by ByteDance
Progressive distillation of SDXL from ByteDance. Available in 1-step, 2-step, 4-step, and 8-step variants via LoRA or full UNet checkpoints. Achieves near SDXL quality in as few as 2-4 steps — significantly faster than SDXL's standard 25-50 steps.
VRAM requirements, GPU fit, and setup notes for SDXL Lightning, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Diffusers support. Best download size: ~6.9 GB at FP16.
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Measured quality metrics for SDXL Lightning outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
Compare which GPUs can run SDXL Lightning at different precisions. FP8 uses less memory than FP16 when available, and the grade shows how comfortably each GPU handles the workload.
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 7.1 GB | S● | S● | A● | S● |
| 768×768 | 7.3 GB | S● | S● | A● | S● |
| 1024×1024 | 7.5 GB | S● | S● | B● | S● |
ControlNets available
Add guided generation with 3 adapters (+1.2 GB VRAM each)
Rich LoRA ecosystem
Customize style, characters, and quality with community LoRAs
from diffusers import StableDiffusionXLPipeline
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"ByteDance/SDXL-Lightning",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=4,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running SDXL Lightning locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyVRAM allocation at 1024×1024 on RTX 4090 24GB (24 GB)
Time per image at 1024×1024, 28 steps, FP16.
Download SDXL Lightning in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
| Format | Precision | Size | Provider | |
|---|---|---|---|---|
| safetensorsRecommended | FP16 | 6.9 GB | official | Download |
3 ControlNets available for SDXL Lightning. ControlNets add guided image generation (edges, depth, pose) at the cost of extra VRAM.
Edge detection for structural guidance. Full SDXL ControlNet compatibility.
Depth-based spatial control for maintaining 3D composition.
Human pose control for character positioning and body language.
Full SDXL LoRA ecosystem compatible. Lightning itself available as LoRA (200MB) for use with any SDXL checkpoint.
Browse all LoRAs on CivitAIFrequently asked questions
SDXL Lightning (2.6B parameters) requires approximately 7.5 GB of VRAM at FP16 precision for standard 1024×1024 image generation. If you want a lighter setup, lower precisions like FP8 can reduce memory use when available.
SDXL Lightning usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.
SDXL Lightning is marked for ComfyUI and Diffusers support in our catalog, so those are the runtimes we recommend first for local setup. If your workflow uses another front end, check the model's available formats and workflow notes above before downloading.
Yes, the RTX 4090 (24 GB VRAM) can run SDXL Lightning comfortably at FP16. Expected generation time is around ~1.5s per image at 1024×1024.
Yes, SDXL Lightning has 3 ControlNet adapters available: Canny Edge, Depth Map, OpenPose. Each ControlNet adds roughly 1.2 GB of extra VRAM.
Full SDXL LoRA ecosystem compatible. Lightning itself available as LoRA (200MB) for use with any SDXL checkpoint. The LoRA ecosystem for SDXL Lightning is rated as "large". Each LoRA adds roughly 0.2 GB of extra VRAM.
On a reference GPU (RTX 4090 24GB), SDXL Lightning generates a 1024×1024 image in approximately ~1.5s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
See also