AI Model2026
A GGUF build of Qwen3.6 (35B) post-processed with the Genesis numerical repair to reduce training noise and restore weight distributions; provides a more stable, uncensored multimodal (image+text) MoE model with long-context support for local use.
Uses pretrained multimodal LLMs as zero-shot, training-free reward models for text-to-image RL by scoring how well the original text prompt can be recovered from a generated image via image-conditioned prompt log-likelihood; includes a Self-SpectraReward closed-loop variant.
AI Model2026
Provides GGUF-quantized Inkling multimodal model weights for local image/audio-to-text and conversational inference. Includes quantization variants (example: 1-bit UD-IQ1_S), Apache-2.0 license, and compatibility with Unsloth Studio, vLLM and common inference stacks.
AI Model2026
GGUF-quantized releases (NEO IMATRIX + MTP) of a multi-stage fine-tuned, uncensored Qwen3.5-9B model with vision enabled and a native 256k context window—optimized for instruction following, reasoning and image-text-to-text workflows; released under Apache-2.0.
Analyzes internal computation of text-to-image diffusion transformers and shows structural template tokens act as implicit semantic registers that maintain object identity during denoising. Introduces a causal interpretability framework (attention decomposition + targeted interventions) and a training-free pruning rule that cuts ~20% attention FLOPs for a ~1.4-point GenEval drop.
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.
AI Model2026
Provides repackaged Mage-Flow model files formatted for ComfyUI, including multiple diffusion variants (bf16, int8, turbo, edit), a Qwen text encoder and a VAE — organized in a ComfyUI directory layout for drop-in use.
AI Dataset2026
Curated set of 20,000 native 24×24 pixel-art sprites with two-level semantic taxonomy labels for tiny text-to-image and discrete visual modeling. Rebalanced, rights-conscious subset with ≤5 colors per sprite and stratified train/val/test splits.
AI Model2026
Delivers image and video understanding plus a built-in event‑gated streaming gate — a unified 4B multimodal foundation model that uses codec-aligned tokenization to cut visual tokens by >75% and yield up to 3.5× wall‑clock inference speedup for streaming and long‑horizon video tasks.
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.