> For the complete documentation index, see [llms.txt](https://docs.pletor.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pletor.ai/model-library/image-models/flux-2.md).
# Flux 2
## Overview
Flux 2 is Black Forest Labs' most capable model to date. It combines generation and editing in a single model while delivering production-grade quality with consistent characters and styles across outputs.
It's a direct competitor to Nano Banana & Seedream 4.0.
### Strengths for marketers
* **Multi-reference support**: Reference up to 10 images simultaneously to maintain strict character, product, and style consistency across generations—no fine-tuning required.
* **Production-grade typography**: Renders complex text, infographics, UI mockups, and magazine layouts with legible fine text that works reliably.
* **HEX color control**: Specify exact brand colors using HEX codes (e.g., `#FF5733`) for pixel-perfect brand consistency across campaigns.
* **Strong prompt adherence**: Follows complex, structured instructions including multi-part prompts and compositional constraints more accurately than previous generations.
### Ideal use cases
* **Product photography**: Generate lifestyle shots with consistent products across different angles, lighting, and environments. Combine product images with model shots and backgrounds.
* **Brand asset creation**: Create marketing materials with exact brand colors, typography, and visual identity maintained across outputs.
* **Fashion shoots**: Combine clothing items, accessories, and style references into cohesive styled outfits on consistent models.
* **Editorial content**: Magazine covers, infographics, and layouts with professional text rendering.
### Weaknesses
* **Less versatile than competitors**: Nano Banana Pro or Seedream 4.0 do better on most visual marketing tasks.
* **Complex scenes**: May struggle with highly complex compositions with many interacting elements.
***
## How to use effectively
#### Model versions
This model is available in three versions on Pletor:
* **Flux 2 Max**: Use when quality matters
* **Flux 2**: Balanced quality and speed.
* **Flux 2 Turbo**: Use when speed matters (fastest generation for rapid iterations and image edits).
#### Prompting
Flux 2 is designed for natural language prompting. Unlike older models that required keyword stacking or quality tags, Flux 2 performs best with clear, descriptive sentences.
The prompt framework
Use this structure for consistent results:
**Subject + Action + Style + Context**
* **Subject**: The main focus (person, object, character)
* **Action**: What the subject is doing or their pose
* **Style**: Artistic approach, medium, or aesthetic
* **Context**: Setting, lighting, time, mood, or atmospheric conditions
**Example**:\
`"Luxury leather handbag, displayed on marble surface, soft directional lighting, warm amber tones"`
Skip the quality tags
Traditional quality tags like "masterpiece, best quality, ultra detailed, 8k" provide minimal benefit with Flux 2. The model's training already includes quality filtering, so focus on describing what you actually want.
Using reference images
When using Flux 2 with a single reference image, describe how you want the reference used:
`"Product shot of the sneaker from the reference image, placed on urban concrete, dramatic side lighting, clean background"`
Flux 2 Flex accepts up to 10 reference images. Reference specific images by index or use the `@` symbol:
`"The person from image 1 wearing the jacket from image 2, standing in the environment from image 3, natural lighting, medium shot"`
Or with `@` syntax:\
`"A portrait of @image1 wearing the jacket from @image2, set in the location of @image3"`
Using color codes
Flux 2 understands HEX codes for exact color matching. Include the color code directly in your prompt:
`"Product photography of running shoe, primary color #FF6B35, secondary accents #004E89, white background, overhead lighting"`
For complex products with multiple color zones, break down components:
`"Ceramic vase with gradient color starting at #02eb3c and finishing at #edfa3c, displayed on marble surface"`
JSON prompting for complex scenes
For maximum control over complex generations, Flux 2 accepts structured JSON prompts. This is especially useful for:
* Production workflows requiring consistent structure
* Precise camera and lighting specifications
* Brand work with exact color requirements
**Example JSON structure**:
```json
{
"scene": "Professional studio product photography",
"subjects": [
{
"description": "Minimalist ceramic coffee mug with steam rising",
"position": "Center foreground",
"color_palette": ["matte black ceramic"]
}
],
"style": "Ultra-realistic product photography",
"lighting": "Three-point softbox setup, soft diffused highlights",
"mood": "Clean, professional, minimalist",
"camera": {
"angle": "high angle",
"distance": "medium shot",
"lens-mm": 85,
"f-number": "f/5.6"
}
}
```
You can include JSON directly in your prompt or flatten it into natural language—Flux 2 understands both formats equally well.
---
# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.pletor.ai/model-library/image-models/flux-2.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.