ERNIE-Image

ERNIE-Image Negative Prompt & CFG Scale Advanced Tuning Guide: The Ultimate Parameter Combinations for Base and Turbo

jul. 12, 2026

ERNIE-Image Negative Prompt & CFG Scale Advanced Tuning Guide: The Ultimate Parameter Combinations for Base and Turbo

Published: 2026-07-12
Published URL: https://ernie-image.app/blog/ei-130-ernie-image-negative-prompt-cfg-english-20260712

Three months after ERNIE-Image's release, the community on Reddit, ComfyUI forums, and Civitai has accumulated extensive hands-on parameter tuning experience. One of the most discussed — and most overlooked — topics is the correct use of Negative Prompts and CFG Scale on ERNIE-Image.

ERNIE-Image comes in two versions — Base (SFT, 50 steps) and Turbo (8 steps) — with inherently different recommended parameters. Base defaults to CFG=4.0, Turbo defaults to CFG=1.0. But what does this difference mean in practice? How does a Negative Prompt work on each version? What combinations yield the best results?

This article systematically answers these questions, from theory to practice.

A Counter-Intuitive Fact: Base Needs Negative Prompts, Turbo Doesn't

There's an easily overlooked difference in the official recommended parameters:

Parameter ERNIE-Image Base ERNIE-Image Turbo
Inference Steps 50 8
guidance_scale (CFG) 4.0 1.0
Optimization SFT DMD + RL
Negative Prompt Highly effective Limited effect

Community testing has confirmed: ERNIE-Image Base (CFG=4.0) responds dramatically to Negative Prompts — well-crafted negative prompts significantly improve image quality, reduce artifacts, and enhance color reproduction. On Turbo (CFG=1.0), however, the effect of Negative Prompts is virtually diluted to zero.

The principle is simple: CFG's formula is uncond + (cond - uncond) * cfg_scale. When cfg_scale=1.0, the conditioning term is multiplied by 1, and the Negative Prompt's (uncond) impact approaches zero. When cfg_scale=4.0, the Negative Prompt's contribution is amplified 4x, producing very noticeable effects.

CFG Scale Deep Dive: Optimal Values for the Base Model

Theoretical Background

CFG (Classifier-Free Guidance) Scale controls how closely the model follows the prompt. Higher values produce results more aligned with the prompt but may sacrifice realism and diversity.

  • CFG < 1.0: The model "goes against" the prompt, producing opposite results (rarely used)
  • CFG = 1.0: Unconditional generation, relying only on the model's inherent distribution
  • CFG = 1.5–3.0: Light guidance, preserving natural feel
  • CFG = 4.0: Official recommended sweet spot — optimal balance of quality and adherence
  • CFG = 6.0–8.0: Strong guidance, may cause color oversaturation and edge sharpening
  • CFG > 10.0: Over-guidance, likely introduces artifacts and deformation

Best CFG Range for ERNIE-Image Base

The official recommendation of CFG=4.0 is an excellent safe starting point. However, fine-tuning CFG for different scenarios yields better results:

Scenario Recommended CFG Rationale
Photorealistic 3.0–4.0 Lower CFG preserves natural lighting and texture, avoiding "plastic look"
Illustration/Anime 4.0–5.0 Slightly higher CFG enhances stylization and line clarity
Text Rendering 4.0–5.5 Stronger prompt adherence ensures accurate text
Complex Composition 4.5–6.0 Multi-object, multi-relation scenes need tighter prompt constraints
Poster/Infographic 4.0–5.0 Balancing structure and text requirements
Creative Exploration 2.0–3.0 Maintaining creative space without over-constraining generation

Why Turbo's CFG is Fixed at 1.0?

ERNIE-Image-Turbo was optimized through DMD (Distribution Matching Distillation) and RL (Reinforcement Learning) — a two-stage process:

  1. DMD Distillation: Compressing the 50-step Base model into an 8-step student model
  2. RL Optimization: Further improving aesthetic quality through preference optimization

The distilled Turbo model has already "internalized" prompt-following capabilities during training. It no longer needs high CFG values to enforce alignment. CFG=1.0 already delivers excellent results, and increasing CFG may actually disrupt the distilled model's learned distribution.

Negative Prompt in Practice: Base Model's Hidden Weapon

Why Base Needs Negative Prompts

ERNIE-Image Base is an SFT model trained on large-scale data. The training data inevitably contains low-quality samples — blurry images, unnatural colors, awkward compositions. Negative Prompts guide the model away from these feature space regions.

Community-Verified Negative Prompt Recipes

The following recipes come from verified testing on Reddit r/StableDiffusion and the broader ERNIE-Image community:

Universal Quality Boost

text, watermark, signature, blurry, low quality, low resolution,
ugly, deformed, distorted, disfigured, bad anatomy, bad proportions,
extra limbs, cloned face, disfigured, gross proportions, malformed limbs,
missing arms, missing legs, extra arms, extra legs, fused fingers,
too many fingers, long neck, mutated hands, poorly drawn hands

Photorealistic

painting, drawing, illustration, cartoon, anime, 3d render,
CGI, artificial, plastic, waxy skin, oversaturated, overexposed,
hazy, foggy, low contrast, grainy, noisy, jpeg artifacts

Anime/Illustration

photograph, realistic, photorealistic, 3d render, CGI,
blurry, low quality, bad lighting, overexposed, underexposed,
flat colors, low contrast, text watermark

Controlling Negative Prompt Intensity

Negative Prompt effectiveness can be tuned through two dimensions:

  1. The wording and length of the Negative Prompt itself — more detailed and specific = stronger effect
  2. CFG Scale coordination — higher CFG amplifies Negative Prompt impact

Rules of thumb:

  • First use: Start with the universal recipe, observe the changes
  • If the image looks overly "pulled" (shadow detail loss or color muting): Reduce Negative Prompt entries, or lower CFG to 3.5
  • If quality improvement is underwhelming: Add more targeted Negative Prompt terms

Base vs Turbo: Negative Prompt Comparison

Scenario Base (CFG=4.0) + Neg Turbo (CFG=1.0) + Neg Verdict
Portrait Skin quality improved, reduced plastic look Virtually no change Base wins
Text Rendering Sharper text, fewer artifacts Slight improvement, barely noticeable Base wins
Anime Cleaner lines, more saturated colors Minor changes Base wins
Rapid Generation Quality gains but needs 50 steps 8 steps, acceptable quality Trade-off

Recommended Parameter Combinations by Scenario

Scenario 1: High-Quality Portrait Photography

model: ERNIE-Image Base
steps: 50
CFG: 3.5
Negative Prompt: [Photorealistic recipe]
PE: on

Rationale: CFG=3.5, slightly below the official recommendation, combined with Negative Prompt to suppress the "plastic look." This is the community's most validated portrait combination.

Scenario 2: Rapid Prototyping

model: ERNIE-Image Turbo
steps: 8
CFG: 1.0
Negative Prompt: [Empty or minimal]
PE: on

Rationale: Turbo generates in 8 steps with CFG=1.0 already delivering good results. No Negative Prompt needed, maximizing iteration speed.

Scenario 3: Poster/Infographic with Text

model: ERNIE-Image Base
steps: 50
CFG: 5.0
Negative Prompt: [Universal quality boost]
PE: on

Rationale: Text rendering requires strong prompt adherence; CFG=5.0 ensures accurate text. Negative Prompt eliminates artifacts around text regions.

Scenario 4: Creative Exploration

model: ERNIE-Image Base
steps: 50
CFG: 2.5–3.0
Negative Prompt: [Minimal or empty]
PE: off

Rationale: Turning off PE yields more direct, less predictable results. Low CFG maintains creative freedom. Ideal for the brainstorming phase.

Scenario 5: Batch E-Commerce Product Images

model: ERNIE-Image Turbo
steps: 8
CFG: 1.0
Negative Prompt: [Lightweight, just watermark/text removal]
PE: on, but use short prompts

Rationale: Turbo's 8-step throughput is 6x+ that of Base, ideal for batch scenarios. Complex Negative Prompts not needed.

Advanced Technique: Scheduler Selection and Combinations

Beyond CFG and Negative Prompts, sampler behavior is also affected by parameter settings. Based on community experience:

  • Euler + Normal: Most stable, works best with Base at CFG=4.0
  • DPM++ 2M + Karras: Most detail-rich, but needs enough steps (minimum 30)
  • DDIM: Good for rapid iteration, but more sensitive to CFG changes

A community-discovered practical recipe:

For ERNIE-Image Base, the preferred community combination is:

sampler: DPM++ 2M Karras
steps: 30 (not 50 — 30 steps reaches ~95% quality)
CFG: 4.0
Negative Prompt: [Universal recipe]

Using 30 steps instead of 50 saves ~40% time with minimal quality loss.

Troubleshooting

Q: The image becomes darker after adding Negative Prompts?
A: Your Negative Prompt may contain color-related terms (e.g., "low contrast", "oversaturated"). Remove color-focused entries or lower CFG to 3.5.

Q: CFG seems ineffective on Turbo?
A: Turbo's default CFG=1.0 is optimal. Increasing CFG above 2.0 usually decreases quality. If you must use high CFG on Turbo, reduce steps to 6 or fewer.

Q: Does the PE toggle affect parameter choices?
A: Yes. With PE enabled, your input is expanded into a detailed long prompt, so CFG can be reduced by 0.5–1.0. With PE disabled, stick with CFG=4.0 or slightly higher.

Summary

Key Finding Explanation
Base must use Negative Prompts CFG=4.0 amplifies Negative Prompt effect 4x — the key to quality improvement
Turbo doesn't need Negative Prompts CFG=1.0 renders Negative Prompts nearly ineffective; focus on speed and throughput
CFG fine-tuning varies by scenario Photorealistic 3.0–4.0, Illustration 4.0–5.0, Text 4.0–5.5
30 steps instead of 50 saves 40% time Only ~5% quality loss — the community-validated efficiency sweet spot
Reduce CFG when PE is enabled Auto-expansion provides richer conditioning, reducing the need for high CFG

ERNIE-Image parameter tuning is an ongoing discovery process. The community on Reddit, Civitai, and GitHub continues to share new findings and recipes. The parameter combinations in this article serve as reliable starting points — fine-tuning for your specific use case will yield the best results.

References:

ERNIE-Image Team