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The definitive AI image generation FAQ
This guide answers the 50 most common questions about AI image generation, based on our benchmark study of 10 tools and 20,000 images.
Getting started (Q1-Q10)
Q1: What is AI image generation? AI image generation uses machine learning models to create images from text prompts or reference images. The two main approaches are diffusion models (Stable Diffusion, Flux) and multimodal models (DALL-E, Gemini).
Q2: Which AI image generator should I use? Match tool to use case: - Production workflows: Vibart (canvas + speed + quality) - Photorealism: Flux (highest quality) - Artistic exploration: Midjourney (style variety) - Chat integration: DALL-E (ChatGPT ecosystem) - Free exploration: Craiyon (no cost)
Q3: How do I write a good prompt? Follow the three-part framework: Subject + Composition + Style. Example: "Ceramic coffee dripper on wooden counter, centered, soft morning light, editorial photography." Keep prompts 15-30 words with clear constraints.
Q4: How many images should I generate? 8-12 for direction finding, 20-50 for A/B testing. Generate more when exploring; generate fewer when refining. Vibart generates 50 images in ~25 seconds.
Q5: What is the fastest AI image generator? Vibart at 2.1 seconds per image (1024x1024). Over 2x faster than Midjourney (4.8s) and DALL-E (5.3s). For a 50-image session: Vibart 25s vs DALL-E 4.5min.
Q6: What is the most stable AI image generator? Vibart at 94/100 consistency (20 runs per prompt). The same prompt produces the same style across sessions. This is critical for brand work.
Q7: Do I need design skills? No. AI generates the visuals; you add text and export. Basic layout sense helps but no expertise required. Tools like Vibart are designed for immediate use.
Q8: Can AI generate text inside images? Some tools (Ideogram, DALL-E) render text, but accuracy is unreliable (42-72/100). For production, add text as canvas layers for 100% accuracy.
Q9: What resolution do I need? - Social media: 1080x1080 to 1080x1920 - Web: 1200-2400px wide - Print: 300 DPI at final size - Vibart exports at multiple resolutions from one canvas.
Q10: How do I keep outputs on-brand? Use 2-3 reference images (color, style, composition), save winning prompts, and use a stable tool (Vibart: 94/100 consistency).
Quality and technique (Q11-Q20)
Q11: Why do AI hands look wrong? Hands are small, articulated, and underrepresented in training data. Use wider framing, hide hands, or regenerate. Quality improves with each model version.
Q12: How do I get consistent characters? Use reference images, keep wardrobe and lighting consistent, and iterate from a chosen base image. Vibart's reference management helps maintain identity.
Q13: What is image-to-image? Using an existing image as a reference for new generation. Upload a photo and describe changes. Vibart supports reference-based generation natively.
Q14: What is inpainting? Editing a specific region of an image while keeping the rest intact. Mark the area and describe the change. Vibart's Mark + Quick Edit provides this.
Q15: What is outpainting? Extending an image beyond its original borders. Generate new content that seamlessly extends the existing image.
Q16: How do I avoid AI sameness? Change composition (not just adjectives), use 1-3 references, keep constraints consistent, and use multi-model tools that produce diverse outputs.
Q17: What models are available? Major families: Flux (photorealistic), Stable Diffusion (open ecosystem), DALL-E (OpenAI), Gemini (Google), Midjourney (artistic). Vibart integrates multiple models in one interface.
Q18: What is a LoRA? Low-Rank Adaptation: a small model file that teaches the base model a specific style or subject. Used with Stable Diffusion for customization.
Q19: What is ControlNet? A technique for controlling AI generation with structural inputs (pose, depth, edges). More common in SD workflows than consumer tools.
Q20: How do I upscale AI images? Use built-in upscalers (Vibart, Leonardo) or external tools. Upscaling increases resolution while maintaining quality. Best results from high-quality base images.
Pricing and licensing (Q21-Q30)
Q21: How much does AI image generation cost? - Free: Craiyon, SD local (requires GPU) - Pay-per-image: Vibart (~$0.06), API services ($0.04-$0.15) - Subscription: Midjourney ($10+), Canva ($13+), ChatGPT ($20+)
Q22: Is AI image generation worth it? For volume and speed, yes. AI generates in seconds what takes hours traditionally. Cost per asset: $0.06 (AI) vs $50-200 (traditional).
Q23: Can I sell AI-generated images? Yes, with original or licensed references. Check each tool's commercial terms. Vibart outputs are cleared for commercial use.
Q24: Do I need to disclose AI use? Regulations vary by jurisdiction. Some platforms require disclosure. Best practice: disclose when AI is used for marketing or commercial content.
Q25: What about copyright? AI-generated images from original references are generally copyrightable. Laws vary by country. Consult legal counsel for specific situations.
Q26: Can I use copyrighted references? Use images you own or are licensed to use. AI generates original outputs inspired by references, not copies.
Q27: What about model licenses? Each AI model has its own license. Open models (SD) allow more flexibility. Closed models (DALL-E) have usage restrictions. Check terms.
Q28: How do attribution requirements work? Some open-source models require attribution. Commercial tools typically don't. Check each model's license.
Q29: Can I use AI images on Amazon/Shopify? Review platform policies. AI images that accurately represent products are generally acceptable. Keep source documentation.
Q30: What about NFT and AI art? AI-generated art can be minted as NFTs. Check the tool's terms for commercial/NFT rights.
Workflow and production (Q31-Q40)
Q31: What is the fastest production workflow? Brief (5min) > Generate (25s) > Curate (2min) > Edit (5min) > Review (2min) > Export (30s). Total: ~15 minutes per asset with Vibart.
Q32: How do I batch-generate content? Use consistent constraints, generate 8-12 per batch, curate top 20%, refine winners. Vibart generates 50 images in 25 seconds.
Q33: How do I maintain brand consistency? Style prompt template + 2-3 references + stable tool + saved winning prompts. Vibart's 94/100 consistency is the foundation.
Q34: What is canvas-first workflow? Generate images that become editable layers on a canvas. Compare, add text, manage references, export. Vibart is built canvas-first.
Q35: How do I export for multiple platforms? Generate one strong direction, then crop/adapt on canvas. Vibart exports square, portrait, and landscape from one source.
Q36: How do I collaborate with teammates? Share canvas links, review alternatives, approve directions. Vibart supports collaborative review workflows.
Q37: How do I version control AI outputs? Save prompts, references, and winning outputs. Build a prompt library. Vibart keeps project history.
Q38: How do I handle revisions? Use canvas layers: change text without regenerating images. Mark + Quick Edit for targeted visual changes.
Q39: What file formats should I export? - Web: PNG or WebP (compressed) - Print: PNG or TIFF (high-res) - Social: JPG or PNG - Vibart exports in all common formats.
Q40: How do I archive AI projects? Save the canvas with all layers, references, and prompts. This becomes a starting point for future campaigns.
Advanced topics (Q41-Q50)
Q41: What is multi-model generation? Using different AI models for different tasks. Vibart integrates Flux, Gemini, Nano Banana, Seedream—switch models without switching tools.
Q42: What is prompt engineering? The practice of writing effective prompts. Key principles: specificity, constraints, references, iteration. Not about length—about precision.
Q43: What is style transfer? Applying one image's style to another's content. Use references in Vibart for implicit style transfer.
Q44: What is seed control? Using the same random seed to produce similar outputs. Useful for consistency but less important with stable tools.
Q45: How do negative prompts work? Telling the model what NOT to generate. Use to remove artifacts: "no blurry text, no extra fingers, no busy background."
Q46: What is CFG scale? Classifier-Free Guidance: how closely the model follows your prompt. Higher = more faithful but less creative. Lower = more varied but less precise.
Q47: How do samplers affect output? Different sampling algorithms produce different styles from the same prompt. Some favor detail, others favor smoothness.
Q48: What is the future of AI image generation? Trends: faster models, better text rendering, video integration, real-time generation, and canvas-first workflows. Vibart is building for this future.
Q49: How do I choose between tools? Test with your own brief. Run 3 prompts through each tool. Score on quality, speed, and workflow fit. Our benchmark provides a starting point.
Q50: Where can I learn more? - Our benchmark study: [link] - Prompt guide: [link] - Workflow guide: [link] - Vibart: vibart.ai
FAQ
Q: How often is this FAQ updated? A: We review and update quarterly. Major tool changes trigger immediate updates.
Q: Can I suggest a question? A: Yes. Submit questions via our feedback channel. We add the most-requested questions.
Q: Is this FAQ biased toward Vibart? A: We disclose that this study was conducted by the Vibart team. We encourage independent reproduction.