In recent years, artificial intelligence has made remarkable strides in image generation. Yet, it’s impossible to scroll through social media without stumbling upon examples of bad AI images, AI art fails, or the outright worst images that look distorted, blurry, or simply off-putting. People often ask, “Why are AI-generated images bad?” or wonder how they can optimize their prompts to avoid these nightmarish fails. If you’ve ever struggled with AI image prompt optimization or want to understand the mysterious CFG Scale, then this comprehensive guide is for you.
In this article, we’ll explore 10 common pitfalls that lead to bad AI art, from poorly defined prompts to mismatched models. We’ll also address how you can fix each problem—such as testing multiple seeds, or leveraging the flux dev best CFG scale setting. By the end, how to coax the best images possible out of the AI model of your choice.
1. Poorly Optimized AI Image Prompts
The prompt, or textual description you feed into your AI image generator, is arguably the most critical factor in getting the results you want. A vague, overly complicated, or contradictory prompt results in an AI image failure because the model can’t zero in on what you’re describing. For instance, if you say you want a “mountain of money” but also insist on having everything neatly stacked in symmetrical rows, the generator may deliver confusing or disjointed images—leading to bad AI images that don’t match your vision.
How to fix it
- Clarity and details are key. Aim for clarity and focus more details in your prompt (please include a detailed description of the scene, subject, lighting, style, texture, dynamic effects, etc. in the prompt). For example, if you want a pile of US dollar bills in a chaotic mound, explicitly say: “A massive pile of chaotically stacked US dollar bills, forming a mountain-like shape, realistic paper textures, vibrant green and white tones, cinematic studio lighting.”
- Start simple, then refine. If your initial prompt is unsuccessful, remove extraneous details and gradually add them back in.
- Avoid contradictory terms. Steer clear of conflicting directives such as “chaotic pile” and “neatly arranged,” unless your intent truly mixes both styles.
A well-structured AI image prompt sets a solid foundation, preventing bad AI art by clearly guiding the AI toward the elements you really want.
2. Missing or Ineffective Negative Prompts
A negative prompt tells the AI image generator what not to include. Without it, you could end up with extra or undesirable details cluttering your final image. Whether you’re getting random backgrounds, off-putting colors, or even comedic distortions, neglecting negative prompts can lead to the worst images imaginable.
How to fix it
- If you notice frequent problems like blurry edges or irrelevant objects, list these issues explicitly as negative prompts. For instance: “blurry, low quality, pixelated” can help eliminate fuzzy results.
- Target stylistic conflicts. Add keywords like “cartoonish,” “unrealistic,” or “overexposed” as negative prompts if you’re aiming for a more realistic style.
- Each time the AI output has an undesirable element—like an accidental cameo of random objects—add that to the negative prompt: “extra objects, irrelevant elements, unnecessary details.”
Negative prompts are a powerful tool to avoid bad AI art; they help the model focus on what you truly want while filtering out unwanted artifacts.
3. Choosing the Wrong Sampler
A sampler controls how the AI navigates through the complex layers of noise and detail in an image. Using the wrong sampler for your intended style can produce muddy or overly smoothed results—thus you’ll end up with bad AI images that disappoint.
Common sampling methods
- Euler A: Fast generation, but the details may not be as fine.
- DPM++ 2M Karras: Rich in detail, suitable for generating high-resolution and complex scenes.
- DPM++ SDE Karras: Smooth and natural, ideal for dynamic visuals or complex lighting and shadow effects.
How to fix it
- If the image details are not clear enough, try using DPM++ 2M Karras.
- If smoother dynamics or complex lighting and shadows are needed, try DPM++ SDE Karras.
Don’t just stick to one sampler. Generating a few versions with different samplers and comparing them side by side can illuminate which method best fits your artistic goals.
4. Inadequate Resolution
Even a perfectly crafted AI image prompt can fail if the resolution is too low. Low-resolution images lose clarity and detail, leading to an image looking bad—especially if you’re generating intricate scenes like a mountain of currency or a bustling cityscape.
Common image Resolution Sizes
A. Landscape:
Close to human natural vision, suitable for natural or everyday scenes
- 4:3 — Classic aspect ratio, widely used in photography and displays (old TVs and computer screens). Example 4:3 resolution: 800x600, 1024x768, 1920x1440
- 16:9 — Modern widescreen monitors, TVs, movies, and YouTube videos. Example 16:9 sizes: 1280x720 (HD), 1920x1080 (Full HD), 3840x2160 (4K UHD)
- 3:2 — Common aspect ratio in photography (default ratio for full-frame cameras). Example 3:2 sizes: 900x600, 1500x1000, 3000x2000
B. Portrait:
Aspect ratio for everyday electronic products
- 4:5 — Instagram portrait, suitable for full-screen browsing on mobile. Recommended 4:5 resolution: 1080x1350
- 9:16 — Short videos (such as TikTok, Instagram Reels, Douyin), phone wallpapers. Example 9:16 sizes: 720x1280, 1080x1920, 1440x2560.
C. Square:
- 1:1 — Social media profiles, product displays on e-commerce platforms, digital advertisements, and official brand covers. Example 1:1 sizes: 512x512, 1024x1024, 2048x2048
How to fix it
- Start with moderate resolutions. Common starting points include 768×512 or 1024×1024.
- Scale up for detailed scenarios. For scenes requiring a high level of realism—like “a mountain of scattered dollar bills”—consider 1024×768 or even higher, like 1536×1024.
- Test hardware limitations. Higher resolutions demand more GPU memory and time. Test smaller images first, then scale up once you’re satisfied with the style and composition.
By matching your resolution to the complexity of your subject, you avoid bad AI images characterized by muddy, pixelated details.
5. Misunderstanding CFG Scale (What Is CFG Scale?)
What is CFG Scale?
The CFG Scale, or Classifier-Free Guidance Scale, is a key parameter in AI image generation that controls how closely the AI model adheres to the provided prompt. It adjusts the balance between the model’s creative freedom and the specificity of your prompt. Essentially, it helps the AI decide how much weight to give to the instructions you provide versus allowing the model to generate more varied, creative results.
A lower CFG scale means the model has more freedom to deviate from the prompt, allowing for more creative and unexpected results. On the other hand, a higher CFG scale forces the model to stick more closely to the prompt, potentially resulting in more rigid or less natural images. The right balance of CFG scale is crucial to achieving the desired outcome—whether you're looking for more accuracy or creative flair in your AI-generated artwork.
Plus tips: The FLUX version of the CFG Scale is typically set at 3.5, while Stable Diffusion's default CFG Scale is set at 7.
Key ranges of CFG Scale
- Below 5: The AI might ignore your prompt details and produce random or irrelevant images.
- Above 12: The output can appear unnatural or distorted.
- 6–10: Generally a sweet spot for balanced detail and creativity.
How to fix it
- Start in the 7–9 range. Stable Diffusion often defaults around 7, which is a good starting point.
- Iterate slowly. If the model seems to ignore your prompt, gradually increase the CFG Scale. If the model is producing stiff or distorted images, lower it.
- Watch for new developments. Some advanced forks or custom models, like those referencing flux dev best CFG scale, might have specialized recommended scales for optimal performance.
Having a grasp on the CFG scale ensures your AI generator is neither too lax nor overly constrained—preventing AI image fails that stem from misaligned guidance.
6. Ignoring the Seed Factor
The seed is the starting point for an AI’s random number generation. If you’re always using the same seed—or ignoring it altogether—you might keep getting unsatisfactory or repetitive results, leading to bad AI images.
How to fix it
- Try multiple seeds. Randomize the seed for fresh perspectives on your subject.
- Keep track of good seeds. When you do get an image you love, note the seed. This allows for easier fine-tuning and consistent replication.
- Leverage seed variations. Slight changes in the seed can yield entirely different details, letting you explore a broad range of possibilities with minimal effort.
By experimenting with seeds, you can break out of a creative rut, avoiding the worst images that come from a stale or perpetually underperforming seed value.
7. Using the Wrong Model or Checkpoint
Different AI image generation models—or checkpoints—are optimized for different styles. If you’re trying to create realistic images of a mountain of U.S. dollars with a cartoon-focused model, you’ll likely end up with bad AI images that bear no resemblance to your vision.
How to fix it
- Match your model to the job. If you’re aiming for hyper-realism, use a checkpoint specifically optimized for realistic imagery.
- Check compatibility. Models like SD 1.5 typically require matching LoRAs, VAE, and other add-ons for the best results. Mismatched versions can create warped or incomplete images.
- Experiment with multiple models. Sometimes you don’t realize a particular model’s limitations until you see a side-by-side comparison. If you see AI art fails repeatedly, switch to a more suitable checkpoint.
Ensuring the right model for your needs avoids bad AI art that arises when the generator’s training and your goals are out of sync.
8. Improper Use of Control Tools (e.g., ControlNet)
Tools like ControlNet add an extra layer of control, letting you guide composition, pose, or style. However, if the reference images or line art you feed into ControlNet are low-quality or mismatched with your main prompt, you risk generating bad-quality AI images.
How to fix it
- Check reference alignment. Make sure your reference images or sketches align well with your textual descriptions.
- Adjust weight values. Sometimes the default ControlNet weight is too high, overshadowing your main prompt. Dial it back if you’re losing key details from your text.
- Use clear sketches. If you supply a line drawing to specify composition, ensure it’s not cluttered or ambiguous.
When used correctly, tools like ControlNet reduce the chances of bad AI art by aligning the AI’s output more closely with your creative direction.
9. Complex Dynamics and Special Effects
AI models often struggle with intricate motion or special effects like flowing water, flying debris, or swirling coins. Overloading the generator with too many dynamic elements can produce bad AI images that appear jumbled or incomplete.
How to fix it
- Focus on core movement. For a scene with falling coins, use specific descriptors like “motion blur,” “spinning coins,” “dynamic composition.”
- Remove extraneous details. If you’re prioritizing a single dynamic effect, cut back on other complicated prompts—don’t also demand swirling dragons and floating cities.
- Consider image editing. In cases where the AI can’t quite nail complex motion, use tools like Photoshop or After Effects to polish or add effects to the post.
Even the best models might create bad AI art when overloaded with too many dynamic demands, so focusing on one main effect often delivers more coherent results.
10. Neglecting Post-Processing
AI can only do so much in one pass. If you rely solely on the generator for photorealism, cinematic lighting, or complex motion effects, you might be stuck asking, “Why are AI-generated images bad?”
How to fix it
If the AI generator struggles to fully meet your expectations, post-processing can help optimize the image. Post-processing involves refining details, adjusting lighting, or adding effects after the image has been generated to enhance its final look.
Recommended Tools:
- Photoshop/GIMP: Adjust details, colors, and lighting effects.
- Figma/Canva: Tweak composition or add text and graphic elements.
- After Effects/Premiere: Add special effects to dynamic scenes.
Post-processing can bridge the gap between a decent AI output and a professional final image, ensuring you avoid the worst images that require multiple rounds of AI regeneration to fix small details.
Conclusion
The worst images generated by AI don’t have to be the norm. By carefully refining your AI image prompt, leveraging negative prompts, adjusting your sampler choice, and using tools like ControlNet effectively, you can avoid bad AI image generation.
The truth is, that AI image generation is a multi-step process. Even when you’ve nailed the perfect prompt and chosen the ideal model, sometimes you’ll still generate bad AI images. But with a clear strategy for revising prompts, testing seeds, and making the most of post-processing tools, you’ll drastically reduce AI image fails and produce images that are far from the worst images you might see circulating online.
For those looking to explore AI art creation more seamlessly, MimicPC offers an all-in-one AI art generation platform. With a variety of pre-installed AI image generation apps like Fooocus, Stable Diffusion WebUI, ComfyUI, and even FLUX 1.1 Pro API, MimicPC makes it easier to experiment, refine, and experience AI art to its fullest. Try MimicPC today and unlock the potential of AI-driven creativity!
