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

pip install torch torchvision safetensors pillow huggingface_hub

2. Python Inference Snippet

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,
)

images[0].save("prism_output.png")

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.

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