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| Latente diffusionsmodeller× | Mamba (State Space Model)× | QLoRA× | |
|---|---|---|---|
| Fagområde | Dyb læring | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning | Machine learning |
| Oprindelsesår≠ | 2022 | 2023 | 2023 |
| Ophavsperson≠ | Robin Rombach | Albert Gu | Tim Dettmers |
| Type≠ | Neural network architecture | Neural network architecture | Training methodology |
| Oprindelig kilde≠ | Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695). DOI ↗ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ | Dettmers, T., Pagnoni, A., Holtzman, A., & Contrastive, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. arXiv preprint arXiv:2305.14314. link ↗ |
| Aliasser≠ | LDM, Stable Diffusion, Latent Diffusion | Mamba, State space models, Selective state space | QLoRA, Quantized LoRA |
| Relaterede | 4 | 4 | 4 |
| Resumé≠ | Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality. | Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power. | QLoRA is an efficient fine-tuning method introduced by Dettmers et al. in 2023 that enables fine-tuning large language models using quantization and low-rank adaptation. By combining 4-bit quantization with LoRA, QLoRA reduces memory requirements by 75%, enabling fine-tuning of 65B-parameter models on single GPUs. |
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