Ideogram 4 Structured JSON Image Reconstruction Workflow

Watch the full video first if you want to understand how this Ideogram 4 structured JSON image reconstruction workflow works in practice. The video shows how a reference image can be analyzed, converted into an Ideogram 4 JSON prompt, and then regenerated through a structured image generation pipeline.

This ComfyUI workflow is designed for Ideogram 4 image reconstruction through structured JSON prompting. Its main purpose is not ordinary text-to-image generation. Instead, the workflow starts from a reference image, analyzes its visible composition, and rebuilds the image as an Ideogram 4-compatible structured JSON prompt. This makes it especially useful for image style reconstruction, layout recovery, poster remaking, visual reference rebuilding, typography layout testing, and design-oriented “image washing” workflows.

The key idea is simple: the user does not need to manually write a long prompt. The workflow uses a reference image as the main input. The visual analysis chain observes the image, extracts the subject, background, composition hierarchy, visible text, color system, lighting, medium, and layout relationship, then generates a structured JSON prompt. That JSON prompt is sent into the Ideogram 4 generation route, allowing the model to recreate the design logic instead of only guessing from a loose natural-language description.

The workflow is built around Ideogram 4 image generation. It uses ideogram4_fp8_scaled.safetensors as the main model, Flux2 VAE for decoding, Ideogram4Scheduler for sampling control, DualModelGuider for guided generation, CFGOverride for guidance behavior, EmptyFlux2LatentImage for canvas creation, SamplerCustomAdvanced for final denoising, VAEDecode for image decoding, and SaveImage for final export. The workflow also includes a structured prompt encoding section where the generated JSON is passed into CLIPTextEncode.

The most important part is the image-to-JSON reconstruction section. The workflow note describes the main route as LoadImage → image_scale_pixel_v2 → RHLLMChatNode image1, combined with a system prompt and target width / height. RH visual completion observes the reference image and outputs an Ideogram 4 JSON prompt. The width and height are used to help plan bounding boxes, layout proportions, and composition scale. This makes the prompt more useful for structured regeneration, especially when the original image contains posters, typography, layered objects, or a strong graphic design layout.

This version intentionally removes the older user-prompt route. It does not focus on asking the user to rewrite the image with text. Instead, it focuses on “image to Ideogram 4 JSON Prompt.” That makes the workflow cleaner and more specialized. The user mainly adjusts the reference image, output ratio, width and height, RH model choice, max tokens, seed, and Quality / Default / Turbo mode.

The workflow also includes a quality preset system. Quality uses 48 steps for more polished output, Default uses 20 steps for balanced testing, and Turbo uses 12 steps for faster previews. Width and height are automatically aligned to valid 16-pixel multiples and kept above the minimum safe size, reducing resolution-related errors.

Compared with ordinary image-to-image workflows, this workflow does not simply encode the source image into latent space. It rebuilds the visual structure as text-level JSON composition. This is useful when you want more controllable reconstruction, cleaner layout reasoning, better prompt reuse, and a reusable Ideogram 4 prompt that can be edited, copied, or expanded later.

Main features:

• Ideogram 4 structured JSON reconstruction workflow

• Reference image to JSON Prompt route

• RH visual completion image analysis

• Image-only reverse prompt generation

• No old user prompt dependency

• Structured composition reconstruction

• Subject, background, color, lighting, and layout extraction

• Bounding box aware prompt planning

• Better poster and typography reconstruction

• Ideogram 4 FP8 main model support

• Flux2 VAE decoding

• Ideogram4Scheduler sampling control

• DualModelGuider guidance structure

• CFGOverride guidance control

• Quality / Default / Turbo preset system

• Width and height auto-aligned to valid values

• SamplerCustomAdvanced generation route

• SaveImage final image output

Suggested workflow:

Prepare a clear reference image first. The image should have readable composition, visible subjects, and a layout worth reconstructing. Upload the image into the workflow, then set the target ratio or width and height. Let the RH visual completion section analyze the image and generate the Ideogram 4 JSON prompt. Check the generated JSON before rendering. If the output loses the original layout, strengthen the background and element descriptions. If the text is unstable, shorten the text and separate title, subtitle, and footer into different text elements. Start with Default mode to test the reconstruction. Use Turbo for quick structure checks and Quality for final polished output. This workflow is best used when you want to turn an existing image into a reusable Ideogram 4 structured prompt rather than manually writing everything from scratch.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2064321335798620162?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/BV1RtEQ6SEsS/

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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/2064321335798620162?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/BV1RtEQ6SEsS/

我会在 夸克网盘 持续更新模型资源：
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