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README.md

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-license: mit
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+# Digit-Spatial-Diffusion's Hugging Face Models
+
+This repository contains several machine learning models developed as part of my project, "Spatial Knowledge Injection into Diffusion-based Image Generation".
+
+## Project Overview
+
+The project focused on the integration of spatial relations knowledge into diffusion-based image generation models. The main objective was to enhance the realism and context-awareness of the generated images. I experimented with four different methods to control the image generation process: baseline, handcrafted approach, typed control, and control dots.
+
+## Models
+
+This repository includes the following models:
+
+1. **Baseline** - Stable Diffusion model trained with digits on our custom dataset
+2. **Handcrafted** - ControlNet with Handcrafted tesnor.
+3. **Typed** - ControlNet with typed digits as control signals.
+3. **Typed dot** - ControlNet with dots as control signals.
+
+## How to Use
+
+Here's a basic example of how to use these models:
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("Krishan3168/Digit-Spatial-Diffusion")
+model = AutoModel.from_pretrained("Krishan3168/Digit-Spatial-Diffusion")
+
+inputs = tokenizer("Example input", return_tensors="pt")
+outputs = model(**inputs)