sd-community/sdxl-flash-lora

[SDXL Flash](https://huggingface.co/sd-community/sdxl-flash) with LoRA in collaboration with [Project Fluently](https://hf.co/fluently)

Introducing the new fast model SDXL Flash, we learned that all fast XL models work fast, but the quality decreases, and we also made a fast model, but it is not as fast as LCM, Turbo, Lightning and Hyper, but the quality is higher. Below you will see the study with steps and cfg.

--> Work with LoRA <--

• —Trigger word:

  <lora:sdxl-flash-lora:0.55>

• —Optimal LoRA multiplier: 0.45-0.6 (the best - 0.55)
• —Optimal base model: fluently/Fluently-XL-v4

Steps and CFG (Guidance)

Optimal settings

• —Steps: 6-9
• —CFG Scale: 2.5-3.5
• —Sampler: DPM++ SDE

Diffusers usage

pip install torch diffusers

import torch
from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
# Load model.
pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16).to("cuda")
# Ensure sampler uses "trailing" timesteps.
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# Image generation.
pipe("a happy dog, sunny day, realism", num_inference_steps=7, guidance_scale=3).images[0].save("output.png")