AI Image Generation Workflow: Prompts, Reference Images, Inpainting, and Batch Production

Who This Is For

This guide is for content operations teams, designers, independent site owners, e-commerce teams, and content creators. If you generate an avatar only once in a while, almost any tool will do. If you continuously need images for articles, product pages, ads, and social media, you need a process.

Step 1: Define the Requirements Before Writing a Prompt

Every usable image needs at least five clearly defined elements: purpose, dimensions, subject, style, and exclusions. Purpose determines information density. An article cover should communicate its subject clearly, a social post should command more attention, and a product image should represent the object accurately. Dimensions shape the composition; a horizontal 16:9 image and a vertical 9:16 image need different directions. Exclusions might prohibit real brands, garbled text, or unrelated people.

Suppose the brief is “a cover for an AI Agent tutorial.” Do not stop at “AI agent illustration.” A better breakdown is: horizontal cover; workflow nodes and tool connections as the subject; a modern technology aesthetic without a dark, dystopian look; no robot faces or complicated text.

Step 2: Write a Structured Prompt

A reliable prompt structure is: subject + setting + composition + style + lighting + detail + exclusions. Chinese or English can both work; consistency matters more than language. For example:

“A clean editorial cover image about AI agent workflow, connected nodes, laptop dashboard, team planning board, modern technology style, bright office lighting, high clarity, no text, no logo, no robot face, 16:9.”

If the tool supports negative prompts, include recurring problems such as watermark, text, logo, blur, and deformed hands. In most article illustrations, it is better not to ask the model to generate typography because many models still produce garbled text.

A ComfyUI / Flux Workflow

In ComfyUI, begin with this minimal node chain:

These settings are a reasonable starting point:

size: 1344x768
steps: 28
cfg: 3.5-7
sampler: dpmpp_2m
seed: fixed for series images
negative: text, watermark, logo, blurry, low quality

Your acceptance screenshot should include the ComfyUI node graph, a four-image candidate grid, and a before-and-after comparison of an inpainted area. Without that comparison, an image tutorial reads more like a list of prompts than a working guide.

Step 3: Use Reference Images to Control Style

Text alone rarely produces a consistent visual style. Reference images can control color, composition, a person's pose, or the angle of a product. Midjourney, Stable Diffusion, Flux workflows, Firefly, Runway, and similar tools all support image references in different ways. A company team can assemble a small set of brand references, with separate examples for color, space, people, products, and iconography.

Pay attention to copyright and likeness rights when using references. Do not upload commercial photography that is not licensed for derivative use, and never use a real person's photograph to create sensitive material. For public-facing work, favor assets you own, licensed materials, or fully generated images.

Step 4: Fix Small Problems with Inpainting

Generated images often contain odd hands, garbled text on screens, unwanted objects in the background, or unsuitable facial expressions. Do not regenerate the entire image because of one small defect. Mask the problem area and use an inpainting instruction such as “remove text on screen,” “fix hand shape,” or “replace background with clean office wall.”

Keep an inpainting prompt short and describe only the local change. A long prompt may pull the style of the whole image in a different direction. Fix one problem at a time, then move to the next.

Step 5: Generate and Name Images in Batches

Content teams need stable output at scale. Create an image-template sheet with fields for article type, dimensions, subject keywords, style keywords, negative terms, and save location. Generate four to eight candidates per request, select one for the cover, and then derive the required sizes from it.

Use consistent filenames as well, such as ai-agent-workflow-cover.webp and ai-agent-tool-calling-step.webp. Clear naming improves SEO, archiving, and future reuse. Image alt text should also be specific; never settle for “image 1.”

Managing Team Assets

When several people create images, maintain a lightweight asset library. For each image that reaches production, record its use case, generation tool, prompt, reference-image source, licensing status, generation date, and owner. This is not paperwork for its own sake. It makes future redesigns, reuse, and copyright audits practical. Source records are especially important for commercial pages, advertising campaigns, and app-store assets.

Brand teams can also maintain approved and prohibited style lists. Approved styles cover color, lighting, composition, and types of people. Prohibited styles might include excessive science-fiction imagery, dark backgrounds, exaggerated expressions, garbled text, and decorative elements unrelated to the brand. Once AI image production scales up, governing the style matters more than making any single image look impressive.

Common Pitfalls

Recommended batch filenames:

article-slug-cover.webp
article-slug-step-01.webp
article-slug-before-after.webp

Alternatives

For authentic product photography, shoot the product or use official assets first. For icons and simple diagrams, hand-drawn SVG or Figma artwork offers more control. For large-scale advertising creative, consider template- and brand-asset-oriented tools such as Canva, Adobe Express, Firefly, and Photoroom.

Summary

The professional way to generate AI images is to turn random experimentation into a repeatable process: define the requirements, write a structured prompt, stabilize the style with references, repair defects through inpainting, and finish with batch exports and consistent naming. That is how generated images become part of a real content-production system instead of remaining an entertaining experiment.