works as well! TODO docs

T2I Adapter

Works as well! TODO docs

Speed Benchmark




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