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How to train a custom Stable Diffusion style with LoRA with Peft

Use PEFT LoRA with Diffusers to teach an image model a subject or style on a modest GPU.

Short answer: Install PEFT with pip install peft and Diffusers; then open the examples/lora_dreambooth/train_dreambooth.py training script from the repo; Prepare a small set of images of your subject or style. All steps.

Who it is forAn artist or designer who wants a model that reproduces their style or product consistently.

The problemFull fine-tuning of Stable Diffusion needs about 27.5GB of GPU memory and produces a large checkpoint.

The outcomeA small LoRA checkpoint for Stable Diffusion trained with as little as about 8GB of GPU memory with gradient checkpointing.

What you need first

  • Python with PyTorch and Transformers
  • A GPU with enough memory for the base model (the README shows a 3B model on far less memory with LoRA)
  • A dataset for your task

Steps

  1. Install PEFT with pip install peft and Diffusers
  2. Open the examples/lora_dreambooth/train_dreambooth.py training script from the repo
  3. Prepare a small set of images of your subject or style
  4. Train with LoRA and gradient checkpointing to keep memory low
  5. Load the resulting adapter in Diffusers to generate images

Prompt for your agent

Using huggingface/peft, run examples/lora_dreambooth/train_dreambooth.py on CompVis/stable-diffusion-v1-4 with my images in <folder>, using LoRA and gradient checkpointing. Report peak GPU memory and save the adapter.

Caveat. The memory numbers in the README are for CompVis/stable-diffusion-v1-4 on an A100; your results will vary.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for huggingface/peft and "Train a custom Stable Diffusion style with LoRA"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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