Style control means directing the aesthetic qualities of AI images—line weight, color palette, shading, texture, composition rhythm—so outputs match a target look. In practice, you’ll combine three layers: 1) prompt-level control (descriptors, weights, negatives, CFG, seed, sampler), 2) model-level control (LoRA, textual inversion, custom fine-tunes), and 3) reference-level control (img2img, IP-Adapter style, ControlNet). Aim for a small, documented recipe you can reuse.

Prompts set the baseline style. Keep content (what) separate from style (how).

• Style descriptors: cel-shaded, flat colors, thick ink lines, screen tones, cross-hatching, soft gradients, painterly, watercolor, retro, minimal, high contrast, limited palette, muted tones.
• Attention/weights: Use parentheses or explicit weights to emphasize style tokens; mirror with negatives for unwanted traits (photorealism, glare, noisy shading, blur).
• CFG scale: Lower (4–7) tends to produce smoother, unified styles; higher (8–12) enforces prompt details but can add artifacts or harsh contrast.
• Seed discipline: Fix a seed for consistency across a series; change only one variable at a time.
• Sampler choice: Clean line work often improves with DPM++ 2M Karras or DDIM; try a short step count first, then refine.
• Resolution and upscaling: Generate at moderate size (e.g., 768–1024 on SDXL), then upscale with a line-preserving or edge-aware method to protect the style.

Use images to transfer or stabilize a look.

• Image-to-image (img2img): Set denoise strength to control how much of the reference’s style transfers. 0.25–0.45 preserves structure and style; 0.5–0.7 explores variations.
• IP-Adapter (style): Feed a reference style image; tune style weight to 0.5–0.9 for strong adoption, lower if it overwhelms content.
• ControlNet guidance: Lineart/softedge for clean outlines, depth/normal for shading cues, pose for character consistency; combine with a palette board to lock colors.
• Palette control: Provide a small swatch image or color grid; mention palette constraints in the prompt and reinforce with negatives like “no neon, no saturated primaries” if needed.

Attach style knowledge directly to the model.

• LoRA: Great for consistent aesthetic packages (line, color, texture). Start at weight 0.6–0.8; adjust until the style is clear but not overpowering. Combine at most 2–3 LoRAs to avoid mud.
• Textual inversion (embeddings): Add a custom token representing a micro-style (e.g., a specific hatching pattern). Useful for lightweight control without heavy training.
• Custom fine-tunes: Train a style-focused model or DreamBooth with style-focused captions. Use mixed, diverse content to avoid overfitting one subject.
• Compatibility: Match base model families (e.g., SD 1.5 vs SDXL). Mismatches often cause artifacts or weak style transfer.

Many comic/anime styles benefit from stable structure before adding detail.

• Two-stage generation: 1) Generate composition/line via ControlNet lineart or pose. 2) Apply style via LoRA/IP-Adapter with moderate denoise.
• High-res fix or tile control: Upscale after style convergence to avoid fuzzy lines. Use tile models for textures or repeating patterns.
• Palette lock: Reuse the same palette reference and seed for a chapter or batch to maintain brand/series identity.

Blend aesthetics intentionally.

• Multi-LoRA: Keep total combined weight under ~1.2–1.5. Prefer one primary style LoRA and one micro-style LoRA (e.g., texture or shading).
• Prompt mixing: Separate style clauses with clear weights; test A/B with fixed seed.
• Step scheduling: Apply style strength later in the diffusion process (where supported) to preserve composition while imprinting style.
• Batch curation: Generate small grids with the same seed, varying one lever (LoRA weight or CFG) to find a stable recipe.

Muddy lines or washed colors: Lower denoise, reduce total LoRA weight, try a cleaner sampler, and upscale with an edge-preserving method. Overbearing style: Drop LoRA/IP-Adapter weight, add negatives (heavy texture, excessive grain), lower CFG. Color drift: Use a fixed palette image, tighten color-related descriptors, add negatives for disallowed hues. Style conflict: Remove competing descriptors/LoRAs; iterate with a fixed seed and change one factor at a time. Inconsistency across panels: Lock seed, sampler, palette, and style weights; document your exact recipe.

Anime cel-shaded portrait: Prompt with clean outlines, flat colors, soft gradient shadows, limited palette; CFG 5–7; DPM++ 2M Karras; optional anime-style LoRA at 0.7; upscale x2 with line-preserving upscaler. Manga screen-tone panel: Generate lineart via ControlNet lineart; keep shading minimal; add screen-tone texture via a micro-style LoRA or post-process halftone overlay; high contrast, black and white. Painterly watercolor scene: Descriptors for soft edges, paper texture, bleeding pigments; IP-Adapter style from a watercolor reference at 0.6–0.8; CFG 6; allow moderate denoise (0.5–0.6) for organic variation.

For repeatability, save: base model, samplers/steps, seed, CFG, resolution, prompt and negatives, LoRA/embedding names and weights, ControlNet models and strengths, any reference images with their weights, and the final upscale method. Reuse this checklist per project.