FLUX.1 [schnell] FP8 vs FLUX.2 [klein] 4B
Head-to-head across 1 challenge
FLUX.1 [schnell] FP8
0.0%
win rate
Ties
0.0%
FLUX.2 [klein] 4B
100.0%
win rate
Challenge Results
The Halloween Invitation
Text-to-Image“Vintage gothic Halloween party invitation. Dark parchment poster, spooky border with webs and thorns, central glowing jack-o-lantern, bats, twisted trees, moody night sky. Add elegant gothic title text saying "Halloween Party Invitation", a small scroll banner saying "You are invited to a night of frights", and event details at the bottom: Date: 30.10.2026 Time: 7pm Location: The Arches, NYC Spooky but polished, cinematic lighting, square format.”
AI Judge Analysis
FLUX.1 [schnell] FP8
- + Features a vibrant glowing jack-o-lantern
- + Composition feels balanced and neat
- − Significant spelling errors throughout the event details and banner
- − Border looks like brown fabric rather than thorny parchment
- − Background lacks detail and depth
FLUX.2 [klein] 4B
- + Excellent atmospheric lighting and moody sky
- + Intricate thorny and webbed border matches the prompt perfectly
- + Superior vintage gothic aesthetic with detailed twisted trees
- − Primary title text contains several spelling errors
- − Minor typos in the banner and date
Verdict: FLUX.2 [klein] 4B captures the 'vintage gothic' and 'spooky but polished' aesthetic much more effectively with its atmospheric background and detailed thorny border. While both models struggled with some text accuracy, FLUX.1 [schnell] FP8 produced severe gibberish in the bottom half of the image, making the invitation unusable.
FLUX.1 [schnell] FP8
FP8 quantized variant of Black Forest Labs' FLUX.1 [schnell] model, offering ~2x faster inference with reduced precision while maintaining high-quality image generation in 4 steps
FLUX.2 [klein] 4B
Black Forest Labs' compact, open-source image generation model with sub-second inference, optimized for production and near real-time applications with multi-reference support