Abstract

Long video generation still suffers from error accumulation, weak temporal coherence, and prohibitive latency, limiting its applicability to interactive scenarios. We present JoyAI-Echo, a framework that breaks these barriers through four key advances. Central to its performance, a cross-modal audio-visual memory bank preserves character appearance and voice timbre consistently over five-minute videos, while a post-training pipeline combines memory-based reinforcement learning with distribution matching distillation for a 7.5× speedup to substantially boost visual quality and alignment. Empowered by these two components, JoyAI-Echo decisively outperforms HappyOyster (directing mode) on long-form generation and even surpasses the short-video specialist Wan 2.6 on human-centric tasks. Beyond raw generation quality, an interactive agent enables real-time user editing through conversational instructions, and a lightweight super-resolution module maintains high definition under streaming latency, further elevating the overall experience and delivering instantly editable, conversation-speed video creation. For the first time, JoyAI-Echo simultaneously achieves long-range cross-modal consistency, real-time inference for minute-long video, conversational interactivity, and high-resolution output — without compromise, inaugurating a new era of interactive video generation. Codes and weights will be open-sourced.

Paper: https://www.researchgate.net/publication/405770309_JoyAI-Echo_Pushing_the_Frontier_of_Long_Audio-Visual_Generation

Code: https://github.com/jd-opensource/JoyAI-Echo

Hugging Face: https://huggingface.co/jdopensource/JoyAI-Echo

Project Page: https://echo-team-joy-future-academy-jd.github.io/Echo-LongVideo-Page/

Abstract

Line drawings are a highly expressive art form that requires the artist to abstract and distill the essence of their subject. We present the first semantics-driven method for automatically generating single-line drawings in vector format, guided either by a text prompt describing the concept or an input image depicting it. Our approach leverages score distillation sampling to optimize the parameters of a uniform rational B-spline (URBS) curve, ensuring that the drawing consists of a single continuous stroke by design. This representation provides fine-grained control over the level of detail, while additional loss terms allow us to steer the final artistic style. We demonstrate that our method outperforms state-of-the-art text-to-image models and optimization pipelines for this task, producing results that are both more aesthetically pleasing and more faithful to the style of continuous line drawing artists. Furthermore, because our method generates a vectorized curve, it directly supports downstream fabrication processes such as embroidery, laser engraving and wire bending. Our code and results are available at https://github.com/tanguymagne/SLDgen.

Paper: https://igl.ethz.ch/projects/sldgen/

Code: https://github.com/tanguymagne/SLDgen

Abstract

Latent-space modeling has been the standard for Diffusion Transformers (DiTs). However, it relies on a two-stage pipeline where the pretrained autoencoder introduces lossy reconstruction, leading to error accumulation while hindering joint optimization. To address these issues, we propose PixelDiT, a single-stage, end-to-end model that eliminates the need for the autoencoder and learns the diffusion process directly in the pixel space. PixelDiT adopts a fully transformer-based architecture shaped by a dual-level design: a patch-level DiT that captures global semantics and a pixel-level DiT that refines texture details, enabling efficient training of a pixel-space diffusion model while preserving fine details. PixelDiT achieves 1.61 FID on ImageNet 256 and 1.81 FID on ImageNet 512, surpassing existing pixel generative models. We further extend PixelDiT to text-to-image generation and pretrain it at the 10242resolution in pixel space. It achieves 0.74 on GenEval and 83.5 on DPG-bench, approaching the best latent diffusion models. Code: this https URL

Project page: https://pixeldit.github.io/

Paper: https://arxiv.org/abs/2511.20645

Github page: https://github.com/NVlabs/PixelDiT

HuggingFace (diffusers): https://huggingface.co/nvidia/PixelDiT-1300M-1024px ComfyUI version: https://huggingface.co/Comfy-Org/PixelDiT

Workflow: https://github.com/Comfy-Org/ComfyUI/pull/14103 (first comment)

Abstract

Users increasingly expect image generation models to quickly adapt to highly diverse and personalized requirements, such as producing images with distinctive styles or characteristics. Traditional approaches rely on fine-tuning, which is costly and difficult to scale. To cope with these limitations, the community has accumulated a growing library of fine-tuned modules and adapters, where each component targets specific generation needs and collectively serves as a foundation for handling new demands. This naturally raises a question: instead of repeatedly training new models, can we systematically exploit this expanding ecosystem to better fulfill user instructions? To this end, we present Polaris, an intelligent retrieval framework that automatically selects and integrates suitable models from the model library based on a user's instructions. The key insight is that harnessing such a massive and heterogeneous pool requires not only finding the most relevant modules among thousands of candidates, but also aligning them effectively for instruction-driven generation and editing. Polaris addresses this challenge by indexing over 6,500 checkpoints and 75,000 adapters, and retrieving the most relevant components given a user's input and instruction. In doing so, it delivers scalable, controllable, and well-aligned generation -- without any additional training.

Paper: https://arxiv.org/abs/2606.01858

Abstract

Generative distillation significantly accelerates text-to-image (T2I) generation by compressing multi-step trajectories into few-step student models while preserving perceptual quality. However, existing methods primarily optimize efficiency and output fidelity, often neglecting critical properties of the original trajectory. In this work, we identify a key missing property: sensitivity to initial noise, whose degradation impairs downstream control methods relying on noise-based optimization and manipulation. We trace this issue to standard distillation objectives that enforce pointwise output alignment, inadvertently flattening the input-output landscape and suppressing the teacher's local geometric structure. To address this, we propose Geometry-Aware Distillation (GAD), a sensitivity-preserving framework that aligns the local functional behavior of teacher and student models. Specifically, GAD matches Jacobian-vector products with respect to input noise, enabling the student to reproduce the teacher's differential response to perturbations. Extensive experiments across multiple T2I paradigms and noise-driven control tasks demonstrate that GAD significantly restores sensitivity and improves diversity while maintaining high visual fidelity. Code is available at this https URL.

Paper: https://arxiv.org/abs/2606.01651

Code: https://github.com/Hannah1102/GAD

Abstract

Text-to-Image (TTI) systems are now everyday infrastructure for journalism, education, advertising, and public communication, and the demographic and cultural stereotypes they inherit from training data (rendering women, people of colour, older adults, and non-Western cultures as under-represented or caricatured) become a population-level harm at deployment scale. Existing mitigations either require costly retraining, infeasible for the closed-source backbones that dominate consumer products, or rely on fixed demographic templates that ignore cultural context. We present KG-FairDiff, a model-agnostic, inference-time framework that formalises fairness-aware prompt refinement as a constrained optimisation problem and operationalises it as a closed-loop pipeline: a knowledge graph of ~1,200 culture- and bias-related triples retrieves structured context, an LLM rewriter proposes refinements, and a validator accepts only prompts that reduce a divergence-based fairness loss while preserving semantic fidelity to the user's original intent. We prove a finite-termination bound for the refinement loop, contribute a mathematically consistent evaluation suite linking Bias-P/Bias-W to divergence from target distributions and ENS to KL divergence, and audit eight widely-deployed backbone generators. KG-FairDiff substantially reduces gender, race, age, and intersectional disparities while preserving prompt semantics, offering a practical, deployment-ready route to more equitable generative AI.

Abstract

Self-supervised novel view synthesis (NVS) remains challenging to scale, despite the abundance of video data, largely due to the brittleness of training on realistic videos and the hard-to-predict scaling behavior of multi-network system designs. We introduce RayDer, a unified, feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering into a single backbone, turning self-supervised NVS into a well-posed single-model scaling problem. A minimal dynamic state, treated as a nuisance factor, absorbs time-varying content and enables stable training on unconstrained real-world video. Importantly, RayDer keeps static-scene NVS as its target task: dynamic content is leveraged purely as scalable supervision, not reconstructed as in dynamic-scene (4D) NVS. Across multiple model sizes and orders of magnitude in data, RayDer exhibits clean power-law scaling with data and compute, and outperforms static-scene data mixtures. On a large number of benchmarks, RayDer achieves strong zero-shot open-set performance competitive with state-of-the-art supervised approaches. Project Page: this https URL

Paper: https://arxiv.org/abs/2605.31535

Code: https://github.com/CompVis/rayder

Weights: https://huggingface.co/CompVis/rayder

Project Page: https://compvis.github.io/rayder

Abstract

Precise camera pose control is critical for video diffusion, yet maintaining geometric consistency remains a challenge. Existing methods that directly inject numerical camera parameters into the diffusion backbone often fail to bridge the gap between abstract coordinates and visual content, leading to structural distortions. To address this issue, we propose CameraNoise, a flow-to-noise warping method that encodes camera motion into a temporally coherent stochastic representation. Unlike conventional conditioning, CameraNoise embeds camera poses directly into the noise space. This decouples motion from scene appearance while faithfully preserving trajectory dynamics. Specifically, we introduce a novel Geometry-guided Reprojection Flow and a noise warping algorithm, which jointly preserve the Gaussian prior of diffusion and ensure consistent noise propagation under camera transformations. By integrating CameraNoise into the diffusion process, our framework delivers stable, high-fidelity videos. Extensive experiments demonstrate that our approach significantly outperforms prior methods in both visual quality and trajectory faithfulness. The project page and code are available at: this https URL.

Paper: https://arxiv.org/abs/2605.30774

Code: https://github.com/gulucaptain/CameraNoise

Weights: https://huggingface.co/gulucaptain/CameraNoise-I2V

Project Page: https://gulucaptain.github.io/CameraNoise/

Whitepaper: https://github.com/PrismML-Eng/Bonsai-Image-Demo/blob/main/bonsai-image-4b-whitepaper.pdf

Bonsai Studio for iPhone: https://apps.apple.com/us/app/bonsai-studio-by-prismml/id6767042620

Models: https://huggingface.co/collections/prism-ml/bonsai-image

WebGPU demo: https://huggingface.co/spaces/webml-community/bonsai-image-webgpu

Bonsai Image demo: https://huggingface.co/spaces/prism-ml/Bonsai-Image-Demo

Code: https://github.com/PrismML-Eng/Bonsai-image-demo

Abstract

We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate lan- guage, image, video, audio, and action sequences within a unified mixture-of-transformers architecture. By supporting highly flexible input-output configurations, Cosmos 3 seamlessly unifies critical modalities for Physical AI—effectively subsuming vision-language models, video generators, world simulators, and world-action models into a single framework. Our evaluation demonstrates that Cosmos 3 establishes a new state-of-the-art across a diverse suite of understanding and generation tasks, demonstrating omnimodal world models as scalable, general-purpose backbones for embodied agents. Our post-trained Cosmos 3 models were ranked as the best open-source Text-to-Image and Image-to-Video models by Arti- ficial Analysis, and the best policy model by RoboArena at the time the technical report was written. To accelerate open research and deployment in Physical AI, we make our code, model checkpoints, curated synthetic datasets, and evaluation benchmark available under the Linux Foundation’s OpenMDW-1.1 License at github.com/nvidia/cosmos and huggingface.co/collections/nvidia/cosmos3 . The project website is available at research.nvidia.com/labs/cosmos-lab/cosmos3 .

Paper: https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf

Code: https://github.com/nvidia/cosmos

Model Collection: https://huggingface.co/collections/nvidia/cosmos3

Project Page: https://research.nvidia.com/labs/cosmos-lab/cosmos3/

Abstract

Multimodal large language models (MLLMs) and diffusion models have each reached remarkable maturity: MLLMs excel at reasoning over heterogeneous multimodal inputs with strong semantic grounding, while diffusion models synthesize images and videos with photorealistic fidelity. We argue that these two families can be unified through a simple division of labor: MLLMs perform semantic planning, while diffusion models render pixels from high-level semantic guidance and low-level visual features. Building on this idea, we propose Bernini, a unified framework for video generation and editing. An MLLM-based planner predicts the target semantic representation directly in the ViT embedding space, and a DiT-based renderer synthesizes pixels conditioned on this plan, augmented by text features and, for editing, source VAE features for detail preservation. Because semantics serve as the interface, the planner and renderer can be trained separately and only lightly co-trained, preserving the pretrained strengths of both components while keeping training efficient. To better handle multiple visual inputs, we introduce Segment-Aware 3D Rotary Positional Embedding (SA-3D RoPE), and further incorporate chain-of-thought reasoning in the planner to better transfer understanding into generation. Bernini achieves state-of-the-art performance across a wide range of video generation and editing benchmarks, with the MLLM's pretrained understanding translating into strong generalization on challenging editing tasks.

Paper: https://arxiv.org/abs/2605.22344

Model: https://huggingface.co/ByteDance/Bernini/tree/main

Project Page: https://bernini-ai.github.io/

Model: https://huggingface.co/nomadoor/flux-2-klein-9B-schematic-lora

Dataset: https://huggingface.co/datasets/nomadoor/flux-2-klein-9B-schematic-dataset

Injects reference image features into Anima's DiT via decoupled cross-attention, enabling character-consistent image generation.

Use with LTX-2.3 IC workflows to add color to black and white footage. It is not perfect by any means but for the best results I recommend to: