How to Run Ideogram 4 Locally in ComfyUI (2026 Guide)

by artokun · June 16, 2026 · ideogram · text-to-image · ComfyUI · model highlight Most image models can paint a gorgeous scene and then butcher the one word you actually needed on the sign. Ideogram 4 is the open-weight model that finally doesn’t — and you can run the whole thing locally in ComfyUI, no API key, no per-image fee. This is the first entry in our model-highlight series, and a fitting flagship: Ideogram 4 is, by the open-weight leaderboards, the best you can self-host today for text-in-image and graphic-design layout. Below: what it’s genuinely best at (with receipts), how it stacks up against Flux / Qwen / Z-Image / ERNIE, the VRAM you need, and the fastest way to get it running — a one-command install with comfyui-mcp and the sidebar Panel, instead of hand-downloading ~50 GB of weights and hand-typing JSON coordinates.

TL;DR — one-command setup. Install comfyui-mcp, apply the ideogram pack (apply_manifest --path packs/ideogram/manifest.yaml, or run the generated install-windows.bat / install-runpod.sh), and drive the graph from your own Claude session via the Panel. Jump to Install.

What is Ideogram 4?

Ideogram 4 (released June 3, 2026) is the first open-weight foundation model from Ideogram — a 9.3B-parameter single-stream Diffusion Transformer, trained from scratch, with a Qwen3-VL-8B text encoder. It renders at 256–2048px and, uniquely, reads a structured JSON prompt that lets you place text, objects, and colors in specific regions of the frame. Open weight, not “open source” — read this first. Ideogram 4 ships under a custom Non-Commercial Model Agreement, not Apache/MIT. Personal use, research, and evaluation are fine; commercial use needs a separate paid license, and the terms forbid using its outputs to train competing models. The HuggingFace download is gated. There’s also an embedded safety filter you can’t disable from ComfyUI. None of that changes how good it is — but if your use case is commercial or you expect an uncensored local model, know it going in.

Why text and layout are its superpower

This is the headline, and it holds up to scrutiny:

~0.97 English OCR accuracy (X-Omni) — the best of any open-weight model at its scale, ahead of much larger ones (Qwen-Image 20B, FLUX.2 dev 32B, HunyuanImage 3.0 80B MoE).
In a designer-preference typography study, it was picked first ~48% of the time and scored highest on “would you use this in real client work.”
#1 open-weight model on Design Arena (Elo ≈ 1285, a ~115-point lead over the next open model).

A fair caveat: the typography study is vendor-run and Design Arena is design-weighted, so the “beats Flux/Qwen” framing is strongest for text and layout specifically — not general photorealism. Reviewers consistently scope the win that way, and so do we.

Ideogram 4 vs Flux, Qwen-Image, Z-Image, and ERNIE

There’s no single “best” — pick by the job:

Pick…	When you need…
Ideogram 4	Readable in-image text, posters/logos/packaging, layout & spatial control (bounding boxes), brand-color accuracy, multilingual headlines, 2K print output
FLUX.2	Photorealism and a mature production default
Qwen-Image	Text + multilingual on a larger, popular dev model
Z-Image Turbo	Speed + low VRAM (~2.5s, ~8 GB) for fast ideation
Z-Image Base	Predictable control and LoRA-training stability
ERNIE-Image	Truly permissive (Apache 2.0) commercial use + CN/JP/EN text

Where rivals win: photoreal portraits (Ideogram trails), raw speed and low VRAM (Z-Image), permissive licensing (ERNIE, Z-Image), and editability — Ideogram’s output is flat pixels, not layers. Each of those models gets its own post in this series; Ideogram 4 earns the spotlight for information-carrying images.

System & VRAM requirements

The full FP8 release is two ~13.8 GB diffusion models plus the encoder — ~50 GB on disk. VRAM:

Build	VRAM	Notes
FP8 (official)	16 GB floor, 24–32 GB comfortable	Two ~13.8 GB UNETs + 8 GB encoder; ComfyUI swaps them in/out
NF4	~12 GB	Quality/VRAM trade
GGUF (e.g. Q4_K)	~8 GB	Community quants for smaller cards

Treat 16 GB as the minimum and 24 GB+ as the smooth-at-2K target. On a 24 GB RTX 4090 you’re in good shape.

Install Ideogram 4 in ComfyUI

The manual route works: update ComfyUI, download the five files into the right folders, and load the text-to-image workflow.

File	Folder
`ideogram4_fp8_scaled.safetensors` (conditional UNet)	`models/diffusion_models/`
`ideogram4_unconditional_fp8_scaled.safetensors` (unconditional UNet)	`models/diffusion_models/`
`qwen3vl_8b_fp8_scaled.safetensors` (text encoder)	`models/text_encoders/`
`gemma4_e4b_it_fp8_scaled.safetensors` (prompt-builder helper, optional)	`models/text_encoders/`
`flux2-vae.safetensors`	`models/vae/`

(Canonical weights live in the Comfy-Org HF repos.)

The fast way — comfyui-mcp + the Panel

Downloading 50 GB to the right folders and wiring two UNETs by hand is exactly the busywork the comfyui-mcp ideogram pack removes. One declarative manifest installs the custom nodes (incl. the KJ Prompt Builder) and pulls every model to the correct folder — and the same manifest drives both an MCP-native install and the generated double-click scripts:

# MCP-native (from a Claude Code session, with COMFYUI_PATH set)
apply_manifest --path packs/ideogram/manifest.yaml

# or one-click
packs/ideogram/install-windows.bat      # Windows
packs/ideogram/install-runpod.sh        # RunPod / Linux

Then load packs/ideogram/workflow.json. Because the pack ships with the plugin, your own Claude session can drive the live graph through the Panel — add/wire nodes, set widgets, and iterate on prompts conversationally, with full Ctrl+Z undo and no extra API keys. Every model URL in the pack is CI-validated for reachability and size, so a link never quietly rots (here’s why that matters).

Structured JSON prompting (the part that sets Ideogram apart)

Ideogram 4 was trained on structured JSON captions, so instead of one loose sentence you hand it a document that “deconstructs” the image into regions — each described, placed, and colored separately. That’s what makes text and layout controllable:

{
  "high_level_description": "one-line summary of the whole image",
  "style_description": {
    "aesthetics": "...", "lighting": "...", "art_style": "...",
    "color_palette": ["#0A1F44", "#FFD200"]
  },
  "compositional_deconstruction": {
    "background": "scene, palette, lighting, mood (required)",
    "elements": [
      { "type": "obj",  "bbox": [200, 120, 760, 880], "desc": "a red sports car, 3/4 view" },
      { "type": "text", "bbox": [40, 100, 180, 900],
        "text": "GRAND OPENING", "desc": "bold condensed sans, gold",
        "color_palette": ["#FFD200"] }
    ]
  }
}

Two things to get right:

bbox = [top, left, bottom, right], on a 0–1000 normalized canvas (not pixels, not x/y/w/h). Canvas width/height must be multiples of 16.
Palettes condition color: up to 16 hex at the image level, up to 5 per element.

You don’t have to type coordinates by hand. The Ideogram 4 Prompt Builder (KJ) node — bundled in the pack — is a visual canvas: in V2 you draw regions with brush/rectangle/ellipse/polyline, get per-region layers (overlaps don’t merge), bucket fill, an eyedropper, and an opacity slider to trace a reference image. Each shape becomes a bbox automatically. The pack also includes 25 ready-made JSON templates (drop them in ComfyUI/user/default/kjnodes/ideogram4/templates). Area-prompting tips that actually help:

One bbox per major subject — don’t split a person into face/hair/clothes.
Reserve zones for posters: title up top, subject center, CTA at the bottom.
3–6 strong, non-overlapping elements beat 20 overlapping ones (overlap garbles text).
Lock global look with style_description so per-element prompts don’t drift.

What to make with it

The pack’s 25 templates map to its sweet spots:

Logos & brand identity — wordmarks, mascot kits, luxury identity sheets
Posters & key visuals — film/event posters with legible headlines
Covers — book, magazine, comic, anime covers
Product & display ads — packaging, e-commerce creatives, menus
Character & game sheets — 2D/3D character sheets, trading cards, item sheets
Merch & signage — t-shirt graphics, print-on-demand, thumbnails

If the image has to carry information — a headline, a price, a brand — this is the open model to reach for.

Settings that matter

The pack’s workflow is tuned already, but for reference: sampler euler, scheduler simple, 28 steps (turbo: 12), cfg 5, ModelSamplingAuraFlow shift 5, denoise 1.0 (0.6 for img2img). Ideogram 4 uses two UNETs — the unconditional model feeds a DualModelGuider for the CFG baseline, so there’s no negative text prompt (it’s zeroed out). The V2 workflow applies any LoRA to both the conditional and unconditional models.

Troubleshooting

Text has a typo / is garbled. Keep rendered strings short and bold; don’t overlap text boxes; proofread before publishing — high OCR ≠ perfect every time.
Gated download / 401. Accept the license on the HuggingFace page and authenticate, or let the pack pull from its mirror.
Safety filter refusals. Baked into the weights; it can’t be disabled from ComfyUI.
OOM at 2K. Drop to a GGUF/NF4 build, generate at lower res first, or use a 24 GB+ GPU.
Same seed, different image across machines. Expected — output varies with GPU/driver/CUDA/ComfyUI version.

FAQ

Is Ideogram 4 open source or open weight? Open weight. The weights are downloadable under a custom Non-Commercial license — not an OSI open-source license. Is it free to use? Free to download and use non-commercially. Commercial use requires a separate paid agreement with Ideogram. Can I run it locally / offline? Yes — fully local in ComfyUI, no API key and no network call at generation time. How much VRAM do I need? ~16 GB minimum for the FP8 build, 24 GB+ for smooth 2K work; GGUF quants bring it down to ~8 GB. How many parameters? 9.3B — a single-stream Diffusion Transformer with a Qwen3-VL-8B text encoder. Is it better than Flux or Qwen-Image? For in-image text and design layout, yes by the open-weight leaderboards. For photorealism, Flux is the stronger default. Why is its text so accurate? It was trained on structured JSON captions and optimized for typography, hitting ~0.97 OCR accuracy — text generation is the model’s core design goal, not an afterthought.

Get it running in one command

Install comfyui-mcp and the Panel — the panel auto-starts a background agent on your Claude subscription (no API keys; sign in with claude once).
Apply the ideogram pack — nodes + ~50 GB of models land in the right folders, validated.
Open the Panel and let the panel’s agent build area-prompts and wire the graph for you.

That’s the whole point of the project: expert ComfyUI setups that install in one step and drive themselves from your own agent session. That wraps the model-highlight series — nine local ComfyUI setups, each one command away. Browse them all on the blog index, or start from the top with WAN 2.2 in ComfyUI.

Blog

Model Highlights

How to Run Ideogram 4 Locally in ComfyUI (2026 Guide)

What is Ideogram 4?

Why text and layout are its superpower

Ideogram 4 vs Flux, Qwen-Image, Z-Image, and ERNIE

System & VRAM requirements

Install Ideogram 4 in ComfyUI

The fast way — comfyui-mcp + the Panel

Structured JSON prompting (the part that sets Ideogram apart)

What to make with it

Settings that matter

Troubleshooting

FAQ

Get it running in one command

​What is Ideogram 4?

​Why text and layout are its superpower

​Ideogram 4 vs Flux, Qwen-Image, Z-Image, and ERNIE

​System & VRAM requirements

​Install Ideogram 4 in ComfyUI

​The fast way — comfyui-mcp + the Panel

​Structured JSON prompting (the part that sets Ideogram apart)

​What to make with it

​Settings that matter

​Troubleshooting

​FAQ

​Get it running in one command

What is Ideogram 4?

Why text and layout are its superpower

Ideogram 4 vs Flux, Qwen-Image, Z-Image, and ERNIE

System & VRAM requirements

Install Ideogram 4 in ComfyUI

The fast way — comfyui-mcp + the Panel

Structured JSON prompting (the part that sets Ideogram apart)

What to make with it

Settings that matter

Troubleshooting

FAQ

Get it running in one command