AI Video2026
Generates minute-scale, temporally coherent dance videos from full music tracks using a hierarchical two-stage approach: global keyframe planning plus local temporal refinement; suitable when long-range musical structure and rhythmic continuity matter.
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 Video Papers2026
Generates interactive long-horizon 24-fps video worlds (540p/720p) from text, image, or video inputs. Uses a 15B video diffusion transformer with a bounded visual context (sink frame, compressed temporal history, geometry-aligned spatial memory, recent-frame conditioning) and a discrete autoregressive distillation that cuts inference to ~4 sampling steps per chunk.
AI Model2026
Generates and reasons about multimodal physical-world content—text, images, video and action trajectories—conditioned on text, images, video and robot/vehicle action inputs. An edge-sized (4B) Mixture‑of‑Transformers omni-model optimized for single‑GPU inference and Physical AI tasks (image→video, action prediction, robot policy).
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 Video Papers2026
Extrapolates long video sequences from very short contexts by restoring memory-writing supervision in autoregressive video diffusion models using a two-pass Self Gradient Forcing (SGF). SGF records a no-gradient rollout at a sampled denoising exit and then recomputes KV context in a second parallel pass so future losses teach earlier latent writes, enabling minutes-long extrapolation from ~5s windows.
AI Video2026
Generates synchronized audiovisual output from text, image, or audio prompts — a diffusion-based multimodal model with componentized weights (video/audio VAEs, multilingual text encoder, distilled transformer) and ready integration with HuggingFace pipelines and ComfyUI.
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 Video2026
Generates synchronized stereo audio and video from multimodal inputs (text, images, video, audio), producing 4–15s clips at 24 FPS with a 768p base and an in‑context regeneration path to 2K; supports first/last‑frame and multi‑reference modes and ships as two task‑specific checkpoints.
Learns a discrete “physical language” from unlabeled videos and uses a reason-then-render pipeline: predict compact state-transition tokens, then decode them into future video. Separates dynamics inference from pixel synthesis to improve physical fidelity, controllable simulation, and zero-shot motion transfer.
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.