SDXL-Lighting-8-steps

1
—
by
HyHorX
Image Model
OTHER
1.0B params
New
0 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
3GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by SDXL-Lighting-8-steps with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

💻 Example Code (Diffusers)pythonpytorch
import torch
from diffusers import StableDiffusionXLPipeline

pipe = StableDiffusionXLPipeline.from_pretrained(
    "HyHorX/SDXL-Lighting-8-steps", 
    torch_dtype=torch.float16, 
    use_safetensors=True
).to("cuda")

prompt = "A majestic cinematic view of a futuristic city with neon lights, high detail, 8k"

image = pipe(
    prompt=prompt, 
    num_inference_steps=8, 
    guidance_scale=0
).images[0]

image.save("result.png")

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