Update README.md

README.md

@@ -1,3 +1,53 @@
+This model is part of the research work described in "FeatureFusion: Merging Diffusion Models Through Representation Correlations" by Murdock Aubry and James Bona-Landry.
+
+<h1>
+Model Description
+</h1>
+
+<h2>Overview</h2>
+This model is an vehicles specialist based on the Stable Diffusion 1.4 architecture. 
+
+<br>
+<h2>Model Details</h2>
+
+Base Model: CompVis/stable-diffusion-v1-4
+Type: Specialist
+Specialization: Vehicles
+Training Data: Vehicles shard
+Model Architecture: UNet-based diffusion model
+
+<h2>Limitations</h2>
+
+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
+
+
+<h1>Training</h1>
+
+<h2>Training Procedure</h2>
+
+Training Data: Pick-a-Pic v1
+Training Method: Finetuning of the UNet component while keeping text encoder and VAE frozen
+
+<h2>Hyperparameters:</h2>
+
+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
+
+<h1>Citation</h1>
+
+If you use this model in your research, please cite:<br>
+@article{aubry2024featurefusion,<br>
+title={FeatureFusion: Merging Diffusion Models Through Representation Correlations},<br>
+author={Aubry, Murdock and Bona-Landry, James},<br>
+journal={},<br>
+year={2025}<br>
+}
+
 ---
 license: mit
 language: