We're Back Baby
After a two hear hiatus, we're back with a brand new Anteros model. Trained on the same imageset used to train the original and highly acclaimed Anteros XXXL of 1.3M professional and amateur pornography, with new high quality generated captions. This model does softcore, hardcore, and everything in-between.
Which Model Should I Use?
Anteros XXX Krea2 Turbo
This is the model for most users. Trained using the same Trajectory Distribution Matching used by Krea to train their turbo model, we find this model strikes a good balance between performance and flexibility. We think you'll love it.
Settings
Sampler: Euler
Scheduler: Simple
Steps: 8
CFG: 1.0
Anteros XXX Krea2 Raw
If you find you can't quite get the result you want from the Turbo model, and are okay spending more time on generation, give this a try. It's the result of our post-finetune alignment and should have more flexibility than Turbo. This is also a good starting point if you want to train your own character LoRAs.
Setttings
Sampler: Euler
Scheduler: beta57
Steps: 40
CFG: 4.5
Anteros XXX Krea2 Unaligned
This is the raw finetune. The images it generates tend to be less visually appealing. The only reason it's here is for trainers or mergers who want to do something big with it. If you don't know why you need this, you don't.
Setttings
Sampler: Euler
Scheduler: beta57
Steps: 40
CFG: 4.5
Caption Style
Long detailed descriptions are best. Detail everything. Here are some sample captions from the actual training set:
A naked blonde woman is posing outdoors next to a tree. She is crouching and leaning forward, looking directly at the camera with a slight smile. Her long blonde hair hangs down her right side. Her breasts are fully exposed, with her left nipple clearly visible. Her right arm rests across her body, and she is wearing orange beaded bracelets on her wrist. Her vulva is exposed and visible between her spread thighs. The background consists of blurred green and orange autumn foliage.
A woman with dark hair lies on her back with her eyes closed while being tittyfucked by a man. Her skin is glistening with sweat or lubricant. She uses her hands to squeeze her large breasts together, trapping the man's large, erect penis within her cleavage. The head of his penis is deeply inserted between her breasts, while the shaft extends outwards to the right. Her hands press firmly against her breasts to maintain the tight enclosure around his genitals. The woman's nipples are visible on her firm, rounded breasts as they envelop the penis.
A woman with dark hair and glasses is giving a blowjob to a man. She is positioned on a white padded table, leaning forward with her buttocks exposed and pushed up, wearing white stockings and black and white sneakers. Her mouth is wrapped around his large erect penis. The image is from the man's point of view, showing his chest and legs. The scene takes place in a room with a grey carpet, a blue wall, a blue exercise ball, and a grey ottoman.
A nude woman and a nude man are having sex on a grey couch. The man is lying on his back, and the woman is positioned on top of him, facing away from the camera while looking back over her right shoulder. Her bare buttocks are centered in the frame, resting on his pelvis. A colorful tattoo is visible on her lower back and the top of her right buttock. The man's tanned chest and shoulders are visible in the foreground. The couch is adorned with grey pillows, and a grey shag rug is on the hardwood floor behind them.
How Was This Made?
As with the original Anteros XXXL, we continue to believe there is too much secrecy around training. We are once again open sourcing the recipe with the hope that others will do the same.
Initial Finetune
We used a custom training harness with the following hyperparameters:
Batch Size: 120
LR: 2e-5
Precision: Original mixed bf16/fp32
Timestep MU: 0.906
Conditioning Dropout: 0.1
Optimizer: AdamW with zero weight decay
L2 Regularization: On
The L2 Regularization is the only real innovation here. Using the ideas from [Explicit Inductive Bias for Transfer Learning with Convolutional Networks](https://arxiv.org/pdf/1802.01483) the weights decay towards a fixed starting point - in our case, the original krea2 model. We use this as a replacement for AdamW's decay and found it to be much less destructive.
We trained this for 3 epochs of the full dataset, resulting in Anteros XXX Krea2 Unaligned.
Alignment
We then used a smaller set of one thousand hand-captioned hand-selected images. We ran this with the same settings for 150 epochs. After that, we generated ten thousand image pairs with saved latents. Using a custom trained classifier, we selected the preferred image (an audit of a significant sample of the results showed the classifier met my preferences about 95% of the time). We used the techniques described in [Diffusion Model Alignment Using Direct Preference Optimization](https://arxiv.org/abs/2311.12908) to further refine the visual appeal of the generated images.
The result is Anteros XXX Krea2 Raw
Turbo
Finally, we performed a 2 epoch run using the dataset's captions to perform the technique described in [Learning Few-Step Diffusion Models by Trajectory Distribution Matching](https://arxiv.org/abs/2503.06674), which is cited by the Krea2 paper. We used the aligned model as teacher and the original Krea2 Turbo model as student.
We once again used our custom training harness, this time with the hyperparameters below:
Batch Size: 120
LR: 1e-5
Precision: Original mixed bf16/fp32
Timestep MU: 1.15
Optimizer: AdamW with zero weight decay
L2 Regularization: On (against the student's original weights)
Teacher CFG: 4.5
Steps: 8
"Fake" LoRA Scale: 10
"Fake" LoRA Rank: 256
"Fake" LoRA Alpha: 256
Hardware
All training was performed using four H200 GPUs on a single host using FSDP.
Description
If you find you can't quite get the result you want from the Turbo model, and are okay spending more time on generation, give this a try. It's the result of our post-finetune alignment and should have more flexibility than Turbo. This is also a good starting point if you want to train your own character LoRAs.
Setttings
Sampler: Euler
Scheduler: beta57
Steps: 40
CFG: 4.5
FAQ
Comments (22)
extremely good model but doesn't mix well with my loras, I will need to retrain them using your base model I guess
Yeah, sorry about that. No way around it - training at this scale really disrupts the latent space.
If you don't mind my asking, how long did it take to train for all 5(?) epochs you mentioned? Really curious to see how long it takes using such a large dataset.
It took a little over one week for each epoch, so ~5wks total.
Thanks very much!
Thanks for the SDXL version back in the day, 1 of the 1st to bring a large dataset to the table. Hopefully I can get a better GPU & try some of the new larger models.
Hooray, the first full-fledged fine-tune (except for Kroma), thanks! Will the quantized versions be released? Anyway I self-quant the model already. The model is flawlessly good!
So we train LoRAs on Unaligned, not Raw, correct?
In my testing, I found the Unaligned version to provide better diversity; the Raw seems overfit the way the stock Krea-2 Turbo model is. However, I may be using it wrong: my standard workflow is loading the Raw (or Unaligned) checkpoint, add the stock Turbo LoRA at 0.5 weight, and use 12 steps / CFG=1 workflow. On stock Krea-2 Raw this provides a perfect balance between creativity (as in seed variance), speed, and quality. (Maybe I should try extracting and comparing the diff LoRAs from Anteros pairs Unaligned/Raw, Raw/Turbo, and Unaligned/Turbo).
My thought was people would train character LoRAs on top of Raw, and larger concepts or styles on top of Unaligned. But do what works for you! You're right that Unaligned has better diversity - tuning for aesthetic steers the model.
Any chance of getting a int8 version of this? I tried converting it myself but all the outputs end up broken for some reason...
I made INT8 in alperktt/Krea-2-SVDQuant-ComfyUI: Native INT8/W4A4 quantization + SVDQuant for Krea 2 on ComfyUI, tuned for Ampere GPUs in a quantize node and it works.
@deGENERATIVE_SQUAD Awesome, thanks!
First, a big thank you for the work you did. This checkpoint is a gem. Second... Any chance of publishing intermediate checkpoints?
After some more testing, I found that the Unaligned version + official Turbo lora @0.5 weight provides the best diversity and quality output.
Anteros Raw and Turbo, on the other hand, seem overly aligned, with classic signs of overtraining: low variety in composition, faces, and styles. In my experience, this means that a much earlier checkpoint should've been chosen as a release candidate instead of the final one.
Do you happen to have intermediate checkpoints, for either Raw or Turbo versions, with much lower epochs? I would be be interested to see how the Raw version would fare around 10, 25, 50, and 100 epochs, particularly pre-DPO checkpoints; same for the Turbo version if they exist. (These are not exact numbers, of course, as there are local ups and downs in the training.) Could you share some pre-DPO intermediate Raw checkpoints please?
Great contribution to the community, its very different in a good way from all the other krea2 checkpoints that I have tested. Tested only the turbo version. The visual aesthetics are phenomenal, only downside is lack of variety in faces, emotions and bodies. Hope you release further updates and other people release LoRAs trained for this model.
Use Anteros Unaligned + the official Krea-2 Turbo LoRA at 0.5 strength. You will get both quality and variety. Perhaps the alignment phase overshoot by a certain margin, but the Unaligned version is near perfect.
@civit77899 I will try I can only fit int8 sadly so will either wait or try making one myself
@civit77899 What are your generation parameters: sample/scheduler, steps, cfg? I am getting horrible blotchy mess
@mqblbhai Are you using the Krea-2 Turbo lora at 0.5 strength? Otherwise it's 12 steps, ER-SDE (Reverse Time) / Beta 57, Cfg = 1. Euler a / Beta also works.
@civit77899 yes I am using this LoRA loras/krea2_turbo_lora_rank_64_bf16.safetensors · Comfy-Org/Krea-2 at main
but no luck :(
If anyone makes an int8 conversion can they link? I liked the model though, it seems like a great addition!
Raw vs. Unaligned comparison: https://civitai.red/posts/31019352 vs. https://civitai.red/posts/31019385





