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by artokun · June 16, 2026 · ernie · image · ComfyUI · model highlight Most “open” image models come with an asterisk: open weights, closed wallet — great until you ship something commercial and the license bites. ERNIE-Image is the rare one that doesn’t. Baidu shipped it under a real Apache-2.0 license, it renders crisp multilingual text (English, Chinese, Japanese) better than almost anything its size, and the Turbo build runs under 8 GB of VRAM in about 8 steps. You can run the whole thing locally in ComfyUI — no API key, no per-image fee, no non-commercial clause hanging over your head. Below: what ERNIE-Image actually is (and the one thing people keep getting wrong about it), how it stacks up against Ideogram 4 and Z-Image, 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 GGUFs and untangling a contaminated installer.
TL;DR — one-command setup. Install comfyui-mcp, apply the ernie pack (apply_manifest --path packs/ernie/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.

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What is ERNIE-Image?

ERNIE-Image (released April 15, 2026 by Baidu’s ERNIE-Image team) is an open-weight, single-stream Diffusion Transformer with about 8B DiT parameters. In ComfyUI it’s paired with a Ministral-3-3B text encoder and the Flux 2 VAE, plus an optional 3B Prompt Enhancer that auto-expands short prompts into richer descriptions. ComfyUI added day-0 support in April 2026. The license is the headline — and it’s genuinely permissive. ERNIE-Image ships under Apache-2.0, which allows commercial use, modification, and redistribution, full stop. That’s a real difference from Ideogram 4, which is open weight but under a Non-Commercial agreement (commercial use needs a paid license). With ERNIE you can put it in a product on day one. No gated download drama, no “for research only” footnote. There are two builds. ERNIE-Image (base) takes ~50 steps for peak quality. ERNIE-Image-Turbo is distilled (Distribution Matching Distillation + RL) and is high-fidelity in ~8 steps at cfg 1 — that’s the one the pack ships, and the one that fits under 8 GB.

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ERNIE-Image vs Ideogram 4 vs Z-Image

There’s no single “best” — pick by the job: Where rivals win: Ideogram 4 gives you explicit spatial control — draw bounding boxes, place text and objects by region — which ERNIE doesn’t expose (you steer ERNIE with descriptive prose). Z-Image is the speed king for fast general ideation. But ERNIE wins decisively on the two axes that matter for production text work: a permissive license and clean multilingual typography. Each of those models gets its own post in this series — this one is ERNIE’s spotlight for commercial-safe, text-rich images.

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System & VRAM requirements

The official non-quantized release targets a 24 GB card. The pack sidesteps that by shipping the Turbo GGUF, so you pick a quant to fit your GPU: The Ministral-3-3B encoder and Flux 2 VAE add a few GB on top. The pack ships Q8_0; if you’re tight on VRAM, edit the quant in the filename (e.g. ernie-image-turbo-Q5_K_S.gguf) before installing. Running the bundled ERNIE↔Z-Image combo pipelines loads two UNets, so budget for both or just run the single-model ERNIE group.

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Install ERNIE-Image in ComfyUI

The manual route works: update ComfyUI, install the GGUF + a handful of node packs, and drop the files into the right folders. (Canonical weights live in the official Comfy-Org/ERNIE-Image HF repo, Apache-2.0, with the same filenames as the third-party mirror.)

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A real-world install gotcha

If you grab the popular third-party installer, beware: it’s contaminated with Z-Image leftovers. The upstream scripts are copy-pasted from a Z-Image pack — their headers literally say “Z-IMAGE-BASE” and they download Z-Image-only files (z_image_turbo-*.gguf, Qwen3-4B-*.gguf, ae.safetensors) that ERNIE never uses. Pull those by mistake and you waste disk and end up wiring the wrong VAE. The comfyui-mcp ernie pack excludes them — it installs only ERNIE’s actual files (ernie-image-turbo, ministral-3-3b, flux2-vae, and the optional enhancer).

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The fast way — comfyui-mcp + the Panel

Untangling that installer by hand is exactly the busywork the comfyui-mcp ernie pack removes. One declarative manifest installs the custom nodes (ComfyUI-GGUF, rgthree, KJNodes, essentials, RES4LYF, and friends) and pulls every model to the correct folder — ERNIE-only, no Z-Image cruft — and the same manifest drives both an MCP-native install and the generated double-click scripts:
Then load packs/ernie/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).

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Why text and layout are its superpower

This is the part that holds up to scrutiny. Per Baidu’s published benchmarks, ERNIE-Image hits:
  • LongTextBench ~0.9733 — state-of-the-art for long, layout-sensitive text rendering among open models.
  • GenEval ~0.8856 overall — strong compositional accuracy, with a notable lead on position understanding and attribute binding.
A character-aware encoder trained on Baidu’s web-scale CJK text-image corpus is the reason Chinese (and Japanese) glyphs come out clean — stroke order and radical composition, not mangled approximations. (Benchmark figures are vendor-reported; treat the “state-of-the-art” framing as scoped to text rendering and structured layout, not general photorealism.) The practical version: if the image has to carry words — a poster headline, a sign, a manga speech bubble, a UI mockup, multilingual packaging — ERNIE is the open model to reach for. Write the exact string you want in quotes and it renders it.

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It is TEXT-TO-IMAGE — not an editor (read this)

The single most common ERNIE confusion: people expect it to edit a photo. It doesn’t. ERNIE-Image is a text-to-image generator. You describe an image and it paints one from scratch. It does not follow grounded edit instructions like “change the shirt to red” or “remove the person on the left.” The shipped workflow does have an “image-to-image” path, and that’s where the confusion starts — but that path is denoise-based refinement (a style/detail pass over one source image at low denoise), not instruction editing. If your job is “change X in this photo,” reach for Qwen-Image-Edit or Flux Kontext instead. ERNIE’s lane is generation, especially text- and layout-heavy generation.

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What to make with it

ERNIE’s sweet spots all share one trait — the image has to say something:
  • Multilingual posters & key visuals — EN/CN/JP headlines that render cleanly.
  • Text-heavy designs — signage, packaging, menus, UI mockups, ad creatives.
  • Manga / anime layouts — multi-panel pages and storyboards with legible speech bubbles (write the literal string in quotes).
  • Structured multi-object scenes — ERNIE’s strong instruction following handles complex, knowledge-heavy prompts.
  • Fast ideation — Turbo’s 8-step generation makes iterating on concepts cheap.
Prompting tip: lead with the exact text you want, in quotes — e.g. a vintage travel poster, bold title reading “KYOTO” at the top — then describe style and layout in plain, structured prose. That’s ERNIE’s headline strength.

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Settings that matter

The pack’s workflow is tuned already, but for reference — these are the Turbo defaults extracted from the shipped graph: A few notes that save grief:
  • Keep ModelSamplingAuraFlow shift at 3.1. It’s applied to the model before sampling; drop it and Turbo output goes soft/undercooked.
  • CFG 1 means negative conditioning is effectively inert. The graph still wires a ConditioningZeroOut as the negative — that’s normal, not a bug.
  • Base (non-Turbo) ernie-image? Bump to ~50 steps and raise cfg (≈3.5–5) since it isn’t distilled.
  • Use the Prompt Enhancer for short prompts, turn it off (ComfySwitchNode false) once you’ve written a detailed prompt yourself.

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Troubleshooting

  • UnetLoaderGGUF / CLIPLoaderGGUF missing → install ComfyUI-GGUF (city96).
  • Power Lora Loader / Image Comparer missing → install rgthree-comfy.
  • ImageResize+ missing → install ComfyUI_essentials.
  • TextGenerate (enhancer) missing → install via ComfyUI-Manager search, or set the ComfySwitchNode to the raw prompt (switch=false) and skip enhancement.
  • CLIP type error on Ministral → set CLIPLoader type to flux2 (not qwen_image / lumina2; lumina2 belongs to the Z-Image encoder).
  • Wrong-VAE artifacts → ERNIE must use flux2-vae.safetensors; ae.safetensors is the Z-Image VAE.
  • Blurry / undercooked output → confirm ModelSamplingAuraFlow shift=3.1 is wired and steps ≥ 8 for Turbo.
  • You wanted to EDIT a photo and it ignored you → expected. ERNIE is txt2img; use Qwen-Image-Edit or Flux Kontext for grounded edits.
  • Installer pulled Z-Image files too → that’s the contaminated upstream script; use the ernie pack, which excludes them.

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FAQ

Is ERNIE-Image open source? Yes — genuinely. It’s released under Apache-2.0, which permits commercial use, modification, and redistribution. Unlike Ideogram 4’s non-commercial license, there’s no separate paid agreement to ship commercially. Can ERNIE-Image edit my photos? No. It’s text-to-image only. The workflow’s “image-to-image” is a low-denoise refine pass, not instruction editing. For “change X in this photo,” use Qwen-Image-Edit or Flux Kontext. How much VRAM do I need? The Turbo GGUF runs in under 8 GB at the Q5_K_S quant; Q6_K wants 8–12 GB and Q8_0 wants 12–16 GB+. The non-quantized official release targets a 24 GB card. How many parameters? About 8B — a single-stream Diffusion Transformer, paired with a Ministral-3-3B text encoder and the Flux 2 VAE. Is it good at non-English text? Yes — that’s a core strength. It renders English, Chinese, and Japanese cleanly thanks to a character-aware encoder trained on a large CJK corpus. ERNIE-Image vs Z-Image — which should I use? Z-Image for the fastest general-purpose ideation; ERNIE when you need a permissive license plus precise multilingual text and structured layouts. Why is the text so accurate? Reported LongTextBench ~0.9733 — ERNIE was designed for layout-sensitive, multilingual text rendering, not as an afterthought. (Figures are vendor-reported; scope the claim to text/layout.)

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Get it running in one command

  1. 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).
  2. Apply the ernie pack — nodes + ERNIE-only models land in the right folders, validated, with the Z-Image cruft stripped out.
  3. Open the Panel and let the panel’s agent write multilingual 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. Next in the series: ANIMA 1.0 — the tiny anime model that generates and trains LoRAs under 6 GB.