Style-targeted prompts are prompts engineered to fix the visual identity of an image—such as a particular era, medium, printing method, or studio production look—before detailing subject matter. They reduce style drift and increase consistency across a set, making them ideal for series, brand guidelines, comics panels, and anime cuts.

Build prompts from repeatable, verifiable blocks. Keep each block independently testable.

• Era and movement: 1980s OVA anime, Golden Age comics, mid-century modern, Y2K webcore
• Medium and process: cel-shaded animation, risograph print, screen tone, halftone dots, lith print, airbrush, oil pastel
• Production cues: limited color palette, three-tone shading, thick ink outlines, off-register print, grain, chromatic aberration
• Camera and layout: flat lighting, orthographic, dutch angle, zoom lens bokeh, 3-panel layout, dynamic foreshortening
• Color and materials: duotone teal-orange, spot colors, neon inks, retro CMYK, metallic gouache
• Constraint/negative list: remove unwanted styles or artifacts (e.g., overly photorealistic, watermark, extra fingers, text artifacts)

Tip: Add one variable at a time and record the visual change. Drop descriptors that do not measurably affect output.

Different models interpret style control differently. Prefer minimal, high-signal tokens.

• Stable Diffusion (SD1.5/SDXL):

  • Weighting: (term:1.2) to emphasize, (term:0.8) to de-emphasize
  • Negative prompt: list unwanted styles/artifacts succinctly
  • Add-ons: LoRA or textual inversion embeddings to lock style; keep strength conservative (e.g., 0.6–0.9)

• Midjourney (v6+):

  • Stylize: --stylize 0–1000. Lower for tight control; higher for expressive style
  • Style modes: --style raw for faithful subject rendering; niji modes for anime-focused stylization
  • References: use image URLs to bias style; combine multiple references for a composite style

• DALL·E-style models:

  • Plain-language style anchors; avoid repeated adjectives
  • Use clear medium/process cues (e.g., halftone screen print, cel animation still)

• General tip:

  • Avoid conflicting anchors (e.g., photoreal portrait + heavy halftone print)
  • Keep style terms before subject for stronger bias

Use these as starting points and iterate with one change at a time.

• Anime cut (cel-era): Style: 1990s cel-shaded anime, three-tone shading, film grain, slight gate weave, painted backgrounds, limited color palette Subject: [character/action] Constraints: clean linework, no watermark, no text artifacts

• Comic panel (print-era): Style: Golden Age comic print, halftone dots, off-register CMYK, bold inking, ben-day shading Subject: [scene] Layout: 3-panel strip, dynamic composition, speech bubble placeholder only Constraints: crisp edges, no moiré banding

• Design/illustration (risograph): Style: risograph, two-spot colors (fluoro pink, teal), heavy texture, paper grain Subject: [object/scene] Constraints: minimal gradients, clean silhouette

• Photobook look (art direction): Style: 1970s film stock, soft vignette, subdued saturation, natural grain Subject: [portrait/location] Constraints: no HDR, no over-sharpening

Image references can lock style faster than text alone.

• Use 1–3 images that clearly express the target style (consistent palette, materials, linework)
• Crop out distracting content (logos, text) to avoid unwanted imitation
• Match aspect ratio and composition to your intended output for better transfer
• Blend text anchors with references: keep text minimal and focused on era/process

A lightweight loop to converge on a stable look across a series.

1. Establish a base: pick 5–7 high-signal style tokens and a short negative list
2. Grid search: test 4–9 variations changing one token or its weight
3. Lock wins: freeze tokens that consistently improve results
4. Scale: generate a small set; check for drift across subjects and scenes
5. Document: save the exact prompt, seeds/parameters, and reference set for reuse

• Overspecification: too many style tokens can conflict; trim to the strongest 4–7
• Domain mismatch: some models tilt to realism; increase process cues (halftone, risograph, cel) to counter
• Style drift in series: fix seed (when available) and keep composition cues consistent
• Muddy linework: raise contrast cues (bold inking, crisp outlines), reduce texture load
• Banned/blocked terms: replace artist names with descriptive era/process and production-era aesthetics
• Negative prompt overload: keep it short; 5–10 precise bans usually outperform long lists