Leonardo AI for Beginners: Common Mistakes & Fixes — LiliDi Blog
Overcome initial hurdles with Leonardo AI. This guide for beginners covers common mistakes and provides practical fixes to help you generate better images fast…
By lilidi editorial
Leonardo AI for Beginners: Common Mistakes and How to Fix Them Starting with any AI image generation platform, including Leonardo AI, can feel like navigating a complex maze. The promise of incredible visuals often meets the reality of oddly rendered hands, illogical compositions, or outputs far from your imagination. This guide is built specifically for beginners using Leonardo AI, focusing not just on features but on the troubleshooting playbook for common mistakes. We will diagnose typical issues and provide actionable, anti hype solutions to help you get the most out of your prompts. 1. Vague Prompts: The Root of Generic Results Mistake: Your AI images lack detail, distinctiveness, or simply look "generic." You might be using prompts like "a cat in a field" or "futuristic city." Why it happens: AI models, while powerful, are fundamentally pattern matching machines. Vague instructions
lead to average outputs because the model defaults to the most common interpretations of your keywords. How to fix it: Embrace specificity. Think like a director describing a scene. Instead of broad terms, provide context, style, mood, and specific elements. Fix Strategy: The "Who, What, Where, When, Why, How" Approach Who: What is the subject? (e.g., "a grumpy ginger cat," "an elderly wizard with a long white beard") What: What is the subject doing or what are its attributes? (e.g., "wearing a tiny crown," "conjuring a blue orb") Where: What is the setting? (e.g., "sitting on a velvet cushion in a dimly lit library," "on a rooftop overlooking a cyberpunk cityscape at dusk") When: Time of day, era, season? (e.g., "golden hour," "Victorian era," "autumn afternoon") Why/How: Mood, action, style. (e.g., "contemplating a complex spell," "dynamic action shot," "cinematic lighting") Example
Transformation: Bad Prompt: dog running in a park Improved Prompt: a golden retriever puppy, mid stride, racing through a sun drenched autumn park, leaves scattering, shallow depth of field, joyful expression, cinematic, octane render 2. Neglecting Negative Prompts: The Unwanted Guests Mistake: Your images frequently include undesirable elements: extra limbs, distorted faces, watermark like artifacts, blurriness, or illogical objects. Why it happens: AI models try to fulfill positive prompts, but they don't inherently "know" what you don't want unless you tell them. Sometimes, common dataset elements (like text or low quality images) creep in. How to fix it: Systematically use negative prompts to filter out unwanted characteristics. This is as crucial as your positive prompt. Fix Strategy: A Comprehensive Negative Prompt List Maintain a standard list of negative prompts you can apply,
then customize it for specific generations. Common Starters: ugly, low resolution, blurry, distorted, poorly drawn, bad anatomy, disfigured, malformed limbs, extra limbs, missing limbs, mangled, fused fingers, wrong number of fingers, extra fingers, too many teeth, too many eyes, out of frame, cropped, signature, watermark, text, error, jpeg artifacts, monochrome, grayscale Contextual Negatives: If you're generating a portrait, add full body, wide shot . If an object, add person, human . If specific colors are unwanted, add those. Leonardo AI, like lilidi.ai, offers robust negative prompting features. Don't skip them. 3. Over Reliance on Initial Generation Settings: One Size Fits None Mistake: You use the default model, prompt strength, or image dimensions for every image without adjustment, leading to inconsistent or suboptimal results. Why it happens: Default settings are general
purpose. They won't be optimal for every artistic style, subject, or desired outcome. How to fix it: Experiment with Leonardo AI's diverse range of settings. Different models excel at different tasks. Fix Strategy: Targeted Parameter Adjustment AI Model Selection: Don't stick to only one. For photorealism, try "PhotoReal" or "Stable Diffusion XL." For illustrative styles, explore "Leonardo Diffusion XL" or community models. Each model has unique strengths. Prompt Strength (Guidance Scale/CFG Scale): This controls how closely the AI adheres to your prompt. A lower value (e.g., 5 7) offers more creativity and variability; a higher value (e.g., 10 15) enforces stricter adherence. Adjust based on how much you want the AI to interpret vs. precisely follow. Image Dimensions: Square (512x512, 768x768) is common, but wide (e.g., 768x512) or tall (e.g., 512x768) formats are better for landscapes
or portraits, respectively. Non standard aspect ratios can introduce distortions, especially on older models. Sampling Method: Different samplers (e.g., Euler, DPM++ SDE Karras) can subtly alter the image's texture and detail. If you're stuck, try switching this up. 4. Ignoring Iteration and Refinement: The "First Try" Fallacy Mistake: You generate a few images, get frustrated if they aren't perfect, and stop. You treat AI generation as a single step process. Why it happens: AI is an iterative tool. Rarely does the first prompt perfectly capture your vision. It's a dialogue, not a dictation. How to fix it: Embrace a workflow of generation, evaluation, refinement, and regeneration. Fix Strategy: The Iterative Loop 1. Generate a Batch: Start with 4 8 images to see a range of interpretations. 2. Evaluate: Identify what works and what doesn't. Specific elements are good? Posing is off?
Lighting is wrong? 3. Refine Prompt: Add more detail for desired elements. Add negative prompts for unwanted elements. Adjust parameters (e.g., CFG scale, seed). 4. Use Image2Image/Referencing: If you get a decent base image but need changes, use it as an input for Image2Image and adjust the "init strength" (or similar setting on lilidi.ai's advanced editor) to control how much the AI adheres to the original vs. your new prompt. 5. Inpainting/Outpainting: Leverage Leonardo AI's inpainting/outpainting tools to fix small areas (like a distorted hand) or expand the canvas. 5. Misunderstanding AI Limitations: Expecting Human Level Comprehension Mistake: You assume the AI understands complex relationships, narrative context, or human nuances as a person would. Why it happens: AI models are excellent at recognizing patterns but struggle with true comprehension, common sense, and the
intricacies of human expression or storytelling. They don't "understand" your prompt; they predict the next best pixel based on training data. How to fix it: Break down complex ideas into simpler, actionable components. Manage your expectations. Fix Strategy: Simplify and Reinforce Break Down Complexity: Instead of a philosopher contemplating the meaning of life amidst the ruins of an ancient civilization with a glowing ethereal orb representing knowledge , try focusing on a stoic philosopher, elderly, seated on a broken column in Greek ruins, pensive expression, dramatic lighting . Then, separately generate a glowing ethereal orb, blue light, floating, magical . You can combine elements in editing or use Inpainting to add the orb later. Emphasize Keywords: Use weights if the model supports it (e.g., (beautiful:1.2) woman ) or repeat keywords. While not all platforms explicitly support
numerical weights, repeating a keyword ( detailed background, detailed background ) can sometimes have a subtle effect. Use Reference Images: When depicting specific characters, poses, or objects, provide an initial image to guide the AI. This is where the Image2Image feature truly shines. Conclusion Mastering Leonardo AI, or any AI image generator, is a journey of continuous learning and adjustment. By actively recognizing and addressing these common beginner mistakes – from vague prompts to neglecting iterative refinement – you will significantly elevate the quality and consistency of your generated images. Remember, the AI is a tool; your skill lies in how effectively you wield it through precise instructions, smart troubleshooting, and a willingness to experiment. Happy prompting! FAQ Q1: Why do my generated faces look distorted or unrealistic? A1: This is a very common issue. It
usually stems from vague prompts, too low a CFG scale (meaning the AI is being too creative), or not using strong negative prompts like bad anatomy, distorted face, ugly, blurry . Try using a model optimized for faces or photorealism, increase your CFG scale slightly, and apply a robust negative prompt list. Q2: What's the best way to get specific artistic styles consistently? A2: To achieve consistent styles, be very explicit in your prompt (e.g., in the style of Van Gogh, impressionist painting, thick brushstrokes or digital painting, concept art, cinematic lighting, artstation ). Also, look for fine tuned models on Leonardo AI that are trained on specific styles, and consider using reference images if you have a very particular aesthetic in mind. Q3: My images keep getting blurry or low quality. What am I doing wrong? A3: Check your initial generation resolution; very low resolutions
(e.g., 512x512) can lack detail. Ensure you're not using negative prompts that explicitly ask for blurriness. After generating, always use Leonardo AI's upscaling and refining tools (e.g., "Upscale Image," "Alchemy Refiner") to enhance detail and resolution. Also, add high resolution, sharp focus, detailed to your positive prompt and blurry, low quality, jpeg artifacts, ugly to your negative prompt. Related on LiliDi How LiliDi compares to Leonardo