AI Model2026
Unified text-to-image and prompt-based image editing model that natively outputs RGBA and accepts up to 10 reference images. Compact 7B visual generator optimized for 2K outputs and efficient inference; research-licensed under the Qwen Research License.
AI Model2026
Generates complete UI, poster and infographic layouts from text prompts, rendering legible in-image text and producing native RGBA output with transparent backgrounds. Suited for text-rich visual design workflows and pairs with a companion model that decomposes designs into editable layers.
AI Image2026
A distilled LoRA adapter for Qwen-Image-2.1 that runs text-to-image generation and instruction-driven image editing in a few transformer passes (shipped as a 6-step r256 LoRA). Samples with a fixed sigma schedule, no classifier-free guidance; non-commercial research license.
Jaihyun Lew, Mingi Jung +3Interdisciplinary Program in AI, Seoul National University, Department of Electrical and Computer Engineering, Seoul National University +1
Uses diffusion-model forking moments as a proxy for perceptual distance to automatically generate pointwise reference-grounded labels, enabling annotation-free training of reference-based image quality assessment metrics.
AI Video Papers2026
Surveys memory mechanisms for autoregressive video generation, framing memory as persistent historical information that influences future generation. Organizes work by Forms, Functions, Operations, Learning, and Evaluation, and synthesizes challenges for long-horizon consistency and memory-aware learning.
AI Video2026
Enables native joint video-and-audio generation and training up to 2K using a dynamic block-sparse attention scheme that cuts attention cost while preserving audio–visual coupling; provides preview checkpoints, training and inference code. High-resolution training requires large multi-GPU (80GB) clusters and CUDA ≥ 12.4.
Trains decoders and diffusion generators to be robust to random subsets of pretrained encoder layers by regularizing layer-fusion during training, narrowing the reconstruction–generation gap in representation autoencoders and improving ImageNet-256 PSNR and gFID.
Arseny Ivanov, Alexander Kolesov +3Affiliation: AXXX, Moscow, Russia, Affiliation: Applied AI Institute, Moscow, Russia
Models sequence generation by unmasking multiple tokens per denoising step and replaces a factorized reverse process with a mixture over discrete routing-based latents from an MoE backbone; improves few-step sampling quality without increasing active parameters.
AI Video Papers2026
Songlin Yang, Xiaotong Zhao +6MMLab@HKUST, The Hong Kong University of Science and Technology, Tencent Video +1
Proposes a forward-process RL method that dynamically localizes gradient updates and adapts multi-reward coordination for joint audio–video diffusion models. Uses bidirectional cross-attention responses for token/layer routing and preference-preserving reweighting to balance competing objectives, improving modality quality, alignment, and synchronization.
Couples a continuous latent diffusion trajectory with discrete token readouts so tokens are read from the latent at every step and fed back as a scaffold, reducing token-independence in parallel decoding and improving structured-task accuracy and LM perplexity.
Introduces a token-adaptive latent recurrence for diffusion language models that allocates computation per token during denoising. Key features: iterative latent refinement, discrete-feedback commits, and learned token-wise schedules to improve parallel decoding quality-efficiency trade-offs.
AI Video Papers2026
Generates 5-second text- or image-conditioned videos with synchronized 44 kHz audio (including lip-sync) and built-in super-resolution to 1920×1080; available in Lite (3B) and Pro (29B) variants with code and checkpoints released under an MIT license.