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AnimeBoysZeroXL
A dedicated model for high-quality anime-style male characters. This model is specifically optimized for males-only content, offering a wide range of aesthetic styles and high versatility.
🚀 Inference Guide
⚠️ Important: This model uses Zero Terminal SNR with V-prediction. Please ensure you are using the correct settings during inference.
ComfyUI Users: Add the
ModelSamplingDiscretenode into your workflow. Setsamplingtov_prediction,zsnrtotrue.Automatic1111 Users: Place the
.yamlconfig file into the model folder. The.yamlfile must have the exact same name as the model file, only with the.yamlextension instead of.safetensors. SetNoise schedule for samplingin settings toZero Terminal SNR.
Prompting: Always begin your prompt with a score tag (e.g.
score_9). You can use any of these styles:Tag soup:
score_X, tag1, tag2, tag3, ...Natural language:
score_X, [your description here]Mixed approach:
score_X, [description], tag1, tag2, ...Tip: If you find the style of the score tags is too strong, you could try dropping them from the prompt.
Negative Prompt: Choose from one of these three presets depending on your needs:
Minimal:
score_1Light:
score_1, lowres, artistic error, scan artifacts, jpeg artifacts, multiple views, too many watermarks, negative space, blank pageHeavy:
score_1, score_2, score_3, lowres, artistic error, film grain, scan artifacts, jpeg artifacts, chromatic aberration, dithering, halftone, screentones, multiple views, logo, too many watermarks, negative space, blank page
CFG Scale: A CFG scale of 3 to 5 is recommended. For finer control, I suggest using dynamic thresholding.
Pro-tip: I set
mimic_scaleto match the CFG scale and set both minimum scales to the same lower value. I useHalf Cosine Upfor both modes.
Resolution: To get started, try these dimensions:
Portrait: 832 × 1216
Square: 1024 × 1024
Landscape: 1216 × 832
Some other supported sizes: 768×1344, 768×1280, 896×1152, 960×1088, 1344×768, 1280×768, 1152×896, 1088×960.
🧪 Training Details
AnimeBoysZeroXL was fine-tuned from Pony Diffusion V6 XL using approximately 950k images. The knowledge cutoff is November 2025.
The following tags were used during training to help you steer the results toward your desired style.
Score tags
Each image is tagged with score_X, where X is a range from 1 to 9.
score_9represents the highest aesthetic quality based on my personal preferences.
Rating tags
rating:general: generalrating:sensitive: sensitiverating:questionable: questionablerating:explicit: explicit
Year tags
Use year YYYY (ranging from 2005 to 2025) to target specific era styles.
Training configurations
Hardware: 4 × Nvidia A100 SXM 80GB
Optimizer: AdaFactor
Gradient Accumulation Steps: 8
Effective Batch Size: 128 (4 × 8 × 4)
Learning Rates:
U-Net: 2e-5
Text Encoders: 1e-5
LR Schedule: Constant with 250 warmup steps
Precision: FP16 Mixed Precision
🔄 Changes from AnimeBoysXL v3.0
Tag Overhaul: Quality tags have been removed. The 5-category aesthetic tags have been replaced with a more granular 9-category score tag system. Renamed rating tags for better clarity. Abolished the tag ordering scheme.
Captions: A subset of highly aesthetic images was trained using natural language prompts for better comprehension.
Emphasis: Highly aesthetic images now have more "repeats" in the training data.
Optimization:
5% caption dropout for unconditional guidance.
Trained with Zero Terminal SNR and V-prediction.
Implemented adaptive loss weighting.
No multi-resolution noise or debiased estimation loss.
Trained with input perturbation noise (gamma=0.1).
Trained with huber loss.
Merging: This model is a merge across several iterations of the same training run for better stability.
License
AnimeBoysZeroXL is a derivative model of Pony Diffusion V6 XL by PurpleSmartAI. Please read their license before using the model.
Description
FAQ
Comments (10)
Hi Koolch, long time no see~! Where's have you been? We missed you so much X'''D
It;s great you are come back with us with another great checkpoint since last one~! ^^
Truth to be told, while I think Illustrious better at making anatomies, I feel the pose felt "samey" at times, so even though It's been a long time since I uses PONY based checkpoint, but now I'm curious to test how things gonna turns out~
Again, thanks for your creation as always~! ;)
Hi, RaidenFan~ Yeah, I just shifted my attention to other stuff haha. I just overhauled my training pipeline, and I wanted to test it on a model I'm familiar with first. I'm planning to get my hands on new model types. Would love to know which models are your current favorite!
Sure~ There's 2 checkpoints I usually uses:
>Newhmenmix: Keep the artstyle and body types of character from lora really well! Rarely get muscular from thin character, and vice versa. Perfect for anime males and rarely made female! My most favorite! The bad: The poses feels samey...
>Kageillustrous: More 2.5 . Kinda made the character more bulkier than it should be, also affects the artstyle of the loras...But the poses are really creative! And also has great anatomy!
Hope it can be help ><
Finally! I've been waiting for your next model and it's finally here.
It’s a new model from my favorite creator, so my eyes lit up the moment I saw it.
But… I was a bit surprised that it was released in the PONY lineage rather than the ILXL or NOOB.
I’m more worried than excited about its performance, but I believe there must be a reason behind that choice.
The model’s art style is very strong and somewhat fixed, which makes it seem suitable for beginners.
However, that strength is too dominant, making diverse use difficult. While it guarantees a decent baseline, the ceiling is low. To be honest, images generated with this model don’t feel new or interesting.
In particular, its performance in generating NSFW images is quite unsatisfactory.
There have already been significant advancements among many SDXL-based models, and the Pony v6 line is clearly outdated.
AnimeBoysXL V3 was innovative at the time.
But AnimeBoysZeroXL feels like it’s stuck in the past.
In today’s landscape, there doesn’t seem to be much reason to use this model.
Sorry for the harsh criticism.
Hi, thanks for the feedback! When I trained this model, I did prioritize learning from images with higher scores, which might have been overkill and sacrificed diversity. As for the base model, I stuck with Pony since, had I changed it, I wouldn't have been able to tell if the outcome was from my new training method or the new base model. I will definitely train on a newer base model later, though.
Try removing the score tags from the prompt! It seems to make the output more diverse.
we've missed you king
miss u so much!
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