README.md

metadata

library_name: diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
  - text-to-image
license: apache-2.0
inference: false

Sub-path Linear Approximation Model (SLAM) LoRA: SDXL

Paper: https://arxiv.org/abs/2404.13903
Project Page: https://subpath-linear-approx-model.github.io/
The checkpoint is a distilled from stabilityai/stable-diffusion-xl-base-1.0 with our proposed Sub-path Linear Approximation Model, which reduces the number of inference steps to only between 2-4 steps.

Usage

First, install the latest version of the Diffusers library as well as peft, accelerate and transformers.

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

We implement SLAM to be compatible with LCMScheduler. You can use SLAM-LoRA just like you use LCM-LoRA.

import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image

model_id = "stabilityai/stable-diffusion-xl-base-1.0"
adapter_id = "alimama-creative/slam-lora-sdxl"

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

# load and fuse lcm lora
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()

prompt = "A brown teddy bear holding a glass vase in front of a grave."

image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=1.0).images[0]

Compare with latent-consistency/lcm-lora-sdxl.

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More examples: