Models
Stability AI
Stable Diffusion 3.5 Large Turbo

Stability AI

Stable Diffusion 3.5 Large Turbo

Name: Stable Diffusion 3.5 Large Turbo
Brand: Stability AI
Price: 0.04 USD
Availability: InStock

AI Image Generation Model

Image

Distilled version of SD 3.5 Large that generates high-quality images in just 4 steps, offering faster inference and reduced costs

Try Model

1024 x 1024

Max Resolution

Supported Modes

Text to Image Image Edit

Active

Details

Model ID

stable-diffusion-3.5-large-turbo

Creator

Stability AI

Family

stable-diffusion-3.5

Ready to integrate?

Access stable-diffusion-3.5-large-turbo via our unified API.

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Available at 1 provider

Starting from

$0.040 /image via Replicate

Prices shown are in USD

Full pricing details

Providers & Pricing (1)

Stable Diffusion 3.5 Large Turbo is available exclusively through Replicate, starting at $0.04/image.

Replicate

Text to Image Image Edit

replicate/stable-diffusion-3.5-large-turbo

Provider Model ID: stability-ai/stable-diffusion-3.5-large-turbo

Output

Image

$0.040 per image

Pricing Notes (2)

• Distilled version that generates high-quality images in just 4 steps
• Offers faster inference and reduced costs

View official pricing • As of Dec 30, 2025

Stable Diffusion 3.5 Large Turbo API OpenAI-compatible

Lumenfall provides an OpenAI-compatible API for Stable Diffusion 3.5 Large Turbo to generate high-quality images with reduced latency using a 4-step distillation process.

Base URL

https://api.lumenfall.ai/openai/v1

Model

stable-diffusion-3.5-large-turbo

Code Examples

Text to Image

/v1/images/generations

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/generations \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -H "Content-Type: application/json" \

  -d '{

    "model": "stable-diffusion-3.5-large-turbo",

    "prompt": "",

    "size": "1024x1024"

}'

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.generate({

  model: 'stable-diffusion-3.5-large-turbo',

  prompt: '',

  size: '1024x1024'

});

// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

console.log(response.data[0].url);

from openai import OpenAI

client = OpenAI(

    api_key="YOUR_API_KEY",

    base_url="https://api.lumenfall.ai/openai/v1"

response = client.images.generate(

    model="stable-diffusion-3.5-large-turbo",

    prompt="",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Image Edit

/v1/images/edits

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/edits \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -F "model=stable-diffusion-3.5-large-turbo" \

  -F "[email protected]" \

  -F "prompt=Add a starry night sky to this image" \

  -F "size=1024x1024"

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

import fs from 'fs';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.edit({

  model: 'stable-diffusion-3.5-large-turbo',

  image: fs.createReadStream('source.png'),

  prompt: 'Add a starry night sky to this image',

  size: '1024x1024'

});

// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

console.log(response.data[0].url);

from openai import OpenAI

client = OpenAI(

    api_key="YOUR_API_KEY",

    base_url="https://api.lumenfall.ai/openai/v1"

response = client.images.edit(

    model="stable-diffusion-3.5-large-turbo",

    image=open("source.png", "rb"),

    prompt="Add a starry night sky to this image",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Parameter Reference

Required Supported Not available

Core Parameters

Parameter	Type	Description	Modes
`prompt`	string	Required. Text prompt for image generation	T2I Edit
`negative_prompt`	string	Negative prompt to guide generation away from undesired content	T2I Edit
`seed`	integer	Random seed for reproducibility	T2I Edit

Size & Layout

Parameter	Type	Description	Modes
`size`	string	Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9") WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.	T2I Edit
`aspect_ratio`	string	Aspect ratio of the output image (e.g. "16:9", "1:1") Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.	T2I Edit
`resolution`	string	Output resolution tier (e.g. "1K", "4K") `1K` Controls scale independently of shape. Higher tiers produce larger images and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.	T2I Edit

1K 9 sizes

Output	`size`		`aspect_ratio` + `resolution`
916 × 1145	`"916x1145"`	or	`"4:5"` + `"1K"`
1145 × 916	`"1145x916"`	or	`"5:4"` + `"1K"`
1024 × 1024	`"1024x1024"`	or	`"1:1"` + `"1K"`
836 × 1254	`"836x1254"`	or	`"2:3"` + `"1K"`
1254 × 836	`"1254x836"`	or	`"3:2"` + `"1K"`
768 × 1365	`"768x1365"`	or	`"9:16"` + `"1K"`
1365 × 768	`"1365x768"`	or	`"16:9"` + `"1K"`
670 × 1564	`"670x1564"`	or	`"9:21"` + `"1K"`
1563 × 670	`"1563x670"`	or	`"21:9"` + `"1K"`

How these parameters work

size

Exact pixel dimensions

"1920x1080"

aspect_ratio

Shape only, default scale

"16:9"

resolution

Scale tier, preserves shape

"1K"

Priority when combined

size › aspect_ratio + resolution › aspect_ratio › resolution

size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.

How matching works

Shape matching – we pick the closest supported ratio. Ask for 7:1 on a model with 4:1 and 8:1, you get 8:1.

Scale matching – providers use different tier formats: K tiers (0.5K 1K 2K 4K) or megapixel tiers (0.25 1). If the exact tier isn't available, you get the nearest one.

Dimension clamping – if a model has pixel limits, we clamp dimensions to fit and keep the aspect ratio intact.

Media Inputs

Parameter	Type	Description	Modes
`image`	file	Required. Input image(s) to edit Supports PNG, JPEG, WebP.	T2I Edit

Output & Format

Parameter	Type	Description	Modes
`response_format`	string	How to return the image `url` `b64_json` Default: `"url"`	T2I Edit
`output_format`	string	Output image format `png` `jpeg` `gif` `webp` `avif` Gateway converts to requested format if provider doesn't support it natively.	T2I Edit
`output_compression`	integer	Compression level for lossy formats (JPEG, WebP, AVIF)	T2I Edit
`n`	integer	Number of images to generate Default: `1` Gateway generates multiple images in parallel even if provider only supports 1.	T2I Edit

Additional Parameters

Parameter	Type	Description	Modes
`cfg_scale`	number	Classifier-free guidance scale — higher values stick more closely to the prompt	T2I Edit
`strength`	number	How much to transform the input image: 0 keeps it unchanged, 1 fully regenerates from the prompt	T2I Edit

Parameter Normalization

How we handle parameters across different providers

Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:

Behavior	What happens	Example
`passthrough`	Sent as-is to the provider	style, quality
`renamed`	Same value, mapped to the field name the provider expects	prompt
`converted`	Transformed to the provider's native format	size
`emulated`	Works even if the provider has no concept of it	n, response_format

Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.

Full API Reference Authentication, endpoints, and more

Gallery

View all 2 images

Stable Diffusion 3.5 Large Turbo FAQ

How much does Stable Diffusion 3.5 Large Turbo cost?

Stable Diffusion 3.5 Large Turbo starts at $0.04 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.

How do I use Stable Diffusion 3.5 Large Turbo via API?

You can use Stable Diffusion 3.5 Large Turbo through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "stable-diffusion-3.5-large-turbo". Code examples are available in Python, JavaScript, and cURL.

Which providers offer Stable Diffusion 3.5 Large Turbo?

Stable Diffusion 3.5 Large Turbo is available through Replicate on Lumenfall. Lumenfall automatically routes requests to the best available provider.

What is the maximum resolution for Stable Diffusion 3.5 Large Turbo?

Stable Diffusion 3.5 Large Turbo supports images up to 1024x1024 resolution.

Overview

Stable Diffusion 3.5 Large Turbo is a distilled version of Stability AI’s most advanced text-to-image model. It is designed specifically for high-speed generation, utilizing adversarial diffusion distillation to produce detailed images in significantly fewer steps than the standard Large variant. This model balances the sophisticated prompt adherence of the SD3.5 architecture with the efficiency required for near-real-time applications.

Strengths

Inference Speed: Capable of generating high-quality images in just 4 sampling steps, compared to the 30–50 steps typically required by non-distilled models.
Text Rendering: Inherits the Multimodal Diffusion Transformer (MMDiT) architecture’s ability to render legible, coherent text and typography within images.
Prompt Adherence: Shows high sensitivity to complex descriptors, maintaining compositional integrity even when given long or nuanced natural language prompts.
Operational Efficiency: Lower computational overhead enables reduced costs per generation and faster iteration cycles for developers and designers.

Limitations

Anatomical Consistency: While improved over previous versions, the model can still struggle with complex human anatomy, particularly limb positioning and finger counts in crowded scenes.
Dynamic Range: The distillation process can sometimes lead to a slight reduction in fine-grained texture detail or a narrow dynamic range compared to the full 8-billion-parameter non-turbo model.
Style Variation: The “Turbo” nature of the model may prioritize speed over extreme artistic flexibility, occasionally leaning toward a specific stylistic aesthetic if prompts are too brief.

Technical Background

The model is built on the Stable Diffusion 3.5 Large framework, which utilizes a Multimodal Diffusion Transformer (MMDiT) architecture featuring separate sets of weights for image and text representations. To achieve its speed, Stable Diffusion 3.5 Large Turbo employs a distillation technique that compresses the sampling process, allowing the model to bypass the standard iterative refinement of traditional diffusion while retaining the majority of its visual fidelity.

Best For

This model is ideal for interactive applications where latency is a critical factor, such as live whiteboarding tools, instant asset generation for games, and rapid prototyping workflows. It is particularly effective for users who need to iterate on visual concepts quickly without sacrificing prompt accuracy. Stable Diffusion 3.5 Large Turbo is available for exploration and integration through Lumenfall’s unified API and playground.

Try Stable Diffusion 3.5 Large Turbo in Playground

Generate images with custom prompts — no API key needed.

Open Playground