---
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.

---

## 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 KL penalty (β=0.1) 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
- **Dropout** (0.1) in attention and MLP for regularization

### Diffusion Process

Standard DDPM (Denoising Diffusion Probabilistic Model) with 1000 timesteps and a linear beta schedule (β₁=1e‑4, βᵀ=0.02). The model is trained to predict the added noise ε. Classifier-free guidance (CFG) is used during training with a text-conditioning dropout probability of 15%.

---

## Training

### Dataset

**[jackyhate/text-to-image-2M](https://huggingface.co/datasets/jackyhate/text-to-image-2M)** — \~2M high-quality text-image pairs in webdataset format. Loaded via the `datasets` library with **streaming** to avoid materializing the full 2TB+ dataset into memory. A rotating **2000-image in-memory buffer** (\~400 MB RAM) is refreshed each epoch from a fresh random stream to provide shuffle diversity without disk I/O bottlenecks.

### Data Augmentation

Random horizontal flip (p=0.5), color jitter (brightness/contrast/saturation ±0.2, hue ±0.05), and random affine transforms (rotation ±10°, translation ±5%, scale 0.9–1.1) via `torchvision`.

### Hyperparameters

| Parameter | Value |
|-----------|-------|
| **Image size** | 256×256 |
| **Batch size** | 8 |
| **Optimizer** | AdamW (β₁=0.9, β₂=0.999, lr=1e‑4, weight decay=0.01) |
| **LR schedule** | Linear warmup (500 steps) + Cosine annealing |
| **Gradient clipping** | 1.0 (norm) |
| **Mixed precision** | FP16 via `torch.cuda.amp.GradScaler` |
| **Dropout** | 0.1 (DiT attention + MLP) |
| **EMA** | Exponential moving average (decay=0.999) applied at every step; EMA weights used for validation and final checkpoint |
| **Early stopping** | Patience of 8 epochs on validation loss (10% held-out split) |
| **KL weight** | 0.1 (β-VAE style) |

### VAE Pretraining

Before diffusion training, the VAE is pretrained for **10 epochs** on reconstruction + KL loss with a higher learning rate (lr=1e‑3) to establish a meaningful latent space. During this phase the DiT and text encoder are frozen.

### Training Phases

1. **VAE Pretraining** (10 epochs) — Train encoder + decoder on image reconstruction to establish a meaningful latent space. DiT and text encoder are frozen.
2. **DiT Diffusion Training** (up to 30 epochs, early-stopped) — Freeze VAE, train DiT to denoise latents conditioned on text embeddings. CFG dropout randomly replaces text embeddings with a learned null embedding to enable classifier-free guidance at inference time.

---

## Usage

### Requirements

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

For training, also install:
```bash
pip install torchvision datasets
```

### 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` automatically downloads weights from Hugging Face via `huggingface_hub` on first use and caches them locally.

### Streaming Generation

View the diffusion process unfold step-by-step:

```python
for img, step_info in pipe.generate_stream(
    prompt="a futuristic city at night",
    num_steps=250,
    cfg_scale=3.0,
    display_every=25,  # emit an image every 25 steps
):
    print(f"Step {step_info['step']}/{step_info['total']}")
    img.save(f"progress_{step_info['step']:04d}.png")
```

---

## 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
- Quality depends on training data distribution — may not match large-scale models like SDXL or Flux
---