---
license: apache-2.0
library_name: diffusers
pipeline_tag: text-to-image
tags:
- diffusion
- dit
- text-to-image
- flow-matching
- safetensors
- rectified-flow
- artistic
- custom-architecture
inference: false
---

# PRISM-1: Diffusion Transformer (DiT) Checkpoint

**PRISM-1** is a custom Diffusion Transformer (DiT) architecture designed for multi-domain text-to-image synthesis, featuring native spectral light modeling, authentic traditional/digital medium emulation, and high geometric consistency.

---

## Model Specifications

| Attribute | Specification |
| :--- | :--- |
| **Model Type** | Diffusion Transformer (`PrismDiT`) |
| **Parameter Count** | 815.46 Million (FP16) |
| **Weights File** | `PRISM-1.safetensors` (1.55 GB) |
| **Latent Dimension** | 16 channels ($8\times$ spatial downsampling) |
| **Patch Size** | $2 \times 2$ latent patches ($16 \times 16$ pixel equivalent) |
| **Text Conditioning** | Cross-Attention (1024-dim context, CLIP / T5-XXL) |
| **Diffusion Formulation** | Rectified Flow / Flow-Matching with Euler ODE solver |
| **Recommended Steps** | **12 – 25 steps** (optimal: 18–20) |
| **Recommended CFG** | **5.0 – 6.5** |
| **Default Sampler** | Euler |

---

## Key Capabilities

- **Zero Style-Bleed:** Seamlessly shifts between pure 2D traditional media (pencil, chalk pastel, aged oil), cinematic photography, and faceted geometric abstraction without carrying over unwanted stylistic tropes.
- **Prismatic Optics:** Built-in comprehension of chromatic dispersion, caustics, and prism flare.
- **Fast Rectified Flow:** High convergence rate requiring only **12 – 25 Euler steps** for full detail fidelity.

---

## Quickstart & Usage

### 1. Installation
```bash
pip install torch torchvision safetensors pillow huggingface_hub
```

### 2. Python Inference Snippet
```python
import torch
from model import PrismDiT
from pipeline import PrismPipeline

# Initialize DiT architecture
dit = PrismDiT(
    input_size=64,
    patch_size=2,
    in_channels=16,
    hidden_size=1152,
    depth=28,
    num_heads=16,
    context_dim=1024,
)

# Load pipeline with PRISM-1.safetensors
pipe = PrismPipeline.from_pretrained(
    checkpoint_path="PRISM-1.safetensors",
    device="cuda" if torch.cuda.is_available() else "cpu",
    dtype=torch.float16,
)

# Generate image (12 - 25 flow steps)
images = pipe(
    prompt="kaleidoscopic radial mandala, abstract geometric human figures, chromatic prism refraction",
    negative_prompt="blurry, muddy colors, noise",
    height=1024,
    width=1024,
    num_inference_steps=20,  # Recommended: 12 - 25 steps
    guidance_scale=6.5,
    seed=42,
)

### 3. Image Variation Snippet (Img2Img)
```python
# Generate a variation from a reference photo
variations = pipe.generate_variation(
    reference_image="path/to/reference.jpg",
    prompt="same scene at night with neon ambient lighting",
    strength=0.65,              # 0.1 (micro-tweak) to 0.9 (broad reimagining)
    num_inference_steps=20,     # Recommended: 12 - 25 steps
    guidance_scale=6.0,
)
variations[0].save("variation_output.png")
```

Or via CLI:
```bash
python variation_demo.py --input "path/to/reference.jpg" --strength 0.65 --prompt "sunset lighting"
```

---

## Repository Files

- `PRISM-1.safetensors`: Main compiled FP16 model checkpoint (815M parameters).
- `config.json`: Architecture configuration for automated loading.
- `model.py`: Full PyTorch implementation of `PrismDiT` and `PrismDiTBlock`.
- `pipeline.py`: Inference pipeline with `PrismFlowScheduler` and CFG support.
- `benchmark_suite.py`: Standard 5-domain evaluation suite.
- `inspect_checkpoint.py`: Command-line inspection utility for safe tensor viewing.

---

## Citation & License

Released under the **Apache-2.0** License.