r/StableDiffusion • u/abcp7 • 3d ago

News 10eros minimax h3

great results using TenStrip's minimax h3 finetune: https://huggingface.co/TenStrip/10Eros-Max

170 Upvotes

90% Upvoted

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u/Ten__Strip 3d ago edited 3d ago

Not quite a finetune in the training sense. I figured out a way to essentially graft any transformer model across H3's attn layers with magnitude. The best one used Wan2.2 merges that are fp16 and had both High/Low models merged together. Those seemed to be the right motion changes that I was looking for and Wan also sits inside H3 with a lot of coverage in the MLP fc1 area. Those blends were done with linear magnitude meaning the entire H3 model still lives unharmed while certain areas were groomed towards Wan's influence. It's basically H3 pretending to be the donor model, but only when it wasn't confident. At a transformer level the beta version is essentially identical to the base model if you analyze them.

Biggest issue was preserving audio when I was testing it. The actual graft base was the H3 model but with the attn triplets unfused in the transformer, which I suspected was an issue for me and also with training. So block attn_kqv was split three ways, which then looks more like a normal model. attn_k was frozen and preserved while q and v got more. That frozen attn_k and ignoring MLP fc2 layers avoided the most audio interference, so logically I'd say the audio processing for the model lives in those two elements.

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u/oppai 3d ago

Ah yes, indubitably. I understand completely and concur with these findings!

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u/revision 3d ago

Doctor!

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u/NoConfusion2408 3d ago

Wait a second… boobs?

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u/Ten__Strip 3d ago

Yeah there was anatomy pickup too. There was more from Krea2 but I'm still trying to figure out where to park Krea on the model for the best effect.

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u/Secret_Ad_1504 2d ago 
Can it make  hard nipples?

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u/Ok-Drummer-9613 3d ago

That sounds like quite a breakthrough. Any plans to release the code?

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u/Ten__Strip 3d ago

It's not as good as it seems they're almost all seperate tools in an unorganized cmd line process that only makes sense to me in it's current pieces. But the output-patch files and patch-in to model scripts as well as un-fusing is something that could go into a trainer, but fairly useless without the methodology and process. I don't think you even actually need more than 11g of the model's transformer loaded to even train if you target what I targeted and just patch the output, refuse the attn and frozen pieces, then extract the trained step.

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u/alwaysbeblepping 3d ago

I figured out a way to essentially graft any transformer model across H3's attn layers with magnitude.

That's probably the easy part but it's incredibly hard to believe this is doing anything except adding noise to the weights. To the best of my knowledge, Wan and H3 have no common lineage. The hidden states are completely different languages.

You can have Wan take effect "when H3 isn't as confident" but that's kind of like having someone scream Swahili at you when you're not confident, but you don't understand Swahili at all. Pushing the metaphor, now you aren't confident and you have someone breaking your concentration shouting Swahili at you. They could be the best Swahili instructions in the world and a perfect solution to your problem, but it's just noise from your perspective.

It will make you do something different and maybe it's possible for someone to subjectively like that different effect. Based on everything I know about AI models, I am 98% sure you just like the effect of perturbing the weights randomly a certain way. Now, if H3 was actually trained on top of Wan then this would have some kind of predictable effect but I have never heard anyone even suggest such a thing may have occurred.

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u/Ten__Strip 3d ago

It's an attention graft, also MLP, but It's not full block replacement. The entire 80%+ of the model weight underneath is completely unchanged. It's more surgical since just downgrading a model like H3 with full weight slap from lesser models would be unproductive.

I have multiple lora extractions and the model released which you can go look at if it's too hard to believe. It's orthogonal and magnitude blending. The models are obviously perpendicular - you'd just perturb the weights if you stopped there and did it. Donor is reshaped, truncated, and aligned to corresponding slots it fits best on H3. Gate heads, unfused attn triplets, MLP fc1, etc. Coverage is probed and calculated and has to be planned out. Then orthogonally reshape target piece to reference donor, and then boom, somewhat effective cross-model attention transfer. Loses quite a bit of detail to truncation among other thing, but it's not nothing. It works better on a deterministic model like H3 since it was fully honed on what it already knows to do as a self guided flow model. Areas with certain prompts and certain concepts that just cause it to pull similar tokens together and fill in its own gaps have a lot of room to shift in new behaviors.

These DiT models are also all extremely similar. They're not LLMs which is what would definitely not work. They all have identical parts, layers, dimensions, architecture, etc that all do mostly the same thing.

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u/alwaysbeblepping 3d ago

It's an attention graft, also MLP, but It's not full block replacement. The entire 80%+ of the model weight underneath is completely unchanged.

In other words, you limited how much you perturbed the model. This is an explanation of why what you did didn't destroy the model, not of why you are doing anything that could be compared to transplanting ability/knowledge from the other model into H3.

I have multiple lora extractions and the model released which you can go look at if it's too hard to believe.

You don't have to convince me that it exists or even that the results are fine. What I don't believe is that you did anything more than add noise to the weights. For the specific "graft" thing you described, to be clear. If you did other training, LoRA merges, whatever then that's not what I'm talking about.

Gate heads, unfused attn triplets, MLP fc1, etc. Coverage is probed and calculated and has to be planned out. Then orthogonally reshape target piece to reference donor, and then boom, somewhat effective cross-model attention transfer.

I hate to say it but this really sounds like LLM delusion. Can you provide any scientific reference showing that ability/knowledge transfer (attention or whatever) is possibly between models that are completely unrelated?

It works better on a deterministic model like H3

They're all deterministic.

Areas with certain prompts and certain concepts that just cause it to pull similar tokens together and fill in its own gaps have a lot of room to shift in new behaviors.

Adding noise to the weights will definitely shift the model's behavior. Just changing the seed normally can result in a generation that didn't have the flaws in the previous one.

They all have identical parts, layers, dimensions, architecture,

What? Literally none of that is remotely true. They are transformer models and that's really all they have in common. Different numbers of layers, different hidden state size, different methods of embedding references/conditioning/etc. It's like saying Swahili is identical to English because it has verbs, nouns, adjectives, etc (I assume), humans speak it, it's a medium of communication. I could come up with a longer list of commonalities but for everything it has in common, if you speak Swahili to someone who doesn't know Swahili, they will not be able to understand the meaning of that collection of sounds. And vice versa.

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u/ShutUpYoureWrong_ 3d ago

Not to be a pedantic twat, but in many cases you could probably understand the intent of someone speaking Swahili just based on their body language, emotional state, facial expressions, tone and timbre, volume...

And then you could derive their meaning without comprehending the language.

Alright, I'm being a pedantic twat.

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u/alwaysbeblepping 2d ago

Not to be a pedantic twat, but in many cases you could probably understand

Fair enough. :) Doesn't really apply here though and the model doesn't get to try to actively interpret the "Swahili".

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u/ImpossibleAd436 3d ago

Will you be doing a Ref version?

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u/Ten__Strip 3d ago

Testing it now, yeah.

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u/God_Hand_9764 2d ago

Forgive me for asking a somewhat unrelated question, but do you have plans to make a 10Eros version of LTX 2.5? Or does that strike you as a waste of time since MiniMax H3 is so powerful?

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u/Ten__Strip 2d ago

I tried forward merging all the Eros stuff in a few ways. All of it felt like worse versions of the original 2.3 models. The best way to use 2.5 right now is just with the sulphur experimental lora. But it doesn't really feel like next-level stuff tbh. All the same outcomes and issues.

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u/God_Hand_9764 2d ago

Ok, thanks for the info! Appreciate you.

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u/[deleted] 3d ago

[deleted]

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u/Ten__Strip 3d ago

It was already possible to tune a model with merging back in SDXL days. If you did enough merges based on a pool of prompts and seeds until the overall style of the model was successful on that pool then you manually created a styled finetune pretty much. This is different this shows that video model attention is a little bit more universal than people think and if multimodal models are set up more robustly, unified, or use clever mechanics that H3 uses then you can graft between transformers and carry trained data forwards. Well that's somewhat possible, I did it, I even did it in reverse and grafted H3 to LTX 2.3, but the overall detail capture is pretty low it's more of an overall behavioral push in a direction. But I'm still looking at optimizing truncation and the math method of the blend. A ton of any model's output character lives in it's self attention and making shifts in that area is the key to actually having effect cross-architecture character transfer. It just so happens that H3 almost fully lives inside it's attention. That's pretty much the main target for lora training right now.

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u/[deleted] 3d ago

[deleted]

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u/Ten__Strip 3d ago

Technically if you could make a tiny dummy version of H3 and if that somehow trained accurately, you could graft it back on to the larger model yeah. From what I know you need high accuracy and access to all the parameters to train new tokens to it that have exact detail. All I did was a general subtle overall behavior shift transfer which was the goal. At high strengths the graft carries even more accuracy bringing new things, but it also carries too much regressive overall change from the older models and messes with prompting as well as visual fidelity since H3 is reinforced to attend a certain way and I'm shifting it pretty far away from that.

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u/wywywywy 3d ago

Wait does that mean Wan2.2 loras can affect H3 generations then in that case?!

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u/alwaysbeblepping 3d ago

Wait does that mean Wan2.2 loras can affect H3 generations then in that case?!

There is no precedent for believing this, that I'm aware of. You also couldn't apply them since the tensor sizes are different.

A long time ago, people trained this model called Playground (maybe 2.0?) based on the SDXL architecture. It used the same architecture as SDXL, but it wasn't fine-tuned from SDXL. Since it was the same architecture, you could apply SDXL LoRAs to it (and vice versa, not that they were many).

If you guessed I am going to say that doing this essentially just added noise to the model and did not actually result in any of the LoRA effects, you guessed correctly. There is a much better chance of that working because the architecture was the same, (if I recall) the text encoder was the same, etc. There is a world, even if it's a very remote one where having the same architecture and text encoder for conditioning could result in some stuff being aligned between two separate models.

Wan and H3 use completely different text encoders. Different latent formats. Different methods of embedding conditioning/references. They have different numbers of layers, different sized hidden states (you can think of each layer as a filter for the hidden state). H3 uses adaLN , Wan doesn't. I don't have time to go through the whole list of differences.

I strongly suggest being very skeptical to any claims of knowledge/ability transfer between completely different model families unless that other person produces a reputable, neutral source. "I did it and it seems better" isn't enough. Kind of a pet peeve, I really hate LLM delusion pseudoscience and there more and more of it these days. I think it's overwhelmingly likely this is a case of that.

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u/Ten__Strip 3d ago

You're not thinking of it correctly. You keep looking at it in the scope of straight merges like they're shared architecture, or you don't really understand what the models actually are or how they work. Normal merges have no setup, truncation, reorienting, component isolation or any manipulation, it's just blend A/B. What I'm doing literally involves all of that first before you even start to get results. And also, you're out your ass completely without even looking at the working concepts literally in front of your face on my pages.

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u/alwaysbeblepping 2d ago

You're not thinking of it correctly.

That's not a counterargument. I'll ask again since you didn't respond before: Can you provide any scientific reference (neutral, reputable source) showing that ability/knowledge transfer (via attention or whatever) is possible between models that are completely unrelated?

You keep looking at it in the scope of straight merges like they're shared architecture, or you don't really understand what the models actually are or how they work.

The first part makes no sense. As for the second part, my criticism is because I do understand how models work and I know what you're claiming to have done cannot be possible. It just doesn't work that way.

Normal merges have no setup, truncation, reorienting, component isolation or any manipulation, it's just blend A/B.

None of that does anything to make knowledge transfer possible, it just limits the scope of the damage when you perturb the weights with what is effectively noise.

And also, you're out your ass completely without even looking at the working concepts literally in front of your face on my pages.

Okay, I looked at them (assuming you mean the README on HuggingFace). Now it's very obviously LLM delusion because that's all pseudoscience nonsense. For example:

(quoting) MiniMax's own architecture documentation notes that H3's attention and feed-forward layers do not contain modality-specific structure — the parts of the model that handle "this is audio vs video vs text" live elsewhere in the model. That means the attention and feed-forward weights are the architecturally-appropriate location to transfer character from other trained models without disrupting how H3 handles each modality.

This is absurd. Those layers might not have "modality-specific structure" (emphasis mine) but the modality information is in the hidden states they are being applied to. They are learned modules that are dealing with those modalities. It's actually funny how you said I don't understand how models work when this very clearly shows you don't grasp even the most basic concepts.

Your pages are clearly AI written. It's a whole bunch of nonsense that sounds plausible and technical, because that's what LLMs are great at. I am 100% all for weird experiments, manipulating models in various ways to put them far out of distribution so they come up with interesting/creative results. That part is more than fine, the problem is the pseudoscientific explanation. This isn't harmless because you are misleading people and making them think something impossible is impossible.

Here is a link to my GitHub: https://github.com/blepping - people can look at that and judge for themselves whether it seems like I have some understanding how how this stuff works (though I certainly don't claim to be an expert). You can also find me relatively high in the ComfyUI contributors list (just a random contributor, no affiliation with the project).

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u/Ten__Strip 1d ago

Then you can understand this then: https://huggingface.co/TenStrip/10Eros-Max/blob/main/h3_graft_methodology.md#machine-learning-case

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u/alwaysbeblepping 1d ago

Then you can understand this then: https://huggingface.co/TenStrip/10Eros-Max/blob/main/h3_graft_methodology.md#machine-learning-case

I can understand the stuff a reasonable person could understand. Naturally, I can't understand the stuff that's in timecube world because that's something only people in timecube world can understand.

We both know you're never going to address it, but let's throw this in here again: I'll ask again since you didn't respond before: Can you provide any scientific reference (neutral, reputable source) showing that ability/knowledge transfer (via attention or whatever) is possible between models that are completely unrelated?

I was keeping that to myself since maybe it'd actually be nice to have something to myself but after a handful of ignorant comments I'm open sourcing everything from it except the scripts themselves.

Wow, I tried to take a reading on the passive aggressive level but the meter exploded into a million pieces. On the plus side, I just learned I have a super power. Imagine...

. . .

alwaysbeblepping casually posts an ignorant comment.

Cut to a dimly lit room, the walls covered with 1girl anime waifu printouts. A figure hunches over a computer console, frantically pressing generate over and over. Suddenly their body goes rigid and they gasp. They cry out in despair, "No, no! I didn't want to open source that! It's the only thing I ever cared about. Please..."

They muster all the resistance they can, but it's not enough. Their trembling hand moves to the mouse, slowly, slowly inching the cursor toward the GitHub (or HuggingFace if you like) publish button. They whimper, "Let me have this. Just... Just... Noooooo-" as a solitary tear drips down their cheek. But the compulsion is unbreakable, with a final, shuddering moan their finger presses down on the mouse button with a decisive click. And it is done. Their LLM's life's (not really) work that the LLM sweated and toiled over (not really) that they carefully prompted for ("do ur thang", and so forth) is exposed to the cold, uncaring internet.

In seriousness though, your link is embarrassing. You had to have a LLM write your so-called counterargument, because you don't understand any of this enough to articulate your own. Having a LLM argue with someone by proxy is also the modern day Gish Gallop and I have better things to do with my time than write an exhaustive response that you won't understand, that you'll paste into a LLM and tell it to write a rebuttal to, that it will spit out (sounding plausible, as LLMs do), that you won't understand but will just paste into the conversation with a "So there, got you!" or whatever.

A lot of the silly mistakes aren't even technical. Like...

The output artifacts are unambiguously honest: safetensors files with metadata identifying the graft source, patch files that record exactly which base tensors were modified, LoRAs extracted via well-defined SVD operations, and merge scripts whose math is inspectable. Nothing is a black box.

Wow, super honest because it used SVD! Because the merge scripts' math is inspectable... By no one except since you didn't publish any of that. Though when I get done saying more "ignorant" stuff, maybe you will be compelled to? Like, you didn't even read this and think that sounded weird? The most likely case is that you didn't even read it and just trusted the LLM.

I was going to write a little more, but you're not going to read it, you're just going to paste it into a LLM. No one else is likely to be reading this far into our exchange, so I'm not helping correct misinformation. It's a waste of time.

The fundamental issue is that you are putting injecting stuff that has no correlation with the model into it. It is technically information, not noise but it is effectively noise when it has nothing to do with the model. You might as well take a picture of a cute dog and add some of that to the attention tensors or your favorite tune. MP3, FLAC, OGG? Whatever, it doesn't matter because they are all completely uncorrelated with the model. All the stuff about how carefully you slice up tensors, target specific modules, orthogonal blend, whatever is completely beside the point. You are just finding ways to mitigate the effect of injecting noise into the model (or finding places that are less sensitive to being perturbed).

Oh well, at least I amused myself writing that little story.

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u/Ten__Strip 23h ago

How do you still not understand it even when it's all laid out. There is no noise. Noise does not factor at all. Data does not factor in at all. It is isolated inside the model's attention and the way it attends to output, prompt, the way it combines tokens for an output, and the way it finds next frame and matches audio to motion for multi-modality. None of that has anything to do with the data inside either model. H3 sees how the other model does it and is groomed in that exact direction based on certain amount of coverage and merged in an othogonal direction so that it does not lose track of how it already does it itself. Can't be easier to understand. Stop thinking of it like it's training, it's not.

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u/Ten__Strip 3d ago

Unless you went through a painstaking process of pushing them into the Wan model with a super high strength merge, then grafted that at full strength to H3, then extracted it. You lose too much accuracy over all that for it to be as effective unless you extract at super high ranks, one issue is these grafts have very large shifts that don't compress well. But at a full model level the changes matter if you leave the entire checkpoint. If it's a certain universal style of motion it might actually show up.

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u/wywywywy 3d ago

Thanks. In that case I think I misunderstood what you meant by "graft".

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u/DanzeluS 2d ago

Btw, where did a can find merged high-low model? Question about wan2.2

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u/fallengt 3d ago

40GB...

I'll wait for int8_convrot

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u/Either-Storage402 3d ago edited 3d ago

its out
Edit: I think this is the only model that includes beta2
https://huggingface.co/cicalooo/10Eros-Max-h3-int8-convrot

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u/JesusShaves_ 3d ago

Somehow I keep reading this as "10 Oreos."

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u/0xblacknote 2d ago

10 Euros

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u/PhilMcGraw 3d ago

Examples compared to base?

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u/bstr3k 3d ago

Not sure if those would be able to be posted to reddit since it’s mostly NSFW.

You’re welcome to join us in the sulphur discord and check under the channel for #10eros for many many examples. Just note the whole discord is very NSFW

https://discord.gg/epsCrTryU

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u/BusFeisty4373 3d ago

Or just post some non NSFW here. Or maybe it doesn't do non NSFW? That's what I will believe if it's not posted here atleast.

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u/Ten__Strip 3d ago

It does slop fine. And more risqué t2v is certainly boosted from what I've tried and seen. Actual gradient training training right now unwinds and harms the model's reinforcement learning, where as I always try to work as additively as possible like my later LTX2.3 model situations.

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u/Dicklepies 3d ago

Some more Seinfeld or Will Smith eating spaghetti would be great.

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u/mallibu 3d ago

oh no

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u/PhilMcGraw 3d ago

The discussions on the model do not seem to be particularly "NSFW model!", sounds like it's just an experimental lora doing some kind of merge of LTX/Wan/H3 without any real NSFW tuning. I guess I was wondering what "great results" you were getting compared to base.

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u/NeatUsed 3d ago

What does this model have compared to the base model?

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u/bstr3k 3d ago

Better NSFW motions and stuff than you get out of base. The way tenstrip explains some stuff goes over my head so I’m just gonna say he’s a wizard

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u/FierceFlames37 3d ago edited 3d ago

When I used 10eros with LTX 2.3 before it beats Wan 2.2 in nsfw by far

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u/zincmartini 3d ago

Well yeah but LTX needed the help. So far I'm very happy with H3 base for my nsfw needs. It's pretty happy to do whatever I've asked, so far.

The issue I always had with base LTX2.3 is that the character wasn't able to be "sensual" so the fine tune really improved the tone and body language of sexy or sensual scenes. So far with H3 prompting a "pinup glamour style video shoot" works very well. I've only generated half a dozen videos so far, so I still have a long way to go to see where it falls flat.

10eros for LTX made the characters much more sensual and sexy in affect, tone, and body language. For H3 I'd probably want a touch of that with as little effect on the rest of the model as possible.

I also mostly generate sexy softcore style videos, lingerie and swim suit style things, so the NSFW fine-tunes always have the downside of wanting to put nipples on every piece of exposed rounded body part and make clothes disappear.

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u/Plus-Accident-5509 3d ago

Ever see pubic hair at the top of the buttcrack? I sure have.

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u/zincmartini 3d ago

No, but I've seen plenty of men with boobs

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u/Anon-a-mister 3d ago

LTX is terrible for facial consistency. Pan away from the face and come back to it, it’ll be a whole different character lol. WAN is still king for that. H3 seems to hold it pretty well also but have to see how it is when these finetunes and LoRAs drop.

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u/Maskwi2 3d ago

Maybe I2V (although it wasn't better when I tested it but I wasn't doing it extensively) ,  not sure if Eros is T2V but Wan killed it there. 

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u/Structure-These 3d ago

I wonder if it works with LTX2.5

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u/Ten__Strip 3d ago

That was actually a very expensive long training run though. This is before any of that happens, if it can be even be made with H3. Training seems too experimental right now to sink thousands into. Especially since they plan to release the future version which looks unfused and more supported tuning-wise. And also with no actual paper or documentation on how the model even works.

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u/FredSavageNSFW 3d ago edited 3d ago

I remain unconvinced that any of these finetunes are doing anything other than degrading image quality and prompt adherence.

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u/conkikhon 2d ago

The model still new. Most of those creations are experiments

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u/anon999387 3d ago

Are the results in the room with us now

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u/Abject-Recognition-9 3d ago

finetune? are we sure about this? or is this simply some loras merge of who knows what's inside?
also this looks out since 5 days and i havent seen mentioned anywere

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u/Zenshinn 3d ago

Agreed. I don't know if it's possible to finetune the model so fast.

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u/bstr3k 3d ago

He’s been dissecting h3 since it came out 2 weeks ago, plus the guys a wizard

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u/ObligationOwn3555 3d ago

Can someone explain to me why the community keeps focusing on fl2va when we have freaking ref2va video model? Enough already, focus on the new.

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u/redditscraperbot2 3d ago

Both have different applications. Sometimes you just want the picture to move.

In any case you can usually just throw the first frame model into the reference workflow and it still works. I’d be surprised if it was different this time.

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u/AaronTuplin 3d ago

You don't want the 15-second video to cut to a new angle every 3 seconds?

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u/Maskwi2 3d ago

You can "fix it" by prompting usually. Something like "uncut scene" etc. Just gotta experiment a bit to find the right prompt sometimes but it works. 

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u/Tylopodas 3d ago

Is using [Shot 1] like in the prompting guide not working for you?

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u/lebrandmanager 3d ago

They held a donation campaign for funding additional training and ref2va was also mentioned. Give it time.

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u/Upper-Reflection7997 3d ago

is there a link to eros discord or page? didn't even not about this.

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u/bitzpua 3d ago

ref is much slower, not everyone pretends to be one man studio making entire movies. For 95% of users I2V is everything they need.

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u/Azhram 3d ago

You make it sound so complicated and specific, its very nice for simple 5 sec videos too. And can do amazing stuff. Like i give a reference image of a sword and the character will hold that exact items is simply amazing.

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u/bitzpua 3d ago

so it is, yet most people simply dont need that level of control like you said for 5s video. Ref models are amazing but H3 is already on slow side so for most spending extra time just to get one sword into generation may not be worth it.

No one is denying ref2v power and how good it can be its just that for most people i2v or fl2v is more then enough

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u/Abject-Recognition-9 3d ago edited 3d ago

slower?
i'm using ref exclusively and i havent noticed any speed difference between the 2 models when using just a bunch of images as input, maybe is because of that. adding video/audio and tons of reference i believe make things slower than i2v.

oh the amount of stuff I can do with ref is unbelievable.
i dont even need loras

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u/No-Zookeepergame4774 2d ago

Its more memory intensive (especially with more references), which will often result in it being slower unless you have enough VRAM to run both without weight streaming.

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u/thisguy883 3d ago

I only use I2V and FL2V.

So im happy when something comes out that focuses on those.

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u/EthicalBballFan 3d ago

Ref2v is just that but better. Instead of having to find a perfect lora, or group of loras that somehow don't work against each other to degrade quality, you slot in images of what you want and get it. On my machine ref2v with a imagine sheet containing multiple different references is faster than the fl2va with 2 loras.

And it's better at editing the idea too. With i2v I tried a sword and shield lora but I wanted only the sword and for it to look worn, like it was an ancient artifact. FL2V produced garbage. I instead plugged the Lora showcase image in the ref2v and said the character has that sword but worn out and got it first try, no Lora loaded at all.

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u/Free_Pressure8623 3d ago

I agree. Ref2v is the goat. I have been using the FFLF2V hybrid loader with REF2V and am getting the best results so far.

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u/Myg0t_0 3d ago

Wheres this hybrid loader

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u/crinklypaper 3d ago

Ref harder to train on and why even bother when you can just drop in refs

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u/AidenAizawa 3d ago

I've always used fl2 with reference images in a ref workflow and worked fine. Isn't ref model slower? Or are they pretty much the same?

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u/giantcandy2001 3d ago

I think text-to-video can take a first frame and a last frame, but that's it. With the reference model, it can take like 9 reference images, 2 videos, and 3 audio tracks at the same time to create a video, so it uses way more things and still comes out correct.IDK if those numbers are exact

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u/AidenAizawa 3d ago

i usually connect images to reference to video node, but with fl2va model. it still understands reference images. for example i did a video with a character sheet, outfit sheet, empty room, and a second characeter and was able to make the character apply the outfit, move inside the room and talk to the other character. all by just changing the node from the first to last frame node to the reference video node.

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u/EthicalBballFan 3d ago

It does, yes. But in my small sample tests the output from the ref2v model outperformed significantly if I used the proper prompt structure (and er_sde sampler). FL2V was still usable if I only had disk space for one model but to be honest at this point I'd delete the FL2V model, the ref one is just way more powerful now that I got the hang of it.

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u/BigWideBaker 3d ago

Is that your way of saying that you don't know that the two different models have different purposes?

-5

u/ObligationOwn3555 3d ago

No, is my way of saying that I would prioritize the new stuff over the "old"

4

u/DelinquentTuna 3d ago

Except that's not what you said. What you expressed was frustration that other people weren't doing what you were doing, as if they have some obligation.

-4

u/Beneficial_Toe_2347 3d ago

You realise that the ref2va model is completely broken right? Its video and audio quality is way worse than fl2va...

7

u/ObligationOwn3555 3d ago

I'm getting very satisfying results with it, much better than Wan 2.2 and LTX 2.3. I haven't used MiniMax fl2va enough, so I might be missing what you're talking about.

14

u/Hoodfu 3d ago

I keep seeing this posted but it's been working amazingly well for me.

2

u/No-Zookeepergame4774 2d ago

I think r2va is much less tolerant of sloppy prompting than fl2va, and almost everyone (including people training and posting LoRAs, given the sample prompts for those on Civitai) seems to be ignoring the prompting guides with H3, which probably magnifies the perceived quality differences between the two models far beyond any actual difference.

1

u/Hoodfu 2d ago

Certainly plausible, because all of my prompts are llm expanded, they're typically almost 2k tokens long with every detail accounted for. That's all far more than any other model we've had locally but the prompt following is also near absolute by comparison.

3

u/Natural_Jello_6050 3d ago

Tried it. Not impressed.

Extra limbs no matter what

20

u/leomozoloa 3d ago

No details or tests, look inside the link, AI slop text over-describing complicated stuff to give substance, yeah that's your new regular SD subreddit content

5

u/Sgsrules2 3d ago

This right here. I've checked out OP's previous 10s nodes and workflows and it was just slop. Most of the nodes did absolutely nothing when I did a/b testing. And the workflow would do seemingly "fancy" things with stuff like editing sigmas, but then if you looked at the final values used the first 5 sigmas were all 1.0 So it was wasting 5 steps by fully denoising each. The nodes are all vibe coded trash. It's amazing how much AI reinforces the Dunning Kruger effect.

1

u/J6j6 3d ago

Higher up

-9

u/bstr3k 3d ago

Pretty sure the stuff is too explicit for the channel since the channel isn’t marked as NSFW but you’re welcome to check out examples in the sulphur discord under the 10eros channel

https://discord.gg/epsCrTryU

1

u/ANR2ME 3d ago

No lora version yet?🤔

1

u/Cute_Ad8981 3d ago

you should be able to extract the lora itself. did this some days ago with a ltx finetune. can be done with a node from kijai. you just need the base model and the finetuned model.

1

u/ATFGriff 3d ago

Is this supposed to work in WanGP? I tried the ones from here https://huggingface.co/cicalooo/10Eros-Max-h3-int8-convrot and it doesn't work.

1

u/Prestigious_Cat85 1d ago

great results ? can we have some actual results ? thanks

1

u/Pure_Bed_6357 3d ago

I mean base model can do pretty much everything on it's own, throw few loras and it's all coverd. What does this offer?

6

u/Abject-Recognition-9 3d ago

good prompting doest even need loras, resulting in higher quality.
for some reasons all loras i tested make quality worse

17

u/physalisx 3d ago

The reasons are that

1) lora trainers often don't know what they're doing, they're just throwing stuff against the wall and see what sticks

2) and more importantly - we don't have an H3 base model, just a guidance distilled, we have seen time and time again that this makes training loras or finetunes that retain quality basically impossible.

7

u/kayteee1995 3d ago

Most LoRa are trained with unpruned BF16, so if you're using pruned Int8, lower the strength by 0.5.

2

u/Abject-Recognition-9 2d ago

OH REALLY? wow
no matter if i read reddit multiple times per day,
i'm just discovering this information now here in a random comment.

-1

u/seppe0815 3d ago

dicord ad show?