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Rapid, lightweight fine-tuning for Stable Diffusion using LoRA
Accelerate Stable Diffusion fine-tuning up to 2× faster than Dreambooth, producing tiny 1‑6 MB LoRA weights compatible with Huggingface Diffusers, inpainting, and multi-model merging.
When teams consider Lora, these hosted platforms usually appear on the same shortlist.
ML hub with curated foundation models, pretrained algorithms, and solution templates you can deploy and fine-tune in SageMaker
Enterprise AI platform providing LLMs (Command, Aya) plus Embed/Rerank for retrieval
API-first platform to run, fine-tune, and deploy AI models without managing infrastructure
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Custom character illustration
Generate consistent images of a new character using a 2 MB LoRA adapter.
Brand‑specific style transfer
Apply a proprietary illustration style across prompts while keeping the model footprint minimal.
Inpainting with specialized textures
Seamlessly fill missing regions using a LoRA‑trained inpainting model.
Rapid prototyping in Colab
Iterate on visual concepts within minutes using the provided notebook example.
LoRA (Low‑Rank Adaptation) trains only a small set of low‑rank matrices that modify the original weights, drastically reducing training time and checkpoint size.
The repository reports up to a 2× speed increase while achieving comparable or sometimes better visual quality.
Yes, using the `--train_text_encoder` flag you can adapt the CLIP text encoder alongside the diffusion model.
A conversion script produces CKPT format that can be loaded into the Automatic1111 repository.
Use the `--mode=ljl` flag with paths to the adapters you wish to combine; the tool outputs a merged LoRA checkpoint.
Project at a glance
DormantLast synced 4 days ago
