Watch the full video first if you want to understand how this Ideogram 4 + KJ Prompt Builder workflow works in practice. The video shows how structured JSON prompts can be used to control poster layout, typography, composition, color palette, object placement, and overall visual direction inside ComfyUI.

This ComfyUI workflow is designed for Ideogram 4 visual composition control using KJ Prompt Builder. Its main purpose is not only to generate a beautiful image, but to make the image generation process more like a visual design system. Instead of writing one loose natural-language prompt, this workflow uses structured JSON-style prompting and a visual composition builder to define background, subject elements, text blocks, bounding boxes, color palettes, style direction, lighting, and output ratio.

The workflow is built around ideogram4_fp8_scaled.safetensors as the main Ideogram 4 model. It also uses ideogram4_unconditional_fp8_scaled.safetensors as the unconditional model branch, qwen3vl_8b_fp8_scaled.safetensors as the Ideogram 4 text encoder, flux2-vae.safetensors as the VAE, Ideogram4Scheduler, DualModelGuider, CFGOverride, EmptyFlux2LatentImage, SamplerCustomAdvanced, VAEDecode, SaveImage, and multiple Ideogram4PromptBuilderKJ nodes.

The most important part of this workflow is Ideogram4PromptBuilderKJ. This node allows the user to organize a visual prompt into a more controllable layout format. You can describe the global image concept, background, style, lighting, medium, color palette, and individual elements. Each element can be treated as an object or text block, with its own position, description, text content, and palette. This is especially useful for Chinese posters, thumbnails, title images, brand visuals, concept art, and design-heavy images where placement matters.

The workflow also includes multiple prepared example groups, such as Chinese movie poster layouts, racing posters, realistic documentary-style scenes, Chinese fantasy worldbuilding, puzzle-adventure environments, F1 racing, MotoGP racing, gaming showcase visuals, action-horror survival scenes, and dystopian underground sci-fi compositions. These examples make the workflow more than a single generation graph; it becomes a practical visual prompt library for learning how structured Ideogram 4 prompting works.

Another important part is the dual-model guidance structure. The main Ideogram 4 model follows the prompt, while the unconditional model provides the baseline. DualModelGuider pushes the generation toward the structured prompt target. CFGOverride controls the guidance behavior during the later sampling process, helping keep the result aligned without over-forcing the model.

The workflow also includes a ResolutionSelector for aspect ratio control. In this version, the default direction is 21:9 ultra-wide, which is especially suitable for cinematic banners, YouTube covers, poster key visuals, and wide-format concept art. Width and height are automatically aligned to valid values, reducing resolution errors.

Compared with ordinary text-to-image workflows, this setup is more suitable for design-oriented generation. It is useful when you need readable text, stable poster layout, clear composition hierarchy, controlled object placement, premium cover design, and repeatable prompt structure.

Main features:

• Ideogram 4 visual composition director workflow

• KJ Prompt Builder structured prompt control

• JSON-style layout and element description

• Supports object blocks and text blocks

• Better control over Chinese poster typography

• Ideogram 4 FP8 main model route

• Ideogram 4 unconditional model branch

• Qwen3-VL 8B Ideogram text encoder

• Flux2 VAE decoding

• DualModelGuider guidance structure

• CFGOverride guidance control

• Ideogram4Scheduler with Quality / Default / Turbo presets

• ResolutionSelector aspect ratio control

• 21:9 ultra-wide poster and cover design support

• Multiple built-in visual prompt examples

Suggested workflow:

Start by choosing the output ratio first. For cinematic covers and poster layouts, use the 21:9 ultra-wide setting. Then write the main idea in the JSON prompt or use Ideogram4PromptBuilderKJ to organize the scene. Define the high-level image description, background, aesthetics, lighting, medium, color palette, and key elements. For poster work, place the title and tagline as separate text elements instead of mixing them into one paragraph. Start with Default mode to test the layout. If the composition is correct, switch to Quality mode for a more polished result. If you only want fast testing, use Turbo mode. If the typography is unstable, simplify the text, reduce unnecessary elements, and keep the layout clean.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2062786765001412609?inviteCode=rh-v1111

If the results meet your expectations, you can later deploy it locally for customization.

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📺 Bilibili Updates (Mainland China & Asia-Pacific)

If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1LC7y6YEoY/

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⚙️打开下方链接即可在线体验，无需安装。
👉 工作流： https://www.runninghub.ai/post/2062786765001412609?inviteCode=rh-v1111
如果觉得效果理想，你也可以在本地进行自定义部署。

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📺 Bilibili 更新（中国大陆及南亚太地区）

如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。
📺 B站视频： https://www.bilibili.com/video/BV1LC7y6YEoY/

我会在 夸克网盘 持续更新模型资源：
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户，方便进行创作与学习。