What's the best open source model for fintuning a large dataset (100k images) of high resolution?
Finding the ideal model to handle fabric textures without losing aesthetic quality...
A Reddit user with a dataset of 100K high-resolution (2K+) fashion images is exploring open-source models for finetuning, preferring the Apache license to avoid licensing issues. They are evaluating three models: Qwen-Image-2512, ZIB, and ZIT. Key concerns include which model best preserves fabric textures, draping, and aesthetic quality after heavy finetuning, and whether anyone has successfully pushed 100K+ images through these models without catastrophic forgetting.
They also debate full-parameter finetuning versus LoRA for this scale, asking about training cost efficiency versus output quality. The goal is a high-end Vogue look—avoiding the plastic AI aesthetic. The community is invited to share real-world experience on stability, detail retention, and alternative SOTA models that could outperform the listed options. The post highlights the practical challenges of scaling generative models for commercial fashion imagery.
- User evaluating Qwen-Image-2512, ZIB, and ZIT for fabric detail retention after heavy finetuning on 100K high-res images.
- Debate between full-parameter finetuning and LoRA for 100K samples, with questions about catastrophic forgetting and efficiency.
- Goal is high-end Vogue aesthetic, rejecting plastic AI look; seeks cost-effective training strategies.
Why It Matters
Real-world insights for generative AI professionals training large-scale fashion models on open-source architectures.