Latent Consistency Model (LCM): SSD-1B
Latent Consistency Model (LCM) was proposed in Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference by Simian Luo, Yiqin Tan et al. and Simian Luo, Suraj Patil, and Daniel Gu succesfully applied the same approach to create LCM for SDXL.
This checkpoint is a LCM distilled version of segmind/SSD-1B
that allows
to reduce the number of inference steps to only between 2 - 8 steps.
Usage
LCM SDXL is supported in π€ 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
Text-to-Image
The model can be loaded with it's base pipeline segmind/SSD-1B
. Next, the scheduler needs to be changed to LCMScheduler
and we can reduce the number of inference steps to just 2 to 8 steps.
from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
import torch
unet = UNet2DConditionModel.from_pretrained("latent-consistency/lcm-ssd-1b", torch_dtype=torch.float16, variant="fp16")
pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", unet=unet, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
prompt = "a close-up picture of an old man standing in the rain"
image = pipe(prompt, num_inference_steps=4, guidance_scale=1.0).images[0]
Image-to-Image
Works as well! TODO docs
Inpainting
Works as well! TODO docs
ControlNet
Works as well! TODO docs
T2I Adapter
Works as well! TODO docs
Speed Benchmark
TODO
Training
TODO
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Base model
stabilityai/stable-diffusion-xl-base-1.0