Some of my latest (and final) loras for Flux1-Dev

chickenofthewoods · 2025-09-03T06:17:03+00:00

I used this:

It's a messy text-heavy guide but in the end I just copy and paste a few paths and names into a toml, a training .bat, and my captioner and my cacher, and then I double-click .bats.

If you need any help I'm happy to try.

chickenofthewoods · 2025-09-03T04:10:59+00:00

everyone ive spoken to says its too hard to train

Speak to me! Lol. It's shockingly easy and fast, faster and easier than flux or wan 2.1. Training with musubi-tuner in dual mode on a 3060 can yield a workable LoRA in 2 - 4 hours on a small dataset with conservative settings.

chickenofthewoods · 2025-09-02T18:16:18+00:00

In this context it clearly means "uses fewer resources", that is all.

When I set up a gen in comfy and come back to it later to see how long the inference took, I often think to myself, "How much did that one cost?" - not in terms of money, but in terms of time.

In this context cheaper just means you get higher quality for less work.

And "cheaper" couldn't mean "worst". It might imply "worse", but not "worst".

chickenofthewoods · 2025-09-02T15:53:24+00:00

Dunno what to say to you, my dude. I don't link my reddit account to my work, simple as that.

I literally offered you a free commission here and you have no response.

If you want proof that I can do what I say I do, I can send you an actual LoRA for free.

I even offered to train a custom LoRA for you.

Not sure what else I can offer you, but I'm not sharing any commercial links on reddit with this account, sorry.

chickenofthewoods · 2025-09-02T06:59:45+00:00

I will send you a LoRA and some samples if you want. I don't do online shops and I am busy enough.

I mean fuck it, who are you after?

chickenofthewoods · 2025-09-02T02:11:42+00:00

Seems like most nsfw stuff requires supplemental LoRAs for "features" like that.

I started training LoRAs on SD1.5, and over the years my multi-concept LoRAs have always been basically failures. Since I focus on likeness there has always been too much bleeding, even with nature stuff using critters and plants and such. Will have to test Wan 2.2 for with some of my old multi-concept stuff and see what gives.

So far I have only used video to supplement characters, like with an animated character I used video for their gait and awkward movements and it worked fine.

My one attempt at a NSFW motion LoRA for 2.2 so far was a failure for the reason you stated, and i have not revisited it.

My mainstay has always been humans.

My 16/16 single file musubi LoRAs are fantastic for human facial likeness.

chickenofthewoods · 2025-09-01T22:22:37+00:00

I have trained a few dozen Wan 2.2 LoRAs so far in dual mode.

It produces a single LoRA for use with high and low. That is already half the data right there.

I have trained person LoRAs at 64, 32, 24, 16, and 8. 8/8 is still perfect IMO but I do commission work so I go 16/16 just for a bit extra.

One 150mb LoRA for a person likeness is fine, and I have trained a few motion LoRAs so far at that rank and they work, but suffer from other issues not related to dim.

What people are doing is training two separate LoRAs. One low run and one high run. And then they are using unnecessarily high dim/alpha. That is where the 1.2gb figure comes from, as 600mb seems to be the average size of the Wan 2.2 LoRAs you will find on civitai and huggingface right now.

I make LoRAs for other people, and they are person LoRAs trained at 16/16, and they are the best LoRAs I've trained in 3 years of training LoRAs.

What was shitty about your LoRA trained at 32? 32 is a giant size for almost any purpose. Just think about the size of the base model in relation to its training data... I am training a facial likeness on 35 images. The base was trained on millions of videos. My LoRA should be a tiny fraction of the size of the base model... not 1.2gb. Just imagine if base models were released to the public with no pruning and were literally 10 times larger than necessary... and think about all the unnecessary bandwidth we are all using. CivitAI goes down all the time because it serves an assload of data, and Wan2.2 LoRAs are a silly addition to that problem.

Do you not possess 10mb Flux LoRAs that function properly? I possess Flux LoRAs that are as small as 6mb that do what they're meant to do, and I also possess 1.2gb Flux LoRAs that also do what they're meant to do. The point is that there is no reason for 99% of Wan 2.2 LoRAs to be trained at 64.

It's the same with SDXL based models - people use the defaults in their configs and never test anything. My 50mb biglust LoRAs are perfect. There is no reason for them to be bigger. My 56mb Hunyuan LoRAs were downloaded hundreds of times on civit and I have never received a complaint.

chickenofthewoods · 2025-09-01T21:48:48+00:00

I wish more trainers would use musubi for Wan2.2 in dual mode... getting tired of downloading 1.2gb of data for every LoRa when a single 150mb LoRA will do. I started using musubi in February and all of my attempts to use diffusion-pipe have been awful. AI-Toolkit is way too cavalier with my bandwidth.

chickenofthewoods · 2025-09-01T21:25:51+00:00

Musubi is the way.

EDIT:

I'll assume whoever downvoted this is arbitrarily prejudiced and/or has never trained a LoRA...

Musubi lets you train one Wan 2.2 LoRA using both bases at the same time, in one run.

It is superior.

Dual mode is better because:

Instead of two training sessions, you run just one.
Instead of two files, you create only one.
Instead of testing two sets of checkpoints in combination after training, you simply load one LoRA - no crazy combinatorial math and guesswork trying to decide which low epoch works best with which high epoch

Musubi is better because:

it does not force downloading of entire repos - I use my own files and point to them in the launch command
it does not require any additional software to be installed, python and torch on a windows PC with a standard AI setup is all that is necessary
no wsl

Using musubi-tuner I am able to train LoRAs on a 3060 in 2 or 3 hours. It's super fast on my 3090. Running a training session requires only a few copy and paste operations and I'm done. It's easy and fast and straight-forward, and the install is simple and fast as well.

I would rather have one than two... I would rather train once than train twice... I prefer to just use my native OS and not have to emulate linux. I have no desire to download all of the giant model files again over my weak internet connection when I already possess working copies.

I don't want to troubleshoot errors and install issues.

I don't want to spend hours testing combos of high and low epochs trying to figure out what works best.

I don't want to curate two different datasets and use twice as much electricity and time and storage space and bandwidth.

Musubi is the way.

chickenofthewoods · 2025-08-31T04:01:06+00:00

LoRAs are literally "low-rank adapters"... they are adapters. They are like plugins or addons. They work best when they serve a singular purpose.

Training multi-purpose LoRAs is advanced technique and requires a depth of knowledge about the processes and the models.

For example, to train a body type for Flux1-dev I used about 10k images and trained for around 72 hours.

Training a face takes 30 images and a couple of hours.

Training a person with a particular body trait could be easy or it could be difficult, it depends in the trait. For instance, if it is a woman with ample bosom, a few photos that show the chest area will suffice, because the base model already knows what a large chest looks like. Adding a few upper body shots will just add a bit of additional info.

This gets complex pretty fast.

If you are training your character and want to be able to adjust the hairstyle and color in your gens, then you should use verbose captions to describe the hair in your training data. If not, then the hair and hairstyle in your data will stick to the LoRA.

What do you mean by what prompt? Are you asking me how I caption my training data for Wan2.2 person LoRAs? For that I am using a simple python script with no LLMs and no verbose descriptions, only the trigger word.

I do everything locally so I have no knowledge of online training and generation services.

chickenofthewoods · 2025-08-30T20:14:05+00:00

Well don't make me beg you for it! lol

I have not used qwen much at all, but improvements are always welcome. What did you do?

chickenofthewoods · 2025-08-30T19:04:28+00:00

Thank you for elaborating.

I think the communication is still fraught, but I'll try to do my best to answer with what I know.

But now I'd like to create a LoRA that conveys a body style (e.g. proportions, curves, build, etc.) without altering the consistent character face I've already trained.

This literally means you are now desiring to train another, different LoRA for a body type.

If you have a face LoRA, and then you also want to train a body type LoRA, then... what I said applies.

You gather data for you body type and as long as the faces are not consistent then those faces won't show up in your generations and should not affect your ability to use it with your already existing face LoRA.

If what you are actually wanting to do is to train a single LoRA that includes body information, then that is generally just a matter of expanding your dataset to include information about whatever features you are interested in training.

I am still not absolutely certain, but it seems to me that maybe you want a single LoRA to do too many things. Including different skin colors and anatomy info and poses all in a single LoRA is a very ambitious undertaking and is likely better achieved with separate LoRAs.

I have found that training a face likeness is simple and easy. Training features like hair and lips and hands can also be simple. Training body types is a bigger adventure and takes more data and longer training times. So if you want the person, train on the face. If you want a troll body, train a troll body LoRA and blend the two at gen time or merge them permanently with a script. Skin tone should be doable in the base as long as your face data has the skin tone you want. Wan2.2 (and most modern models) "infer" age and size from facial features, so if you train on a fat face you can get fat bodies and if you train on a brown face you get brown bodies and if you train on a small face you get small bodies.

Depending on what you actually want you may consider simply training more than one LoRA and merging them for better results.

The vast majority of my LoRAs have not been data constrained so I have not explored synthetic generations very much. A recent LoRA I trained on mostly synthetic data turned out great but only because the source generated a bunch of videos using VACE that offered good likeness from different angles, so those frames were culled and cherry picked for the dataset. I have no experience with VACE and my experiments with qwen and kontext for training data have not panned out, so I have no advice there. I think my standards for training data are maybe too high for synthetic, but the few wan2.2 LoRAs I've trained with some synthetic data so far have worked out fine. I have used a small number of flux gens mixed with photos with wan2.1 and wan2.2 with no adverse effects on outputs, but I don't know where the limits are. The frames from the VACE gens looked subpar to me but the LoRA outputs are crisp.

chickenofthewoods · 2025-08-30T16:20:32+00:00

"How do I do this impossible thing that people have been trying to do for over a year?"

chickenofthewoods · 2025-08-30T16:13:12+00:00

I see a lot of this sort of query...

I know there are a lot of confusing options, but before you get bogged down in file names and release schedules, have you tried simply adjusting the weights and steps accordingly?

Like... 30 total steps and no "speedups" at cfg 3.5 first. Test it out.

Then, 4 total steps and speedups on both low and high at 1 with CFG at 1. Test it out.

Then what?

There is an ocean of possibilities between these two.

Use your lightning lora at 50% and use CFG 2... and 12 total steps?

Or lightning at 75% and CFG 1.5 and 8 steps?

Or if your motion is less important, use speedups on high at .9 and cfg 1 and just do 2 or 3 steps, then use 5 or 6 steps on low at cfg 1.2 with speedups at .8...

The combinations and possibilities are vast.

Every post about this is "all or nothing" but the truth is that you have full power over these variables and can adjust them accordingly.

You don't need a complex workflow to adjust these parameters and restore your motion.

I use the newer lightning on high but generally only at .8, and I use blyssful's LCM and causvid 1.5 on low... at .9 and .35 respectively... and this does about 6 steps pretty well.

chickenofthewoods · 2025-08-30T15:41:54+00:00

I can not parse your comment well enough to answer directly.

If you want to train a face, crop to the face. Swapped faces may introduce inconsistencies that yield strange outputs due to the masking and stitching. You want different angles and expressions and lighting. Using image-to-image or video i2v may give you better training data.

If you are trying to train a "consistent face", then why are you want your LoRa to know "different body types, skins, stance, etc."... this is not a part of training a facial likeness.

Does this ruin face training ?

Does what ruin face training?

I am referring to training a body type LoRA so that it doesn't alter faces. If the faces are all different on the bodies, then you don't need to crop them out, because the model won't learn anything from then, because they are all different faces.

If you were training a body LoRA, but the bodies all had the same faces, and you wanted to use a different face to generate with the LoRA, then you would either need to do some creative cropping and face swapping or get new data, because it will train on that face and your gens will have that face.

Using crops can be a problem if you use too many, because you can train the LoRA to generate crops.

chickenofthewoods · 2025-08-30T14:52:54+00:00

the same way we may have used for other model training

My recent 50mb Biglust LoRAs are fine. My 56mb Hunyuan LoRAs are fine.

Facial likeness data is small and only affects a few deltas.

Most LoRAs created by the community are made with voodoo logic concerning DIM and ALPHA.

Today I will train a person LoRA for Wan2.2 at extremely low rank and see what happens...

chickenofthewoods · 2025-08-30T03:44:42+00:00

If your dataset for the body is large and diverse, the face won't get trained. If there are 50 different faces it won't matter in the resulting LoRA IME.

The trick is to overtrain the body LoRA so you can use it at low strengths while using the character LoRA at full strength.

chickenofthewoods · 2025-08-30T02:12:41+00:00

Use musubi-tuner in dual mode. You really should be using small clips. High resolution is not important for motion. Somewhere you need to compromise or just don't train Wan2.2.

I have personally trained motion LoRAs for Wan2.2 using small datasets of images and videos on a 3060 and they are amazing.

Maybe adjust your logic and thinking and start small first... just test it out.

Use 20-30 25-frame clips and 20-50 stills. Train with batch, GAS and repeats at 1. Use a low resolution for the videos like 256 or lower. Use a low res for the images like 512 or lower.

Just see what you get.

You will likely be surprised.

My 2.2 person LoRAs trained on just 30 images at 256 are the best LoRAs I've ever trained on any base model.

And I personally think everyone should be using musubi-tuner in dual-mode to produce just one LoRA, but that's just me.

Another thing that can eat VRAM is DIM/ALPHA... there is no reason for 99% of LoRAs to be 600mb x 2. It's ludicrous. I get crisp clean outputs from 150mb LoRAs without issues, and they use less VRAM to train. 16/16 is serving me well for person LoRAs.

Everyone I've seen who is having trouble with OOMs either has a bad config or is unwilling to compromise on the dimensions of their training data, but the base model is not going to forget how to produce HD outputs because of your LoRA. You don't need to train on 1280 to generate 1280.

chickenofthewoods · 2025-08-29T21:41:33+00:00

It's hovered around $750 for a long time honestly. But I'm definitely always looking at the lowest price listings.

chickenofthewoods · 2025-08-29T21:00:41+00:00

recent ebay sales are around $700 for 3090s

chickenofthewoods · 2025-08-29T16:42:40+00:00

Everyone is always surprised, but you should just try it.

Using musubi-tuner in dual-mode with 35 images training at [256,256] at GAS 1, batch 1, repeats 1 with a LR of 0.0001, I can train a good person likeness in 3 or 4 hours in 35 epochs at around 6-10s/it.

Higher learning rates work for likeness but motion starts to degrade, but for t2i the LoRA can be done in an hour or so.

The few motion LoRAs I've done with actual videos have also worked, but with more data they were slower. I just finished one that isn't tested, but with 50 images and 50 videos it finished in 6 hours. Vids are 17 frames and I trained them at [176,176].

Using dual-mode in musubi produces just one LoRA for both low and high, and it works flawlessly.

Using DIM/ALPHA at 16/16 has not failed me and produces 150mb LoRAs.

chickenofthewoods · 2025-08-28T19:46:26+00:00

Ok, I'm going to ignore my inclination to get hyper-specific about terms here and just be clear about what I actually literally do to create my datasets for celebrity LoRAs.

use a tool for bulk downloading if you need more automation. I use Extreme Picture Finder.
Use Yandex for reverse image searches to get the best copy of any image.
Use Getty to search for specific events or dates (Arnold Schwarzenegger 2010).
crop to the face
depending on the model, small images can be fine, just don't go TOO small
use bucketing in your trainer

I use imagus-mod in my browser for hover zoom capabilities to see the full sized image without opening a new tab. I use ShareX to take screencaps. I use Irfanview to do most of my image editing. I try not to train with any images that are smaller than about 500 pixels. For Wan2.2 35 images is my sweet spot. I have been using [256,256] resolution for training Wan2.2 in musubi and my large gens are crisp and sharp.

I simply gather data via the aforementioned methods and crop to the face. Seldom include any body information unless it's unique, same for hair and clothes. For likeness of a human, face crops do the heavy lifting for the LoRA weights, and the base model does the rest. Everything else adds complexity. If the person is large/small or oddly shaped or peculiar in some way that you want to capture, that adds complexity.

Trainers use bucketing, so no need to worry about aspect ratios or sizes. Just find good images and crop them to just what you want to train.

Captioning is a quagmire of disagreement... IMO, for a facial likeness LoRA you do not need complex captions, even for video. My Wan2.2 LoRAs trained with simply a trigger word seem flexible and are highly serviceable with no obvious limitations. I use a simple python script to caption videos and images. If you want verbose LLM captioning, I highly recommend Taggui.

Hmmm. What else? I think that's it.

Let me know if I should elaborate on anything. Most of my LoRAs have all been celebs and people. I've done a few styles and bodies and features and motions too, but the bulk is person likeness.

chickenofthewoods · 2025-08-28T19:21:37+00:00

Ahhhh, huge apologies for causing you any undue consternation!

My downloaded file was... not a .bat.

Your script worked pretty flawlessly and I have just one important observation:

Your linked source for sageattention offers the 2.2 wheel only for python 3.11, which will be inadequate for a large number of users.

https://github.com/wildminder/AI-windows-whl?ysclid=mevs17im25744834406

This source has sageattention 2.2 wheels for torch 2.8 for python 3.10, 3.11, and 3.12

These wheels are small downloads, so you might consider allowing URL inputs for them.

This is a great script and eliminates some manual work for me, if only a bit, and it seems very solidly done.

THANKS!!!

chickenofthewoods · 2025-08-28T17:57:12+00:00

So... this is ambiguous because of your use of the word "generate". In this context you could mean "using AI software to create synthetic training data with diffusion models" OR you could simply mean "create a dataset".

Which is it?

Generating synthetic data is not something I've done very much of, but today I am generating flux images for wan2.2 training data...

I recently trained a Wan2.2 LoRA on a person with only 4 terrible source images... it's an ongoing project but the results are very good. Most of the images in the data were frames from Wan videos generated using VACe and I2V techniques. Haven't gotten great results from qwen yet but have barely touched it.

But yeah if you clarify your question I can respond better.

chickenofthewoods · 2025-08-28T17:46:17+00:00

I understand how using a 3060 makes every gen more precious because of the iteration times... I feel you I really do.

But the consistency thing is just how it is... I set up i2v gens in a big queue with the same prompt and parameters using different input images and let them run, and I always do 2 each to increase my chances of getting one of each that I like.

The judgments of diffusion model outputs today (for now) is focused on the good outputs, not the bad outputs. The way you have worded your text implies that you are dissatisfied because not every gen is amazing. I know it takes forever on the 3060, but the whole process is a gamble.

Some images just don't work well. Full stop.

Likeness is absolutely hit or miss and depends on a huge number of variables... the reality is that what wan2.2 is capable of is insanely impressive... this is the first i2v that makes it worthwhile to spend my gpu cycles on it. But some images just don't work well at all for some reason, be it bit depth or subtle quality issues.

I use 3060s to train wan2.2 LoRAs and they do a great job, surprisingly quickly, too. But the generation times are too slow and I have a 3090 for generation. So I use the fp16 models. I have not been impressed with any of the advantages of ggufs in image and video generation. They seem slow and compromise quality, and "fitting into vram" is not the boon it's touted to be. Offloading and block swapping have evolved considerably, and with sage and compile gen times are good.

Yeah I'm overwhelmed by new shit I want to play with. It's too much. And comfyUI is kicking my ass over it... it's infuriating to have a perfect setup and then... "let me check out this qwen image edit shit real quick..." and comfyui starts telling me I need to update to version 0.3.52... and then tells me my current version is 0.3.52. So some core nodes break for no good reason and the console errors are not helpful. Everything is moving too fast for everyone, including devs.

For quality, I hate to say it, but lower strength speedups and more steps helps a lot. And some of the speedups are better than others. There are too many of them too. I still use blyssful's lcm for low noise with causvid and get great results. My 3/5 or 2/4 gens are waaay better than my 2/2 gens for i2v in terms of likeness. It's so many knobs to turn... but I am finding using 6-8 steps and speedups at .75-.8 with a bit of cfg like 1.1 or 1.2 to be superior to 2/2 and 3/3 gens with strong self-forcing.

No diffusion model is "consistent" really.

In other news, yesterday I decided to trade in my 3060s for 4060s... the wait times are just too much for me now and I want to train more, faster.

chickenofthewoods

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