I’ve been experimenting with AI image tools for a while now, and one thing keeps coming up: it’s easy to generate interesting one-offs, but much harder to produce a cohesive set of visuals that feel like they belong together. Whether you’re building a personal series, a brand asset library, or a portfolio of concept art, consistency matters. In this piece I’ll share practical prompt strategies, workflows, and examples I use to generate consistent visual concepts across multiple images with tools like Midjourney, Stable Diffusion (including DreamStudio and open-source checkpoints), and Imagen-style systems.

Start with a clear concept and a short style brief

Before touching any generator, I write a one-paragraph concept brief and then distill it into a compact style token—this becomes my north star. The brief answers: what is the theme, what emotions should the visuals evoke, and what practical constraints do I have (colors, aspect ratios, usage context)?

Example concept brief I might use:

“A series of three urban dusk portraits exploring solitude and warmth in neon-lit European streets. Soft film-like grain, muted teal-and-amber palette, shallow depth of field, mid-century coat silhouettes, 3:4 portrait crop for editorial use.”

From that I extract a short style token I prepend to every prompt, for instance: “film-grain neon portraits — muted teal & amber — shallow DOF — editorial 3:4”. Having that short token saves me repeating the descriptive backbone every time.

Prompt structure I use

I follow a consistent prompt structure so each generated image has the same scaffolding. My template looks like this:

  • Style token (the brief distilled into a few keywords)
  • Main subject (person/scene/object)
  • Environment & lighting
  • Compositional constraints (crop, perspective)
  • Details & textures
  • Camera & lens (if relevant)
  • Negative prompts (what to avoid)
  • Post-processing cues (film grain, color grading)

Filled in, the tokenised prompt might read:

“film-grain neon portraits — muted teal & amber — shallow DOF — editorial 3:4 — young woman in mid-century coat, standing under a cafe awning, rainy cobblestone street, soft rim lighting, subtle bokeh, 85mm portrait, low saturation, warm highlights — avoid exaggerated faces, no logos, no text — added film grain and light leak.”

Use anchors: fixed keywords that lock the style

Anchors are a small set of keywords I never drop. They act like a style signature across prompts. My typical anchors include:

  • Palette: e.g., “muted teal & amber palette”
  • Grain & texture: e.g., “35mm film grain, soft halation”
  • Depth & focus: e.g., “shallow depth of field, soft bokeh”
  • Lighting: e.g., “warm rim light, neon reflections”

By repeating anchors, you bias the model to reuse similar lighting, contrast, and color treatments. Even when subjects or poses change, anchored descriptors maintain a visual throughline.

Seed images and reference images: consistency through visual anchors

When you need tighter consistency, feed the model reference images. I keep a small folder of 3–5 visuals that exemplify the look: two color grades, one texture close-up (film grain or paper), and one compositional reference. When using Stable Diffusion img2img or Midjourney’s image prompt, I prefix the prompt with the reference URL or upload token and then the text prompt. This does three things:

  • Anchors color grading and contrast
  • Improves repeated rendering of specific props or garments
  • Helps produce consistent silhouettes and framing

Tip: slightly vary the strength of the image prompt. For Stable Diffusion img2img, I usually set denoising between 0.35–0.5 for consistent results that still allow variation.

Parameter habits that promote uniformity

Parameters matter. I try to standardize some across a batch:

  • Aspect ratio: lock it (e.g., --ar 3:4 in Midjourney or 3:4 canvas in SD)
  • Seed: for variations, reuse the same seed for closely related images; change seeds deliberately when you want variation
  • CFG Scale/Guidance: keep it consistent (e.g., 7–9) to balance creativity and fidelity
  • Denoising: standardize for img2img (0.35–0.5) to maintain structural consistency

Using the same camera and lens tokens (e.g., “85mm f/1.8 portrait”) helps preserve perspective and bokeh across images.

Prompt recipes for different needs

Below are ready-to-use prompt recipes adapted to typical creative tasks. Remember to prepend your style token.

Editorial portrait series

“[style token] — subject: young woman in mid-century coat, three-quarter profile, hands in pockets — environment: neon cafe exterior, rainy cobblestones — lighting: soft rim light, neon reflections — camera: 85mm, shallow depth of field, bokeh — texture: 35mm film grain, light halation — negative: no logos, no text”

Concept product mockups (consistent brand look)

“[style token] — subject: minimalist wireless speaker, matte black finish, subtle brand stamp — environment: terrazzo tabletop with soft window light, warm highlights — composition: top-down 45° perspective, centered, consistent padding — details: soft shadows, filmed color grade, muted teal accents — negative: no human models, no busy backgrounds”

Environmental concept art set

“[style token] — subject: coastal town at dusk, clustered cottages, fog rolling in — lighting: low warm sun, strong rim glows, volumetric light — mood: serene, nostalgic — composition: wide panorama, rule-of-thirds focal point, 16:9 — texture: painterly brush strokes, visible canvas grain — negative: avoid futuristic elements, no neon”

Batch generation workflow

Here’s a workflow I follow to produce a cohesive batch:

  • Draft concept brief and style token.
  • Collect 3 reference images (color, texture, composition).
  • Create a master prompt template with anchors.
  • Generate 8–12 variations with the same AR, guidance, and seed (or adjacent seeds).
  • Cull and pick the top 3–5 images. Note what worked in the prompts.
  • Refine: iterate on the chosen images using img2img plus lower denoising to fine-tune faces or logos.
  • Apply unified post-processing (Lightroom/Photoshop/LUT) to the final set for identical color grading.

Post-processing: the secret sauce

Even the best prompts benefit from consistent post-processing. I export the raw AI images and apply the same LUT or a stack of adjustments: exposure, contrast, split toning (teal shadows / warm highlights), film grain, and a light vignette. Doing this in batches in Lightroom or Affinity Photo ensures the final set reads as a single visual family.

Practical tips and traps to avoid

  • Avoid changing too many variables at once. Tweak one element per iteration—color, lens, or seed—so you can identify what produces the effect you want.
  • Be explicit about what to avoid. Negative prompts are powerful—use them to strip out artifacts, text, or unwanted styles.
  • Save your best prompt versions. Keep a living “prompt recipe” doc and version-control successful prompts.
  • Respect usage rights. If you use a reference photo (especially a person), make sure you have appropriate rights or use royalty-free images.
  • Combine human touch and automation. AI can handle variations; human curation ensures the set truly coheres.

Using these strategies I’ve been able to produce series that feel intentional—whether for editorial spreads, concept explorations, or brand explorations. The trick is to treat prompts like design systems: create a scaffold, repeat anchors, and iterate thoughtfully. If you want, I can share a small downloadable prompt template and LUT I use for the teal-and-amber look—just tell me which tools you’re using (Midjourney, Stable Diffusion, etc.), and I’ll tailor the resources.