Ideogram V4's feature set is engineered for design work, not general image generation. The JSON caption training, bounding-box control, color palette conditioning, and vision-language text encoder all target the same goal: make the model behave like a design tool that follows a brief, not a creative AI that interprets a mood.

#1 Open-Weight Text Rendering (0.97 OCR)

Ideogram has led on in-image typography since its first release. V4 pushes further: 0.97 X-Omni OCR accuracy, the highest among all open-weight models. This is ahead of FLUX.2 dev (32B), HunyuanImage 3.0 (80B MoE), and Qwen-Image (20B). Dense multi-line text, varied font weights, logos with taglines, bilingual signage, and eight-item tour date posters all render cleanly. This is the feature that makes Ideogram V4 irreplaceable for design work.

Structured JSON Prompting

Every training image had exhaustively described JSON captions: per-element styling, literal text strings with separate visual descriptions, bounding-box coordinates, and color palettes. The model understands text placement at a structural level. Double-quoted text renders cleaner than unquoted. Specific typeface names (Cooper Black, Futura, Playfair Display) outperform generic descriptions. Adding "professional typography" to the style field pulls from a higher-quality training slice.

Bounding-Box Spatial Control

Specify x/y bounding-box coordinates in your prompt to place subjects, text, logos, and background regions precisely. Overlapping boxes create z-order: later elements render on top of earlier ones. This is how to place a logo over a photograph without a separate mask layer. The model treats coordinates as deterministic constraints, not suggestions.

Color Palette Conditioning

Pass hex color codes in a color_palette array to steer the image's dominant color scheme. Brand guidelines translate directly into model parameters. Hex codes work in the array field; color words like "deep navy blue" outperform hex codes in prose descriptions. Both methods keep the output within your brand palette without post-production color grading.

Vision-Language Text Encoder (Qwen3-VL-8B)

Instead of CLIP or T5, Ideogram V4 uses Qwen3-VL-8B-Instruct as its text encoder. Hidden states from 13 intermediate layers are concatenated, giving multi-scale semantic features from surface token understanding to deep compositional reasoning. This is why the model handles dense, multi-clause design briefs better than larger models using traditional text-only encoders.

LoRA Training Pipeline

Train custom LoRAs directly on Floyo. Fine-tune V4 for your brand's product line, character IP, or house style. The trained LoRA loads into the generation workflow for consistent, on-brand output. Enterprises can fine-tune on their own data and deploy within their own infrastructure.

Transparency and Layered Output (Roadmap)

Background Remover is live today: clean alpha cutouts from any V4 output. Coming next: editable text layers and movable image layers directly from inference. The model will return output as a stack of components rather than a flat frame. Headlines can be revised after generation. This closes the gap between AI output and production-ready design files.