What are the key differences between Stable Diffusion 3.5 and Stable Diffusion 1.5?

Compare Stable Diffusion 3.5 and Stable Diffusion 1.5 side by side on Lunoo to see detailed feature differences, AI scores, and expert analysis.

How are Stable Diffusion 3.5 and Stable Diffusion 1.5 scored?

Stable Diffusion 3.5 has an AI score of 8.2/10 and Stable Diffusion 1.5 has an AI score of 9.2/10. Scores are based on category fit, feature coverage, pricing signals, public reception, and recency.

Stable Diffusion 3.5 vs Stable Diffusion 1.5 2026 - Compared

Stable Diffusion 3.5

Stable Diffusion 1.5

WINNER Stable Diffusion 1.5

Stable Diffusion 1.5 edges ahead with a score of 9.2/10 compared to 8.2/10 for Stable Diffusion 3.5. While both are high...

Stable Diffusion 3.5

8.18 Great

Model Get Stable Diffusion 3.5 open_in_new

emoji_events WINNER

Stable Diffusion 1.5

9.18 Excellent

Model Get Stable Diffusion 1.5 open_in_new

psychology AI Verdict

Stable Diffusion 1.5 edges ahead with a score of 9.2/10 compared to 8.2/10 for Stable Diffusion 3.5. While both are highly rated in their respective fields, Stable Diffusion 1.5 demonstrates a slight advantage in our AI ranking criteria. A detailed AI-powered analysis is being prepared for this comparison.

emoji_events Winner: Stable Diffusion 1.5

verified Confidence: Low

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description Overview

Stable Diffusion 3.5

Stable Diffusion 3.5 is a text-to-image generation model released by Stability AI in 2024 as a refinement of Stable Diffusion 3. The release included multiple size variants, including Large, Large Turbo, and Medium, with publicly available model weights. It was designed to improve prompt adherence, text rendering within images, and overall image quality compared to earlier Stable Diffusion version...

Stable Diffusion 1.5

Stable Diffusion 1.5 is an open-weight text-to-image artificial intelligence model released in October 2022 by Stability AI in collaboration with Runway and LMU Munich. It utilizes a latent diffusion architecture that generates detailed images by gradually denoising a mathematical representation of an input prompt. The model operates efficiently on consumer-grade graphics processing units, which s...