AI Model2026
Depth-conditioned LoRA for Krea‑2 that extracts a depth map from any input image and generates new images preserving the original 3D structure and composition while changing content and style. Single 862MB LoRA, works with Krea‑2‑Raw and Krea‑2‑Turbo.
Efficient 4B-scale image generation and editing model family that pairs a lightweight VAE tokenizer (Mage-VAE) with a native-resolution multimodal diffusion transformer, reducing tokenization cost by an order of magnitude and enabling few-step high-resolution generation and editing.
AI Model2026
Efficient 4B native-resolution diffusion foundation model for text-to-image generation and instruction-based image editing. Uses a lightweight Mage‑VAE tokenizer and a 4B NR‑MMDiT backbone to produce 512–2048 outputs with low memory and fast inference; ships in Base, RL-aligned and few-step Turbo variants.
AI Image2026
Performs instruction-based image editing from reference images using a 4B native-resolution diffusion transformer; the Turbo variant uses 4-step distillation for interactive latency (≈1.02 s per 1024² edit on A100) while supporting semantic, appearance, structure-aware and restoration edits.
Generates polygonal meshes from images using flow matching for fast, native mesh synthesis. Decodes vertices, edge connectivity, and face winding in one parallel pass, preserves artist-authored topology without vertex quantization or welding, supports a user-set vertex budget for face-count control, and completes image-to-mesh in ~6s median.
A continuous-latent diffusion language model that preserves a high-capacity, decodable text latent and directly models its distribution via a block-causal diffusion transformer and query-based encoder–decoder; achieves top results on OpenWebText and XSum while scaling to 1B parameters.
AI Video Papers2026
Uses video generation only as a training signal to co-train a pretrained video expert and a lightweight action expert, then discards the video branch at inference to produce a low-latency end-to-end driving planner; enhanced with RL for compositional driving rewards.
AI Audio2026
Generates complete songs (up to five minutes) from lyrics and a music description, producing 32 kHz stereo WAV with expressive vocals and long-range musical structure. Uses hierarchical LLMs fused with flow-matching/Flow-VAE synthesis for coherent arrangement and timbre; requires CUDA and integrates with Diffusers and SGLang-Omni.