Technical Specifications of black-forest-labs/flux-kontext-max
| Item | Details |
|---|---|
| Model ID | black-forest-labs/flux-kontext-max |
| Provider | Black Forest Labs |
| Category | Image generation |
| Primary capability | Context-conditioned image generation from text with optional reference inputs |
| Input modalities | Text, reference image inputs |
| Output modality | Generated images |
| Core approach | Diffusion-based generation with multimodal conditioning |
| Best for | Brand-consistent creatives, product mockups, character continuity, moodboard-driven ideation |
| Workflow style | Reference-guided image synthesis and controlled variation generation |
What is black-forest-labs/flux-kontext-max?
black-forest-labs/flux-kontext-max is a context-conditioned image generation model in the FLUX line, designed to produce images from text prompts while optionally incorporating reference inputs. This makes it well suited for workflows where image outputs need to stay grounded in a supplied visual direction rather than relying on text alone.
The model supports reference-guided generation use cases such as preserving style cues, maintaining subject consistency, and creating controlled variations based on existing images or moodboards. By combining text instructions with visual context, it helps teams generate outputs that are more aligned with brand, product, or creative requirements.
Typical use cases include marketing asset creation, product visualization, concept exploration, and character or scene continuity across multiple generations. Its diffusion-based architecture and multimodal conditioning make it a practical option for pipelines that need both creative flexibility and tighter visual control.
Main features of black-forest-labs/flux-kontext-max
- Context-conditioned generation: Creates images from text prompts while leveraging optional reference inputs to steer composition, style, or subject matter.
- Reference-guided workflows: Supports image generation tasks where supplied visual context helps ground outputs and reduce drift from the intended creative direction.
- Style preservation: Helps maintain a consistent aesthetic across generations, which is useful for brand-aligned creative production and iterative campaigns.
- Subject continuity: Enables more stable visual treatment of recurring products, characters, or design motifs across multiple outputs.
- Controlled variations: Makes it easier to produce alternate versions of an image concept while retaining important visual anchors from references.
- Multimodal conditioning: Combines text instructions with reference imagery for more directed synthesis than text-only prompting.
- Creative production support: Fits use cases such as mockups, ideation boards, campaign concepts, and visually consistent asset generation.
How to access and integrate black-forest-labs/flux-kontext-max
Step 1: Sign Up for API Key
To get started, create an account on CometAPI and generate your API key from the dashboard. This key will authenticate your requests and let you access the black-forest-labs/flux-kontext-max API.
Step 2: Send Requests to black-forest-labs/flux-kontext-max API
Once you have your API key, send requests to the CometAPI endpoint using black-forest-labs/flux-kontext-max as the model identifier. Include your prompt and any supported input parameters, such as reference inputs, according to your application needs.
curl --request POST \
--url https://api.cometapi.com/v1/responses \
--header "Authorization: Bearer YOUR_COMETAPI_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "black-forest-labs/flux-kontext-max",
"input": [
{
"role": "user",
"content": [
{ "type": "input_text", "text": "Generate a polished product hero image with a premium studio look." }
]
}
]
}'
Step 3: Retrieve and Verify Results
After submission, CometAPI will return the generation result payload for black-forest-labs/flux-kontext-max. Parse the response in your application, retrieve the generated image output, and verify that the result matches your requested prompt, reference context, and quality expectations before using it in production workflows.