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.
Recovers editable design files from raster images by growing an editable layer hierarchy via an agentic pipeline that selects and composes modality-specific tools. Introduces graceful verification (accept/prune/retry) to prevent error accumulation and presents the Figma Edit Replay Benchmark (909 files, 14,796 edits) to measure editability across layout, color, and text edits.
AI Image2026
Provides a single-file ComfyUI-compatible LoRA plus full-weight RMSNorm/modulation .diff deltas that reproduce a Krea 2 fine-tune when applied to Krea 2 checkpoints. Rank-256 adapters, ~1.88 GB, MIT-licensed.
Provides a unified multimodal framework for large-scale 3D understanding, text-to-3D generation, and instruction-guided 3D editing. Trains on an 87M-sample 3D multimodal corpus (25M understanding, 50M generation, 12M editing) and pairs a vision-language model with a diffusion-based 3D synthesizer to preserve structure and enable part-aware edits; suited for researchers building text-driven 3D asset pipelines but requires large compute and data.
AI Model2026
Provides ComfyUI-ready INT8 MiniMax‑H3 checkpoints (conditioning encoder plus optional generation tail) for a Heretic-edited Qwen3‑VL‑32B source; preserves the vision tower in BF16 and uses row-wise ConvRot INT8 quantization to reduce VRAM needs for ~32GB GPUs. Not a full Transformers generation repository.
AI Model2026
ComfyUI-ready H3 conditioning encoder builds for Qwen3-VL-32B: a BF16 full-precision checkpoint, an INT8 ConvRot quantized checkpoint, and an optional generation tail (layers 50–63). Retains vision tower in BF16 and targets H3 workflows and lower-VRAM systems.
AI Model2026
A quickly trained 2D "tine" VAE for MiniMax‑H3 that speeds up preview renders of video outputs and typically outperforms latent2rgb for preview use. Currently only compatible with the ModelPreviewOverride node in ComfyUI‑KJNodes and intended for previewing rather than production-grade decoding.
AI Agent Papers2026
Generates large-scale, explorable 3D open-world scenes from open-ended text prompts, producing editable instance-level assets and a consistent global terrain. Uses agentic planning to convert text into region/terrain/asset specifications and a coarse-to-fine pipeline for terrain construction, mesh reconstruction, and render-based refinement.
AI Agent Papers2026
Orchestrates reasoning, external tool use, and native image generation under one unified multimodal agent policy via post-training. Introduces RAD-GRPO for agentic reinforcement fine-tuning and releases training data plus the full post-training infrastructure.