This model is part of the research work described in "FeatureFusion: Merging Diffusion Models Through Representation Correlations" by Murdock Aubry and James Bona-Landry.

Base Model: CompVis/stable-diffusion-v1-4 Type: Specialist Specialization: Animals Training Data: Animals shard Model Architecture: UNet-based diffusion model

The model has the same limitations as the base Stable Diffusion model Best performance is achieved when prompts relate to the model's specialization May produce unexpected results for concepts outside its training distribution

Training Data: Pick-a-Pic v1 Training Method: Finetuning of the UNet component while keeping text encoder and VAE frozen

Optimizer: AdamW Learning rate: 1e-6 Schedule: Cosine with warmup Training steps: 5 epochs on 1000 data samples Memory optimization: Gradient accumulation (4 steps), attention slicing, VAE slicing, gradient checkpointing

If you use this model in your research, please cite:
@article{aubry2024featurefusion,
title={FeatureFusion: Merging Diffusion Models Through Representation Correlations},
author={Aubry, Murdock and Bona-Landry, James},
journal={},
year={2025}
}

license: mit language: