Ideogram 4 Official Image Generation Workflow

Watch the full video first if you want to understand how this Ideogram 4 official image generation workflow works in practice. The video shows how Ideogram 4 can be used inside ComfyUI for structured poster design, typography-heavy visuals, brand-style images, collage compositions, and layout-controlled image generation.

This ComfyUI workflow is designed as a clean Ideogram 4 image generation route. Its main purpose is to provide a stable official-style output pipeline for creators who want to test Ideogram 4 without building a complicated multi-branch graph. The workflow focuses on model loading, prompt encoding, dual-model guidance, quality preset control, resolution handling, sampling, decoding, and final image export.

The workflow is built around ideogram4_fp8_scaled.safetensors as the main generation model. It also uses ideogram4_unconditional_fp8_scaled.safetensors as the unconditional branch, qwen3vl_8b_fp8_scaled.safetensors as the Ideogram 4 text encoder, and flux2-vae.safetensors as the VAE. These are the core files required for local deployment. The graph also includes CLIPTextEncode, ConditioningZeroOut, CFGOverride, DualModelGuider, Ideogram4Scheduler, RandomNoise, KSamplerSelect, EmptyFlux2LatentImage, SamplerCustomAdvanced, VAEDecode, and SaveImage.

The most important feature of this workflow is its structured prompt design. Ideogram 4 is especially strong for images that need readable text, graphic layout, poster composition, logo-like visuals, magazine-style design, and clear object placement. Instead of relying only on a loose natural-language prompt, this workflow encourages JSON-style captions. The JSON prompt can define the high-level description, background, visual style, lighting, color palette, and composition elements. This gives the model clearer design intent and makes it more useful for cover images, YouTube thumbnails, Chinese posters, advertising visuals, and social media key art.

The dual-model guidance structure is another key part. The main Ideogram 4 model reads the prompt and tries to follow the target image direction. The unconditional model provides a baseline. DualModelGuider compares the two branches and pushes the output toward the prompt target. CFGOverride then controls how guidance is applied during sampling, helping the workflow stay aligned without making the image overly rigid.

The workflow also includes a quality preset table. Quality mode uses 48 steps for better detail and final polish. Default mode uses 20 steps as a balanced setting. Turbo mode uses 12 steps for faster testing. This makes the workflow practical for different stages of creation: Turbo for quick ideas, Default for layout testing, and Quality for final output.

The resolution section is designed to reduce user error. Width and height inputs are processed through math nodes so they automatically align to valid 16-pixel multiples and stay above the minimum safe size. This avoids many common resolution problems when running Ideogram 4 locally.

Compared with ordinary text-to-image workflows, this Ideogram 4 workflow is more suitable for design-oriented generation. It is not only for realistic images; it is better for visual systems, typography, layout, posters, banners, thumbnails, product graphics, and high-impact AI design assets.

Main features:

• Ideogram 4 official image generation workflow

• Clean ComfyUI image output route

• Ideogram 4 FP8 main model support

• Ideogram 4 unconditional model branch

• Qwen3-VL 8B text encoder

• Flux2 VAE decoding

• JSON-style structured prompt support

• Better layout and typography control

• DualModelGuider guidance structure

• CFGOverride guidance timing control

• Ideogram4Scheduler sampling control

• Quality / Default / Turbo preset system

• Width and height auto-aligned to valid values

• SamplerCustomAdvanced generation route

• Final SaveImage output

Suggested workflow:

Start with Default mode first. Write a clear prompt or JSON prompt that defines the image type, subject, layout, text content, color palette, background, and visual style. For poster or thumbnail work, separate the title text, tagline, main subject, and background into clear composition instructions. Run the workflow once to check whether the layout is correct. If the structure is wrong, adjust the prompt before increasing quality. Use Turbo mode only for fast concept testing. Use Quality mode when the layout is already stable and you want a more polished final image. If text becomes unstable, reduce the amount of text, make the wording shorter, and keep the visual hierarchy clean.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2062772528212439042?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/2062772528212439042?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
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