Ideogram 4 High-quality cover image

This is an advanced text-to-image design workflow based on Ideogram 4 Open Weights + Qwen3-VL Text Encoder + Flux2 VAE + Dual-Model Guidance + JSON Structured Prompts. Its core objective is not merely generating a realistic image, but tackling visual design tasks that require higher control: posters, covers, title text graphics, logos, brand visuals, social media ads, and layout-driven commercial images.

The biggest difference between this and ordinary text-to-image generation is that: Ordinary text-to-image leans more towards "describing a scene", whereas this Ideogram 4 workflow leans more towards "designing a scene".
Users can not only write natural language prompts but also use structured JSON Prompts to break down the image into overall description, style description, background, elements, text, colors, spatial relationships, and composition areas. This is much more valuable for images that require titles, text, layouts, and a sense of branding.

The current default case is a typical design-type Prompt: instead of simply writing "a skateboarding youth", it breaks the image down into multiple design elements: a blue sky background, huge white 3D "COMFY" typography, a cutout photograph of the skateboarding youth, a red stamp, a torn paper banner on the left, and a rough paper strip with slogan text at the bottom. This structured writing approach is extremely well-suited for creating covers, posters, commercial promotional images, AI workflow cover images, and Xiaohongshu/Bilibili/YouTube thumbnails.

This workflow has four core advantages.

First, its text and layout capabilities are better suited for design scenarios.
The Ideogram series is inherently better at handling text images, title graphics, and layout designs. This workflow further separates text, elements, positions, and colors using JSON Prompts, making it much better suited than regular natural language for cover images with "conspicuous titles, clear subjects, and controllable layouts".

Second, dual-model guidance enhances prompt adherence.
The workflow contains two branches: the main model and the unconditional model. The main model is responsible for reading the prompt, the unconditional model provides a baseline, and the DualModelGuider pushes the generation direction toward the prompt's target. In short, it makes the image listen more to the prompt rather than relying on completely random generation.

Third, aspect ratio and quality modes are better tailored for application packaging.
This workflow already includes basic open parameters such as width, height, random seed, and quality mode. Users can directly select 3:4 posters, 16:9 covers, 1:1 social media images, 9:16 vertical images, and switch between Turbo, Default, and Quality based on their needs.

Fourth, it is exceptionally well-suited for RunningHub to build "image generation officer" style applications.
The frontend only needs to expose: Prompt / JSON Prompt, width, height, random seed, quality mode, prompt adherence strength, and final output. The main model, unconditional model, CLIP, VAE, Scheduler, ConditioningZeroOut, CFGOverride, etc., are recommended to be locked on the backend. This prevents users from being distracted by technical parameters, allowing them to directly input their requirements and generate highly polished design images.

Suitable application scenarios include: AI cover images, WeChat Official Account headers, Xiaohongshu covers, Bilibili/YouTube video thumbnails, poster designs, brand visuals, logo inspiration images, event promotional graphics, commercial ads, product concept art, workflow release covers, and course covers.

When using, it is recommended to focus on controlling three items: Prompt determines the design content, width and height determine the aspect ratio, and quality mode determines speed and details.
If users need strict layout control, using JSON Prompts is recommended; if they just want quick image generation, regular natural language can be used; if creating commercial promotional graphics, the prompt must explicitly state the title text, subject position, color system, background simplicity, and avoid cluttered decorations.

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