library_name: diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- text-to-image
license: openrail++
inference: false
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 🤗 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
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=8.0).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