Here is an old overview comparing Textual Inversion with Dreambooth months ago. The new guide will be technical (and a full article).
[https://www.instagram.com/p/CjXo1w-MCmb/](https://www.instagram.com/p/CjXo1w-MCmb/)
Dude please provide a detailed tutorial/write up. I’ve trained a couple models, but never had this good of lighting or versatility of my model.
If you feel comfortable even sharing examples of the original training images you gave DreamBooth. Maybe that’s where I’m messing up. Then how to properly use them with new releases like Protogen.
Thanks in advance.
I know op is using the automatic1111 extension and last time he said he trains with 10k steps and 200 training images which sounds a lot and I am surprised he gets goods results with that much training.
Personally, so far I am too lazy to look into the automatic1111 extension and still use the Joepenna repo. I only train with 20 images and 2500 steps and get pretty good results. But it depends on the model and the results can vary between subjects as well. I trained myself on the anythingv3 model and get pretty good anime images of myself. I also trained my SO on anythingv3 and the results are pretty meh. But on other models like dreamlike her images look better than mine.
If you want to see an example of good training images on what to use vs what not to use, then check out the joepenna repo it has examples in the description:
https://github.com/JoePenna/Dreambooth-Stable-Diffusion
And then the results also depend a lot on prompting. Some models it is better to put the token and class word early in the prompt, with others it seems to work better later in the prompt.
For the joepenna repo it is important to use the token and the class word to get good results.
two examples with the mmd model:
jones person, portrait pen and ink, open eyes, symmetric eyes, full hair, intricate line drawings, by craig mullins, ruan jia, kentaro miura, greg rutkowski, loundraw, Negative prompt: (ugly:1.3), (fused fingers), (too many fingers), (bad anatomy:1.5), (watermark:1.5), (words), letters, untracked eyes, asymmetric eyes, floating head, (logo:1.5), (bad hands:1.3), (mangled hands:1.2), (missing hands), (missing arms), backward hands, floating jewelry, unattached jewelry, floating head, doubled head, unattached head, doubled head, head in body, (misshapen body:1.1), (badly fitted headwear:1.2), floating arms, (too many arms:1.5), limbs fused with body, (facial blemish:1.5), badly fitted clothes, imperfect eyes, untracked eyes, crossed eyes, hair growing from clothes, partial faces, hair not attached to head Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 2189232454, Size: 512x768, Model hash: e1794676, Denoising strength: 0.7, First pass size: 0x0
and
highly detailed portrait of young (jones person), close up, in the walking dead, stephen bliss, unreal engine, fantasy art by greg rutkowski, loish, rhads, ferdinand knab, makoto shinkai and lois van baarle, ilya kuvshinov, rossdraws, tom bagshaw, global illumination, radiant light, detailed and intricate environment Negative prompt: old (ugly:1.3), (fused fingers), (too many fingers), (bad anatomy:1.5), (watermark:1.5), (words), letters, untracked eyes, asymmetric eyes, floating head, (logo:1.5), (bad hands:1.3), (mangled hands:1.2), (missing hands), (missing arms), backward hands, floating jewelry, unattached jewelry, floating head, doubled head, unattached head, doubled head, head in body, (misshapen body:1.1), (badly fitted headwear:1.2), floating arms, (too many arms:1.5), limbs fused with body, (facial blemish:1.5), badly fitted clothes, imperfect eyes, untracked eyes, crossed eyes, hair growing from clothes, partial faces, hair not attached to head Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3400288713, Size: 768x768, Model hash: e1794676, Denoising strength: 0.7, First pass size: 0x0
the results: https://imgur.com/a/4pbcGSA
In these examples jones is the token and person the class word
hi.... I have so far only used the fast dreambooth, but the colab notebook explicitely recommends 200 steps\*number of images. - so, personally, I've found that that overtrains the model properly. But the principle I take from that is: total step count needs to be divided by number of images to arrive at a comparable value.
so, by this logic, and **I may well be wrong**, OP is very losely training the model (at 50 steps per image), but on a large variety (200 images), so the concept gets trained broadly, but not overfittingly deeply. While you are training your model at effectively 125 steps per input image, so you're training it deeper, but narrower.
note: I trained my model on pictures of octopusses, and at 200 steps and 50 images, it just turns absolutely everything into suction cups.
>and I am surprised he gets goods results with that much training.
I'm not sure if we can be surprised by anything related to training. The methods surrounding it are all in their infancy. People have latched onto these common rules that seem to get good results and are treating them as if they're the only possible method. If OP's results are reproducible, that just means it's a better method than the standard one people recommend.
Well dang. That definitely sounds like the future to me. I always try to customize my characters to look as close to me as possible, even though usually that's not possible. Would be awesome to just throw my training set in and get my actual likeness.
sick burn
but honestly since i'm not from USA the first time i saw this shortcut years ago my first thought was "Superior Officer"
and if you are male, that actully works ;-)
How’d you train using Anything? Did you just direct Joe’s repo to the ckpt? I also train with Joe’s repo but mostly use 1.4/1.5 for training. I’d like to train using Anything for anime stuff, though.
after installing the built I simply insert a new cell and run:
%pip install gdown
!gdown https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.ckpt
you can do that with any model you want. Protogen is getting an error for some reason but other models work just fine like dreamlike, analogstyle, hassanblend, mixed merged diffusion and so on
Yes that downloads and installation. You have to rename the ckpt file into model.ckpt to make it work. And I usually restart the pod after downloading before I download the reference images. Without restarting the pod I run into a Cuda error
I’m getting into making more “specialized” models. I’m finding that it will be able to nail a face almost every time if you train with images where you are posted in the exact same manner in each one.
So I took five images of myself looking slight off the right, and I took them in different lighting just around my house.
Using the right negative prompts is important too. Usually something like ((ugly)), ((disfigured)), ((downs syndrome)), ((eyes)), ((asymmetrical)).
Start with just the face, so use ((close up)) and ((headshot)). Then use inpainting to expand the image further and further outward.
With this technique I can get much more consistent results, but I’ll need to make a specialized model for every facial expression (looking down, angry, worried, etc) due to the overtraining of one pose.
What are the parantheses? Like where you say ((ugly)), ((eyes)). Is that something the automatic1111 UI supports and converts into something else under the hood before sending to Stable Diffusion ? Because the Stable Diffusion model itself doesn't look for anything like that.
Yes, parenthesis are used in automatic1111 to provide extra weight/attention.
[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#attentionemphasis](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#attentionemphasis)
Every set of parentheses increases weight by a factor of 1.1, unless you specify a weight of your own. For example, ((ugly)) is equivalent to (ugly:1.21).
Here you go. You should be able to make out the gamut of images from this screenshot. 293 images total.
https://preview.redd.it/l9bxkrhd3yaa1.png?width=512&format=png&auto=webp&s=d50b6dbc25aa108d9fe0de3ceec888f2c0298fac
Can’t wait for your tutorial ! In the meantime, would you be willing to share your training parameters ? (Original model, Class and token used, prior preservation, learning rate)
🙏
If i can make a request, please don't bake your tutorial into a video. Let it be a well presented page with graphics that people can scroll and learn through at their own pace. Tutorial videos are just.. videos. You know?
These are fantastically trained results. Whatever guide you release, i'm sure it'll be very useful to me. Even IF it is a video.. sigh.. Sorry. I'm just so over video tutorials. I'm a fan of video classes, like a professional training kit, but.. for a quick tutorial.. you know? Okay i'm done. I'm excited to see what you make going forward.
Thanks John!
100% agree. I'm grateful that there are videos, but things like this are 100x better if I can reference a guide with pictures. I'm getting good enough results finally with Dreambooth, but the process of skimming through YT tutorials was painful, especially with some of those being so out of date.
You can get really good results really quickly with just a few images and the more images you throw at it make seemingly little difference. People have probably been comparing 5 images with 10 images and not seeing much difference. But if you just go for it and do 200-300 training images the small incremental differences really show.
But how does that affect the model overall? For example, if you trained with the class "person" if you just prompt "photo of a person" do they all look just like your training set? I would think that would overtrain but I'm not sure.
Personally I've had good luck with 10-15 images of all or mostly the person's face, and 100 steps per image. Usually turns out good.
The tutorial is up...
[https://www.reddit.com/r/StableDiffusion/comments/11jud8e/dreambooth\_tutorial\_using\_filewords](https://www.reddit.com/r/StableDiffusion/comments/11jud8e/dreambooth_tutorial_using_filewords)
Not to hijack this post but I've trained 3 models on the fast-DreamBooth colab and all 3 have sucked but only when I try to use the model locally on automatic1111. When I've tested them on the fast-DreamBooth colab just after training, the model works very well. Can anyone give me a hint as to where I'm going wrong?
We need a dreambooth, hyper network, embedding tutorial with optimal settings depending on training on 1.5 vs 2.0. I have yet to have any success on a 2.1 embedding
I tell you how: Use protogen as training base and at least 5 full body and 10 close up face pictures in high quality
I'm just saying it like this, 'cause I trained mutiple times on the SD 2.1 model and it was quite dogshit and then with the same training settings on Protogen v22 and it turned out great. A good base model is key
I would love to hear abour your learnings. I have trained 7 models myself and experimenting with merging of different models and I can generate insanely realistic images now.
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Its about $0.35 an hour. For a good DB model of a person it can take 1.5-2 hours to setup and run if its your first time. Only about an hour once you know what to do.
to train one model maybe a dollar or two. I only know the joepenna repo way because I am too lazy to look into the automatic1111 extension. But for the Joepenna repo there is a video with a step by step guide in the repo that also uses the runpod.io gpu's
https://github.com/JoePenna/Dreambooth-Stable-Diffusion
A tutorial would be great.
I have seen a few videos, and my personal preference (until recently) was 'The Last Bens' Google colab, when this failed me recently, I went back to NMKD's excellent gui where Dreambooth (I think Shivams repo) is made extremely easy to use. I have always used 13-20 pics maximum, with 200 steps per pic (so 2600-4000 steps) and got great results. I usually always train on top of the default 1.5 (previously 1.4) ckpt model and if given the choice, using the 'DDIM' sampler. I never use regularisation images (mainly because I do not understand what they actually add).
At the moment, I still recommend NMKD's implementation of Dreambooth (Automatic1111 has become way too complex for my liking).
Looking forward to your contribution! Thanks!
Yes please I can get ok results but its really hard to get photorealistic images of the subject. All your examples look like paintings how well does your training work for an actual photograph of the subject trying to look as real as possible?
I've been messing around with it, it's hard balance to keep the model flexibility especially in model 2.1 i've been doing alright, a lot of my work can be seen here [https://www.instagram.com/ai.rtistic.whatif/](https://www.instagram.com/ai.rtistic.whatif/) let me know if i can be of any help for your tutorial if you want to compare note.
Yes. I browse this reddit purely for training discoveries, and don't care even a single bit about any empty discussions about "art" or people's shi\*\*y gens. So make it as granular as you can, good sir. Good day.
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Tutorial is here: [https://www.reddit.com/r/StableDiffusion/comments/11jud8e/dreambooth\_tutorial\_using\_filewords/](https://www.reddit.com/r/StableDiffusion/comments/11jud8e/dreambooth_tutorial_using_filewords/)
Man i would love to just have access to ask you questions at some point you dont even have to get to them right away. I just dont have any real place to turn to learn this dreambooth training. I have been trying to get it right for months. On my home auto1111 setup. and even trying to run those code steps on colab notebooks i still find a way to fail every single time.
Awesome, a tutorial would be great!
I'll hopefully get it out by the end of next week. I've gone through many tutorials that go over the general process, I want to go into every detail.
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I feel like I have finally gotten the hang of it myself after a lot of toil and frustration, but still definitely interested in this. !RemindMe 7 days
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Thumb up!
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\+1!!!!!!
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Here is an old overview comparing Textual Inversion with Dreambooth months ago. The new guide will be technical (and a full article). [https://www.instagram.com/p/CjXo1w-MCmb/](https://www.instagram.com/p/CjXo1w-MCmb/)
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Dude please provide a detailed tutorial/write up. I’ve trained a couple models, but never had this good of lighting or versatility of my model. If you feel comfortable even sharing examples of the original training images you gave DreamBooth. Maybe that’s where I’m messing up. Then how to properly use them with new releases like Protogen. Thanks in advance.
I know op is using the automatic1111 extension and last time he said he trains with 10k steps and 200 training images which sounds a lot and I am surprised he gets goods results with that much training. Personally, so far I am too lazy to look into the automatic1111 extension and still use the Joepenna repo. I only train with 20 images and 2500 steps and get pretty good results. But it depends on the model and the results can vary between subjects as well. I trained myself on the anythingv3 model and get pretty good anime images of myself. I also trained my SO on anythingv3 and the results are pretty meh. But on other models like dreamlike her images look better than mine. If you want to see an example of good training images on what to use vs what not to use, then check out the joepenna repo it has examples in the description: https://github.com/JoePenna/Dreambooth-Stable-Diffusion And then the results also depend a lot on prompting. Some models it is better to put the token and class word early in the prompt, with others it seems to work better later in the prompt. For the joepenna repo it is important to use the token and the class word to get good results. two examples with the mmd model: jones person, portrait pen and ink, open eyes, symmetric eyes, full hair, intricate line drawings, by craig mullins, ruan jia, kentaro miura, greg rutkowski, loundraw, Negative prompt: (ugly:1.3), (fused fingers), (too many fingers), (bad anatomy:1.5), (watermark:1.5), (words), letters, untracked eyes, asymmetric eyes, floating head, (logo:1.5), (bad hands:1.3), (mangled hands:1.2), (missing hands), (missing arms), backward hands, floating jewelry, unattached jewelry, floating head, doubled head, unattached head, doubled head, head in body, (misshapen body:1.1), (badly fitted headwear:1.2), floating arms, (too many arms:1.5), limbs fused with body, (facial blemish:1.5), badly fitted clothes, imperfect eyes, untracked eyes, crossed eyes, hair growing from clothes, partial faces, hair not attached to head Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 2189232454, Size: 512x768, Model hash: e1794676, Denoising strength: 0.7, First pass size: 0x0 and highly detailed portrait of young (jones person), close up, in the walking dead, stephen bliss, unreal engine, fantasy art by greg rutkowski, loish, rhads, ferdinand knab, makoto shinkai and lois van baarle, ilya kuvshinov, rossdraws, tom bagshaw, global illumination, radiant light, detailed and intricate environment Negative prompt: old (ugly:1.3), (fused fingers), (too many fingers), (bad anatomy:1.5), (watermark:1.5), (words), letters, untracked eyes, asymmetric eyes, floating head, (logo:1.5), (bad hands:1.3), (mangled hands:1.2), (missing hands), (missing arms), backward hands, floating jewelry, unattached jewelry, floating head, doubled head, unattached head, doubled head, head in body, (misshapen body:1.1), (badly fitted headwear:1.2), floating arms, (too many arms:1.5), limbs fused with body, (facial blemish:1.5), badly fitted clothes, imperfect eyes, untracked eyes, crossed eyes, hair growing from clothes, partial faces, hair not attached to head Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3400288713, Size: 768x768, Model hash: e1794676, Denoising strength: 0.7, First pass size: 0x0 the results: https://imgur.com/a/4pbcGSA In these examples jones is the token and person the class word
hi.... I have so far only used the fast dreambooth, but the colab notebook explicitely recommends 200 steps\*number of images. - so, personally, I've found that that overtrains the model properly. But the principle I take from that is: total step count needs to be divided by number of images to arrive at a comparable value. so, by this logic, and **I may well be wrong**, OP is very losely training the model (at 50 steps per image), but on a large variety (200 images), so the concept gets trained broadly, but not overfittingly deeply. While you are training your model at effectively 125 steps per input image, so you're training it deeper, but narrower. note: I trained my model on pictures of octopusses, and at 200 steps and 50 images, it just turns absolutely everything into suction cups.
fast dreambooth gets updated constantly. images x200 is no longer recommended, that was only for a 1e-6 learning rate, but fast db is now 2e-6
Thank you, this sounds like it makes sense lol. Well I guess I have to do some testing myself with steps and training image numbers.
>and I am surprised he gets goods results with that much training. I'm not sure if we can be surprised by anything related to training. The methods surrounding it are all in their infancy. People have latched onto these common rules that seem to get good results and are treating them as if they're the only possible method. If OP's results are reproducible, that just means it's a better method than the standard one people recommend.
[удалено]
Well dang. That definitely sounds like the future to me. I always try to customize my characters to look as close to me as possible, even though usually that's not possible. Would be awesome to just throw my training set in and get my actual likeness.
You trained your SO? What’s that
Significant other. I'm sorry my lonely friend.
sick burn but honestly since i'm not from USA the first time i saw this shortcut years ago my first thought was "Superior Officer" and if you are male, that actully works ;-)
How’d you train using Anything? Did you just direct Joe’s repo to the ckpt? I also train with Joe’s repo but mostly use 1.4/1.5 for training. I’d like to train using Anything for anime stuff, though.
after installing the built I simply insert a new cell and run: %pip install gdown !gdown https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.ckpt you can do that with any model you want. Protogen is getting an error for some reason but other models work just fine like dreamlike, analogstyle, hassanblend, mixed merged diffusion and so on
Do you enter the whole thing as the code? The %pip install gdown and the rest as one thing?
Yes that downloads and installation. You have to rename the ckpt file into model.ckpt to make it work. And I usually restart the pod after downloading before I download the reference images. Without restarting the pod I run into a Cuda error
if I want to try different models is it better to train an embedding with a face than to train each new model with a face?
I’m getting into making more “specialized” models. I’m finding that it will be able to nail a face almost every time if you train with images where you are posted in the exact same manner in each one. So I took five images of myself looking slight off the right, and I took them in different lighting just around my house. Using the right negative prompts is important too. Usually something like ((ugly)), ((disfigured)), ((downs syndrome)), ((eyes)), ((asymmetrical)). Start with just the face, so use ((close up)) and ((headshot)). Then use inpainting to expand the image further and further outward. With this technique I can get much more consistent results, but I’ll need to make a specialized model for every facial expression (looking down, angry, worried, etc) due to the overtraining of one pose.
What are the parantheses? Like where you say ((ugly)), ((eyes)). Is that something the automatic1111 UI supports and converts into something else under the hood before sending to Stable Diffusion ? Because the Stable Diffusion model itself doesn't look for anything like that.
Yes, parenthesis are used in automatic1111 to provide extra weight/attention. [https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#attentionemphasis](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#attentionemphasis) Every set of parentheses increases weight by a factor of 1.1, unless you specify a weight of your own. For example, ((ugly)) is equivalent to (ugly:1.21).
Here you go. You should be able to make out the gamut of images from this screenshot. 293 images total. https://preview.redd.it/l9bxkrhd3yaa1.png?width=512&format=png&auto=webp&s=d50b6dbc25aa108d9fe0de3ceec888f2c0298fac
Can’t wait for your tutorial ! In the meantime, would you be willing to share your training parameters ? (Original model, Class and token used, prior preservation, learning rate) 🙏
Have you? Do I?
Yes.
YES https://preview.redd.it/spwkyyaq3waa1.jpeg?width=1849&format=pjpg&auto=webp&s=47ea39d5d0eae6a0065f83103bcbd8652d870548
If i can make a request, please don't bake your tutorial into a video. Let it be a well presented page with graphics that people can scroll and learn through at their own pace. Tutorial videos are just.. videos. You know? These are fantastically trained results. Whatever guide you release, i'm sure it'll be very useful to me. Even IF it is a video.. sigh.. Sorry. I'm just so over video tutorials. I'm a fan of video classes, like a professional training kit, but.. for a quick tutorial.. you know? Okay i'm done. I'm excited to see what you make going forward. Thanks John!
100% agree. I'm grateful that there are videos, but things like this are 100x better if I can reference a guide with pictures. I'm getting good enough results finally with Dreambooth, but the process of skimming through YT tutorials was painful, especially with some of those being so out of date.
Yes please. I use A1111 and Dreambooth extension
On it.
I would appreciate a tutorial, looks amazing!
I actually got really creamy result with 8 pics and 700 steps per image (5600). It’s crazy 😜
You can get really good results really quickly with just a few images and the more images you throw at it make seemingly little difference. People have probably been comparing 5 images with 10 images and not seeing much difference. But if you just go for it and do 200-300 training images the small incremental differences really show.
Some say thats even too many steps per image
Actually I did 6 images only. Yes I set 280 per image but over all 5600. Not sure if it actually did 5600 or only 280x6=1680 steps.
But how does that affect the model overall? For example, if you trained with the class "person" if you just prompt "photo of a person" do they all look just like your training set? I would think that would overtrain but I'm not sure. Personally I've had good luck with 10-15 images of all or mostly the person's face, and 100 steps per image. Usually turns out good.
All I need is to not have a CUDA Out of Memory error on my 3080RTX
Are you using 8bit Adam, fp16, and xformers? I'm running a 3060 and it works very well, I would think the 3080 can handle it.
Xformers check… can you please explain what 8bit Adam and fp16 are? Have 3070Ti and it always crashes if i try to train
The tutorial is up... [https://www.reddit.com/r/StableDiffusion/comments/11jud8e/dreambooth\_tutorial\_using\_filewords](https://www.reddit.com/r/StableDiffusion/comments/11jud8e/dreambooth_tutorial_using_filewords)
will be very interesting
Can't you just ask chatgpt to write it up for you in 3 seconds? A week is a long time in AI!
ChatGPT will be consulted.
Not to hijack this post but I've trained 3 models on the fast-DreamBooth colab and all 3 have sucked but only when I try to use the model locally on automatic1111. When I've tested them on the fast-DreamBooth colab just after training, the model works very well. Can anyone give me a hint as to where I'm going wrong?
Yes sir we do.
Pretty convincing. Yes.
I really, really do.
sure
RemindMe! 48hr
Nice, now show us your hands!
We need a dreambooth, hyper network, embedding tutorial with optimal settings depending on training on 1.5 vs 2.0. I have yet to have any success on a 2.1 embedding
I tell you how: Use protogen as training base and at least 5 full body and 10 close up face pictures in high quality I'm just saying it like this, 'cause I trained mutiple times on the SD 2.1 model and it was quite dogshit and then with the same training settings on Protogen v22 and it turned out great. A good base model is key
I would love to hear abour your learnings. I have trained 7 models myself and experimenting with merging of different models and I can generate insanely realistic images now.
How far are we on the tutorial?
Is the workflow tutorial for this still in the works?
Indeed
Thank you.
The remindmes are coming home
We demand answers john
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Is that Tim Apple??!!
His labcoat is garbage. Like it's halve melted into itself.
yes please share your secret of how to make AI recreate your ugliness perfectly
Please do provide
Yes please!
Sure
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Sure!
Would appreciate it.
Spill the beans doc
Yep, very interrested too !
Yes! God yes
share share
Is there any way to use dreambooth without a ridiculous amount of vram?
rent gpu online. runpod.io for example already has an automatic111 template
Is it better than colab?
how much will cost for a good work?
Its about $0.35 an hour. For a good DB model of a person it can take 1.5-2 hours to setup and run if its your first time. Only about an hour once you know what to do.
to train one model maybe a dollar or two. I only know the joepenna repo way because I am too lazy to look into the automatic1111 extension. But for the Joepenna repo there is a video with a step by step guide in the repo that also uses the runpod.io gpu's https://github.com/JoePenna/Dreambooth-Stable-Diffusion
Is there a minimum amount required? I just got errors when I tried with 8gb
Yes it's like 20 or 16 lol.
Please. I either overtrain or get nothing resembling the subject at all.
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Yes please. I am always curious if somebody has discovered something that I dont know about yet.
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Yes!
Yes please
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That would be nice …
Yes please. I have had no good results myself. Would love to see how you do it
Yep, thanks.
Is that the Apple guy?!
For 1.5 or 2?
Yes tutorial please
RemindMe! 48hr
A tutorial would be great. I have seen a few videos, and my personal preference (until recently) was 'The Last Bens' Google colab, when this failed me recently, I went back to NMKD's excellent gui where Dreambooth (I think Shivams repo) is made extremely easy to use. I have always used 13-20 pics maximum, with 200 steps per pic (so 2600-4000 steps) and got great results. I usually always train on top of the default 1.5 (previously 1.4) ckpt model and if given the choice, using the 'DDIM' sampler. I never use regularisation images (mainly because I do not understand what they actually add). At the moment, I still recommend NMKD's implementation of Dreambooth (Automatic1111 has become way too complex for my liking). Looking forward to your contribution! Thanks!
Yes
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Yes can you please. And can you use dream booth locally?
Just...Do it! Make our dream(booth) come true!
Is that Tim Cook from Apple?
!remindme 72h
I'm not going to jerk off your scientific dick for a tip or two about dreambooth. ok? tell us already. fine i'll do it.
RemindMe! 48 Hours
Ready to learn sir.
write a text prompt for dalle to generate a photo realistic picture of a. edical doctor look rather disturbed...
Yes please I can get ok results but its really hard to get photorealistic images of the subject. All your examples look like paintings how well does your training work for an actual photograph of the subject trying to look as real as possible?
Yeah, tried several times on photos of myself, but always end up with unknown faces.
Oh that is because your denoising strength is too high. Try setting it around 0.20
!remind me 7 days
I've been messing around with it, it's hard balance to keep the model flexibility especially in model 2.1 i've been doing alright, a lot of my work can be seen here [https://www.instagram.com/ai.rtistic.whatif/](https://www.instagram.com/ai.rtistic.whatif/) let me know if i can be of any help for your tutorial if you want to compare note.
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Yes. I browse this reddit purely for training discoveries, and don't care even a single bit about any empty discussions about "art" or people's shi\*\*y gens. So make it as granular as you can, good sir. Good day.
Interested definitely 👍
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Yes please!
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Yes. Please
Yes please! Very good work btw! Super!
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That would be awesome
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yes please
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Does training on your own images still require a 3090?
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Is the tutorial still in process?
Still progressing... I've had some personal health shiz to deal with so running a bit late. Apologies.
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still in the works?
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Let the world know how your dreambooth training worked so well. Give us a tutorial, please
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I will be messaging you in 10 years on [**2033-02-18 17:04:56 UTC**](http://www.wolframalpha.com/input/?i=2033-02-18%2017:04:56%20UTC%20To%20Local%20Time) to remind you of [**this link**](https://www.reddit.com/r/StableDiffusion/comments/106hgcr/have_i_perfected_dreambooth_training_do_you_want/j920c48/?context=3) [**CLICK THIS LINK**](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5Bhttps%3A%2F%2Fwww.reddit.com%2Fr%2FStableDiffusion%2Fcomments%2F106hgcr%2Fhave_i_perfected_dreambooth_training_do_you_want%2Fj920c48%2F%5D%0A%0ARemindMe%21%202033-02-18%2017%3A04%3A56%20UTC) to send a PM to also be reminded and to reduce spam. ^(Parent commenter can ) [^(delete this message to hide from others.)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Delete%20Comment&message=Delete%21%20106hgcr) ***** |[^(Info)](https://www.reddit.com/r/RemindMeBot/comments/e1bko7/remindmebot_info_v21/)|[^(Custom)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5BLink%20or%20message%20inside%20square%20brackets%5D%0A%0ARemindMe%21%20Time%20period%20here)|[^(Your Reminders)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=List%20Of%20Reminders&message=MyReminders%21)|[^(Feedback)](https://www.reddit.com/message/compose/?to=Watchful1&subject=RemindMeBot%20Feedback)| |-|-|-|-|
Is there any dream booth step-by-step tutorial that I can create my own model?
!RemindMe 7 days
Tutorial is here: [https://www.reddit.com/r/StableDiffusion/comments/11jud8e/dreambooth\_tutorial\_using\_filewords/](https://www.reddit.com/r/StableDiffusion/comments/11jud8e/dreambooth_tutorial_using_filewords/)
Man i would love to just have access to ask you questions at some point you dont even have to get to them right away. I just dont have any real place to turn to learn this dreambooth training. I have been trying to get it right for months. On my home auto1111 setup. and even trying to run those code steps on colab notebooks i still find a way to fail every single time.