README.md

---
license: apache-2.0
pipeline_tag: text-to-image
---
# ShellD (Shell Diffusion)

**Small DiT-based Text-to-Image Latent Diffusion Model**

ShellD is a lightweight text-to-image model that generates **256×256** images from natural language prompts. It uses a Diffusion Transformer (DiT) backbone operating in a compact VAE latent space, making it feasible to train and run on consumer GPUs.

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## Model Architecture

```
Text Prompt → [MiniLM-L6-v2 (frozen)] → Text Embedding (384-d)
                                                      ↓
Random Noise → [VAE Encoder] → Latent (16ch) → [DiT (12 blocks)] → Denoised Latent → [VAE Decoder] → 256×256 Image
```

| Component | Details | Params |
|-----------|---------|--------|
| **Text Encoder** | `sentence-transformers/all-MiniLM-L6-v2` (frozen) | 22.71M |
| **VAE** | Encoder + Decoder with residual blocks, 3 down/up stages, latent dim=16 | 23.43M |
| **DiT** | 12-layer Transformer with self-attention, cross-attention (text), and adaptive timestep conditioning. Patch size=4, hidden dim=256, 8 heads | 20.80M |
| **Total** | | **66.95M** (trainable: **44.23M**) |

### VAE (Autoencoder)

The VAE compresses 256×256 RGB images into a **16-channel latent** with spatial size 32×32 (downsampled by 8×). It uses residual blocks with GroupNorm and SiLU activations. During training, a small KL penalty keeps latents close to a standard normal distribution.

### DiT (Diffusion Transformer)

The DiT operates on patched latents (patch size 4 → 8×8 = 64 patches). Each block includes:
- **Self-attention** for spatial relationships
- **Cross-attention** conditioned on text embeddings
- **Adaptive timestep conditioning** via an MLP-projected sinusoidal embedding

### Diffusion Process

Standard DDPM (Denoising Diffusion Probabilistic Model) with 1000 timesteps, cosine-like linear beta schedule (β₁=1e‑4, βᵀ=0.02). The model is trained to predict the added noise ε.

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## Training

- **Dataset**: Midjourney v5 202304 Clean (~20K image-text pairs, with procedural fallback)
- **Image size**: 256×256
- **Batch size**: 8
- **Optimizer**: Adam (β₁=0.9, β₂=0.999, lr=1e‑4)
- **LR schedule**: Linear warmup (500 steps) + Cosine annealing
- **Gradient clipping**: 1.0
- **Mixed precision**: FP16 via GradScaler
- **VAE pretraining**: 1 epoch of reconstruction + KL loss (lr=1e‑3) before diffusion training

### Training Phases

1. **VAE Pretraining** — Train encoder + decoder on image reconstruction to establish a meaningful latent space.
2. **DiT Diffusion Training** — Freeze VAE, train DiT to denoise latents conditioned on text embeddings.

---

## Usage

### Requirements

```bash
pip install torch safetensors sentence-transformers pillow numpy huggingface-hub
```

### Inference (standalone — loads from Hugging Face)

```python
from inference import ShellDInference

pipe = ShellDInference("FlameF0X/ShellD")
image = pipe.generate("a serene lake surrounded by mountains")
image.save("output.png")
```

`ShellDInference` will automatically download the weights from Hugging Face using `huggingface_hub` on first use, then cache them locally.

See [`inference.py`](./inference.py) for the complete standalone inference script.

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## Model Files

| File | Description |
|------|-------------|
| `config.json` | Model hyperparameters (ShellDConfig) |
| `model.safetensors` | All weights: VAE, DiT, and text encoder |

### Loading the weights manually

```python
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
import json

model_path = snapshot_download("FlameF0X/ShellD")

with open(f"{model_path}/config.json") as f:
    config = json.load(f)

state_dict = load_file(f"{model_path}/model.safetensors")
print(state_dict.keys())  # prefixed: vae.*, dit.*, text_encoder.*
```

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## Intended Use

- Educational exploration of diffusion transformers
- Lightweight text-to-image generation on consumer hardware
- Starting point for fine-tuning on custom datasets

## Limitations

- 256×256 resolution only (no upscaling built in)
- Limited prompt understanding due to small DiT and frozen lightweight text encoder
- Procedural training data substitutes real images — quality depends on the real/fine-tuned checkpoints

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