This is gold for those using generative Ai to create sets of images that go together such as icons and emotes. I have personally used this technique to create more consistent image sets. it can be pasted into the likes of ChatGPT and you can ask to start the process and take it from there. it is essentially a refinement to the art generation capabilities of chatgpt through a formal structure of documentation and heuristic rules. in essence, I've reverse engineered the essence of professional asset production as a task in ChatGPT. using this and some manual editing for those emotes that are repetitive/memes, I was able to produce 22 emote images that are relatively consistent and generally look human made, in less than a day, I think I did that in 6-8 hours.
this is not perfect, this is not a "fix the Ai's inadequecies" magic spell, but it is really, really good at working with you to iterate over output (as long as you remember to say stop and go back to the documentation/rule setting phase to fix whatever went wrong and reinforce what you want), and to correct errors in output until you get something that fits what you're looking for. this is great for fine tuning output. being obsessive over detail here IS the art. commitment to this process of trial and error is what makes this more of a serious tool and less of a lazy man's way out.
junior asset artists worldwide may want to take note of this, please note however that your muse cannot be captured by a computer, the biggest value you have is your perspective and experiences and views that inform your creative ideas specifically, you may end up prompting a computer, but what's important here is that everything I produced via this method still drew heavily on what I wanted and what inspired me, it's just a tool. the key to avoiding it for now is to be able to better match the specific demands of your clients than an Ai can, with how people can be about artworks and creative projects, that is still a pretty low bar to get over in many cases these days.
truth be told, emotes and icons and such are the exact kind of assets I expected to end up being produced by automated processes, nearly two decades ago when the first tiniest glimmer of Ai potential started to show in research here and there.
This document describes a general-purpose, repeatable workflow for producing consistent visual assets using generative image models, especially in domains where:
- Style consistency matters more than novelty
- Small-format readability is critical
- “Almost correct” outputs are worse than rejection
- Iteration and constraint refinement are unavoidable
The method applies to:
- Emotes & emojis
- Icons & UI elements
- Sprite sheets
- Character variants
- Meme-format art
- Brand-locked illustration systems
Generative image models optimize for plausibility, not compliance.
In constrained visual domains, this leads to:
- Style drift (“generational loss”)
- Hallucinated anatomy or props
- Inconsistent lighting and rendering
- Reintroduction of forbidden elements
- Increasing variance over iterations
Naïve prompt tweaking fails because the model fills ambiguity with learned priors.
Solution: Remove ambiguity by progressively constraining the solution space.
The model should be treated as:
A stochastic renderer that must be boxed in by rules
Not as:
A creative partner with taste or intent
Creativity is allowed only where explicitly granted.
Anything not explicitly forbidden will eventually appear.
If something must never happen, it must be:
- Explicitly disallowed
- Repeated
- Enforced through rejection
Absence is not implied.
Absence must be commanded.
Errors are not setbacks. They are data.
Every repeated mistake reveals:
- A missing rule
- An ambiguous constraint
- An unprotected invariant
The workflow is built on three documents:
- Style Bible – what never changes
- Iteration Protocol – how changes are introduced
- Acceptance Gate – what “done” means
All three are required.
The Style Bible defines everything that must remain stable across assets.
Define, explicitly:
- Canvas rules (size, padding, transparency)
- Line art:
- Thickness
- Opacity
- Consistency
- Color rules:
- Fixed palette or ranges
- Saturation limits
- No drift over generations
- Lighting model:
- Flat / cel / graphic
- Explicitly forbid glow, bloom, vignette if unwanted
- Texture policy:
- Allowed vs forbidden textures
- When damage or noise is acceptable
These are non-negotiable.
For characters or objects:
- Fixed silhouettes
- Fixed proportions
- Fixed structural elements
- Explicit bans on:
- Extra parts
- Shape reinterpretation
- Expressive shortcuts
If an element must not be used for expression, say so explicitly.
Define:
- Which props are canonical
- How many props are allowed
- Where props may appear
- How props interact with the subject
Avoid symbolic interpretation unless explicitly desired.
Maintain a running list of recurring failure modes.
Examples:
- Style drift
- Painterly rendering
- Unwanted anatomy
- Abstract or surreal reinterpretation
- Softening of edges
- Added lighting effects
This list is referenced in every locked prompt.
This is the operational process.
Each iteration introduces exactly one new variable:
- A pose
- An emotion
- A prop
- An interaction
Everything else is frozen.
Before generating, write brief notes covering:
- Intended viewer read (what should be understood instantly)
- Composition plan
- Interaction physics (if relevant)
- Anticipated failure modes
This primes constraint extraction later.
All prompts should be structured, not prose.
Recommended sections:
Condensed Style Bible.
Anatomy, geometry, or object rules.
The only new information introduced.
Explicit list of forbidden elements.
Background, resolution, edge quality, etc.
Once written, the prompt is considered locked.
- Generate conservatively
- Reject aggressively
- Do not “hope” the next run fixes systemic issues
Critique using categories, not vibes.
Examples:
- StyleMismatch
- AnatomyViolation
- PropUnreadable
- PhysicsIncorrect
- DriftDetected
This keeps feedback precise and repeatable.
Each repeated failure results in:
- A new explicit rule
- A tightened constraint
- A promotion from soft to hard constraint
Failures are folded back into the system.
As needed, escalate:
- Conceptual freedom
- Guided generation
- Locked style
- Locked composition
- Locked geometry (rearrange only, no invention)
Escalation continues until outputs stabilize.
Define when an asset is finished.
An asset is accepted only if:
- It matches the reference set at a glance
- It violates zero hard rules
- It reads correctly at target display size
- It shows no degradation vs previous accepted assets
Always test at final usage size.
If it fails small, it fails.
For each accepted asset, store:
- The locked prompt
- The delta introduced
- The primary failure modes encountered
This builds institutional memory.
To reproduce the process across teams or time:
Reusable, parameterized templates.
A fixed taxonomy for feedback.
A small, immutable anchor set that defines “on-model”.
This process succeeds because:
- Errors become constraints
- Iteration is intentional, not exploratory
- Style is defended, not rediscovered
- The model’s degrees of freedom are progressively removed
This is not prompt engineering.
It is design system enforcement for generative tools.