AI image generation is more than typing a prompt: real work uses a handful of workflows. Text-to-image creates an image from a description…
Learn text-to-image and image-to-image, the two starting points.
Two workflows start most projects. Text-to-image generates a fresh image from a prompt alone — pure creation, maximum freedom, least control over exact composition. Image-to-image takes an existing image plus a prompt and transforms it, keeping some of the original's structure and colors; a strength setting controls how much it changes.
Use text-to-image to explore ideas from scratch, and image-to-image when you have a reference — a sketch, a photo, a rough composition — you want the model to reinterpret while preserving its layout.
Fix and extend images with inpainting and outpainting.
Inpainting regenerates only a masked region while leaving everything else untouched — remove an object, change a face, fix a distorted hand, or swap a background. You paint a mask over the area and prompt what should go there, and the model fills it to match the surroundings.
Outpainting is the opposite direction: it extends the image beyond its original borders, generating new content that continues the scene, so you can widen a crop or turn a square into a landscape. Together these turn generation into editing — precise, local changes instead of regenerating the whole image and hoping.
Get better results through specific prompts and refinement.
Prompt quality drives output quality. Be specific about subject, style, composition, lighting, and mood ('a golden retriever puppy, soft morning light, shallow depth of field') rather than a bare noun. Negative prompts say what to avoid ('blurry, extra fingers'), steering the model away from common failures.
Then iterate. Generation is probabilistic, so treat the first result as a draft: adjust the prompt, regenerate variations, then switch to inpainting to fix specific flaws. The polished images you see are usually the product of several passes, not one lucky prompt.
Add finer control and avoid the common errors.
For more control than prompts alone, techniques and tools help: control methods that condition generation on a pose, depth map, or edge outline to lock composition; fine-tuned or LoRA models for a specific style or character; and node-based tools that let you chain these steps into a repeatable pipeline. You reach for these when you need consistency or precise structure across images.
Watch for: vague prompts that leave too much to chance; expecting one prompt to be perfect instead of iterating; regenerating the whole image to fix one flaw when inpainting would preserve the good parts; and ignoring rights and consent when using real people's likenesses or copyrighted styles. Be specific, iterate, edit locally, and generate responsibly.
AI image generation uses several workflows: text-to-image creates from a prompt, image-to-image transforms a reference, inpainting edits a masked region, and outpainting extends beyond the borders. Specific prompts plus negative prompts, and treating the first output as a draft to iterate on, drive quality. For precise control, use pose/depth/edge conditioning, fine-tuned or LoRA style models, and node-based pipelines. Avoid vague prompts, one-shot expectations, whole-image regeneration for small fixes, and misuse of likenesses or copyrighted styles.
You have a rough sketch of a product and need a polished marketing image. Describe the sequence of workflows you'd use — image-to-image from the sketch, prompt refinement, inpainting fixes — and one control or rights consideration you'd keep in mind.
What is the difference between text-to-image and image-to-image?
Text-to-image is pure creation with most freedom; image-to-image reinterprets a reference while preserving its layout, controlled by a strength setting.
What is inpainting?
Inpainting makes precise local edits — removing objects, fixing flaws — by regenerating just the masked area to match its surroundings.
What does a negative prompt do?
Negative prompts push the model away from named failures, complementing the positive prompt's description of what you want.
What is a common image-generation workflow mistake?
Good results come from specific prompts, iteration, and local edits; vagueness and full regeneration for small fixes waste effort.