R

black-forest-labs/flux-kontext-pro

每次請求:$0.048
black-forest-labs/flux-kontext-pro 是一個用於情境感知影像生成的多模態擴散模型。它可根據文字提示與可選的參考影像合成影像,並保留構圖與風格線索,以產生貼合參考的結果。典型用途包括品牌資產製作、產品視覺,以及透過情緒板或示例照片進行視覺發想。技術亮點包括文字與影像輸入、參考條件化取樣,以及透過種子控制實現可重現的輸出。
商業用途

Technical Specifications of black-forest-labs/flux-kontext-pro

SpecificationDetails
Model IDblack-forest-labs/flux-kontext-pro
ProviderBlack Forest Labs
CategoryMultimodal diffusion model
Input ModalitiesText prompts and optional reference images
Output ModalityGenerated images
Core CapabilityContext-aware image generation grounded by prompt and visual references
Reference ConditioningSupports image-guided synthesis to preserve composition and style cues
ReproducibilitySeed control for repeatable outputs
Typical Use CasesBrand asset creation, product visuals, visual ideation, mood-board-driven generation
Integration TypeAPI-based image generation workflow

What is black-forest-labs/flux-kontext-pro?

black-forest-labs/flux-kontext-pro is a multimodal diffusion model designed for context-aware image generation. It creates images from natural language prompts and can optionally use reference images to guide the final result. This allows developers and creative teams to generate visuals that are not only prompt-compliant but also better aligned with desired composition, framing, tone, or stylistic direction.

Because it combines text understanding with image-based conditioning, black-forest-labs/flux-kontext-pro is well suited for workflows where consistency and grounding matter. Teams can use it to produce marketing graphics, product-centric imagery, concept explorations, and other visuals that benefit from example-based steering. Seed control also helps make outputs more reproducible, which is useful when iterating on creative assets or maintaining repeatable generation pipelines.

Main features of black-forest-labs/flux-kontext-pro

  • Multimodal prompting: Accepts text prompts and optional reference images, enabling more controlled image generation than text-only workflows.
  • Context-aware generation: Uses prompt context and visual guidance together to synthesize outputs that better reflect the intended subject, layout, and style.
  • Reference-conditioned sampling: Leverages example images to preserve composition and stylistic cues for grounded creative results.
  • Creative consistency: Helps teams maintain more coherent visual direction across asset variations, especially in branding and product content workflows.
  • Seed-controlled reproducibility: Supports repeatable generations through seed parameters, which is useful for testing, refinement, and production pipelines.
  • Visual ideation support: Works well for mood boards, example-shot-driven exploration, and concept development where visual references accelerate iteration.
  • Production-friendly applications: Suitable for brand asset creation, product visuals, campaign mockups, and other commercial image-generation scenarios.

How to access and integrate black-forest-labs/flux-kontext-pro

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 is required to authenticate every request and route it to the black-forest-labs/flux-kontext-pro model.

Step 2: Send Requests to black-forest-labs/flux-kontext-pro API

Once you have your API key, send requests to the CometAPI endpoint with black-forest-labs/flux-kontext-pro as the model. Include your prompt and any optional reference-image inputs required by your workflow.

curl --request POST \
  --url https://api.cometapi.com/v1/responses \
  --header "Authorization: Bearer $COMETAPI_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "black-forest-labs/flux-kontext-pro",
    "input": [
      {
        "role": "user",
        "content": [
          { "type": "input_text", "text": "Create a premium studio product image of a minimalist skincare bottle with soft natural lighting." }
        ]
      }
    ]
  }'

Step 3: Retrieve and Verify Results

After submission, CometAPI returns the model output in the response payload. Parse the returned data, store the generated image artifacts, and verify that the result matches your prompt intent, reference constraints, and any seed-based reproducibility requirements before using it in production.