LoRA adapters break on distilled video models, long post explains why
burkov · x · 2026-09-30
Video diffusion models are the standard foundation for high-fidelity synthesis, and practitioners distill them (via full fine-tuning) to cut latency and inference cost, while LoRA remains the go-to tool for customizing style without retraining.
The problem: LoRA modules trained on a base model often produce severe visual artifacts, character duplication, and style degradation when applied directly to distilled variants. Retraining modules for every new distilled model requires private user data and large compute budgets, creating an operational bottleneck.
burkov's article digs into why this mismatch happens and what to do about it.
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