flux-best-practices

To write effective prompts for FLUX image generation, use descriptive, prose-based structures that maximize model adherence and ensure consistent, high-quality visual asset creation across the BFL model families.

Choosing between FLUX.2 and FLUX.1 models requires evaluating your specific needs for speed, quality, typography rendering, or image-to-image editing to select the right tool for your text-to-image generation task.

Yes, you can use FLUX models for image-to-image editing and style transfer. The framework supports advanced workflows including object modification and complex multi-reference composition.

Yes, FLUX supports structured JSON prompting. This technique helps manage complex multi-reference compositions and ensures precise control over text-to-image generation outputs.

FLUX handles typography rendering by utilizing specific models within the FLUX.2 and FLUX.1 families optimized for text generation, ensuring high-quality text integration within your image-to-image and text-to-image outputs.