ERNIE-Image ComfyUI Community Workflow Guide: Sampler, CFG & LoRA Best Practices
Audience: ComfyUI users with basic ERNIE-Image knowledge who want to optimize output quality
Core content: Sampler comparison, CFG strategies, PE toggle, curated community workflows
Introduction: Why ERNIE-Image Needs Its Own Tuning Guide
Three months after ERNIE-Image's open-source release, the community has accumulated significant hands-on experience in ComfyUI. Unlike SD-family models, ERNIE-Image uses a pure DiT architecture with Flow Matching training—its sampler preferences, CFG behavior, and Negative Prompt response differ significantly from traditional U-Net diffusion models.
This article systematically consolidates the most valuable community insights from Reddit r/comfyui, r/StableDiffusion, and Civitai workflow shares—giving you production-ready configurations.
Chapter 1: Ultimate Sampler Comparison
Base Model (50-step inference)
The ERNIE-Image Base model requires more inference steps and is more sensitive to sampler choice:
| Sampler | Scheduler | CFG | Steps | Rating | Notes |
|---|---|---|---|---|---|
| DPM++ 2M | Karras | 4.0 | 25-30 | ⭐⭐⭐⭐⭐ | Highest quality, rich detail |
| Euler | Simple | 4.0 | 30-50 | ⭐⭐⭐⭐ | Most stable, best compatibility |
| DPM++ 2M SDE | Karras | 4.0 | 25-30 | ⭐⭐⭐ | Slightly slower than 2M |
| DPM++ 3M SDE | Karras | 4.0 | 30-40 | ⭐⭐⭐ | Better with higher steps |
Community consensus: DPM++ 2M + Karras is the best combination for Base model. Euler + Simple serves as a reliable fallback with extremely stable output.
Turbo Model (8-step inference)
The Turbo model, distilled via DMD + RL training, has a narrower sampler selection range:
| Sampler | Scheduler | CFG | Steps | Rating | Notes |
|---|---|---|---|---|---|
| Euler | Simple | 1.0 | 8 | ⭐⭐⭐⭐⭐ | Official recommendation, community verified best |
| DPM++ 2M | SGM Uniform | 1.0 | 8 | ⭐⭐⭐⭐ | Slightly better detail than Euler |
| DDIM | Uniform | 1.0 | 8 | ⭐⭐⭐ | Softer output |
| LCM | - | 1.0 | 4-8 | ⭐⭐ | Fast but significant quality loss |
Key insight: At CFG=1.0, all samplers produce very similar results with Turbo. Don't overthink sampler selection—Euler + Simple is always reliable.
Chapter 2: CFG Scale & Negative Prompt Strategy
Turbo Model: CFG=1.0 is the Law
The Turbo model, having undergone DMD distillation, has a highly optimized output distribution. Community verification consistently shows:
- CFG=1.0: Best results, faithful prompt following, highest aesthetic quality
- CFG > 1.5: Excessive contrast, oversaturated colors, prone to artifacts
- CFG < 1.0: May produce blurry or undersaturated results
Negative Prompt is essentially ineffective with Turbo. At CFG=1.0, there's no classifier guidance for Negative Prompts to influence the denoising process.
Base Model: CFG=4.0 + Negative Prompt is Standard
The Base model retains the complete guidance mechanism, making CFG and Negative Prompt behavior similar to traditional models:
Recommended config:
- CFG: 4.0 (range: 3.5-5.0)
- Sampler: DPM++ 2M Karras
- Steps: 25-30
Recommended Negative Prompt recipes:
standard: worst quality, low quality, bad anatomy, ugly, distorted, blurry, watermark, text signature, extra digits
Targeted Negative Prompts:
| Issue | Negative Prompt Recipe |
|---|---|
| Bad hands | poorly drawn hands, extra fingers, fused fingers, bad hand anatomy |
| Plastic skin | plastic skin, airbrushed skin, smooth skin, wax skin |
| Text artifacts | jumbled text, garbled characters, misspelled text |
| Overexposure | overexposed, blown out highlights, too bright |
| Grid artifacts | checkerboard pattern, grid artifact, diagonal grid (see EI-046) |
Chapter 3: PE (Prompt Enhancer) Toggle Strategy
PE is a distinctive feature of ERNIE-Image, but it's not a "always-on" solution:
| Scenario | Recommended PE | Reason |
|---|---|---|
| Base + detailed prompt | ON | PE adds structural descriptions, significantly improving quality |
| Base + Chinese prompt | ON | PE's enhancement is especially effective for Chinese prompts |
| Turbo + short prompt | ON | Enriches detail, compensates for Turbo's detail loss |
| Turbo + precise instruction | OFF | PE rewrites prompts, may deviate from original intent |
| Precise text rendering needed | OFF | PE may modify text content, affecting accuracy |
| img2img workflow | OFF | Preserves original composition; PE may over-modify the image |
Rule of thumb: Try with PE ON first. If the output deviates from your intent, turn PE OFF and regenerate.
Chapter 4: Curated Community Workflow Recommendations
1. NVFP4 Quantized Workflow — Lowest VRAM
Civitai: #2561360
Use case: Running ERNIE-Image on 6GB-12GB VRAM GPUs
Core config:
- Model: ERNIE-Image NVFP4 (~4.78GB VRAM)
- Optional: Turbo LoRA, second-pass refinement, PE
- Tested: RTX 5060 Ti 16GB runs smoothly
Setup:
- Download NVFP4 model →
ComfyUI/models/unet/ - Load with UNET Loader (not Diffusion Model Loader)
- Optionally load PE (→
ComfyUI/models/checkpoints/) - Run — NVFP4 reduces VRAM usage by 40%+
2. Two-Stage Sampler Workflow — Quality First
Inspiration: Reddit r/comfyui community
Use case: Maximum output quality, willing to spend extra time
Stage 1 (Coarse composition):
Sampler: Euler
CFG: 1.0
Steps: 20
Denoise: 0.8-1.0
Stage 2 (Detail refinement):
Sampler: DPM++ 2M Karras
CFG: 4.0
Steps: 15-20
Denoise: 0.5-0.7
- Latent Upscale 1.5x-2x
Implementation: Chain KSampler → LatentUpscale → KSampler(Advanced) in ComfyUI.
3. GGUF Workflow — Lowest Barrier
Use case: Quick testing, sub-24GB VRAM GPUs
Setup:
- Download GGUF weights from Unsloth
- Use ComfyUI UNET Loader (GGUF) node
- Standard VAE (FLUX.2 VAE) + Text Encoder (Ministral 3B)
- Recommended: Q4_K_M or Q5_K_M quantization
4. Turbo LoRA Enhancement Workflow
Source: Civitai community
Description: Community-trained Turbo-style LoRA that simulates fast inference on the Base model
- Load with: LoRA Loader node
- Recommended strength: 0.6-0.8 (higher causes oversaturation)
- Usage: Base model + 8-15 steps + CFG 1.5-2.0
Chapter 5: Scene-Specific Best Practices Quick Reference
| Scene | Model | Sampler | Scheduler | CFG | Steps | PE | Negative Prompt |
|---|---|---|---|---|---|---|---|
| Daily T2I | Turbo | Euler | Simple | 1.0 | 8 | ON | Not needed |
| High-quality T2I | Base | DPM++ 2M | Karras | 4.0 | 25-30 | ON | Recommended |
| Chinese prompts | Base | DPM++ 2M | Karras | 4.0 | 30 | ON | Recommended |
| Text rendering | Base | DPM++ 2M | Karras | 4.0 | 25 | OFF | Recommended |
| img2img | Base | DPM++ 2M | Karras | 4.0 | 25 | OFF | On demand |
| Batch generation | Turbo | Euler | Simple | 1.0 | 8 | ON | Not needed |
| Low VRAM (6-8GB) | Turbo NVFP4 | Euler | Simple | 1.0 | 8 | ON | Not needed |
| Maximum quality | Base 2-stage | Euler→DPM++ | - | 1.0→4.0 | 20+15 | OFF | Recommended |
Chapter 6: Common Issues & Solutions
Q1: Grid artifacts with Turbo?
→ See EI-046: Turbo may produce diagonal grid artifacts at specific resolutions. Fix: use multiples of 8 (e.g., 1024×1024, not 1000×1000), or use community grid-fix LoRAs.
Q2: Base model output oversaturated?
→ Lower CFG to 3.5; check that you're not using Negative Prompts designed for SD models (some can cause color shifts).
Q3: Bad faces?
→ Add face-related Negative Prompts (see Chapter 2); or use community face-fix LoRAs like Jibs_European_Face_Fix.
Q4: PE deviates from original prompt?
→ Turn PE OFF with use_pe=False. For partial enhancement, describe the scene more manually in your prompt.
Q5: All-black output?
→ Check model file integrity; verify correct VAE (ERNIE-Image requires FLUX.2 VAE specifically).
Summary
ERNIE-Image tuning in ComfyUI boils down to three golden rules:
- Model determines base strategy: Base → DPM++ 2M Karras + CFG 4.0 + Negative Prompt; Turbo → Euler Simple + CFG 1.0, no negative prompt needed
- PE is not universal: Detailed/precise prompts → turn PE OFF; short/abstract prompts → turn PE ON
- Choose workflow by resources: Ample VRAM → two-stage sampler for quality; limited VRAM → NVFP4 quantization for smoothness
The community continues contributing new workflows and tuning insights. Subscribe to the ERNIE-Image tag on Civitai and follow Reddit r/comfyui to stay updated on the latest best practices.