lightweight-diffusion-ldm

anilegin/lightweight-diffusion-ldm

Custom lightweight latent diffusion text-to-image model.

This repository contains inference-only files:

• VAE config and stripped VAE weights
• LDM/UNet config and stripped LDM weights
• diffusion/sampler code needed for DDPM and DDIM
• a simple inference.py script
• generation defaults in generation_config.yaml

The checkpoints are stripped to contain model weights only; optimizer state, scheduler state, and training logs are not included.

Install

git clone https://huggingface.co/anilegin/lightweight-diffusion-ldm
cd lightweight-diffusion-ldm
pip install -r requirements.txt

Generate images

python inference.py \
  --prompt "a small dog sitting on a red couch" \
  --sampler ddim \
  --num-steps 50 \
  --guidance-scale 3.0 \
  --precision bf16 \
  --output-dir outputs/example

For offline/local-only CLIP loading, make sure openai/clip-vit-large-patch14 is cached locally and add:

--local-files-only

Notes

This is a custom PyTorch implementation, not a native Diffusers pipeline. The included source code is required for inference.

Training data

Trained/evaluated with COCO-style image-caption data. Add more precise dataset, metrics, and limitations here before making the repo public.

Citation

If you use this model, please cite the project/repository.