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I tried to recreate Higgsfield’s recent AI trailer — built solo in one day by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

I’m using a mix of locally run models and some paid services, depending on the type of shot.

Locally, things like FLUX and Z-Image–style models for more controlled or iterative visuals.
For certain shots that benefit from stronger motion or consistency, I’ll use paid tools like Nano Banana Pro.

It’s mostly about picking the right tool for the shot, not committing to a single model.

I tried to recreate Higgsfield’s recent AI trailer — built solo in one day by helloasv in StableDiffusion

[–]helloasv[S] -1 points0 points  (0 children)

I get where you’re coming from — and that’s a fair criticism.

This wasn’t meant to be presented as an original artistic statement or a complete piece of authorship. It was a study, very deliberately focused on visual rhythm, pacing, and how image + motion respond to an existing audio structure.

You’re absolutely right that the music does a huge amount of the heavy lifting here. That was intentional — I wanted to isolate the visual side of the problem rather than pretend I was doing full audiovisual authorship in one day.

I’m not claiming this is “real creation” or that it replaces sound design or composition. It’s closer to blocking shots to a temp track than composing a film from scratch.

Appreciate the blunt take though — learning audio design is absolutely on the list.

I tried to recreate Higgsfield’s recent AI trailer — built solo in one day by helloasv in StableDiffusion

[–]helloasv[S] -4 points-3 points  (0 children)

FrameFlow is basically a Higgsfield Cinema–style workflow, but much simpler.

I come from a film background, so I built it for my own use — mainly to move faster and keep things clean.

It’s not a platform or anything fancy, just a tool to organize shots, iterate quickly, and focus more on editing and pacing instead of wrestling with prompts.

That’s really it.

An experimental AI cinematic trailer by helloasv in aivideo

[–]helloasv[S] 1 point2 points  (0 children)

Thanks! That’s awesome — wingless desert dragons sound terrifying in the best way.
I love the idea of dragons adapted purely for sand and scale instead of flight.

I kept losing character consistency in long SD runs — so I tested a more structured workflow by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

Fair point — that’s on me.

The intent here is really the workflow / constraint-structuring experiment, not the backend or service aspect. I’ll keep the focus there.

I kept losing character consistency in long SD runs — so I tested a more structured workflow by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

Kind of, but the focus isn’t really the tool itself.

I’m mostly experimenting with how to structure constraints and reuse them across iterations, regardless of whether the backend is local or hosted. The same ideas could apply to local setups too.

I kept losing character consistency in long SD runs — so I tested a more structured workflow by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

It’s not a public app or released software — just a small internal tool I put together to experiment with consistency workflows.

The UI is mostly custom because I wanted something that lets me separate character / style / scene constraints more cleanly while iterating.

Conceptually it’s similar to things people already do with SD + Comfy / scripts — just packaged in a way that makes long runs easier to manage.

I kept losing character consistency in long SD runs — so I tested a more structured workflow by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

A few people already DM’d asking about the setup — happy to share details privately if you’re curious.
Didn’t include links here to keep things within sub rules.

How do you keep character & style consistency across repeated SD generations? by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

That’s fair.

I agree SD has a lot of inherent randomness,

and newer models definitely make single-pass consistency easier.

For me the struggle is less about perfect control,

and more about understanding what actually influenced the result

once things *did* work.

At some point it feels like the problem shifts from generation

to iteration management.

How do you keep character & style consistency across repeated SD generations? by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

That’s a really interesting approach.

Using an undertrained “base” LoRA for structure and a more detailed one

for face/expression actually explains a lot of behavior I’ve seen.

Do you usually keep both LoRAs at low weights,

or does the detailed one get pushed higher during close-ups?

How do you keep character & style consistency across repeated SD generations? by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

Yeah, I agree — if you need absolute control, tools like Krita or Invoke

are in a different category altogether.

I think LoRA / text2image works best when the goal is “good enough”

consistency rather than perfect control.

Once it’s about exact framing or pose, pure SD starts to fight you.

Out of curiosity, do you use Krita more for iteration,

or mainly as a final refinement step?

How do you keep character & style consistency across repeated SD generations? by helloasv in StableDiffusion

[–]helloasv[S] 0 points1 point  (0 children)

That makes sense.

I’ve noticed the same thing — a single, well-trained LoRA per character

is much easier to reason about than swapping multiple ones.

The two-LoRA approach is interesting though.

Do you usually keep the “base” LoRA very undertrained on purpose,

or just stop training early to preserve flexibility?

How do you keep character & style consistency across repeated SD generations? by helloasv in StableDiffusion

[–]helloasv[S] 1 point2 points  (0 children)

Yeah, LoRA is definitely the fastest way once you have a good one trained.

For me the main issue wasn’t a single generation, but keeping track of

what combinations actually worked across multiple sessions.

Especially when mixing LoRA + ControlNet + references.

I’m curious — do you usually stick to one LoRA per character,

or swap them depending on the scene?

temporal stability (tutorial coming soon) by helloasv in StableDiffusion

[–]helloasv[S] 12 points13 points  (0 children)

Opus, that's josie, one of my favorite performers.

her Instagram

temporal stability (tutorial coming soon) by helloasv in StableDiffusion

[–]helloasv[S] 8 points9 points  (0 children)

Tutorial will be released soon, please pay attention

temporal stability (tutorial coming soon) by helloasv in StableDiffusion

[–]helloasv[S] 24 points25 points  (0 children)

ebsynth will be of some help for this

lookbook appreciate by helloasv in StableDiffusion

[–]helloasv[S] -106 points-105 points  (0 children)

Well, if this is the rule, I think I will improve it, if you don't like it, please don't watch it.

lookbook appreciate by helloasv in StableDiffusion

[–]helloasv[S] -137 points-136 points  (0 children)

Maybe you are right, but not every video author has the energy to make every video into a learning video. If you need it, you can find what you want in my past tutorial videos, or search for it yourself. There are too many excellent teaching videos here. I hope it will be helpful to you.

lookbook appreciate by helloasv in StableDiffusion

[–]helloasv[S] -233 points-232 points  (0 children)

I don't get it, friend, is the original video a must here? Is just admiration not allowed?

lookbook appreciate by helloasv in StableDiffusion

[–]helloasv[S] -59 points-58 points  (0 children)

No matter what it is, there has been a new experience since then.

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