Nai by MyAiMeN: The Ultimate Male-Focused Krea-2 Model

The Meaning of "Nai" In Thai, "Nai" (นาย) is a versatile word deeply rooted in masculinity. It is commonly used as a formal title (similar to "Mr."), a friendly and polite second-person pronoun among male peers, and historically, it translates to "Boss" or "Master." It embodies masculinity in a culturally unique, respectful, and authoritative way.

The Motivation Most models based on the Krea-2 architecture heavily lean towards generating female subjects. From body types and facial features to clothing, composition, and overall aesthetic styles, the base weights are overwhelmingly female-centric. I created the Nai model originally for my personal use to completely shift this balance, dedicating an entire model exclusively to high-quality male generation.

Feedback & Support Your feedback is always welcome. If you run into any issues or have suggestions for future versions, feel free to drop a comment. If you love the results, tipping some Buzz to support my work is highly appreciated. Enjoy!

Key Features

• Dedicated Male Focus: Massively trained on male-centric datasets, significantly shifting the core generation weight toward male subjects and aesthetics.

• Enhanced Demographics: Increased diversity in ethnicities and ages, making it much easier to generate men from various regions around the world.

• Built-in Male NSFW: Highly trained on male NSFW concepts. You can easily generate specific anatomy, poses, and intimate activities without needing to stack additional LoRAs.

• Seamless LoRA Compatibility: Designed to work flawlessly with male character LoRAs. The model adapts easily without distorting or overpowering the specific facial and bodily features of your applied LoRAs.

• Hyper-Realistic & Social Media Aesthetic: The model is heavily biased towards photorealism, particularly mimicking the look of mobile phone photography and casual social media selfies. However, it remains highly versatile—if you want different art styles (e.g., illustration, cinematic, anime), you can easily achieve them by explicitly specifying the desired style in your prompt.

Known Quirks & Prompting Tips (To be improved in future versions)

• Contextual/Environmental Bias: Because of the added diversity, conflicting environmental prompts can sometimes confuse the model's ethnic output. For example, prompting "An Asian man with blonde hair standing in a German castle" might yield a European man, because the combined weights of "blonde hair" and "German castle" overpower the ethnicity tag.

  • Tip: Weigh your ethnicity tags heavier, use specific cultural descriptors, or anchor the appearance with a character LoRA.

• Extreme NSFW Sensitivity: This is an inherently NSFW model. If you do not explicitly prompt for clothing, the model will default to nudity. Furthermore, if you prompt for clothing but overly describe the anatomy underneath (e.g., "He wears a LONG THICK bulge jockstrap"), the model's strong anatomical understanding will cause the anatomy to literally break through the fabric. Keep clothing prompts simple if you want them to stay on!

• Sweat Rendering: Prompting for "sweat" on a naked body can occasionally render as a thick white fluid instead of clear water droplets.

• Body Hair Dynamics: If you want a smooth body, you must include "hairless body" in your prompt, otherwise random chest hair may appear. Conversely, prompting for facial hair (e.g., "beard") on certain ethnicities like Arab men will almost always trigger heavy full-body hair generation.

💡 Prompting Tips: Anatomy & Size Control

Because this V1 model was trained to emphasize a highly dominant, masculine aesthetic (RAW | MALE | MASTER), the baseline generations naturally lean toward very large sizes. Here is how you can better control the anatomy in your prompts:

• Controlling Direction: The model responds well to directional prompts. Example: "His [anatomy] is pointing LEFT-UpWARD."

• Achieving Realistic Sizes: Since the base size is already large, using modifiers like "long" or "very long" is enough for realistic proportions. Avoid terms like "HUGE" or "Extra large" unless you are aiming for extreme, unnatural proportions. You can also use body landmarks to define length (e.g., "long slender flaccid [anatomy] loose hanging down length above knee's level").

• Prompting for Smaller Sizes: Getting smaller sizes can be tricky in this version. Use words like "small" or "short" alongside body landmarks rather than exact measurements. Example: "short slender flaccid [anatomy] pointing upward length under the navel." (Do not use "20 cm", the model doesn't process exact measurements).

• 🤝Mosly work small size example "His thin short and small penis is pointing downward"

• ⚠️ The "Tiny" Warning: Avoid using words like "very small" or "very tiny." The model will shrink the anatomy to the point where it might generate female anatomy instead to maintain its realism logic. (If a gender-bending result is your actual goal, this is a great way to achieve it without needing an extra LoRA!).

🤝 Call for Dataset Contribution: Currently, my dataset for smaller anatomy sizes is highly limited. If anyone has access to high-quality reference data for this (must be consensual, legally obtained, and ethically sourced), please feel free to reach out to me via Direct Message. Your contributions will greatly help in balancing the dataset for the next major update!

Recommended Settings

• Steps: 8 - 9

• Sampler: Euler

• Scheduler: Simple

• Text Encoder: qwen3vl_4b_uncensored_int8_convrot.safetensors OR qwen3-vl-4b-heretic_int8.safetensors

• VAE: krea2RealVae_v10.safetensors

A Note on Formats

Which format should you choose? While the file sizes of int8_convrot and FP8 are nearly identical, I highly recommend using BF16 or int8_convrot if your system can handle it. int8_convrot preserves the model's weight precision much better than FP8, giving you sharper details, more accurate anatomy, and superior overall generation quality. Choose FP8 only if extreme VRAM optimization is your absolute priority.

Note: All sample images provided with this model were generated purely via Text-to-Image without the use of any LoRAs.