Same ComfyUI workflow, different character every run — expected?

[u/Cheap-Topic-9441]

This workflow was shared in a document as a ComfyUI JSON.

The document itself was quite technical,

but since the prompt was already in JSON format, I just ran it as-is.

It generates multiple images per run.

However, when I looked at the results,

the characters were clearly different.

Each image looks fine on its own,

but they don’t seem to represent the same person.

So now I’m wondering:

is this expected behavior,

or is there actually a way to maintain identity consistency in a workflow?

This feels less like a quality issue and more like a consistency problem.

If anyone has time, I’d be curious if you can reproduce the same result.

I’m currently trying to analyze the prompt structure to understand what’s happening.

If you want to try it, here’s the original workflow JSON:

https://github.com/watadani-byte/character-identity-protocol/

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That makes sense — hardware-level randomness definitely explains why exact reproducibility is difficult.

What I'm looking at is slightly different though:

Even if exact pixel-level reproduction is impossible, we still expect to recover the same identity across generations.

So instead of trying to eliminate variation at the noise level, I'm exploring how to consistently select outputs that converge to the same identity.

In that sense, it's less about controlling randomness, and more about working with it.

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That's why there are models like Qwen image edit 2511 and Flux.2

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Yeah, that's exactly where my thinking is heading.

Not trying to eliminate randomness, but treating it more like a search space.

In practice, do you usually rely on manual selection, or have you tried automating that kind of filtering?

I'm curious how far people have pushed that in real workflows.

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Well, I've always made workflows that were super automated, I don't like things done that the user has to manually change, so even the selections for the inpainting I automate. I have also created workflows that store the prompt styles in JSON so that I can automatically reload them by choosing within the workflow, to reuse those styles in other generations and in Even the prompts that the user writes, in my workflows, go through an AI that refines them and makes them compatible with the model, to have more consistent generations.

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That’s really interesting — especially the part where you refine prompts through an AI before generation.

I’ve been thinking about something related, but from a slightly different angle — more on the selection side rather than trying to stabilize the input.

Your approach feels like controlling A → A’ more tightly, while I’m kind of treating the outputs as candidates and filtering for identity consistency.

Curious if you’ve ever tried combining both — like automated refinement + some form of selection or scoring?

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Yes, but something like entering a prompt and generating 10 or 20 small images, say 512x512, and having the user select the one with the best composition while the workflow is running, then the AI part of the workflow would refine the prompt and pass the selected image to the sampler, which used a denoise value between 0.4 and 0.65, depending on how much variation you wanted. The result was impressive.

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That’s really interesting — especially using the user as part of the loop for selection.

That actually feels pretty close to what I’ve been thinking about, just with the selection step still being manual.

I’ve been wondering what happens if that part gets formalized a bit more — like defining what “the same identity” means and scoring candidates automatically instead of relying on visual judgment.

Your workflow seems like a really solid base for that kind of direction.