Some AI tools don’t just generate images—they inherit, mix, and echo a wide range of influences. The final output is really a highly customized stack of data made visible.
Sometimes, it’s hard to tell where the human input stops and the AI influence on an image begins.
In a single generated image, there can be dozens—sometimes hundreds—of influences working together: a model’s training data, a casual color choice, a reference image you forgot you uploaded three versions ago. These fragments build up like sediment, layering into the output image.
It’s getting harder to define what it means to “create” in the age of AI. And maybe that’s the wrong question anyway. A better one might be: Where did the image come from?
Mapping the Inputs
Let’s start breaking it down. An AI-generated image can emerge from a mix of the following inputs—some chosen consciously, others inherited quietly by default. These inputs can mix, stack, and override one another in unpredictable, sometimes even black box, ways.
Here’s my quick attempt at outlining, at a forensic-level, potential influences on an AI-Generated image:
Model-Level Influences:
• Base model training data (billions of scraped images across styles, eras, and cultures)
• Fine-tuned checkpoint on a specific aesthetic (e.g. cyberpunk, Pixar, ukiyo-e)
• Embedded aesthetic biases (learned from dominant content in the training corpus)
• Hallucinated visual patterns based on latent space clusters
• LoRA modules for specific artists, media, or genres
• Prompt embeddings trained for common phrases (e.g. “moody lighting” or “anime girl”)
Prompt-Level Influences:
• Descriptive words that imply color or lighting (“sunset,” “neon,” “underwater”)
• Named references (e.g. “Van Gogh style” or “Ghibli background”)
• Negative prompts that remove specific aesthetics or color tones
• Prompt length and token weighting (e.g. “blue” appearing more than once)
• Prompt templating tools that auto-structure prompts from UI sliders
• Accidental leftover prompt memory from reused sessions (in some tools)
Image-Based Inputs:
• Non-AI source image uploaded as a seed or reference
• AI-generated image used as an iterative base (and often remixed multiple times)
• Style transfer from photography, paintings, digital art, scans, etc.
• ControlNet or pose reference guides
• Depth maps or segmentation masks guiding object structure
• Edge maps or scribbles from user input
• Sketches or outlines from external drawing tools
Colors in the final image may have come from:
• A non-AI seed image
• An AI-generated seed image
• A previously generated AI image remixed into the prompt
• A style transfer image (AI or non-AI) with embedded color data
• A manually uploaded color reference (mood board, still frame, art piece)
• A prompt describing color (e.g. “washed-out pastels” or “vivid neon”)
• Negative prompts suppressing certain color ranges
• A color palette generator (e.g. Adobe Color, Coolors)
• A hand-picked palette using hex or RGB values
• Post-edits made in Photoshop, Procreate, or Krita
• Real-time painting over the image using a tablet or brush tool
• Grayscale image colorized by AI (either automatically or guided)
• Color grading filters applied during or after generation
• Automatic tone mapping by the rendering engine
• HDR lighting maps in pseudo-3D workflows
• LUT (lookup table) filters applied in video workflows
• Interpolated color from tweening frames or upscaling steps
• Color defaults embedded in the model version (some versions lean warm, cool, soft, bold)
• App-level rendering engine differences (e.g. how NightCafe vs Leonardo interprets inputs)
• Screen calibration on the editor’s display (perceived color affects final adjustments)
• Export compression and format (e.g. JPEG artifacts, WebP saturation shift)
Post-Generation Tools & Edits
Even after generation, many users manipulate their images with:
• Layer masks to refine or recolor sections
• AI generative fill tools
• Texture overlays or brush noise for visual depth
• Manual touch-ups on faces, eyes, or hands using clone/healing tools
• Vectorization software (e.g. Illustrator’s Image Trace)
• Edge enhancement, blur passes, or sharpening filters
• Third-party filters like Lightroom, Snapseed
• Upscaling algorithms that add or shift pixel data
• Outpainting workflows where new parts change the image context or palette
With all this in mind, I start to wonder: what part of the image is considered mine? Afterall, that expectation is still there. That mental model of using a creative tool and owning the creative product output - the result of your interaction with the tool - still exists.
Sometimes authorship doesn’t live in any one step. Sometimes it lives in the pattern of decisions: the language in a prompt, the eye that picked the palette from an obscure 1970s ad, the hands that pulled pieces together across tools, models, and sessions.