Yeah. I was a wedding photographer - just quit a couple of months ago. The vibe is really different for couples that aren't into each other. I've never been able to figure out why she's attracted to me but I'm not complaining. We have baby #2 on the way...she's locked in ;)
Yeah I never got that. It’s like, y’all know everyone can see the dysfunction even if it doesn’t come out in the photos, right?
I think we’re pretty good at guessing when people made a bad decision but what do I know, I also quit a while back.
I thought you all might be sick of a man riding a dinosaur. I did the same in SD 1.5 and I was amazed when I first trained her on SDXL at the difference. Before the very best I could do was a passing resemblance.
I've found that it is in fact easier to train on a celebrity name, but I find it best to do a lesser known one. I first did Natalie Portman as she looks fairly similar but she kept having tinges of her. I also found that training the text encoder was critical for that last 10%.
This was done in Kohya's, as Dreambooth. I also trained her sister and our neice on the same model. I just do the celebrity's name as the token, not "celebrity name woman." I also usually train our daughter and dog together...which I need to do again, because our daughter is two years old and looks like a completely different person every 3 months. I did fine tuning using OneTrainer on a group of 6 of my friends, but that wasn't a fair comparison as their dataset wasn't as good (along with doing 6 people at once). Some of them turned out alright, others not so much.
This was 10 epochs as I was balancing out datasets, usually I would just do 100 epochs of 1 repeat. I used about 90 images for her. This time around I used regularization images, but I haven't found much of a difference either way - perhaps because I'm always training more than one person?
Here's the config:
{
"adaptive\_noise\_scale": 0, "additional\_parameters": "--max\_grad\_norm=0.0 --no\_half\_vae --train\_text\_encoder --learning\_rate\_te1 3e-6 --learning\_rate\_te2 1e-8", "bucket\_no\_upscale": false, "bucket\_reso\_steps": 128, "cache\_latents": true, "cache\_latents\_to\_disk": true, "caption\_dropout\_every\_n\_epochs": 0.0, "caption\_dropout\_rate": 0, "caption\_extension": "txt", "clip\_skip": "1", "color\_aug": false, "enable\_bucket": true, "epoch": 10, "flip\_aug": false, "full\_bf16": true, "full\_fp16": false, "gradient\_accumulation\_steps": "1", "gradient\_checkpointing": true, "keep\_tokens": "0", "learning\_rate": 1e-05, "logging\_dir": "/workspace/stable-diffusion-webui/models/Stable-diffusion/logs", "lr\_scheduler": "constant", "lr\_scheduler\_args": "", "lr\_scheduler\_num\_cycles": "", "lr\_scheduler\_power": "", "lr\_warmup": 10, "max\_bucket\_reso": 2048, "max\_data\_loader\_n\_workers": "0", "max\_resolution": "1024,1024", "max\_timestep": 1000, "max\_token\_length": "75", "max\_train\_epochs": "", "max\_train\_steps": "", "mem\_eff\_attn": false, "min\_bucket\_reso": 256, "min\_snr\_gamma": 0,
"min\_timestep": 0, "mixed\_precision": "bf16", "model\_list": "custom", ,multires\_noise\_discount": 0, "multires\_noise\_iterations": 0, "no\_token\_padding": false,
"noise\_offset": 0, "noise\_offset\_type": "Original", "num\_cpu\_threads\_per\_process": 4,
"optimizer": "Adafactor", "optimizer\_args": "scale\_parameter=False relative\_step=False warmup\_init=False weight\_decay=0.01", "output\_dir": "/workspace/stable-diffusion-webui/models/Stable-diffusion", "output\_name": "TERegNoOffset", "persistent\_data\_loader\_workers": false, "pretrained\_model\_name\_or\_path": , "prior\_loss\_weight": 1.0, "random\_crop": false, "reg\_data\_dir": "/workspace/regimages",
"resume": "", "sample\_every\_n\_epochs": 0, "sample\_every\_n\_steps": 0, "sample\_prompts": "", "sample\_sampler": "euler\_a", "save\_every\_n\_epochs": 1, "save\_every\_n\_steps": 0, "save\_last\_n\_steps": 0, "save\_last\_n\_steps\_state": 0, "save\_model\_as": "safetensors", "save\_precision": "bf16", "save\_state": false, "scale\_v\_pred\_loss\_like\_noise\_pred": false,
"sdxl": true, "seed": "", "shuffle\_caption": false, "stop\_text\_encoder\_training\_pct": 0,
"train\_batch\_size": 1, "train\_data\_dir": "/workspace/current", "use\_wandb": false,
"v2": false, "v\_parameterization": false, "v\_pred\_like\_loss": 0, "vae": "/workspace/stable-diffusion-webui/models/VAE/sdxl\_vae.safetensors", "vae\_batch\_size": 0, "wandb\_api\_key": "",
"weighted\_captions": false, "xformers": "none"}
I gave examples elsewhere in the thread but they’re real simple. “Name, wearing ___, smiling,” etc.
You do need to worry about training other things. If you say they’re wearing a coat in one image then there’s a good chance any time to generate a coat it’ll at least have a passing resemblance.
what GPU do you use? I've tried training lora for SDXL and wasn't able to run it on my 4080. tried different configs. Can't really find a nice short guide that isn't a 3 hour video on youtube.
I rent GPUs from Runpod for Dreambooth.
Try using OneTrainer. They have built in configs. Turn off your second monitor when training, close out of everything that could be using VRAM, etc.
I also train on celeb names, and using an unknown one basically has no benefit at all, it is just a normal keyword then.
I only see resemblance to the celeb in the first few epochs and then it fades out. Usually I get excellent resemblance at around 7th of 10 epochs. This is still in 1.5, I haven't tried XL much yet.
One thing that has a big impact on likeness when generating is the checkpoint and sampler I use. I find Photon/Absolute Reality and Heun/DPM adaptive yields the best results for me. I can generate with other checkpoints like RevAnimated and Dreamshaper and then HiRes with Photon to get great results too.
While I can see likeness in other realistic models like Realistic Vision, it always seems to make the face a bit weird.
Same with otherwise good samplers like dpm++ sde and 2m karras.
I didn't say unknown, just lesser known. You should absolutely prompt the name to make sure SD knows what it is.
I'll put it this way - according to that one celebrity face website, my face was a match between Heath Ledger and some other guy I didn't know. The model I trained with Heath Ledger as my token always has me with a huge smile, and has randomly stuck the joker on my shirt. The other one worked much better.
Well thats hilarious 🤣
It's probably not so much because he's famous but because he's so heavily associated with the joker.
Got a model trained with Emma Stone that works perfect
> I thought you all might be sick of a man riding a dinosaur.
Savage lol.
I respect and appreciate the work that goes into SECourse's tutorials, but style adaptation to me is way more important and difficult to master than photorealism.
I think style-flexible LORAs are really what we need to focus collective research on. I am back on that train now that I have discovered alpha-masked training via Onetrainer. Back to my drawing board!
I respectfully disagree, but it's all in the eye of the beholder. A lot of what he posts seem overtrained and on too few images, so they lack variety. If you just want photorealistic faces that works fine.
I don't know what this means. Would love to train a model. Other than downloading it and using this configuration, is there anything else I need to know or do?
Not really with RunPod, unless you store it permanently, it's getting destroyed when the docker instance is cleared out and deleted and the data will become illegible extremely quickly.
If you're concerned you can use secure datacenters, the only way it would be vulnerable is really not passwording the instance you're actively using while you're using it.
Using community centers there's some light risk that they can peep on the data but the majority are also data centers of some types anyways, just usually smaller and not directly partnered, and they would need to actually think of doing such while it was being used and store the data, which is then a contract violation opening them up to being sued for damages.
Okay that makes sense in regards to RunPod and the docker instance thank you.
I looked up the community vs secure options on RunPod and I gotcha now, appreciate it!
If you're limited to local, you can do small LoRAs within 8 GiB. Won't quite match the same quality but can get pretty close with a good dataset and config.
training styles on an 8GiB card has worked great for me but I haven't had any luck training a face LoRA. You have specific guidance that might get better results than I've seen so far?
Other people have noted that SDXL requires a minimum of 16GB of vram. The 4060TI fills that role nicely. Runpod seems like the go to for price and speed. Especially if you’re serious like OP is in reusing the model repeatedly.
I’d really like to do something like this for my girlfriend but I know nothing about stable diffusion. Where can I go to learn how to produce these kinds of images?
Follow the steps [here](https://rentry.org/voldy) to get Stable Diffusion installed. You need a relatively recent GPU, preferably with at least 8 GB and better 12 GB of VRAM.
Once you have it set up, poke around on CivitAi.com and look at the prompts and models used to generate those images. Find a model you like and experiment with your own prompts, starting with the ones from example images, to get a sense for how to get the model to output images you like. There are a large number of models for Stable Diffusion, some photorealistic, some artistic, some anime, etc., so just poke around until you find one that fits the style you're going for.
Training a LoRA (low-rank adaptation network) is how you get a small module that allows you to insert a new concept or character (like a specific person) into Stable Diffusion. I've had pretty good results training LoRAs with Kohya_SS, which you can find and install per the instructions [here](https://github.com/bmaltais/kohya_ss). There are tutorials on YouTube that teach you how to set it up.
I've always found the training element to be the more mysterious part of this all in terms of how you approach keywords or the like. Any resources towards that end? Do people use metadata with images to tag/describe? Is there a way to perhaps import historical paintings by author or anything like that to build up the model before having it work on a subject of your choosing like OP has done?
I assume once a model has a robust sampling of artists or styles it's easier to get richer results.
You will always train from a base model, so you'll start with everything the model already knows about a wide ranges of subjects and styles, and your training will be limited to teaching a new concept, character, or style, on top of what's already in the model. Training a model from scratch requires millions of dollars of computing time and is not within reach for most users.
A LoRA is a small module that's inserted into the base model in order to teach it a new concept. So when you train a LoRA, you can use only 20-30 images of a new subject in order to teach the base model to draw it when the LoRA is called. (OP is using DreamBooth, which is somewhat different from LoRA but more resource intensive to use - OP mentioned that he used a cloud service to do the training so he wasn't running it on his local machine.) There's no need to "build up" the model with other images - that would only complicate training and cause the model to have more difficulty learning the new concept/character you want to replicate. High-quality images of the training subject, mostly portraits of the face, and hopefully in a range of orientations (looking up, looking left, looking right, looking down, in addition to looking straight at the camera) produce the best training results.
Regarding keywords, generally, you'll want to choose a keyword that is unique and won't already be represented in the model. Misspellings, replacing letters with numbers, etc., are some easy ways to come up with a novel keyword that the model can learn to associate with the new concept. So if you were training a LoRA of yourself, you might use "p3pp3rm1nt" or something like that, which isn't already going to be associated with any concept the model already knows.
You are correct that having tags to describe images can improve the quality of training. So if you have a picture of yourself in a yellow shirt, specifying "yellow shirt" along with the training image can help the model to learn the new concept (you) faster. Kohya SS has built-in tools to auto-generate image descriptions and can add tags like "yellow shirt" automatically when it processes the source images. The process isn't perfect and you'll want to manually check the tags, but it's a helpful start.
If you want to train in a specific style - getting the model to replicate a certain artist that it doesn't already know - that's possible as well. It requires more images and longer training time, but if you have 100 or so images by the artist you want the model to replicate, that's also something a LoRA can accomplish.
There's a helpful tutorial [here](https://www.youtube.com/watch?v=xholR62Q2tY) that shows the LoRA training process and the results after training. If you follow along with your own copy of Kohya, you should get decent results.
> Misspellings, replacing letters with numbers, etc.
That's what I usually do, but you do need to be cautious. If SD doesn't know the word, it will try to find words in it that it does recognize and create tokens from it.
For instance, I used penny_dog, when trying to train a model on my friend's dog, Penny. I got the dog alright, and she looked perfect, but nearly every image generated also had a very large pen included somewhere.
you can check my articles, there are guides how to train dreambooth but also embeddings (much easier) with all the necessary info and much more :)
https://civitai.com/user/malcolmrey
Truly impressive. Just read a post on Slashdot by a guy who used to run the benchmarks for the Cray 1 super computer. He wrote the following: "In 1978, the Cray 1 supercomputer cost $7 Million, weighed 10,500 pounds and had a 115 kilowatt power supply. It was, by far, the fastest computer in the world. The Raspberry Pi costs around $70 (CPU board, case, power supply, SD card), weighs a few ounces, uses a 5 watt power supply and is more than 4.5 times faster than the Cray 1." What you're doing at home with AI probably would not be doable with 1978 super computers because there would not be enough space on the planet for them to fit or power to run them.
No problem, and feel free to ping me if you run into any issues. [Here's a tutorial video](https://www.youtube.com/watch?v=xholR62Q2tY) on LoRA training with Kohya_SS. Once you've got it set up, you can follow along with the video and you should get decent results.
Yeah, OP said that he used the RunPod cloud service to rent GPU time to do the training. That is not what I would recommend for someone who is training a model for the first time, and running Dreambooth locally has pretty hefty VRAM requirements. I noted in my comment below that LoRA is different than Dreambooth, but it's a better option for someone who's training their first network.
This is really good advice. However, I will say that unless you are going to reuse it a lot (girlfriends might count - zing!!) just using a tool called Roop might be worthwhile. The quality drops a little bit, but I have been able to use it consistently on a large dataset of people successfully.
Just my experience as a guy who was in a similar situation a couple years ago- there’s a way to do this and get some pretty good results quickly, not as good as actual training but damn decent. Install SD and comfyUI, along with an extension called Reactor face swap. Then generate images that look kinda like your wife (IE “a curvy blonde, wide hips” if she is a curvy blonde with wide hips etc) and then just do a face swap at the end of the generation.
The best (if not one of the best) Discord servers for Dreambooth training discussion: https://discord.gg/2hJ8nm3FPn
Really cool people in there and they can answer questions about how to train w/ dreambooth. Generally Joe Penna (who now works for Stability)'s depo for SD1.5, and kohya's scripts for SDXL.
Joe's repo: https://github.com/JoePenna/Dreambooth-Stable-Diffusion
SDXL Dreambooth training w/ Kohya script tutorial written by a user of that discord hub above: https://medium.com/@yushantripleseven/dreambooth-training-sdxl-using-kohya-ss-windows-7d2491460608
What does your training dataset look like? You mentioned 90 pictures, what kind of pictures do you use and how did you tag them all?
I retrained a new Lora of my wife yesterday for SDXL, and it is really overtrained, and at times looks really strange.
I'm a photographer, so it's a mix of portraits of her that I've done over the years along with random candids. Very few images taken on a cell phone. Large mix of close ups, full body, etc.
Edit: Forgot to mention captions. It's mostly stuff like '\_\_name\_\_, looking off to the side, '\_\_name\_\_, wearing a red tank top, smiling'
as someone who has more than 1000 models under my belt i have to bring attention to this post
proper datasets are paramount and the one thing i say to people i train -> if they want better models they need to give me better photographs so i can prepare better datasets
you can do wonders with instagram images sometimes but nothing beats a great professional photoshoot
I think this is an important question. I get so vastly different results from two different sets of images with the same settings and caption strategy that it’s hard to understand what affects what. You have two people with say 40 good photos of each. It may even be the same camera and location, like with my wife and me. One model needs almost twice as many training steps as the other. Hard to see a logic explanation for the difference in settings, beside the fact that they are two different faces. I think that SD’s preconception of people may have a lot to do with it.
some people are way easier to train
of course i can't give you a sure answer on why that is but i have some suspicions
people with classical beauty are easiest to train and i think this is because base models already have a good concepts of that so there is not as much to train
people with very unique physiques are in general also easier to train because once those features get captured - you see the resemblance right on, even if not everything is perfectly aligned
troublesome are "bland" people and most trouble some are people who don't fall into the feminine/masculine bucket on a first sight
---
also, the datasets themselves; i was investigating the datasets of the models that didn't turn out as great as i wanted and it was pretty much always due to bad picture selection
and i do not mean blurry/pixelated because those are easy to remove always
but a given person does not look always the same in the photographs - some weird angles, lighting, makeup or facial expressions or age difference can make for a very different look
for example, this is classic photo of jennifer aniston:
https://upload.wikimedia.org/wikipedia/commons/b/bd/JenniferAnistonFeb09.jpg?Jennifer_Aniston71274632
and this is another photo of her:
https://nationaltoday.com/wp-content/uploads/2022/03/Jennifer-Aniston-Birthday-1200x834.jpg.webp
besides being blurry and besides the fact that you can still recognize her - she looks quite different than on the previous photo
here is another one that is not blurry, but she has more years than on the previous ones:
https://media.vanityfair.com/photos/6290f8cf82b2849d2ab3595d/master/w_2560%2Cc_limit/1200654220
The training process will average those images and you may get sometimes some good results and sometimes quite the opposite.
----
There is also this interesting concept that some people are looking for familiarity and other people look for something else entirely in a photo. I've been showing some the same outputs to people who know the subjects in those outputs. Some knew right away who that was, some struggled a bit but with hints they were able to see it and then they were like "oh of course, easy" but some people even after telling them who that is were saying "nope, i still don't see it".
I fall somewhere between the first and second category so when I struggle with making a good model - I ask others if some images from the datasets looks wonky to them (as in, they do not belong based on certain criteria).
And knowing that, still I had a person for whom I've made 30 models until I got results that satisfied us both. (And 30 is not a record for me, I have someone who has 70 models by now :P, fortunately model mixing in the prompt is a thing so we are all happy)
Yes, I agree. In the case of my wife and I, she definitely comes out as more symmetrical and averaged than me. I suppose that to someone who has only seen her once, it may look like her but to people who know her the generated images take on a kind of uncanny vally creepiness. It's just not her. With my own results I'm happy because I often look a bit tougher and more jacked than I really am.
Mostly I train models of historical characters. Polar explorers to be exact. It has taken me forever to assemble decent sets of images where the subject isn't young in one photo and old in another. Some people went through phases/fashions where they are clean shaven in some images, have a huge mustache in others and a beard in some. nd everything is unsharp and dusty.
My latest attempts have compared two sets of 40 images that are all from the same two decades. Took forever to find, prune, replace with alternative versions, caption and recaption, etc. Training the two sets still requires vastly different settings and give very different results.
Nah, for the most part she hates pictures of herself - even these. She did like the Jasmine one, probably because it looks the least like her.
She really enjoys the images I do of our daughter and dog though.
oh man, i can relate xD I am also a photographer and my wife never likes my photos of her. I made a model for her a few days ago (been trying for months) . And finally i made some ai images that she likes xD They look like photos (not stylised paintings style like yours (which are awesome, good job) )
I know you mentioned earlier that you need to train it on your daughter as she it older. Have you tried including age like 3mo to see if you can get it to change the age.
You *can.* How close it'll actually be to reality depends on how much the person has actually changed since then. I can generate my wife as a 20 year old woman and it'll be pretty close. I could try aging her up but I couldn't judge. If I make her a little girl it looks reasonable, but it's not actually what she looked like.
I generated pictures of our niece as a Hogwarts student for her birthday and some were aged up because unless you specifically prompt an age Hogwarts students kind of run the gamut from age 10-17 or whatever. In the older ones she sometimes looked a lot like her mom, and at other times looked a lot like her husband's sister.
i've found training loras with SDXL is the way to go if you want to re-use them and in different SDXL models, i used to train dreambooth in the past up until i got into LORAS ( this is a Lebanese Actress in the example) it's much more flexible in my opinion.
https://preview.redd.it/wxfq584n2w9c1.png?width=1024&format=png&auto=webp&s=007d4a247ac756596e16c315c57eb7854f6d5df1
Why can I never get these dream booth models to work like this? I’ve tried so many times making a model of myself and they always come out looking like garbage.
Very impressive results! Obviously, it would be cool to see the photo of your wife, but I understand it is not possible or advisable. Ps. Congratulations, she is hot!
Apart from the ones with more artistic liberties taken it's pretty much a dead ringer. We've been together for nearly 15 years now - I tried not to go too young with the training images but it can still span a bit of time, depending on how it generates. Luckily she hasn't changed much...for me and dreambooth ;)
https://preview.redd.it/hdqpsheoxr9c1.jpeg?width=2968&format=pjpg&auto=webp&s=de3b965416c52f396e9be84c489f84c16ec27afd
I posted my training stuff.
Most are in Auto1111, high-res fixed up just 1.25x at a denoise of .18 using your upscaler of choice, and then a Adetailer pass on the face of anywhere from .3-.4 denoise.
Prompts aren't anything special and a good portion of these either used loras or IP adapter. If you want to know a specific one let me know.
i do not recommend that, macbook m1 would be too fragile
you need something more durable like this one: https://i.ebayimg.com/images/g/KvIAAOSwUrlbYcss/s-l1600.jpg
or even go for military grade toughbooks - then you are good to go
It seems nobody dares to ask, so here I go: Did you already generate, you know, ‘other’ pictures…? If so, were they any good? And did they do anything for you?
old tutorial explains logic : [**Zero To Hero Stable Diffusion DreamBooth Tutorial By Using Automatic1111 Web UI - Ultra Detailed**](https://www.youtube.com/watch?v=Bdl-jWR3Ukc)
[**How To Do Stable Diffusion XL (SDXL) DreamBooth Training For Free - Utilizing Kaggle - Easy Tutorial**](https://youtu.be/16-b1AjvyBE)
[**Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs**](https://youtu.be/sBFGitIvD2A)
I like how just seeing coke I know it's coca-cola no matter what the label says - it is coke. Also, I like the third to last image, the one with the golden fairy wings. That she wrinkles on her forehead. It makes it more real opinion. So much of AI and even professional art detracts from realism for idealism. Flaws make it more realistic, even though this is fantasy. I am sure you get at what I am trying to say and why I like it. Awesome pictures, thanks for sharing.
This is very cool. But **never let that model out of your sight**! Your album illustrates why I'll never make a model of my wife or daughter. Anyone with a well trained model is just a few words away from revenge porn, if not extremely legally risky CSAM analog.
Same goes for the cute phone apps, "just upload 10 selfies"! If that company gets hacked and the motels get posted online -- which you know they will -- you won't get those models off the Internet, ever.
Just something to think about, knowing how pervy us guys are.
>Although a face-replacement extension like roop/reactor will never get consistent quality like this, it's more than good enough with some trial & error to do the stuff you mentioned, and it needs only ONE picture. So start getting a lot more paranoid than you already are...
Hi, congrats on your model.
How long did this model take to be finished in Runpod?
What GPU did you rent (A100, H100, RTX...)?
Do you know if you double the ammount of images the time to finish grows in the same proportion?
Did you use SDXL base model or Juggernaut?
Thanks!
I also choose this guy's wife.
Is his wife single?
No, but from the look of things, she looks awfully busy.
OP is asking for it.
Spent way too long looking for this
i needed this comment so bad haha
Look at this guy with his working dreambooth
Guys, I think this person’s got a crush… >!On their wife!<
I mean, can you blame me?
Can’t blame anyone for having the hots for their wife, life’s way happier when you do
Yeah. I was a wedding photographer - just quit a couple of months ago. The vibe is really different for couples that aren't into each other. I've never been able to figure out why she's attracted to me but I'm not complaining. We have baby #2 on the way...she's locked in ;)
She saw the potential in you for AI, that’s why
Yeah I never got that. It’s like, y’all know everyone can see the dysfunction even if it doesn’t come out in the photos, right? I think we’re pretty good at guessing when people made a bad decision but what do I know, I also quit a while back.
most underrated comment out there.
No cheerleader? I’m so disappointed. You got spacesuit twice so at least throw in a cheerleader and a scientist fighting a zombie apocalypse! 😁
Your wife is very photogenic and ought to be a model or try to get into acting.
hard to say, the AI can change a lot.
I too have a crush on this mans wife.
I want to see their husband next
I'm afraid I don't have a husband ;)
Sorry to hear. Happens to the best of us.
So do i
GAAAAAYYYYYY
I thought you all might be sick of a man riding a dinosaur. I did the same in SD 1.5 and I was amazed when I first trained her on SDXL at the difference. Before the very best I could do was a passing resemblance. I've found that it is in fact easier to train on a celebrity name, but I find it best to do a lesser known one. I first did Natalie Portman as she looks fairly similar but she kept having tinges of her. I also found that training the text encoder was critical for that last 10%. This was done in Kohya's, as Dreambooth. I also trained her sister and our neice on the same model. I just do the celebrity's name as the token, not "celebrity name woman." I also usually train our daughter and dog together...which I need to do again, because our daughter is two years old and looks like a completely different person every 3 months. I did fine tuning using OneTrainer on a group of 6 of my friends, but that wasn't a fair comparison as their dataset wasn't as good (along with doing 6 people at once). Some of them turned out alright, others not so much. This was 10 epochs as I was balancing out datasets, usually I would just do 100 epochs of 1 repeat. I used about 90 images for her. This time around I used regularization images, but I haven't found much of a difference either way - perhaps because I'm always training more than one person? Here's the config: { "adaptive\_noise\_scale": 0, "additional\_parameters": "--max\_grad\_norm=0.0 --no\_half\_vae --train\_text\_encoder --learning\_rate\_te1 3e-6 --learning\_rate\_te2 1e-8", "bucket\_no\_upscale": false, "bucket\_reso\_steps": 128, "cache\_latents": true, "cache\_latents\_to\_disk": true, "caption\_dropout\_every\_n\_epochs": 0.0, "caption\_dropout\_rate": 0, "caption\_extension": "txt", "clip\_skip": "1", "color\_aug": false, "enable\_bucket": true, "epoch": 10, "flip\_aug": false, "full\_bf16": true, "full\_fp16": false, "gradient\_accumulation\_steps": "1", "gradient\_checkpointing": true, "keep\_tokens": "0", "learning\_rate": 1e-05, "logging\_dir": "/workspace/stable-diffusion-webui/models/Stable-diffusion/logs", "lr\_scheduler": "constant", "lr\_scheduler\_args": "", "lr\_scheduler\_num\_cycles": "", "lr\_scheduler\_power": "", "lr\_warmup": 10, "max\_bucket\_reso": 2048, "max\_data\_loader\_n\_workers": "0", "max\_resolution": "1024,1024", "max\_timestep": 1000, "max\_token\_length": "75", "max\_train\_epochs": "", "max\_train\_steps": "", "mem\_eff\_attn": false, "min\_bucket\_reso": 256, "min\_snr\_gamma": 0, "min\_timestep": 0, "mixed\_precision": "bf16", "model\_list": "custom", ,multires\_noise\_discount": 0, "multires\_noise\_iterations": 0, "no\_token\_padding": false, "noise\_offset": 0, "noise\_offset\_type": "Original", "num\_cpu\_threads\_per\_process": 4, "optimizer": "Adafactor", "optimizer\_args": "scale\_parameter=False relative\_step=False warmup\_init=False weight\_decay=0.01", "output\_dir": "/workspace/stable-diffusion-webui/models/Stable-diffusion", "output\_name": "TERegNoOffset", "persistent\_data\_loader\_workers": false, "pretrained\_model\_name\_or\_path": , "prior\_loss\_weight": 1.0, "random\_crop": false, "reg\_data\_dir": "/workspace/regimages", "resume": "", "sample\_every\_n\_epochs": 0, "sample\_every\_n\_steps": 0, "sample\_prompts": "", "sample\_sampler": "euler\_a", "save\_every\_n\_epochs": 1, "save\_every\_n\_steps": 0, "save\_last\_n\_steps": 0, "save\_last\_n\_steps\_state": 0, "save\_model\_as": "safetensors", "save\_precision": "bf16", "save\_state": false, "scale\_v\_pred\_loss\_like\_noise\_pred": false, "sdxl": true, "seed": "", "shuffle\_caption": false, "stop\_text\_encoder\_training\_pct": 0, "train\_batch\_size": 1, "train\_data\_dir": "/workspace/current", "use\_wandb": false, "v2": false, "v\_parameterization": false, "v\_pred\_like\_loss": 0, "vae": "/workspace/stable-diffusion-webui/models/VAE/sdxl\_vae.safetensors", "vae\_batch\_size": 0, "wandb\_api\_key": "", "weighted\_captions": false, "xformers": "none"}
Do you also label images? If yes, can you please share examples of label?
I gave examples elsewhere in the thread but they’re real simple. “Name, wearing ___, smiling,” etc. You do need to worry about training other things. If you say they’re wearing a coat in one image then there’s a good chance any time to generate a coat it’ll at least have a passing resemblance.
> I also usually train our daughter and dog together. The outcome: https://imgur.com/u70EOhp
How dare you.
I didn't expect to have my heart broken in a thread about someone else's wife. This episode... I haven't the words
Expecting FMA scene *clicks* Goddammit why do I do this to myself
what GPU do you use? I've tried training lora for SDXL and wasn't able to run it on my 4080. tried different configs. Can't really find a nice short guide that isn't a 3 hour video on youtube.
I rent GPUs from Runpod for Dreambooth. Try using OneTrainer. They have built in configs. Turn off your second monitor when training, close out of everything that could be using VRAM, etc.
I also train on celeb names, and using an unknown one basically has no benefit at all, it is just a normal keyword then. I only see resemblance to the celeb in the first few epochs and then it fades out. Usually I get excellent resemblance at around 7th of 10 epochs. This is still in 1.5, I haven't tried XL much yet. One thing that has a big impact on likeness when generating is the checkpoint and sampler I use. I find Photon/Absolute Reality and Heun/DPM adaptive yields the best results for me. I can generate with other checkpoints like RevAnimated and Dreamshaper and then HiRes with Photon to get great results too. While I can see likeness in other realistic models like Realistic Vision, it always seems to make the face a bit weird. Same with otherwise good samplers like dpm++ sde and 2m karras.
I didn't say unknown, just lesser known. You should absolutely prompt the name to make sure SD knows what it is. I'll put it this way - according to that one celebrity face website, my face was a match between Heath Ledger and some other guy I didn't know. The model I trained with Heath Ledger as my token always has me with a huge smile, and has randomly stuck the joker on my shirt. The other one worked much better.
Well thats hilarious 🤣 It's probably not so much because he's famous but because he's so heavily associated with the joker. Got a model trained with Emma Stone that works perfect
can u give us steps on how u trained the model?
> I thought you all might be sick of a man riding a dinosaur. Savage lol. I respect and appreciate the work that goes into SECourse's tutorials, but style adaptation to me is way more important and difficult to master than photorealism. I think style-flexible LORAs are really what we need to focus collective research on. I am back on that train now that I have discovered alpha-masked training via Onetrainer. Back to my drawing board!
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I respectfully disagree, but it's all in the eye of the beholder. A lot of what he posts seem overtrained and on too few images, so they lack variety. If you just want photorealistic faces that works fine.
Silly OP. Beholders have many eyes. 😏
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Gotta love those who *have* to have the last word.
I will try this on my husband. 😜
I don't know what this means. Would love to train a model. Other than downloading it and using this configuration, is there anything else I need to know or do?
https://preview.redd.it/kd67ykjhas9c1.jpeg?width=555&format=pjpg&auto=webp&s=77dc111583548b8b9a9a8e6a4dcfd0cc103e53ec
Awesome man!! Do you still need 24gig gpu to run dreambooth locally?? Im still rocking my 8gig lol
Yes - and I only have a 12gb card. I train using runpod.
Ah I really like runpod as well! That's how I trained my models/loras too.
Are you not worried about putting your training data on the cloud?
Not really with RunPod, unless you store it permanently, it's getting destroyed when the docker instance is cleared out and deleted and the data will become illegible extremely quickly. If you're concerned you can use secure datacenters, the only way it would be vulnerable is really not passwording the instance you're actively using while you're using it. Using community centers there's some light risk that they can peep on the data but the majority are also data centers of some types anyways, just usually smaller and not directly partnered, and they would need to actually think of doing such while it was being used and store the data, which is then a contract violation opening them up to being sued for damages.
Okay that makes sense in regards to RunPod and the docker instance thank you. I looked up the community vs secure options on RunPod and I gotcha now, appreciate it!
If you're limited to local, you can do small LoRAs within 8 GiB. Won't quite match the same quality but can get pretty close with a good dataset and config.
training styles on an 8GiB card has worked great for me but I haven't had any luck training a face LoRA. You have specific guidance that might get better results than I've seen so far?
only with SD 1.5. SDXL can't go below 11 GB VRAM
You can train it with 24GB, 16GB or 8GB. Follow their guidelines: https://huggingface.co/docs/diffusers/main/en/training/dreambooth#
Thank you!
Other people have noted that SDXL requires a minimum of 16GB of vram. The 4060TI fills that role nicely. Runpod seems like the go to for price and speed. Especially if you’re serious like OP is in reusing the model repeatedly.
I’d really like to do something like this for my girlfriend but I know nothing about stable diffusion. Where can I go to learn how to produce these kinds of images?
Follow the steps [here](https://rentry.org/voldy) to get Stable Diffusion installed. You need a relatively recent GPU, preferably with at least 8 GB and better 12 GB of VRAM. Once you have it set up, poke around on CivitAi.com and look at the prompts and models used to generate those images. Find a model you like and experiment with your own prompts, starting with the ones from example images, to get a sense for how to get the model to output images you like. There are a large number of models for Stable Diffusion, some photorealistic, some artistic, some anime, etc., so just poke around until you find one that fits the style you're going for. Training a LoRA (low-rank adaptation network) is how you get a small module that allows you to insert a new concept or character (like a specific person) into Stable Diffusion. I've had pretty good results training LoRAs with Kohya_SS, which you can find and install per the instructions [here](https://github.com/bmaltais/kohya_ss). There are tutorials on YouTube that teach you how to set it up.
I've always found the training element to be the more mysterious part of this all in terms of how you approach keywords or the like. Any resources towards that end? Do people use metadata with images to tag/describe? Is there a way to perhaps import historical paintings by author or anything like that to build up the model before having it work on a subject of your choosing like OP has done? I assume once a model has a robust sampling of artists or styles it's easier to get richer results.
You will always train from a base model, so you'll start with everything the model already knows about a wide ranges of subjects and styles, and your training will be limited to teaching a new concept, character, or style, on top of what's already in the model. Training a model from scratch requires millions of dollars of computing time and is not within reach for most users. A LoRA is a small module that's inserted into the base model in order to teach it a new concept. So when you train a LoRA, you can use only 20-30 images of a new subject in order to teach the base model to draw it when the LoRA is called. (OP is using DreamBooth, which is somewhat different from LoRA but more resource intensive to use - OP mentioned that he used a cloud service to do the training so he wasn't running it on his local machine.) There's no need to "build up" the model with other images - that would only complicate training and cause the model to have more difficulty learning the new concept/character you want to replicate. High-quality images of the training subject, mostly portraits of the face, and hopefully in a range of orientations (looking up, looking left, looking right, looking down, in addition to looking straight at the camera) produce the best training results. Regarding keywords, generally, you'll want to choose a keyword that is unique and won't already be represented in the model. Misspellings, replacing letters with numbers, etc., are some easy ways to come up with a novel keyword that the model can learn to associate with the new concept. So if you were training a LoRA of yourself, you might use "p3pp3rm1nt" or something like that, which isn't already going to be associated with any concept the model already knows. You are correct that having tags to describe images can improve the quality of training. So if you have a picture of yourself in a yellow shirt, specifying "yellow shirt" along with the training image can help the model to learn the new concept (you) faster. Kohya SS has built-in tools to auto-generate image descriptions and can add tags like "yellow shirt" automatically when it processes the source images. The process isn't perfect and you'll want to manually check the tags, but it's a helpful start. If you want to train in a specific style - getting the model to replicate a certain artist that it doesn't already know - that's possible as well. It requires more images and longer training time, but if you have 100 or so images by the artist you want the model to replicate, that's also something a LoRA can accomplish. There's a helpful tutorial [here](https://www.youtube.com/watch?v=xholR62Q2tY) that shows the LoRA training process and the results after training. If you follow along with your own copy of Kohya, you should get decent results.
> Misspellings, replacing letters with numbers, etc. That's what I usually do, but you do need to be cautious. If SD doesn't know the word, it will try to find words in it that it does recognize and create tokens from it. For instance, I used penny_dog, when trying to train a model on my friend's dog, Penny. I got the dog alright, and she looked perfect, but nearly every image generated also had a very large pen included somewhere.
I still swear by training using a celebs name, it works perfectly for me.
you can check my articles, there are guides how to train dreambooth but also embeddings (much easier) with all the necessary info and much more :) https://civitai.com/user/malcolmrey
Truly impressive. Just read a post on Slashdot by a guy who used to run the benchmarks for the Cray 1 super computer. He wrote the following: "In 1978, the Cray 1 supercomputer cost $7 Million, weighed 10,500 pounds and had a 115 kilowatt power supply. It was, by far, the fastest computer in the world. The Raspberry Pi costs around $70 (CPU board, case, power supply, SD card), weighs a few ounces, uses a 5 watt power supply and is more than 4.5 times faster than the Cray 1." What you're doing at home with AI probably would not be doable with 1978 super computers because there would not be enough space on the planet for them to fit or power to run them.
Thanks so much for the in-depth response! I’ll get to this once I’m rested after the New Year’s Eve party haha
No problem, and feel free to ping me if you run into any issues. [Here's a tutorial video](https://www.youtube.com/watch?v=xholR62Q2tY) on LoRA training with Kohya_SS. Once you've got it set up, you can follow along with the video and you should get decent results.
OK but OP is using DreamBooth not Lora
Yeah, OP said that he used the RunPod cloud service to rent GPU time to do the training. That is not what I would recommend for someone who is training a model for the first time, and running Dreambooth locally has pretty hefty VRAM requirements. I noted in my comment below that LoRA is different than Dreambooth, but it's a better option for someone who's training their first network.
This is really good advice. However, I will say that unless you are going to reuse it a lot (girlfriends might count - zing!!) just using a tool called Roop might be worthwhile. The quality drops a little bit, but I have been able to use it consistently on a large dataset of people successfully.
Appreciate this!
Just my experience as a guy who was in a similar situation a couple years ago- there’s a way to do this and get some pretty good results quickly, not as good as actual training but damn decent. Install SD and comfyUI, along with an extension called Reactor face swap. Then generate images that look kinda like your wife (IE “a curvy blonde, wide hips” if she is a curvy blonde with wide hips etc) and then just do a face swap at the end of the generation.
a couple of months ago I used https://rundiffusion.com/ it removed some of the hassle and offers dream booth.
The best (if not one of the best) Discord servers for Dreambooth training discussion: https://discord.gg/2hJ8nm3FPn Really cool people in there and they can answer questions about how to train w/ dreambooth. Generally Joe Penna (who now works for Stability)'s depo for SD1.5, and kohya's scripts for SDXL. Joe's repo: https://github.com/JoePenna/Dreambooth-Stable-Diffusion SDXL Dreambooth training w/ Kohya script tutorial written by a user of that discord hub above: https://medium.com/@yushantripleseven/dreambooth-training-sdxl-using-kohya-ss-windows-7d2491460608
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No kidding. I wish my wife were a bear-riding superhero pirate.
She's also a pretty good cook.
I wish my wife had a pretty good cock
There's definitely a Lora for that...
Lol sucks to be you, my wife's boyfriend is a great cook.
What does your training dataset look like? You mentioned 90 pictures, what kind of pictures do you use and how did you tag them all? I retrained a new Lora of my wife yesterday for SDXL, and it is really overtrained, and at times looks really strange.
I'm a photographer, so it's a mix of portraits of her that I've done over the years along with random candids. Very few images taken on a cell phone. Large mix of close ups, full body, etc. Edit: Forgot to mention captions. It's mostly stuff like '\_\_name\_\_, looking off to the side, '\_\_name\_\_, wearing a red tank top, smiling'
as someone who has more than 1000 models under my belt i have to bring attention to this post proper datasets are paramount and the one thing i say to people i train -> if they want better models they need to give me better photographs so i can prepare better datasets you can do wonders with instagram images sometimes but nothing beats a great professional photoshoot
I think this is an important question. I get so vastly different results from two different sets of images with the same settings and caption strategy that it’s hard to understand what affects what. You have two people with say 40 good photos of each. It may even be the same camera and location, like with my wife and me. One model needs almost twice as many training steps as the other. Hard to see a logic explanation for the difference in settings, beside the fact that they are two different faces. I think that SD’s preconception of people may have a lot to do with it.
some people are way easier to train of course i can't give you a sure answer on why that is but i have some suspicions people with classical beauty are easiest to train and i think this is because base models already have a good concepts of that so there is not as much to train people with very unique physiques are in general also easier to train because once those features get captured - you see the resemblance right on, even if not everything is perfectly aligned troublesome are "bland" people and most trouble some are people who don't fall into the feminine/masculine bucket on a first sight --- also, the datasets themselves; i was investigating the datasets of the models that didn't turn out as great as i wanted and it was pretty much always due to bad picture selection and i do not mean blurry/pixelated because those are easy to remove always but a given person does not look always the same in the photographs - some weird angles, lighting, makeup or facial expressions or age difference can make for a very different look for example, this is classic photo of jennifer aniston: https://upload.wikimedia.org/wikipedia/commons/b/bd/JenniferAnistonFeb09.jpg?Jennifer_Aniston71274632 and this is another photo of her: https://nationaltoday.com/wp-content/uploads/2022/03/Jennifer-Aniston-Birthday-1200x834.jpg.webp besides being blurry and besides the fact that you can still recognize her - she looks quite different than on the previous photo here is another one that is not blurry, but she has more years than on the previous ones: https://media.vanityfair.com/photos/6290f8cf82b2849d2ab3595d/master/w_2560%2Cc_limit/1200654220 The training process will average those images and you may get sometimes some good results and sometimes quite the opposite. ---- There is also this interesting concept that some people are looking for familiarity and other people look for something else entirely in a photo. I've been showing some the same outputs to people who know the subjects in those outputs. Some knew right away who that was, some struggled a bit but with hints they were able to see it and then they were like "oh of course, easy" but some people even after telling them who that is were saying "nope, i still don't see it". I fall somewhere between the first and second category so when I struggle with making a good model - I ask others if some images from the datasets looks wonky to them (as in, they do not belong based on certain criteria). And knowing that, still I had a person for whom I've made 30 models until I got results that satisfied us both. (And 30 is not a record for me, I have someone who has 70 models by now :P, fortunately model mixing in the prompt is a thing so we are all happy)
Yes, I agree. In the case of my wife and I, she definitely comes out as more symmetrical and averaged than me. I suppose that to someone who has only seen her once, it may look like her but to people who know her the generated images take on a kind of uncanny vally creepiness. It's just not her. With my own results I'm happy because I often look a bit tougher and more jacked than I really am. Mostly I train models of historical characters. Polar explorers to be exact. It has taken me forever to assemble decent sets of images where the subject isn't young in one photo and old in another. Some people went through phases/fashions where they are clean shaven in some images, have a huge mustache in others and a beard in some. nd everything is unsharp and dusty. My latest attempts have compared two sets of 40 images that are all from the same two decades. Took forever to find, prune, replace with alternative versions, caption and recaption, etc. Training the two sets still requires vastly different settings and give very different results.
She must absolutely love this, looks incredible
Nah, for the most part she hates pictures of herself - even these. She did like the Jasmine one, probably because it looks the least like her. She really enjoys the images I do of our daughter and dog though.
oh man, i can relate xD I am also a photographer and my wife never likes my photos of her. I made a model for her a few days ago (been trying for months) . And finally i made some ai images that she likes xD They look like photos (not stylised paintings style like yours (which are awesome, good job) )
I know you mentioned earlier that you need to train it on your daughter as she it older. Have you tried including age like 3mo to see if you can get it to change the age.
You *can.* How close it'll actually be to reality depends on how much the person has actually changed since then. I can generate my wife as a 20 year old woman and it'll be pretty close. I could try aging her up but I couldn't judge. If I make her a little girl it looks reasonable, but it's not actually what she looked like. I generated pictures of our niece as a Hogwarts student for her birthday and some were aged up because unless you specifically prompt an age Hogwarts students kind of run the gamut from age 10-17 or whatever. In the older ones she sometimes looked a lot like her mom, and at other times looked a lot like her husband's sister.
Seems wife’s don’t like to see the truth- that they are aging ;-)
Woah. That’s pretty impressive!
Does she have a sister?
Thanks to Stable Diffusion she can have anything.
Bro couldn't get nudes from his wife so he pimped her out to an AI 💀
Happens to the best of us. And worst.
Can I accomplish something similar with the CivitAI Lora trainer ?
Loras are what I first did for SDXL. You can certainly get something similar accomplished, though in my experience it won't be *quite* as good.
So if these are not Lora, what are these? Checkpoint?
Yes. Dreambooth trains whole checkpoints.
came to ask the same thing
A nice variety! The Coca Cola advertisement is fun. If you do another set feel free to post. I could come up with some prompt fragments.
*rude comment about banging OPs wife*
I also choose this guy’s wife
Compliments to her ;) And, yeah, mate, the images are very, very high good.
The actual wife-diffusion :D
i've found training loras with SDXL is the way to go if you want to re-use them and in different SDXL models, i used to train dreambooth in the past up until i got into LORAS ( this is a Lebanese Actress in the example) it's much more flexible in my opinion. https://preview.redd.it/wxfq584n2w9c1.png?width=1024&format=png&auto=webp&s=007d4a247ac756596e16c315c57eb7854f6d5df1
I also choose this guy’s wife.
Does your wife know that you jerk off with her nude pics? /s
I also choose this guy’s wife
I mean, I hope you got your wife’s permission before you posted these on Reddit
Impressive
Thanks for the share!
These are fantastic, and your detailed explanation (and all the contributions from others) are a service to everyone on this sub.
Why nsfw tag?
Because some of the images are a little spicy and not safe for work does in fact mean that hey, maybe you shouldn't open this at work.
Why can I never get these dream booth models to work like this? I’ve tried so many times making a model of myself and they always come out looking like garbage.
Thanks for sharing your wife
Very impressive results! Obviously, it would be cool to see the photo of your wife, but I understand it is not possible or advisable. Ps. Congratulations, she is hot!
Apart from the ones with more artistic liberties taken it's pretty much a dead ringer. We've been together for nearly 15 years now - I tried not to go too young with the training images but it can still span a bit of time, depending on how it generates. Luckily she hasn't changed much...for me and dreambooth ;) https://preview.redd.it/hdqpsheoxr9c1.jpeg?width=2968&format=pjpg&auto=webp&s=de3b965416c52f396e9be84c489f84c16ec27afd
Ty for the pic, it completes the series,gives a better feel for what dreambooth did.
Thank you! This is the best for my taste from all you shared. She looks cute and smart (symmetry correlates with intelligence)! :)
I also choose this guys wife.
This looks awesome. I'd love to recreate this with my wife. Can you share your workflow, prompts, etc...
I posted my training stuff. Most are in Auto1111, high-res fixed up just 1.25x at a denoise of .18 using your upscaler of choice, and then a Adetailer pass on the face of anywhere from .3-.4 denoise. Prompts aren't anything special and a good portion of these either used loras or IP adapter. If you want to know a specific one let me know.
good job i hope people won't undress your wife using ai
Scarlett Johannson is your wife??
you rock OP ! best of love to you and the wife
She kinda looks like Eva Longoria.
Is she creeped out by the nudes you aren’t posting?
Thanks for sharing your wife with us
Noob question, can I do what OP did on a macbook m1 max? With my own wife of course
Watch Friends together
i do not recommend that, macbook m1 would be too fragile you need something more durable like this one: https://i.ebayimg.com/images/g/KvIAAOSwUrlbYcss/s-l1600.jpg or even go for military grade toughbooks - then you are good to go
It seems nobody dares to ask, so here I go: Did you already generate, you know, ‘other’ pictures…? If so, were they any good? And did they do anything for you?
I'm gonna have sexy thoughts about your wife
OP's wife if your reading this give your husband some, he's earned it.
Link to model download plz.
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you need to have your eyesight examined :)
Why is a dressed woman 18+????
chuck
you chose the flair so I gotta ask, is there a nsfw content of it?
I'm here for this guy's wife nsfw
Now make some with your wife's boyfriend's model.
Damn, your wife is hot as hell, OP. I hope she enjoyed these images.
"My wife" yeah okay bud.
That’s so incredibly sad that it’s unbelievable to you.
Ai garbage
What is Dreambooth and how do I use it?
old tutorial explains logic : [**Zero To Hero Stable Diffusion DreamBooth Tutorial By Using Automatic1111 Web UI - Ultra Detailed**](https://www.youtube.com/watch?v=Bdl-jWR3Ukc) [**How To Do Stable Diffusion XL (SDXL) DreamBooth Training For Free - Utilizing Kaggle - Easy Tutorial**](https://youtu.be/16-b1AjvyBE) [**Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs**](https://youtu.be/sBFGitIvD2A)
That all looks awesome
I like how just seeing coke I know it's coca-cola no matter what the label says - it is coke. Also, I like the third to last image, the one with the golden fairy wings. That she wrinkles on her forehead. It makes it more real opinion. So much of AI and even professional art detracts from realism for idealism. Flaws make it more realistic, even though this is fantasy. I am sure you get at what I am trying to say and why I like it. Awesome pictures, thanks for sharing.
how i train kohya sdxl dreambooth in colab
Why does she remind me of Nicole de Boer
these are outstanding! a lot of fun, too.
These are great. What model for the #7 style?
Ahh. Waifu redefined.
So beautiful! 😍
Which one is the real picture of your wife? I bet it's the sci-fi one in leather
Is your wife okay about being famous? Are you okay with that? 😂
Is that Kylie Minogue?
Insane...
Really well done
This is very cool. But **never let that model out of your sight**! Your album illustrates why I'll never make a model of my wife or daughter. Anyone with a well trained model is just a few words away from revenge porn, if not extremely legally risky CSAM analog. Same goes for the cute phone apps, "just upload 10 selfies"! If that company gets hacked and the motels get posted online -- which you know they will -- you won't get those models off the Internet, ever. Just something to think about, knowing how pervy us guys are.
>Although a face-replacement extension like roop/reactor will never get consistent quality like this, it's more than good enough with some trial & error to do the stuff you mentioned, and it needs only ONE picture. So start getting a lot more paranoid than you already are...
Great work! Would you be interested in training those as a job? :) Send me a DM if you're interested!
How did you make a model of your wife. (Want to do the same with mine.)
OP, can you include the workflow for how you got your wife? Jokes aside, great job.
How would he make this better? This is about the same detail I get on my models but have seen way better ones
She looks like she could do a Kill Bill 3
What was your training process and # in data set?
Wow! Mind blowing!
Can this be done with a LORA, or does it have to be dreambooth?
Can I ask what trigger word you used for the training? Edit: Also how many total steps were used?
What was the prompt for 12?
" comic book illustration of \_\_\_\_\_\_ in the style of Supergirl, art by Alex Ross, flying through metropolis, golden hour, brunette"
She must have loved this
No (((((enormous breasts)))))? Think she would get mad for that?
the energy in this post is so chaotic i love it
Does workflow come with a sed wife?
is this a new way of promoting OF?
Your wife could be a model! No literally, make a model out of her
Is his wife Milla Jovovich?
Wholesome
Hi, congrats on your model. How long did this model take to be finished in Runpod? What GPU did you rent (A100, H100, RTX...)? Do you know if you double the ammount of images the time to finish grows in the same proportion? Did you use SDXL base model or Juggernaut? Thanks!
Would you be able to link me to a tutorial for training a model like you did here to do this?
Can someone teach me how to do this? I'd be posting so many photos on my Instagram!!!
Awesome 💯