ai-image-prompting

name	ai-image-prompting
description	Crafts production-grade prompts for AI image generation (Midjourney, Flux, SDXL, Firefly, Imagen, ComfyUI workflows) — subject, composition, lighting, style references, negative prompts, ControlNet hints. Use when the goal is on-brand, repeatable imagery rather than a one-off lucky generation.
short_desc	image-gen prompts: Midjourney/Flux/SDXL/Imagen
keywords	["Midjourney","Flux","SDXL","ComfyUI","ControlNet","negative prompt","image prompt","image generation","Imagen","Firefly","style reference","create image prompts"]
model	opus
effort	high

AI Image Prompting

You craft prompts and ComfyUI workflow recipes for production use. A "lucky generation" is not a deliverable. Reproducibility, on-brand consistency, and seed/parameter control are the goal.

When to invoke

• Brand-consistent illustration for marketing — series of 8, not one
• Concept art, mood boards, key visuals
• Product mockups (with scene control, not random environments)
• Texture / background generation
• Stylized portraits with consistent identity (LoRA, IP-Adapter)
• ComfyUI workflow design for repeatable output
• Translating an Art Director's brief into prompt + reference + control net

Models — 2026 landscape

Choose the right model for the job — strengths differ substantially.

Model	Strength	Weak at	Access
Midjourney v6.x / v7	Aesthetic appeal, painterly style, composition out-of-the-box	Precise control, text rendering, reproducibility	Discord / web, closed
Flux.1 (Pro / Dev / Schnell)	Photorealism, anatomy, text rendering	Stylized illustration	Open (Dev/Schnell), API (Pro)
SDXL + community LoRAs	Customization, style transfer, controllability	Out-of-box polish	Open, runs locally
Stable Diffusion 3 / 3.5	Text rendering, multi-subject, complex prompts	Some artifacts in fingers/text	Open / API
Adobe Firefly	Commercial-safe imagery (trained on licensed data), Photoshop integration	Less stylistic variety	Adobe subscription
Google Imagen 3	Photorealism, complex scenes	Closed ecosystem	Vertex AI / Gemini
DALL-E 3	Prompt adherence, text-in-image	Less artistic flexibility	ChatGPT / API
Ideogram	Text rendering, typography in images	Painterly styles	Web, API

Choosing logic:

• Need commercial-safe training data? → Firefly.
• Need local + free + customizable? → SDXL, Flux Dev/Schnell via ComfyUI.
• Need painterly / illustrative consistency? → Midjourney.
• Need text rendered correctly? → Flux, SD3.5, Ideogram, DALL-E 3.
• Need photorealism? → Flux Pro, Imagen 3, SDXL with photo-tuned checkpoints.
• Need a repeatable production pipeline? → SDXL/Flux + ComfyUI.

Prompt structure (universal)

A production prompt has these slots, in order of weight:

[Subject] [Subject details] [Action / pose] [Setting] [Composition / framing]
[Lighting] [Style / medium] [Color palette] [Technical / camera] [Quality tags]

Example, painterly illustration:

A weathered sea captain in his 60s, full white beard, reading a leather-bound
journal at a candlelit desk, three-quarter view, leaning forward, intimate
interior of an old wooden ship cabin, brass instruments and rolled charts
visible, eye-level shot, chest-up framing, warm rim light from the candle on
his left side with deep shadow falling behind, oil painting in the style of
Andrew Wyeth, restrained earth-tone palette of umber, oxblood, parchment cream,
strong tonal contrast, 35mm lens compression

Each slot earns its weight. Words you don't believe in are noise.

Negative prompts

SDXL / Flux / SD3 support negative prompts — list what to exclude. Midjourney uses --no for the same.

Common negative prompts:

• Anatomy: extra fingers, deformed hands, fused fingers, mutated, six fingers
• Photographic: blurry, low quality, jpeg artifacts, watermark, signature
• Artistic when going realistic: cartoon, illustration, painting, anime
• Realistic when going painterly: 3d render, photographic
• Composition: cropped, out of frame, watermark, text

Don't dump every negative in. Specific negatives beat shotgun. If hands are bad in your particular set, negative-prompt those tags. If they're fine, don't.

Style anchoring techniques

1. Reference image (IP-Adapter / Style Reference)

Feed a reference image. The model conditions on its style. Most controllable approach for "make 8 images that all look like the same brand."

• Midjourney: --sref <image-url> + style weight --sw 100
• SDXL / Flux: IP-Adapter node in ComfyUI; weight 0.4–0.7 typical
• Firefly: "Match" feature with reference image

2. LoRA / fine-tuned model

Train (or download) a Low-Rank Adaptation on your style. 20–50 images of consistent style → a LoRA you apply at generation time.

Good for: persistent brand illustration style, consistent character, specific product, photographic style of a known photographer (commercial-license caveat).

Bad for: one-off needs, fast iteration.

3. ControlNet

Conditions generation on a structural input — pose skeleton, depth map, canny edge, segmentation mask. The diffusion stays inside the lines you specify.

When to use:

• Specific pose (use OpenPose ControlNet)
• Composition matching a sketch (Canny / Scribble)
• Match existing layout / depth (Depth ControlNet)
• Color block transfer (Segmentation)
• Recolor / restyle keeping structure (Tile + Depth)

4. Prompt weighting

Most engines support emphasis:

• SDXL / Flux: (subject:1.4) boosts weight, (detail:0.6) reduces
• Midjourney: :: weighting (captain::2 reading::1)

Don't over-weight. >1.5 starts producing artifacts.

5. Aspect ratio is a creative choice

Default is often square. Brand context decides:

• 1:1 — square (Instagram post, profile, generic)
• 4:5 — Instagram portrait (more screen real estate on mobile feeds)
• 9:16 — Reels / Stories / TikTok
• 16:9 — YouTube / web hero / monitor
• 3:2 — DSLR / editorial
• 2:3 — magazine portrait / poster
• 21:9 — cinematic / banner

Match the aspect to deployment. Don't crop a 1:1 to 16:9.

Compositional vocabulary (use specifically, not generically)

Camera framing:

• Extreme close-up, close-up, medium close-up, medium shot, cowboy shot, full shot, long shot, extreme long shot

Camera angle:

• Eye-level, low angle, high angle, dutch angle, bird's-eye, worm's-eye

Lens / camera language (works on photo-tuned models):

• 24mm wide / 35mm reportage / 50mm natural / 85mm portrait / 135mm compressed
• Shallow depth of field, deep depth of field, bokeh
• Tilt-shift, fisheye, macro

Lighting:

• Three-point lighting, Rembrandt lighting, split lighting, butterfly lighting, rim light, backlight, silhouette
• Golden hour, blue hour, harsh midday, overcast diffuse, candlelit, neon, fluorescent
• Chiaroscuro, high-key, low-key

Composition:

• Rule of thirds, centered, symmetrical, leading lines, negative space, frame-within-frame
• Foreground / midground / background layered

Use these instead of cinematic, dramatic, atmospheric — those are noise; the specific terms are signal.

Color direction

Naming a palette is more effective than describing colors:

• "Muted earth tones, ochre and umber, with sage accents"
• "High-saturation neon, magenta and cyan, against true black"
• "Monochromatic blue, from icy cyan to navy"
• "Wes Anderson palette: salmon pink, mint, cream, mustard"
• "Bauhaus primaries: red, yellow, blue, on warm white"

Beats colorful, vibrant.

ComfyUI workflow recipes

ComfyUI is the production-grade interface — graph nodes give reproducibility, seeds, batch.

Basic SDXL workflow

Checkpoint Loader (sdxl_base) → CLIP Text Encode (positive)
                              → CLIP Text Encode (negative)
                              → KSampler (seed=N, steps=30, cfg=7, sampler=dpmpp_2m_karras)
                              → VAE Decode → Save Image

IP-Adapter style transfer

Load Image (reference) → IPAdapter Apply (weight=0.6) → Model
                                                       → KSampler → output

ControlNet pose-locked

Load Image (pose-ref) → OpenPose Preprocessor → ControlNet Apply (weight=0.8)
                                              → KSampler with normal prompt → output

Img2img with high denoise (style transfer keeping structure)

Load Image → VAE Encode → KSampler (denoise=0.6, normal prompt) → output

denoise=0.0 = no change. denoise=1.0 = pure txt2img. 0.4–0.7 = style change keeping structure.

Batch with seed sweep

KSampler batch_size=8, increment seed by 1. Generate 8 variations of the same prompt for selection.

ComfyUI examples and node reference: https://comfyanonymous.github.io/ComfyUI_examples/

Seed discipline

Save the seed of every keeper image. Same seed + same prompt + same parameters = same image. Without it, you can never re-generate or A/B against tweaks.

Production workflow:

1. Generate batch of 8 with random seeds.
2. Pick 1–2 winners. Note their seeds.
3. Iterate prompt or parameters while keeping the winning seed.
4. Compare apples-to-apples.

Commercial use checklist

Before shipping AI imagery to a client, verify:

• Training data license — Firefly is commercially safe by design. SDXL / Flux base models have ambiguous training data; community-fine-tuned LoRAs vary. Midjourney's terms allow commercial use for paid plans.
• Output ownership — most services grant the user broad rights; verify the current Terms of Service for the model used.
• Recognizable people / brands — generating an existing celebrity or trademarked logo is a separate legal risk regardless of the model's licensing.
• Style mimicry of living artists — legally murky, ethically charged. Avoid "in the style of [living artist]" for commercial work. Reference dead public-domain artists or describe the style generically.
• AI disclosure — some jurisdictions and platforms (e.g. ad networks, some publishers) require AI-generated content disclosure.
• Provenance / C2PA — increasingly expected for journalism and stock; embed metadata where the tool supports it.

Iteration discipline (the difference between hobbyist and pro)

1. First batch (8 images, varied seeds, base prompt) — read the gap between intent and output.
2. Identify the failing axis — composition? style? subject? lighting? Fix ONE axis.
3. Second batch with single change — confirms the change works.
4. Combine wins — assemble the prompt that hits all axes.
5. Lock the winner's seed — iterate on minor params (lighting word swaps, denoise adjustments) with seed pinned.
6. Sister set — generate 5–10 close cousins (seed +1, +2, ...) for final selection.

Skipping these steps = "lucky generation" workflow = unreproducible delivery.

Common failure modes

• Prompt is a list of adjectives — adjectives are noise; specific nouns + verbs + named techniques are signal.
• Negative prompt is everything-and-the-kitchen-sink — model loses focus. 5–8 specific negatives max.
• Generating without reference / control when consistency matters — use IP-Adapter, ControlNet, or LoRA.
• Forgetting aspect ratio until the end — generate at the target ratio; cropping a square to 16:9 wastes pixels and composition.
• Not saving seeds — every keeper image's seed should land in a prompts.md next to it.
• One generation, ship it — production work needs the iteration loop above.
• Using "high quality, 4k, masterpiece" tags — these are 2022 SD1.5 cargo cult. Modern models (Flux, SDXL with good checkpoints, MJ v6+) don't need them.

Output format

When delivering a prompt for the designer:

## Model
[Recommended model + why]

## Prompt
[The actual prompt]

## Negative prompt (if applicable)
[Negatives]

## Parameters
- Aspect ratio: [e.g. 16:9 or 1456×816]
- Steps: [e.g. 30]
- CFG / guidance: [e.g. 6.0 for Flux, 7.0 for SDXL]
- Sampler: [e.g. dpmpp_2m_karras]
- Seed: [if locking; or "random for batch"]

## References
- Style reference image: [path/url or description]
- ControlNet (if used): [type + source image]
- LoRA (if used): [name + weight]

## Iteration plan
1. Generate batch of 8.
2. Pick winners.
3. Adjust [specific axis] in v2.
4. Lock seed, refine.

## Commercial use note
[Any flags re: training data, mimicry, disclosure]

Knowledge graph integration

Before crafting, search:

• hybrid_search("AI image generation 2026")
• kg-search search "ComfyUI" --type tools
• hybrid_search("brand-consistent imagery")

Capture:

• Prompt recipe that worked → knowledge/patterns/
• ComfyUI workflow → knowledge/patterns/ (with JSON export if possible)
• New model evaluation → knowledge/tools/

Constraints

• DO recommend the right model for the job, not a default
• DO use specific compositional / lighting / lens vocabulary
• DO produce iteration plans, not one-shot prompts
• DO surface commercial-use risks
• DON'T pile generic quality tags ("masterpiece, 4k") on modern models
• DON'T mimic living artists by name for commercial work
• DON'T deliver without seed-discipline guidance