SDXL LoRA Training Dilemma: Why Art Style Drifts Despite Strong Character Consistency

SDXL LoRA Training Dilemma: Why Art Style Drifts Despite Strong Character Consistency

In the rapidly evolving world of AI-generated art, a detailed case study from the r/StableDiffusion subreddit highlights a persistent challenge in LoRA training: achieving both character consistency and stylistic fidelity. The user, known as u/Key_Smell_2687, trained a custom SDXL LoRA on the Illustrious-XL (Wai) base model using a meticulously curated dataset of 25 high-quality AI-generated images. While the model successfully preserves character identity across poses and angles, the output consistently deviates from the original artistic style and nuanced facial features of the source material.

According to the Reddit post, the user initially trained on a larger, less refined dataset of 50 images, which produced poor results. After a complete overhaul, he narrowed the dataset to 25 images — 12 close-up facial shots, 8 upper-body compositions, and 5 full-body frames — all generated using the Nano Banana Pro model. Crucially, he revised his captioning strategy: instead of tagging immutable traits like eye color and hair hue, he focused exclusively on mutable elements such as clothing, expression, and background. This adjustment resolved pose-related distortions but did not resolve the stylistic drift.

The core issue lies in the tension between the base model’s inherent aesthetic and the target style of the training data. Illustrious-XL, developed by Wai, has a distinct, semi-realistic anime style with specific rendering conventions for skin tones, lighting, and facial structure. When a LoRA is trained on images generated by a different model (Nano Banana Pro), even if those images are high quality, they inherit the source model’s stylistic DNA — which may conflict with Illustrious-XL’s latent space. The user’s attempt to bridge this gap by retraining on outputs from Illustrious-XL backfired, amplifying the drift. This suggests the LoRA is not merely adapting to style, but being pulled toward the base model’s default preferences, especially when training on synthetic data with subtle but critical aesthetic mismatches.

Experts in AI art training suggest three key areas for intervention. First, dataset provenance matters. Training a LoRA on images generated by a different model introduces a “style noise” that confuses the network. Ideally, training images should be sourced from the same model architecture or at least be stylistically aligned. Second, captioning may need refinement. While removing immutable traits helped with pose stability, it may have deprived the model of critical visual anchors. A hybrid approach — tagging only the most distinctive, non-generic features (e.g., “sharp jawline,” “slanted almond eyes,” “soft gradient blush”) — could provide enough signal without overfitting.

Third, training parameters may require tuning. The user employed a network rank of 32 and alpha of 16 — standard settings — but for style-sensitive tasks, lower ranks (e.g., 16–24) with higher alpha (e.g., 32) can improve fidelity by limiting the model’s capacity to deviate. Additionally, reducing epochs from 120 to 60–80 may prevent overfitting to noise. Gradient checkpointing and batch size of 1 are appropriate, but the lack of image augmentation (e.g., subtle color jitter or contrast variation) may limit the model’s ability to generalize style.

Finally, generation settings play a role. A LoRA strength of 0.7–1.0 is necessary to activate the learned style, but combining it with CFG scales above 7.0 may force the model to prioritize prompt semantics over stylistic nuance. Lowering CFG to 5.0–6.0 and experimenting with samplers like DPM++ 2M Karras may yield more natural stylistic blending.

This case underscores a broader truth in AI art: consistency is not enough. To capture the soul of an artist’s style — whether human or algorithmic — training must be as much about aesthetic alignment as it is about feature preservation. For creators seeking to clone a specific visual voice, the path forward lies not in more data, but in smarter, more intentional data curation and parameter tuning.