Stable Diffusion Turbovs.Lumina-Image 2.0

Which one is better?

In our own test, Lumina-Image 2.0 leads. Neither is in the arena. Neither has an API price.

Test images

Same task, the first image of each. The task with the biggest difference is preselected.

Stable Diffusion Turbo: Character and detail, first image from the fixed test run — Stable Diffusion Turbo5 out of 100

Lumina-Image 2.0: Character and detail, first image from the fixed test run — Lumina-Image 2.023 out of 100

Result for “Character and detail”:Lumina-Image 2.0 slightly ahead

Prompt given to both modelsCreate a square cinematic full-body portrait of a fictional bicycle courier waiting under a transparent umbrella at a rainy tram stop in Hamburg at blue hour. She wears a mustard raincoat, navy trousers, red sneakers, a silver helmet, and carries a teal messenger bag. Keep both hands visible, render the bicycle correctly, use realistic wet-street reflections, and include no readable brand names.

What the rating looks at: Anatomy, clothing, and spatial consistency.

“Ahead”: better in all three rating rounds. “Slightly ahead”: a higher value, but not in every round.

Test protocol of the images

Each test attempt uses the same archived prompt and one request. The first output of that attempt counts. Repeats after technical test failures are disclosed for the affected images. Files are normalized to 1,024 × 1,024 pixels without cropping. Provider resolution, randomness, quality tier and prompt processing can still differ. The images are a practical visual test, not a reconstruction of the arena configuration.

Character and detail

Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=5483236571890770311; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE; native tokenizer truncation: tokenizer 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 6:17 PM. Source

Lumina-Image 2.0: fal, requested 1024x1024, n=1, seed=provider random, estimated cost $0.150000, generated Nov 1, 2026, 9:38 PM. Source

Protocol gradually-image-comparison-v1. Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6

Stable Diffusion Turbo image SHA 256: 291b69ba3d375bfa017c782f61521b705d67708ed5e1be9350d55d486ad6f1d1

Lumina-Image 2.0 image SHA 256: 8747df0401d91598486735bfff5d6bac6c139727a8bc50c60bf7f697f678b85f

Infographic

Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=8161577731918994504; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:18 PM. Source

Lumina-Image 2.0: fal, requested 1024x1024, n=1, seed=provider random, estimated cost $0.150000, generated Nov 1, 2026, 9:38 PM. Source

Protocol gradually-image-comparison-v1. Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f

Stable Diffusion Turbo image SHA 256: 80a2687dbc408395b416994d4853332427d09fccead1a6229564cc25b32de7e6

Lumina-Image 2.0 image SHA 256: b937b28f3c3209280c5828e18c85341d3ca443175020ca1e407dae035b5df14f

Product photography

Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=2124044582169182899; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE; native tokenizer truncation: tokenizer 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 6:16 PM. Source

Lumina-Image 2.0: fal, requested 1024x1024, n=1, seed=provider random, estimated cost $0.150000, generated Nov 1, 2026, 9:38 PM. Source

Protocol gradually-image-comparison-v1. Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3

Stable Diffusion Turbo image SHA 256: 13dcbe6eb77495317a03f3eed013c4358ae7466fca00d9f869e512279a8a2af6

Lumina-Image 2.0 image SHA 256: 74b79fcbad7565e29995c941d2b488d2edde884b307ed99e47fad994424e9dfc

Typography and layout

Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=2303175353176269810; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:16 PM. Source

Lumina-Image 2.0: fal, requested 1024x1024, n=1, seed=provider random, estimated cost $0.150000, generated Nov 1, 2026, 9:37 PM. Source

Protocol gradually-image-comparison-v1. Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419

Stable Diffusion Turbo image SHA 256: 7def19e4f4adf68244ff69e5873b9f465ecd158fb4abab0273443bfc3804438e

Lumina-Image 2.0 image SHA 256: f132c159d659fb76c94cd82f1decb617c475b9c4095b177d02b73f09f2954df0

The numbers at a glance

Arena ratingfrom votes by users · higher is better

n/a

Stable Diffusion Turbo

not in the arena

n/a

Lumina-Image 2.0

not in the arena

GraduallyBenchour own rating, 0 to 100 · higher is better

Stable Diffusion Turbo

Rank 108 of 108

Lumina-Image 2.0

Rank 97 of 108

Price per imagedollars through the API · lower is better

n/a

Stable Diffusion Turbo

no API price

n/a

Lumina-Image 2.0

no API price

Ranks among the 21 image models with an arena rating and the 108 with a GraduallyBench run; prices compared with 28 models with an API price.

Stable Diffusion Turbo

Subscription: n/a No plan in Germany names exactly this model.
Via API: No API price known. Access: local.
License: Open weights.

Lumina-Image 2.0

Subscription: n/a No plan in Germany names exactly this model.
Via API: No API price known. Access: local.
License: Open weights.

Subscriptions only count plans that name exactly this model. Subscription prices include VAT, API prices are in dollars.

GraduallyBench from Nov 3, 2026 · Subscription database from Sep 29, 2026 (38 days old)

All data side by side

The better value is bold. For ratings only when the gap is clear.

Kennzahl	Stable Diffusion Turbo	Lumina-Image 2.0
Ratings
Arena ratingfrom votes by users, higher is better	n/a	n/a
Arena rank	n/a	n/a
Arena votesmore votes, a safer rating	n/a	n/a
GraduallyBenchour own rating, 0 to 100	3	18
GraduallyBench by task
Typography and layoutLegible text, hierarchy, and composition	0	18
Product photographyMaterials, reflections, and fine details	1	16
Character and detailAnatomy, clothing, and spatial consistency	5	23
InfographicNumbers, labels, and visual organization	5	16
API price
Per imagelower is better	n/a	n/a
100 images	n/a	n/a
Profile
Released	n/a	Jan 25, 2025
Weightsopen means: runs locally	open	open
Lifecycle	Older open fast checkpoint; no active hosted API ID claimed in the reviewed model list	no notice
Access	local	local
Parameters	not published	2.6 billion
Largest output	not consistently published	not consistently published
Plans in Germanynaming exactly this model	n/a	n/a

API prices for one image at 1,024 × 1,024 pixels according to Artificial Analysis.

Sources: Stable Diffusion Turbo model source, status source (verified Nov 1, 2026) · Lumina-Image 2.0 model source (verified Nov 1, 2026)

Arena ratings by category

From blind votes on LMArena. “Ahead” means the lead is clear.

No shared arena score in the stored snapshot

The stored benchmark database contains no measurement for Stable Diffusion Turbo and Lumina-Image 2.0 from the same arena and category. Older model versions are not used as substitutes.

More comparisons

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