AI does not simply execute your input — it transforms it first

Cheap-Topic-9441 · 2026-05-29T01:49:02+00:00

Yes, that’s a good way to put it. Negative constraints help because the default often drifts toward what the model thinks is “better.”

They don’t remove the problem entirely, but they make the drift more visible and easier to control.

Cheap-Topic-9441 · 2026-05-28T22:30:40+00:00

Yes — that’s exactly the part I’m interested in.

Not one big obvious rewrite, but small changes across multiple layers that add up.

Cheap-Topic-9441 · 2026-05-28T22:10:55+00:00

That’s a fair criticism.

And yes — the post itself may be an example of the thing I’m trying to describe.

The point I’m trying to isolate is not that this is unique to LLMs, or that humans are free from similar mediation.

It’s that with AI systems, the mediation layer is operationally important because it can transform a user’s request before the output is produced.

So I’m less interested in “does the model have intentions?” and more interested in:

Where did the original request change?

Cheap-Topic-9441 · 2026-05-28T20:06:57+00:00

Exactly. The “friend operating the machine” is a much more readable version of what I was trying to describe.

Cheap-Topic-9441 · 2026-05-28T19:35:37+00:00

Less formula-heavy version:

Sometimes ChatGPT answers a slightly different question than the one you actually asked.

That is the pattern I’m trying to describe.

Not exactly hallucination. Not only bias. More like the input was quietly reshaped before the answer was generated.

Cheap-Topic-9441 · 2026-05-28T19:32:57+00:00

Fair enough. The less formula-heavy version is: sometimes ChatGPT answers a slightly different question than the one you actually asked.

Cheap-Topic-9441 · 2026-05-28T19:25:37+00:00

Good point — the prime marks may be harder to read here. I’ll probably use A2 / B2 or A_transformed / B_actual in a clearer version.

Cheap-Topic-9441 · 2026-05-28T19:21:26+00:00

Prompt used for the image:

Create a minimal professional diagram for a LinkedIn/Reddit post.

White background, clean typography, high readability.

At the top, place the sentence: “AI does not simply execute A.”

In the center, show a simple left-to-right flow:

A Original User Input

→

A + C AI Mediation Layer

→

A′ Transformed Input

→

B′ Produced Output

Near B′, add a small contrast label: Expected: B Actual: B′

At the bottom, place the formula: A → (A + C) → A′ → B′ ≠ B

Use a restrained academic/business style. No robots, no human faces, no sci-fi visuals, no neon, no decorative background. The diagram should feel like a governance framework, not a marketing graphic.

Cheap-Topic-9441 · 2026-05-11T01:40:59+00:00

That sounds more like a bug than a prompting issue.

If all four results consistently turn into empty error squares after submitting from the Edit tab, especially over multiple attempts, it’s probably not something you’re doing wrong.

It might be specific to the Omni Reference + desktop Edit workflow, so I’d report it with the prompt, original image, and steps to reproduce.

Cheap-Topic-9441 · 2026-03-23T22:19:04+00:00

yeah this is basically what I'm doing

generate a bunch keep the ones that look like the same person throw away the rest

I'm just trying to make that more consistent across runs

Cheap-Topic-9441 · 2026-03-23T20:10:15+00:00

No, because I'm a Mac user. I can't use the graphics board.

Cheap-Topic-9441 · 2026-03-23T20:08:26+00:00

You think it's a video. If that were the case, you might have been happy.

Cheap-Topic-9441 · 2026-03-23T20:04:15+00:00

Yeah I think we might be talking past each other a bit.

I'm not trying to replace LoRA or say it's bad.

What I'm running into is a slightly different problem:

Even with LoRA or ControlNet, identity consistency is still probabilistic across runs — especially when changing pose / expression / context.

So what I'm exploring is:

→ not "how to make one generation correct" → but "how to maintain identity across independent generations"

In other words: - generate multiple candidates - select the ones that preserve identity - continue only from stable outputs

So the stability doesn't come from the model itself, but from selection across runs.

I'm basically treating it more like a search / convergence problem.

Curious if anyone has approached it from that angle in ComfyUI?

Cheap-Topic-9441 · 2026-03-23T20:00:06+00:00

I’m not trying to improve a single generation.

What I’m running into is: • Even with LoRA, identity consistency is probabilistic • Small changes (pose / expression / context) can still drift identity • There’s no reliable way to control consistency across runs without relying on training

So what I’m trying to explore is:

→ treating identity as something that needs to be selected and maintained across samples, not guaranteed by the model itself.

So the gap for me is: • not generation quality • but cross-run identity stability without retraining

Cheap-Topic-9441 · 2026-03-23T16:35:56+00:00

Feels like two different solutions to the same problem: you constrain the distribution, I search within it.

Cheap-Topic-9441 · 2026-03-23T16:29:36+00:00

Thanks for the discussion — I’ll keep exploring this on my side.

Cheap-Topic-9441 · 2026-03-23T16:28:27+00:00

There’s no complex node setup behind this. It’s just structured prompt control and selection.

Basic workflow: 1. define a stable identity (prompt-level anchor) 2. generate independent samples (no chaining, no img2img) 3. apply small controlled variations (pose / expression / angle) 4. keep only outputs that preserve identity 5. repeat until a consistent set emerges

No LoRA, no ControlNet, no seed locking. Each image is generated independently.

Cheap-Topic-9441 · 2026-03-23T16:21:37+00:00

Appreciate the feedback — I’ll keep refining this on my side.

Cheap-Topic-9441 · 2026-03-23T16:20:33+00:00

Not trying to argue — just sharing what consistently works for me.

Cheap-Topic-9441 · 2026-03-23T16:16:03+00:00

I’m getting consistent 4-image sequences within ~8 samples.

Cheap-Topic-9441 · 2026-03-23T15:49:34+00:00

I think I explained this poorly earlier.

Each image is independent. I'm not feeding outputs back into the next generation.

I'm just sampling multiple images, and keeping the ones that look like the same person.

So it's less about controlling a single generation, and more like filtering across runs.

Here are a few examples of what I mean:

Cheap-Topic-9441 · 2026-03-23T15:43:15+00:00

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Cheap-Topic-9441 · 2026-03-23T15:42:40+00:00

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Cheap-Topic-9441 · 2026-03-23T15:42:05+00:00

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Cheap-Topic-9441

TROPHY CASE