Training a Z‑Image Turbo LORA on Male Genitalia – Why Results Are Poor
Investigate why Z‑Image Turbo LORA struggles with male genitalia despite 10k images.
Experiment with balanced training data and alternative rank settings to reduce deformities.
Summary
The author trained a Z‑Image Turbo LORA using over 10,000 images of male genitalia from various angles and zoom levels. Despite the large dataset, the generated results were unsatisfactory, with correct shapes but many deformities. The author compares this difficulty to the model’s struggles with hands and fingers, suggesting that male genitalia may be a similarly challenging anatomical category. They speculate about possible data poisoning or bias in the training set. Other community members have reportedly succeeded with similar subjects, raising questions about the training approach. The author experimented with different LORA rank sizes (32, 64, 128) and learning rates, yet still hit a performance wall.
This post highlights the challenges of fine‑tuning diffusion models for specific anatomical details and suggests that dataset quality and training hyperparameters are critical factors. It also points to the need for more balanced and diverse training data for sensitive subjects.
Developers should consider alternative training strategies or data augmentation techniques to mitigate these issues.
Key changes
- Trained Z‑Image Turbo LORA with 10,000 images of male genitalia.
- Results show correct shapes but many deformities.
- Difficulty similar to hands/fingers, indicating a hard anatomical category.
- Possible data poisoning or bias in training set.
- Other users have succeeded with similar subjects.
- Tried rank sizes 32, 64, 128 and varied learning rates.
- Still hitting a performance wall.