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Black Forest Labs/FLUX 2 PRO

每次請求:$0.06
FLUX 2 PRO 是 FLUX 2 系列的商用旗舰模型,提供最先进的图像生成能力,带来前所未有的质量与细节。面向专业与企业级应用打造,具备卓越的提示词遵循性、照片级写实输出以及非凡的艺术创作能力。该模型代表了 AI 图像合成技术的最前沿水准。
新
商用
概览
功能亮点
定价
API

Key features (what FLUX.2-Pro offers)

  • Production-oriented quality: Targeted for commercial pipelines with predictable latency and high visual fidelity (photoreal outputs up to ~4 megapixels).
  • Multi-reference conditioning: API support for up to 8 references via API and maintains character/style consistency across outputs — useful for brand or character continuity.
  • Improved typography & layout: Stronger, more legible text rendering for UI, infographics and logos compared with many prior models.
  • Deterministic, low-variance outputs: Pro tier optimized to reduce iterative prompting and cycle time in production.
  • Content provenance & safety tooling: API applies cryptographically-signed C2PA metadata to outputs; hosted endpoints include filters and inference-time moderation.
  • Low-latency, predictable inference ( “sub-10-second” generation speeds and SLAs for Pro).

Technical details of FLUX.2 Pro

  • Core architecture: FLUX.2 uses a latent flow-matching approach with a rectified-flow transformer operating in a learned latent space. The design couples that transformer backbone with a Mistral-3 24B vision-language model to provide semantic grounding and world knowledge during synthesis.
  • VAE and latent redesign: BFL released an updated FLUX.2 VAE (Apache-2.0) that rebalances compression, reconstruction fidelity and learnability — enabling higher-quality editing at multi-megapixel resolutions. The shared VAE underpins all FLUX.2 variants for interoperability and more consistent editing results.
  • Inference behavior / training techniques: The Dev checkpoint was trained with techniques such as guidance distillation to make sampling more efficient and to allow lower-step high-quality sampling; hosted Pro may use further engineering and sampling pipelines to reduce latency.

Model name: black-forest-labs/flux-2-pro

Benchmark performance

Black Forest Labs’ own evaluations and independent coverage published at launch report that FLUX.2 shows measurable gains vs several contemporary image systems in human evaluation win rates for text→image and editing tasks:

  • Text→image: reported win rate ~66.6% in head-to-head human comparisons vs selected models (sampled comparisons cited in the press).
  • Single-reference editing: ~59.8% win rate vs Qwen-Image in the reported comparisons; multi-reference editing: ~63.6% win rate . These win-rate figures were emphasized by media at launch as evidence of consistent quality and editing accuracy.

FLUX.2 vs Nano Banana Pro vs Qwen-Image

  • Nano Banana Pro / Google Gemini image tiers: BFL positions FLUX.2 as matching closed-source leaders on prompt fidelity and visual quality while being lower cost per image (BFL published per-MP pricing comparisons). Proprietary competitors may still claim absolute top ELOs in some curated tests but at higher per-image costs.
  • Hunyuan Image / Qwen-Image / other open models: FLUX.2 is reported to outperform many contemporary open checkpoints in head-to-head win-rate tests across T2I and editing tasks (per BFL’s published comparisons). Differences tend to be strongest in multi-reference consistency and typography.
  • FLUX.1 lineage: FLUX.2 is a full architectural redesign (not a drop-in replacement) that improves DiT blocks, autoencoder, and VLM coupling. Expect noticeable gains in editing fidelity and multi-reference coherence versus FLUX.1.

How to access Flux.2 Pro API

Step 1: Sign Up for API Key

Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.

Step 2: Send Requests to Flux.2 Pro API

Select the “black-forest-labs/flux-2-pro ”endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account.

Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Retrieve and Verify Results

Process the API response to get the generated answer. After processing, the API responds with the task status and output data.

See also Gemini 3 Pro Image( Nano Banana Pro) API

CometAPI Now Supporting Replicate Format Models: 🔹 black-forest-labs/flux-2-pro 🔹 black-forest-labs/flux-2-dev 🔹 black-forest-labs/flux-2-flex

Limited Time Promotion: Lower than Replicate Official Pricing!

👇 Start Building Now Create Predictions – API Doc

⚡ Flexible Selection:

  • Pro: Designed for high-efficiency production and fast delivery.
  • Flex: Maximizes image quality with adjustable parameters.
  • Dev: Developer-friendly optimization.

Black Forest Labs/FLUX 2 PRO 的功能

了解 Black Forest Labs/FLUX 2 PRO 的核心能力,帮助提升性能与可用性,并改善整体体验。

Black Forest Labs/FLUX 2 PRO 的定价

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Comet 价格 (USD / M Tokens)官方定价 (USD / M Tokens)折扣
每次請求:$0.06
每次請求:$0.075
-20%

Black Forest Labs/FLUX 2 PRO 的示例代码与 API

获取完整示例代码与 API 资源,简化 Black Forest Labs/FLUX 2 PRO 的集成流程,我们提供逐步指导,助你发挥模型潜能。
Python
JavaScript
Curl
import os
import requests

COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"

url = "https://api.cometapi.com/replicate/v1/models/black-forest-labs/flux-2-pro/predictions"

headers = {
    "Authorization": f"Bearer {COMETAPI_KEY}",
    "Content-Type": "application/json"
}

payload = {
    "input": {
        "prompt": "Glossy candy-colored 3D letters in hot pink, electric orange, and lime green on a sun-drenched poolside patio with bold terrazzo tiles. Shot on Kodachrome film with a Hasselblad 500C, warm golden afternoon sunlight, dramatic lens flare. Text reads 'FLUX.2 PRO'",
        "input_images": [
            "https://replicate.delivery/xezq/Q9jOVfT4nmT6YaUGe7fwMe85kYPNziFq18j850Sg6YvPlk0WB/tmpzq6ncmxa.jpg"
        ],
        "aspect_ratio": "16:9",
        "resolution": "2 MP",
        "output_format": "jpg",
        "safety_tolerance": 2,
        "seed": 42
    }
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()

print(f"Status Code: {response.status_code}")
print(f"Task ID: {result.get('id')}")
print(f"Status: {result.get('status')}")
print(f"Model: {result.get('model')}")

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