What is Flux.2 and Flux 2 is now available on CometAPI

FLUX.2 is a newly announced family of image-generation and editing models from Black Forest Labs that offer production-grade fidelity, multi-reference editing (up to 10 references), and deployable variants ranging from open-weight Dev to production Pro and a controllable Flex tier. This guide pulls together the latest release notes, provider integrations, architecture notes and practical code examples so you can start building with the FLUX.2 API today.

What is FLUX.2?

FLUX.2 is Black Forest Labs’ production-grade image generation + editing family that merges multi-reference conditioning, a re-worked latent space (VAE), and advanced control primitives (hex color steering, JSON prompting, pose guidance) to deliver consistent, high-fidelity results for creative and commercial workflows. It supports both text→image generation and multi-reference image editing in a single model family, and BFL ships hosted API endpoints as well as open-weight artifacts for research and local inference. The offering exists across multiple distribution channels: open weights for researchers/developers (FLUX.2 [dev]), hosted production models such as Flux.2 Pro  , and customizable hosted endpoints like Flux.2 Flex.

Key capabilities

• Multi-reference editing: combine up to 8–10 reference images for a single output while maintaining identity and style consistency. This is especially useful for advertising, product mockups, or character continuity across creative variants.
• High resolution (up to 4MP): outputs up to 4 megapixels (for example 2048×2048 and larger, depending on aspect ratio).
• Photorealism + fine detail: improvements in hands, faces, textures and spatial reasoning vs earlier open models.
• Structured prompting & JSON prompts: FLUX.2 supports structured/JSON prompts that map naturally to UI controls (scene, subjects[], style, lighting, camera), enabling programmatic and reproducible generation.
• Typography and color fidelity: unusually good text rendering and exact color (hex) steering for brand-sensitive workflows.
• Content-provenance & safety: the Pro API applies cryptographically-signed C2PA metadata to produced images and operates layered filtering for disallowed content categories.

Pro vs Flex vs Dev: Which Model to Choose?

When to pick which

• Choose dev if you must run locally, need algorithmic research, or require open-weight customizations (and accept high hardware needs).
• Choose pro when you need predictable, low-latency production images with built-in safety and provenance features.
• Choose flex if you are iterating on generation hyperparameters (tuning steps, guidance scale, etc.) and want a managed endpoint that exposes that control.

How does FLUX.2 work?

FLUX.2 brings together three main architectural elements:

1. Rectified-flow transformer backbone

At its core FLUX.2 uses a flow-matching / rectified-flow transformer architecture that operates in a learned latent space (a modern alternative to diffusion for some production pipelines). This backbone enables high-fidelity rendering and spatial reasoning that improve consistency across multiple references. The “flow matching” approach offers different tradeoffs in sampling speed and fidelity compared with classical diffusion.

2. New variational autoencoder (VAE)

A purpose-built autoencoder compresses images into a latent representation optimized for FLUX.2’s generation and editing tasks. BFL states that the new VAE improves compressibility and fidelity (better learning dynamics and higher quality reconstructions than prior generations). The VAE is a key contributor to clean upscaling to 4MP and improved detail.

3. Long-context vision–language model (VLM)

A VLM (reported to be related to Mistral-class visual–language encoders in published notes) provides the language-conditioning and real-world knowledge that makes prompts more faithful and the model better at following complex instructions (pose guidance, contextual edits, etc.). Combining a VLM with a flow backbone allows FLUX.2 to reason about composition and semantics at larger context windows.

How these pieces interact (runtime flow)

1. Encode input(s): reference images are encoded via the VAE into latent tokens; text prompts are encoded by the VLM.
2. Cross-modal fusion: the transformer backbone ingests image latents + text tokens and models spatial relationships, identity features, and editing instructions.
3. Flow-based generation: the rectified-flow samplers generate or edit latent images conditioned on the fused representation.
4. Decode: the VAE decodes latents back to pixel space, optionally applying final color constraints and watermark/C2PA metadata.

Why this architecture matters

This combination yields three practical advantages: (1) multi-reference coherence because identity and style are modeled explicitly in the latent; (2) better text and typography due to tighter integration between VLM and image latent space; (3) scalable deployment options — the same basic model family can be shipped as open weights for local use (dev), as a managed low-latency service (pro), or as a tunable service for developers (flex).

How does FLUX.2 Good ?

Perform in benchmarks

Black Forest Labs published comparative evaluations and charts showing FLUX.2 [Dev] outperforming several open-weight contemporaries in head-to-head human preference/win-rate tests and in ELO vs. cost analyses. Reported highlights from the published vendor/press summary include:

• Text→Image win-rate: FLUX.2 [Dev] reported ≈66.6% win rate (vs ~51.3% Qwen-Image, 48.1% Hunyuan Image 3.0).
• Single-reference editing: ≈59.8% win rate (vs ~49.3% Qwen-Image, 41.2% FLUX.1 Kontext).
• Multi-reference editing: ≈63.6% win rate (vs ~36.4% for Qwen-Image).
• ELO vs cost: FLUX.2 family (Pro, Flex, Dev) cluster in an upper-quality, relatively low-cost band (ELO ≈1030–1050 while operating at ~2–6 cents per image in the vendor’s pricing chart).

Multi-Reference Generation

One of FLUX.2’s biggest features is its ability to generate multiple consistent outputs using multiple reference images.

For example, when photographing a product, you can upload multiple photos taken from different angles, under different lighting conditions, and against different backgrounds, and generate multiple variations of the same image at once.

This feature allows you to quickly batch generate product catalog photos for e-commerce websites, advertising banners, social media image sets, and more.

Unlike traditional single-image generation, this multi-reference mechanism is ideal for real-world workflows that emphasize consistency and integrity.

High Resolution, Business Quality (Up to 4MP)

FLUX.2 supports output up to 4 megapixels (approximately 2000-3000 pixels), providing image quality suitable for practical applications such as advertising, print, signage, and posters.

It handles text, logos, UI mockups, infographics, and more perfectly, making it suitable not only for artistic creation but also for design and commercial use.

Meanwhile, the rendering quality of fonts and text has also been improved, making it suitable for creating advertising banners and product labels.

Supports Local GPU Execution: Low Cost, Low Barrier to Entry

To date, many high-performance image generation models are only practical in data centers with massive computing resources. However, FLUX.2 is optimized to run on standard GPUs (such as NVIDIA RTX) with less VRAM consumption.

Models no longer need to be accessed through the cloud; they can be edited and generated locally, significantly reducing costs and increasing operational flexibility.

This is a major advantage not only for companies but also for individual creators and small teams.

Unified Creation and Editing Workflow

FLUX.2 supports not only text-to-image (text → image generation) but also image-to-image (editing and styling existing images).

This allows you to consistently use a single model for tasks such as “drawing a new image from scratch,” “editing and retouching existing photos,” and “reusing multiple images to create uniform variations.”

For example, it’s easy to change the background of a product photo to a different atmosphere or resize it for social media.

How to Access Flux.2 API

We are pleased to announce that CometAPI has integrated the Flux.2 API. Now Supporting Replicate Format Model(Lower than Replicate Official Pricing), FLUX.2 Endpoints:

• black-forest-labs/flux-2-pro
• black-forest-labs/flux-2-dev
• black-forest-labs/flux-2-flex

Start Building Now Create Predictions – API Doc,

Want to try first? Test FLUX.2 [pro] and [flex] in our playground after registering and logging in to CometAPI, if you want to start building with API now: Create Predictions – API Doc.

FLUX.2 is not merely another model drop; it is a family-level product strategy that addresses production realities: fidelity, editability, multi-reference coherence, and practical deployment pathways (managed APIs and open checkpoints). For organizations that produce visual content at scale, FLUX.2 promises meaningful productivity gains — provided teams pair technical adoption with robust licensing governance and quality control.

Main Uses and Intended Use Cases of FLUX.2

Product Visuals/E-commerce Catalog Creation

E-commerce businesses and brands have a high demand for taking numerous product photos from multiple angles, using different lighting, backgrounds, and color modes.

• With FLUX.2, you can quickly generate multiple visually consistent effects without actually shooting any content.
• This allows you to quickly expand your product catalog while reducing photography costs, time, and management costs.

Advertising and Marketing Material Creation

The demand for design materials is broad, including advertising banners, social media post images, promotional campaign visuals, and public relations posters.

• Simply provide a text description to obtain images with the desired style, composition, and atmosphere, greatly reducing the burden on designers and advertisers.
• Furthermore, because variations can be generated using multiple reference images, it is also suitable for A/B testing of creative ideas and creating materials compatible with multiple languages ​​and regions.

User Interface/User Experience Design, Prototyping

FLUX.2 also supports editing logos, fonts, layouts, and backgrounds, making it suitable not only for photo generation but also for the visual design of digital products.

• You can quickly create preliminary designs, wireframes, event websites, application screen mockups, and more.
• This is a cost-effective production solution, especially suitable for startups and small design teams.

Art/Creative Works and Personal Use

Of course, it can also be used purely for “artwork,” “illustrations,” or “graphic design.”

• Expand your creative horizons by creating works in various moods and styles using text prompts and reference images.
• You can also use image editing features to freely repurpose existing photos into artistic styles, or experiment with fantastical landscapes or character designs.

Differentiated from Existing Models and Competitors—Why Choose FLUX.2?

Comparison with Other AI Image Generation Models

Currently, there are many models (open-source and commercial) in the field of AI image generation, such as traditional diffusion models and the latest competing models. So, why is FLUX.2 so compelling? The reasons are as follows:

• Integrated Generation and Editing: Many models focus either on “generation (text to image)” or “editing (image to image).” FLUX.2 supports both functions simultaneously, achieving a highly consistent workflow.
• Multiple Reference Inputs: Utilize multiple reference images for easy product photography and consistent visual consistency.
• Commercial Quality and High Resolution: Supports 4MP for advertising, product photography, and print.
• Easy Local Execution: It is cloud-independent and can run on standard GPUs, offering advantages in both cost and flexibility.
• Flexible Model Selection: Offers a variety of models covering everything from standard to commercial and research applications, allowing you to choose the one that best suits your needs and budget.

This makes FLUX.2 a powerful choice for professional workflows, commercial use, high-volume production, and projects where cost and speed are critical.

Final thoughts:

FLUX.2 sits at a pragmatic intersection: it offers open-weight research options for teams that need control and reproducibility, and managed, production APIs for teams that prioritize low latency, predictable outputs, and provenance. By shipping both open and managed variants (dev/pro/flex), BFL acknowledges that different workflows — experimentation, iterative design, and production — require different trade-offs between fidelity, speed, customization, and governance

Developers can access Flux.2 Dev API, Flux.2 Flex API and Flux.2 Pro API through CometAPI. To begin, explore the model capabilities of CometAPI in the Playground and consult the Continue API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

Ready to Go?→ Sign up for CometAPI today !

If you want to know more tips, guides and news on AI follow us on VK, X and Discord!