Flux AI Prompt Engineer
Create optimized prompts for Flux.1 - The advanced open-source AI image generation model
Enhance Your Flux.1 Prompt
Enter your base prompt idea and use the categories below to enhance it for better results with Flux.1
Enhance with Categories:
Enhance your topic with specific details about the subject, scene, action, and setting. Be explicit about what you want to see.
Define the medium or rendering style of your image.
Define the artistic style of your image.
Reference specific artists to influence the visual style.
Reference popular art platforms to enhance quality.
Add terms to ensure clear, crisp image quality.
Add atmospheric and mood-enhancing details.
Specify color schemes and shading.
Specify lighting effects to enhance mood and dimensionality.
Negative Prompts
Select negative prompts to exclude unwanted elements from your generated image.
Common negative prompts to improve image quality across all types.
Specific negative prompts for anime-style images.
Negative prompts for realistic photo generation.
Negative prompts specific to facial features.
Negative prompts to improve hand rendering.
Negative prompts for better eye rendering.
Negative prompts for portrait images.
Negative prompts for SFW content.
Selected Negative Prompts:
Negative Prompt Output:
Enhanced Positive Prompt:
Explanation:
About Flux.1
Flux.1 is an open-source AI image generation model launched by Black Forest Labs, founded by Robin Rombach, a former core member of Stability AI. With up to 12 billion training parameters, it outperforms models like SD3 Medium, Midjourney, and DALL-E 3 across various metrics.
Flux.1 Versions:
- FLUX.1 [pro]: Closed-source model for commercial use via official API
- FLUX.1 [dev]: Open-source model for non-commercial use, distilled from FLUX.1 [pro]
- FLUX.1 [schnell]: Open-source model for commercial use, fastest with minimal memory usage
Key Features:
- Exceptional image generation quality, especially for complex subjects
- Hybrid architecture combining multimodal and parallel diffusion Transformer modules
- Flow-matching training methods and rotational positional embeddings