ERNIE-Image / ERNIE-Image-Turbo Workflows

What this is (read first)

ERNIE-Image is Baidu's open-weight TEXT-TO-IMAGE model, an ~8B single-stream Diffusion Transformer (DiT), Apache-2.0, released April 2026 and repackaged for ComfyUI by Comfy-Org. It is not an instruction-based image editor.

• ERNIE-Image (base): ~50 steps for peak quality.
• ERNIE-Image-Turbo: distilled (Distribution Matching Distillation + RL), high-fidelity in ~8 steps at cfg 1. The downloaded pack uses Turbo (ernie-image-turbo-*.gguf).

Pick ERNIE when the job is precise text/typography rendering (multilingual, including Chinese), posters/signage/UI mockups, manga/anime storyboards and multi-panel layouts, or structured multi-object scenes from a complex prompt. Do not pick ERNIE for "edit this photo / change the shirt / swap the background". That is instruction-grounded editing, which ERNIE does not do. Use qwen-image-edit or Flux Kontext for those. ERNIE's "image-to-image" here is plain denoise-based refinement (style pass / detail pass), not reference-grounded editing.

Niche vs siblings. ERNIE is the best open-weight text rendering + layout T2I. Qwen-Image-Edit does instruction editing. Flux Kontext does reference editing. Z-Image Turbo does fast general T2I, and this same pack pairs the two; see Combo pipelines.

Separated packs (render-verified)

The original ernie monolith was a single toggle-template graph (every pipeline shipped bypassed; you activated one via the rgthree group toggles). It's now split into standalone, single-purpose packs, each a clean activated graph that renders headlessly with no group-toggling:

Pack	Use	Models	VRAM
ernie-txt2img	text-to-image (flagship)	ERNIE only (4)	<8GB
ernie-img2img	denoise refine of a source image	ERNIE only (4)	<8GB
ernie-combo	ERNIE × Z-Image-Turbo combo pipelines	ERNIE + Z-Image (7, ~32GB)	12GB+

Working details verified live: the prompt-enhancer LLM is OFF by default (the ENHANCE PROMPT boolean is false; leave it off unless you want the 3B enhancer to rewrite the prompt). The grain/sharpen post-proc (FastFilmGrain/FastLaplacianSharpen, comfyui-vrgamedevgirl) needs librosa installed. In ernie-combo the Z-Image half's VAE is saved as z-image-ae.safetensors; its weights differ from Flux/ERNIE's ae.safetensors despite the same size, and the rename avoids a filename clash.

Source of truth & a provenance warning

This skill is derived from the actual pack files in C:\Users\Artokun\Downloads\:

• ERNIE-IMAGE-ULTRA-WORKFLOW.json (authoritative; the ComfyUI graph)
• ERNIE-IMAGE_ULTRA-MODELS-NODES_INSTALL.bat, ...-COMFYUI-MANAGER_AUTO_INSTALL.bat, ...-AUTO_INSTALL-RUNPOD.sh

Installer warning (verified). The three install scripts are copy-pasted from a Z-Image pack. Their headers literally say "Z-IMAGE-BASE"/"Z-IMAGE Base", and they download both ERNIE and Z-Image files. The model URLs/folders below are taken from those scripts but mirror this confusion. They pull z_image_turbo-*.gguf, Qwen3-4B-*.gguf, and ae.safetensors, which belong to the Z-Image half of the combo, not ERNIE. The ERNIE-only files are flagged below. All weights come from a third-party mirror huggingface.co/Aitrepreneur/FLX, not the official huggingface.co/Comfy-Org/ERNIE-Image (which hosts the same filenames; see Official sources).

Models

ERNIE-Image (the files ERNIE actually uses)

Confirmed from the workflow's virtual wires (Set_*/GetNode): the nodes tagged "ERNIE" resolve to these exact files.

Component	Node (type)	File (in workflow)	Folder	Notes
UNet (GGUF)	UnetLoaderGGUF	ernie-image-turbo-Q8_0.gguf	models/unet/	Turbo DiT. Q5_K_S / Q6_K / Q8_0 quants offered by installer
Text encoder	CLIPLoader (type=flux2)	ministral-3-3b.safetensors	models/text_encoders/	Ministral-3-3B is ERNIE's text encoder. Loaded with CLIP type flux2
VAE	VAELoader	flux2-vae.safetensors	models/vae/	ERNIE reuses the Flux 2 VAE
Prompt enhancer	CLIPLoader (type=flux2) → TextGenerate	ernie-image-prompt-enhancer.safetensors	models/text_encoders/	3B LLM that auto-expands a short prompt into a rich description (see Prompt enhancer). Optional, toggled per-pipeline

Quant guidance from the installer: Q5_K_S for GPUs <8 GB · Q6_K for 8 to 12 GB · Q8_0 for 12 to 16 GB+.

Z-Image Turbo (bundled in the same pack — the "ZIT" half)

The workflow also wires a parallel Z-Image Turbo pipeline for ERNIE→ZIT / ZIT→ERNIE combos. These files are Z-Image's, not ERNIE's. Do not confuse them:

Component	Node	File	Folder
UNet (GGUF)	UnetLoaderGGUF	z_image_turbo-Q8_0.gguf	models/unet/
Text encoder	CLIPLoaderGGUF (type=lumina2)	Qwen3-4B-UD-Q6_K_XL.gguf	models/text_encoders/
VAE	VAELoader	ae.safetensors	models/vae/

LoRAs (referenced in the Power Lora Loader, off by default)

hirohiko-araki-style-ERNIE_000001250.safetensors and ernie-anime-v1.safetensors are community ERNIE style LoRAs, loaded via Power Lora Loader (rgthree) (both toggled off in the shipped graph). Not in the installer; user-supplied.

Upscalers / post (shared)

4x-ClearRealityV1.pth, RealESRGAN_x4plus_anime_6B.pth → models/upscale_models/.

Installation

Custom nodes (git clone into ComfyUI/custom_nodes/)

All three installers clone the same set:

Node pack	Repo	Why it's needed
ComfyUI-Manager	https://github.com/ltdrdata/ComfyUI-Manager.git	management
ComfyUI-GGUF	https://github.com/city96/ComfyUI-GGUF	UnetLoaderGGUF, CLIPLoaderGGUF
rgthree-comfy	https://github.com/rgthree/rgthree-comfy	Power Lora Loader, Label, Fast Groups Bypasser, Image Comparer
ComfyUI-Easy-Use	https://github.com/yolain/ComfyUI-Easy-Use	easy cleanGpuUsed, easy clearCacheAll
ComfyUI-KJNodes	https://github.com/kijai/ComfyUI-KJNodes	utility nodes
ComfyUI_essentials	https://github.com/cubiq/ComfyUI_essentials	ImageResize+
wlsh_nodes	https://github.com/wallish77/wlsh_nodes	Upscale by Factor with Model (WLSH)
comfyui-vrgamedevgirl	https://github.com/vrgamegirl19/comfyui-vrgamedevgirl	FastFilmGrain, FastLaplacianSharpen
RES4LYF	https://github.com/ClownsharkBatwing/RES4LYF	advanced samplers

The graph also uses TextGenerate, TextBox1, StringReplace, ComfySwitchNode, PreviewAny, SetNode/GetNode, PrimitiveBoolean, ModelSamplingAuraFlow, ConditioningZeroOut, EmptySD3LatentImage, EmptyFlux2LatentImage. Most are builtin or come from the packs above. SetNode/GetNode are from KJNodes. TextGenerate (runs the prompt-enhancer LLM) has an unverified pack origin; verify which pack provides it via ComfyUI-Manager if it shows as missing.

Model downloads (exact URLs from the installer)

Base URL HF = https://huggingface.co/Aitrepreneur/FLX/resolve/main (third-party mirror). !MODEL_VERSION! ∈ {Q5_K_S, Q6_K, Q8_0}.

# ERNIE (the files ERNIE actually uses)
unet/ernie-image-turbo-<Q>.gguf                <HF>/ernie-image-turbo-<Q>.gguf?download=true
text_encoders/ministral-3-3b.safetensors       <HF>/ministral-3-3b.safetensors?download=true
text_encoders/ernie-image-prompt-enhancer.safetensors  <HF>/ernie-image-prompt-enhancer.safetensors?download=true
vae/flux2-vae.safetensors                       <HF>/flux2-vae.safetensors?download=true

# Z-Image half (bundled; only needed for the ZIT combo pipelines)
unet/z_image_turbo-<Q>.gguf                      <HF>/z_image_turbo-<Q>.gguf?download=true
text_encoders/Qwen3-4B-UD-Q6_K_XL.gguf          <HF>/Qwen3-4B-UD-Q6_K_XL.gguf?download=true
vae/ae.safetensors                               <HF>/ae.safetensors?download=true

# Upscalers
upscale_models/4x-ClearRealityV1.pth            <HF>/4x-ClearRealityV1.pth?download=true
upscale_models/RealESRGAN_x4plus_anime_6B.pth   <HF>/RealESRGAN_x4plus_anime_6B.pth?download=true

Official sources (prefer these over the mirror)

The same filenames are hosted officially at huggingface.co/Comfy-Org/ERNIE-Image (unet|diffusion_models/, text_encoders/, vae/). Apache-2.0. Original model: github.com/baidu/ERNIE-Image. Comfy day-0 docs: docs.comfy.org/tutorials/image/ernie-image/ernie-image. The official repo also ships non-GGUF ernie-image.safetensors / ernie-image-turbo.safetensors (load with UNETLoader instead of UnetLoaderGGUF).

How the pipeline works (at a glance)

The big graph is a menu of group-boxed pipelines built from the same blocks. Core ERNIE-Image text-to-image flow:

UnetLoaderGGUF (ernie-image-turbo) ──► Power Lora Loader (rgthree) ──► ModelSamplingAuraFlow (shift=3.1) ──► MODEL
CLIPLoader (ministral-3-3b, type=flux2) ──► CLIP ──► CLIPTextEncode (positive)
                                              └──► ConditioningZeroOut  ──► negative   (cfg=1, so negative ≈ unused)
VAELoader (flux2-vae) ──► VAE
EmptySD3LatentImage (1920×1088) ──► LATENT
        │
KSampler (steps≈8–9, cfg=1, euler, simple, denoise=1) ──► VAEDecode ──► SaveImage / post

In the optional prompt enhancer path, the short user prompt + {width}/{height} are templated into a Chinese system prompt, fed to TextGenerate (which runs ernie-image-prompt-enhancer), and a ComfySwitchNode chooses raw prompt (switch=false) vs. enhanced prompt (switch=true) before CLIPTextEncode.

The image-to-image (refine) path is not editing. The graph's "ERNIE IMAGE TO IMAGE" groups take a LoadImage → ImageResize+ (1024, keep proportion, lanczos) and VAEEncode it, then run KSampler at low denoise (0.35 to 0.4) to refine or restyle. This is a denoise pass over a single source image; it does not follow edit instructions.

About the ~5 LoadImage + ~5 VAEEncode nodes: they are not multi-reference compositing. Each LoadImage feeds a separate pipeline variant (single-image img2img, or the combo refine stages). One source image per pipeline. The extra VAEEncodes are the encode steps for those independent img2img / two-pass refine chains.

The combo pipelines (ERNIE↔ZIT), with group titles ERNIE ---> ZIT COMBO, ZIT ---> ERNIE COMBO, TWO TIMES COMBO ..., chain ERNIE and Z-Image Turbo as a two-pass generate→refine, with optional film-grain (FastFilmGrain) or sharpening (FastLaplacianSharpen) finishing and SIMPLE UPSCALE.

Settings (extracted from the shipped KSamplers)

Pipeline	Steps	CFG	Sampler	Scheduler	Denoise	Shift
ERNIE text-to-image (Turbo)	8–9	1	euler	simple	1.0	3.1
ERNIE image-to-image refine	8	1	euler	simple	0.4	3.1
Combo refine pass (2nd stage)	9	1	euler	simple	0.25–0.35	3.1

• ModelSamplingAuraFlow shift = 3.1 is applied to the ERNIE model before sampling (flow-matching shift). Keep it.
• CFG = 1 for Turbo, so negative conditioning is effectively inert; the graph still wires a ConditioningZeroOut as the negative.
• The shipped latent is 1920×1088 (EmptySD3LatentImage). ERNIE is a high-res-capable DiT; 1024 to 2048 on the long edge is reasonable. Use EmptySD3LatentImage for ERNIE latents.
• For base (non-Turbo) ernie-image, bump steps to ~50 and raise cfg (e.g. 3.5 to 5) since it is not distilled.

Prompt / instruction style

ERNIE rewards descriptive, structured natural-language prompts, and is unusually strong at literal text rendering. Write the exact text you want to appear in quotes.

A vintage travel poster of Kyoto in autumn, bold title text reading "KYOTO" at the top,
maple leaves, Mount fuji silhouette, clean vector layout, muted warm palette

A 3-panel manga page: panel 1 a samurai drawing his sword, panel 2 close-up of his eyes,
panel 3 a wide shot of cherry blossoms falling, black-and-white ink, speech bubble "参る"

• For typography/signage, state the literal string ("a neon sign that says 'OPEN'"), placement, and font feel.
• For layout, name the panel/grid structure and what goes in each region.
• Multilingual prompts (incl. Chinese) work; the built-in enhancer's system prompt is Chinese.
• This is txt2img phrasing, not edit phrasing. Do not write "change the…/remove the…" expecting grounded edits.

Complete API-format workflow (ERNIE-Image-Turbo text-to-image)

Derived from the source graph, flattened to API format (no subgraphs/virtual wires). The enhancer is omitted for clarity; CLIPTextEncode takes the prompt directly.

{
  "1": { "class_type": "UnetLoaderGGUF", "inputs": { "unet_name": "ernie-image-turbo-Q8_0.gguf" } },
  "2": { "class_type": "CLIPLoader", "inputs": { "clip_name": "ministral-3-3b.safetensors", "type": "flux2", "device": "default" } },
  "3": { "class_type": "VAELoader", "inputs": { "vae_name": "flux2-vae.safetensors" } },
  "4": { "class_type": "ModelSamplingAuraFlow", "inputs": { "model": ["1", 0], "shift": 3.1 } },
  "5": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["2", 0], "text": "A vintage travel poster of Kyoto in autumn, bold title text reading \"KYOTO\" at the top, maple leaves, clean vector layout, muted warm palette" } },
  "6": { "class_type": "ConditioningZeroOut", "inputs": { "conditioning": ["5", 0] } },
  "7": { "class_type": "EmptySD3LatentImage", "inputs": { "width": 1920, "height": 1088, "batch_size": 1 } },
  "8": { "class_type": "KSampler", "inputs": {
    "model": ["4", 0], "positive": ["5", 0], "negative": ["6", 0], "latent_image": ["7", 0],
    "seed": 997032332094579, "steps": 9, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1
  } },
  "9": { "class_type": "VAEDecode", "inputs": { "samples": ["8", 0], "vae": ["3", 0] } },
  "10": { "class_type": "SaveImage", "inputs": { "images": ["9", 0], "filename_prefix": "ernie_image" } }
}

Image-to-image (refine) variant

Replace the empty latent with an encoded source image and lower denoise. This restyles or refines a single image; it is not instruction editing.

{
  "11": { "class_type": "LoadImage", "inputs": { "image": "source.png" } },
  "12": { "class_type": "ImageResize+", "inputs": { "image": ["11", 0], "width": 1024, "height": 1024, "interpolation": "lanczos", "method": "keep proportion", "condition": "always", "multiple_of": 0 } },
  "13": { "class_type": "VAEEncode", "inputs": { "pixels": ["12", 0], "vae": ["3", 0] } }
}

Then in the KSampler set "latent_image": ["13", 0] and "denoise": 0.4.

Adding LoRAs

Insert a Power Lora Loader (rgthree) between the UNet loader and ModelSamplingAuraFlow (model: ["1",0] → loader → ["4"].model). In API format you can substitute LoraLoaderModelOnly with lora_name: "ernie-anime-v1.safetensors", strength_model: 0.5.

VRAM

• ERNIE-Image-Turbo GGUF: Q5_K_S for <8 GB · Q6_K for 8 to 12 GB · Q8_0 for 12 to 16 GB+ (installer's own guidance).
• The Ministral-3-3B encoder + Flux2 VAE add a few GB. The graph includes easy cleanGpuUsed / easy clearCacheAll nodes between stages. Keep them for the combo/two-pass pipelines so VRAM is freed before swapping models.
• Running the ERNIE↔ZIT combos loads two UNets; budget for both or run the single-model ERNIE group only.

Troubleshooting

• UnetLoaderGGUF / CLIPLoaderGGUF missing. Install ComfyUI-GGUF (city96).
• Power Lora Loader / Image Comparer / Label missing. Install rgthree-comfy.
• ImageResize+ missing. Install ComfyUI_essentials.
• TextGenerate missing (prompt enhancer). Install via ComfyUI-Manager search; pack origin unverified. If unavailable, set the ComfySwitchNode to use the raw prompt (switch=false) and skip enhancement.
• CLIP type error on ministral. Ensure CLIPLoader type is flux2 (not qwen_image/lumina2). The lumina2 type belongs to the Z-Image (Qwen3) encoder, not ERNIE.
• 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; for base ernie-image use ~50 steps + higher cfg.
• You wanted to EDIT a photo and it ignored the instruction. Expected. ERNIE is txt2img; use the qwen-image-edit skill or Flux Kontext for grounded edits.
• Installer pulled Z-Image files too. Expected (the scripts are Z-Image-derived). Harmless; those files only feed the combo pipelines.

Tips

1. Lead with the literal text you want rendered, in quotes. That's ERNIE's headline strength.
2. Use the prompt enhancer for short/lazy prompts; turn it off (ComfySwitchNode false) when you've written a detailed prompt yourself.
3. Use get_workflow (action:"analyze") before executing the shipped graph. It has dozens of group-boxed variants gated by Fast Groups Bypasser (rgthree); the analyzer summary is far easier than reading raw JSON.
4. Most groups are bypassed (mode 4) by default in the source file. Enable only the pipeline you want via the group bypasser, or build the clean API workflow above.
5. To choose a model: ERNIE for text/layout T2I, Z-Image Turbo for fast general T2I, Qwen-Image-Edit / Flux Kontext for actual editing.

Sources

• Official: https://github.com/baidu/ERNIE-Image and https://docs.comfy.org/tutorials/image/ernie-image/ernie-image
• Empirical: sampler values and combo wiring from the pack graphs (see Source of truth above); installer scripts are Z-Image-derived.