AI Model2026
Instruction-driven LoRA fine‑tune for identity‑preserving image edits: give an image plus a plain‑language instruction and it edits pose, outfit, objects or scene while keeping unasked content and subject likeness. Requires the ComfyUI‑Krea2Edit node pack; distributed under the Krea 2 Community License.
AI Model2026
Provides GGUF-quantized builds of the Qwythos-9B-v2 LLM for local runtimes, with multiple quant levels, optional MTP-enabled variants, a 1,048,576-token context window, and an optional BF16 vision projector for multimodal use.
Reconstructs 4D dynamic human scenes from sparse, low-overlap multi-camera captures by decoupling background synthesis and human modeling. Synthesizes hundreds of camera-controlled background views with a video diffusion model, initializes deformable Gaussian humans via cross-view identity and triangulated keypoints, then applies motion-adaptive recursive enhancement to reduce artifacts.
AI Model2026
A GGUF-distributed Qwen3.6 35B MoE model variant repaired with a
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.