2026Paper
veltneon-Lumen: Latent Diffusion at 4K with 1-Step Refinement
A new architecture that produces 4K imagery in a single refinement step, reducing inference cost by 6× on NVIDIA Hopper nodes.
veltneon Labs
Pushing the frontier of generative imaging.
Our research team works on diffusion models, controllability, efficient inference and safety. We publish what we learn — read our papers, model cards and tech reports.
Labs Infrastructure
NVIDIA-Powered Model Training.
At veltneon Labs, we train our foundation latent diffusion models on NVIDIA DGX H100 clusters. By harnessing FP8 Mixed Precision via the NVIDIA Transformer Engine, we reduce training epochs by 40% while preserving gradient accuracy.
Our researchers specialize in quantization research, enabling full model checkpoints to execute on standard NVIDIA Tensor Core servers with zero structural drift.
Transformer Engine FP8DGX Compute Node ClusterCUDA-X Deep Learning Labs
Model Specs & Optimization
OptimizedBASE CHECKPOINT
12B Params
TRAINING PRECISION
FP8 / FP16
INFERENCE COST
-83.3% Latency
REFINE MECHANISM
1-Step Latent
FID SCORE VS DENOISING STEPS
1-Step Distillation
One-Step Latent Denoising
Diffusion models generally resolve image noise across 30 to 50 sequential steps, creating high processing queues. Our Lumen distillation process maps noise to target anchors in a single refinement pass.
Attention Maps
Cross-Attention Latent Grids
To keep layout geometry aligned with input text, veltneon cross-attention layers assign spatial weights to specific text tokens. This binds objects to their exact coordinate bounds.
ATTENTION WEIGHT HEATMAP
"Bottle"
0.94
"Background"
0.12
"Liquid"
0.34
"Neon Glow"
0.88
Orthogonality
Brand-Lock Style Orthogonality
To prevent style leakage, Brand-Lock models restrict weights to null-space vectors orthogonal to other generative layers. This keeps custom fine-tunes completely isolated.
Safety Spaces
Multimodal safety projections
We project text prompts and image latent arrays into a unified 3D vector space. Safe content is mapped to separate clusters away from trademarks, copyrighted symbols, and NSFW markers.
EMBEDDING SPACE CLUSTERS
Benchmarks
GenEval evaluation performance.
Standard comparative metrics demonstrating alignment scores against general industry frameworks.
Spatial Layout Accuracy
veltneon: 94.2%
Text Rendering Fidelity
veltneon: 89.6%
Brand Color Consistency
veltneon: 98.8%
Detail Preservation
veltneon: 92.4%
Score Ingestion Model
We score veltneon against open GenEval vectors weekly. By training text-image controllers on layout spatial indices, our models score 30% higher on composition rules compared to vanilla setups.
Model distillation pipeline
Our foundation checkpoints originate at 12B parameter density. We deploy a multi-stage distillation process that compresses weights down to a 4B parameter matrix optimized specifically for fast edge VRAM pipelines.
1. Foundation Training
12B parameter model trained in FP16/FP8 precision on custom image sets.
2. Student Distillation
Model parameters compressed to 4B while matching visual output fidelity.
3. FP8 Triton Run
Checkpoints loaded directly on Hopper core nodes in native FP8 formats.
Labs Architecture
Lumen-V2 Diffusion Pipeline
The internal mechanics of veltneon's low-latency design compiler, mapping text descriptions to compliant layouts.
Input Layer
Layout Specs
Prompt text specifications
Processing
Latent Encoder
Refines inputs to feature tokens
Alignment
Brand-Lock LoRA
Locks product silhouettes & palette
Hopper Denoiser
NVIDIA H100 Core
1-Step Latent Refinement pass
Output Layer
4K Rendered Image
High-fidelity branding asset
Lab visuals
Observing intelligence at multiple resolutions.
Recent publications
Papers & releases.
Read →
2026Paper
Brand-Lock: Constraining Diffusion Outputs to Visual Identity
A LoRA-based fine-tuning scheme that holds brand colors, typography and product silhouettes invariant under prompt drift.
2025Pre-print
Prompt Distillation for Faithful Generation
We show distilled prompt encoders reduce hallucinations by 38% on the GenEval benchmark.
2025Tech report
Safety in the Loop: Real-Time IP & NSFW Filtering at Scale
Our production safety stack and how we keep false-positives under 0.4% across 10M+ daily generations.
Research focus
Four questions we care about.
Controllability
How do we let users steer composition, lighting and style without losing fidelity?
Efficiency
Smaller, faster, cheaper inference — without giving up image quality.
Safety & IP
Building generative systems that respect creators, trademarks and consent.
Evaluation
Better metrics for what humans actually consider a 'good' generated image.
Want to collaborate?
We work with academic labs, independent researchers and partner companies.
Research collaborators
