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 8.18/10 and Stable Diffusion 1.5 has an AI score of 9.18/10. 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 model released by Runway in October 2022, building on the Stable Diffusion project associated with Stability AI and researchers at LMU Munich. It uses latent diffusion, generating images by gradually removing noise from a compressed representation guided by a text prompt. Working in that compressed space helps reduce computing demands compared w...