FLUX.1 Kontext [dev]

Name: FLUX.1 Kontext [dev]
Brand: Black Forest Labs
Price: 0.025 USD
Availability: InStock

AI Image Editing Model

Image

Black Forest Labs' open-weights multimodal flow transformer for in-context image generation and editing, available for non-commercial use with character consistency and style transfer capabilities

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2048 x 2048

Max Resolution

Supported Modes

Text to Image Image Edit

Active

Details

Model ID

flux.1-kontext-dev

Creator

Black Forest Labs

Family

flux.1-kontext

Max Input Images

Ready to integrate?

Access flux.1-kontext-dev via our unified API.

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Available at 2 providers

Starting from

$0.025 /image via fal.ai, Replicate

Prices shown are in USD

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Providers & Pricing (3)

FLUX.1 Kontext [dev] is available from 3 providers, with per-image pricing starting at $0.025 through fal.ai.

fal.ai

Text to Image

fal/flux.1-kontext-dev

Provider Model ID: fal-ai/flux-kontext/dev/text-to-image

$0.025 /image

View pricing details

fal.ai

Image Edit

fal/flux.1-kontext-dev-edit

Provider Model ID: fal-ai/flux-kontext/dev

$0.025 /image

View pricing details

Replicate

Text to Image Image Edit

replicate/flux.1-kontext-dev

Provider Model ID: black-forest-labs/flux-kontext-dev

$0.025 /image

View pricing details

FLUX.1 Kontext [dev] API OpenAI-compatible

Integrate FLUX.1 Kontext [dev] into your application via the Lumenfall OpenAI-compatible API to perform advanced text-to-image generation and precise image editing using flow transformer technology.

Base URL

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

Model

flux.1-kontext-dev

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": "flux.1-kontext-dev",

    "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: 'flux.1-kontext-dev',

  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="flux.1-kontext-dev",

    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=flux.1-kontext-dev" \

  -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: 'flux.1-kontext-dev',

  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="flux.1-kontext-dev",

    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. Edit instruction for the image	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") `auto` `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

Output	`size`		`aspect_ratio` + `resolution`
Flexible
Auto	`"auto"`	—	Model chooses optimal dimensions
Custom 1–14142px per side	`"WxH"`	—	Any pixel dimensions within model constraints

1K 11 sizes

Output	`size`		`aspect_ratio` + `resolution`
1183 × 887	`"1183x887"`	or	`"4:3"` + `"1K"`
916 × 1145	`"916x1145"`	or	`"4:5"` + `"1K"`
1145 × 916	`"1145x916"`	or	`"5:4"` + `"1K"`
1024 × 1024	`"1024x1024"`	or	`"1:1"` + `"1K"`
887 × 1182	`"887x1182"`	or	`"3:4"` + `"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
`acceleration` fal	string	The speed of the generation. The higher the speed, the faster the generation. `high` `none` `regular`	T2I Edit
`disable_safety_checker` replicate	boolean	Disable NSFW safety checker	T2I Edit
`enable_safety_checker` fal	boolean	If set to true, the safety checker will be enabled.	T2I Edit
`enhance_prompt` fal	boolean	Whether to enhance the prompt for better results.	T2I Edit
`num_inference_steps`	integer	The number of inference steps to perform.	T2I Edit
`output_quality` replicate	integer	Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs	T2I Edit
`resolution_mode` fal	string	Determines how the output resolution is set for image editing. - `auto`: The model selects an optimal resolution from a predefined set that best matches the input image's aspect ratio. This is the recommended setting for most use cases as it's what the model was trained on. - `match_input`: The model will attempt to use the same resolution as the input image. The resolution will be adjusted to be compatible with the model's requirements (e.g. dimensions must be multiples of 16 and within supported limits). Apart from these, a few aspect ratios are also supported.	T2I Edit
`sync_mode` fal	boolean	If `True`, the media will be returned as a data URI and the output data won't be available in the request history.	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 3 images

FLUX.1 Kontext [dev] FAQ

How much does FLUX.1 Kontext [dev] cost?

FLUX.1 Kontext [dev] starts at $0.025 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.

How do I use FLUX.1 Kontext [dev] via API?

You can use FLUX.1 Kontext [dev] through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "flux.1-kontext-dev". Code examples are available in Python, JavaScript, and cURL.

Which providers offer FLUX.1 Kontext [dev]?

FLUX.1 Kontext [dev] is available through fal.ai and Replicate on Lumenfall. Lumenfall automatically routes requests to the best available provider.

What is the maximum resolution for FLUX.1 Kontext [dev]?

FLUX.1 Kontext [dev] supports images up to 2048x2048 resolution.

Overview

FLUX.1 Kontext [dev] is an open-weights multimodal flow transformer developed by Black Forest Labs, designed specifically for in-context image generation and editing. It extends the foundational FLUX.1 architecture to allow for complex image-to-image workflows, enabling users to maintain consistent characters or styles across different compositions. This model is intended for non-commercial development and research, offering a high-fidelity bridge between text prompts and visual reference inputs.

Strengths

Character Consistency: The model excels at maintaining the identity and features of a specific subject across multiple generated frames by leveraging reference images as “context.”
Zero-Shot Style Transfer: It can adapt the aesthetic, color palette, and texture of a target image onto a new prompt without requiring specific LoRA training or fine-tuning.
Complex Attribute Mapping: It demonstrates high accuracy in following dense textual instructions while respecting the spatial constraints and structural information provided in the input image.
Prompt Adherence: Like other models in the FLUX.1 family, it minimizes common artifacts in hand rendering and manages high-density text within images effectively.

Limitations

Non-Commercial License: The [dev] version is released under a restrictive license that prohibits revenue-generating applications, making it unsuitable for production environments without further licensing.
Hardware Intensity: Due to the flow transformer architecture and the multimodal input requirements, it demands significant VRAM and compute compared to standard latent diffusion models.
Prompt Sensitivity: Achieving the perfect balance between the input image context and the text prompt can require iterative testing, as the model may occasionally over-index on the reference image at the expense of prompt instructions.

Technical Background

FLUX.1 Kontext [dev] is built on a multimodal flow transformer architecture, a departure from traditional U-Net-based diffusion models. This approach uses flow matching to improve training efficiency and sampling quality. By integrating text and image embeddings into a shared latent space, the model treats visual context as a primary input alongside textual tokens, allowing for more natural in-context learning during the generation process.

Best For

FLUX.1 Kontext [dev] is best suited for storyboarding, character design sheets, and stylistic exploration where visual continuity is required across a series of images. It is an excellent choice for developers experimenting with advanced image-editing pipelines or researchers studying multimodal integration in large-scale generative models. You can experiment with its in-context capabilities through the Lumenfall unified API and playground, which simplifies the integration of its multimodal inputs into your development workflow.

Try FLUX.1 Kontext [dev] in Playground

Generate images with custom prompts — no API key needed.

Open Playground