SDXL Flash with LoRA in collaboration with Project 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")
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Model tree for sd-community/sdxl-flash-lora
Base model
stabilityai/stable-diffusion-xl-base-1.0
Finetuned
fluently/Fluently-XL-v4