Discord Server: https://discord.gg/EwKE8KBDqD

Abstract

While 10B-level industrial foundation models have pushed the boundaries of image inpainting, their prohibitive computational costs severely hinder practical deployment. Constructing a highly optimized task-specific specialist offers a promising solution; however, extreme structural compression inevitably triggers a severe representation bottleneck. To conquer this, we propose Moebius, a highly efficient lightweight inpainting framework. We systematically reconstruct the diffusion backbone by introducing the Local-λ Mix Interaction (LλMI) block. Comprising Local-λ and Interactive-λ modules, it elegantly summarizes spatial contexts and global semantic priors into fixed-size linear matrices, preserving complex latent interactions while drastically shedding parameters. Furthermore, to unlock the full representational capacity of this highly compact architecture, we synergistically pair it with an adaptive multi-granularity distillation strategy. Operating strictly within the latent space to avoid expensive pixel-space decoding, this strategy dynamically balances multiple gradient-based losses to achieve high-fidelity alignment. Extensive experiments across natural and portrait benchmarks demonstrate that this optimal synergy enables Moebius to rival or even surpass the generation quality of the 10B-level industrial generalist FLUX.1-Fill-Dev. Remarkably, Moebius achieves this using less than 2% of the parameters (0.22B vs. 11.9B) while delivering a >15× acceleration in total inference time, setting a new efficiency standard for high-fidelity inpainting.

Paper: https://arxiv.org/abs/2606.19195

Code: https://github.com/hustvl/Moebius

Weights: https://huggingface.co/hustvl/Moebius

Project Page: https://hustvl.github.io/Moebius/

Release: https://github.com/rupeshs/fastsdcpu/releases/tag/v1.0.0-beta.500

Workflows: https://github.com/WhatDreamsCost/WhatDreamsCost-ComfyUI/tree/main/example_workflows

Technical report: https://www.krea.ai/blog/krea-2-technical-report

Code: http://github.com/krea-ai/krea-2

Code and weights: http://krea.ai/krea-2-open-source

Hugging Face: huggingface.co/krea/Krea-2-Raw, huggingface.co/krea/Krea-2-Turbo

Video: https://www.youtube.com/watch?v=vGxZIS4J0Mg

Boogu-Image-0.1 is a competitive Apache-2.0 open-source unified image generation and editing model family, including Base, Turbo, Edit, and other variants that provide stable, practical capabilities for high-quality text-to-image generation, fast generation, image editing, and Chinese-English text rendering. Closed-source multimodal understanding and generation systems like Nano Banana Pro and GPT-Image-2 achieve remarkable performance not because of a single model, but through a highly unified suite of system capabilities. However, under training compute that is extremely limited compared with closed-source systems, we find that systematically improving a model's understanding ability, data quality, and training pipeline can still significantly improve image generation and editing performance. Specifically, compared with some existing open-source models, our training data scale is roughly one order of magnitude smaller. We hope our empirical study and open-source release will help advance the open-source ecosystem for multimodal generation and understanding.

Techical Report: (Coming Soon)

Code: https://github.com/boogu-project/Boogu-Image

Models: https://huggingface.co/Boogu

Project Page: https://boogu.org/

Workflow: https://civitai.com/models/2707066/scail-2-unlimited-length-workflow-and-nodes

Higgs Audio v3 Tags: https://github.com/diodiogod/TTS-Audio-Suite/blob/main/docs/HIGGS_AUDIO_V3_INLINE_TAGS.md

Discord Server: https://discord.gg/EwKE8KBDqD

Abstract

Few-step diffusion distillation has become increasingly mature for 4-8-step generation, yet pushing further to 2 steps remains challenging. In this work, we introduce Z-Image Turbo++, a high-quality 2-step image generation model distilled from the 8-step Z-Image Turbo teacher. Our method addresses the central bottlenecks of increased task difficulty and limited model capacity in 2-step generation through three simple but effective design choices tailored to this regime. First, we propose Distribution-Aligned Adversarial Learning, which uses teacher-generated images rather than external real images as real samples for GAN training, providing a more attainable and informative adversarial target. Second, we adopt Step-Decoupled Parameterization, assigning independent model parameters to the two denoising steps to better match their distinct capacity demands. Third, we perform End-to-End Training with Iterative Regularization, allowing the first step to receive gradients from final image quality while preserving a meaningful intermediate generation through an explicit step-1 loss. Together, these designs substantially narrow the quality gap between 2-step and 8-step generation in both qualitative and quantitative evaluations, highlighting the potential of carefully tailored distillation strategies for improving the quality-efficiency trade-off in few-step generation.

Paper: https://arxiv.org/abs/2606.12575

Abstract

Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation. Even the latest open-source Unified Multimodal Models (UMMs) exhibit limited performance in this regard. In this paper, we introduce InterleaveThinker, the first multi-agent pipeline designed to endow any existing image generator with interleaved generation capabilities. Specifically, we employ a planner agent to organize the image-text input sequence, instructing the image generator on the required execution at each step. Subsequently, we introduce a critic agent to evaluate the generator's outputs, identify samples that deviate from the planned instructions, and refine the instructions for regeneration. To implement this pipeline, we construct the Interleave-Planner-SFT-80k and Interleave-Critic-SFT-112k to perform a format cold-start. Then we develop Interleave-Critic-RL-13k to reinforce the step-wise instruction correction capability within a generation trajectory using GRPO. Since a single interleaved generation trajectory may involve over 25 generator calls, optimizing the entire trajectory is computationally impractical. Therefore, we propose accuracy reward and step-wise reward, allowing single-step RL to effectively guide the entire generation trajectory. The results show that InterleaveThinker improves performance across various image generators. On interleaved generation benchmarks, it achieves performance comparable to Nano Banana and GPT-5. Surprisingly, it also significantly enhances the base model on reasoning-based benchmarks; for example, on 4-step FLUX.2-klein, we observe substantial gains on WISE and RISE.

Paper: https://arxiv.org/pdf/2606.13679

Code: https://github.com/zhengdian1/InterleaveThinker

Project Page: https://zhengdian1.github.io/InterleaveThinker-proj/

Models:

• https://huggingface.co/InterleaveThinker/InterleaveThinker-Planner-8B

• https://huggingface.co/InterleaveThinker/Critic-SFT-8B

• https://huggingface.co/InterleaveThinker/InterleaveThinker-Critic-8B

Abstract

Controlled character animation requires transferring motion from a driving sequence to a reference character. Prior works heavily rely on intermediate representations — including pose skeletons to represent motion or masked background to represent environment — which inevitably leads to information loss. Skeleton maps suffer from inherent ambiguity under complex scenarios; character masks limit body-shape flexibility; and depth-ambiguous overlapping skeletons cause misinterpretation in multi-character interactions.

To address this, we present SCAIL-2, a framework that bypasses those intermediates and achieves end-to-end character animation. By directly concatenating driving videos latents to the sequence, the model obtains all required visual information from the input. To overcome the lack of end-to-end data, we unify sub-tasks of character animation with decoupled conditions and curate a pipeline to synthesize MotionPair-60K — a heterogeneous dataset of 60K motion pairs spanning animation, replacement, and multi-character tasks. We introduce in-context mask conditioning and mode-specific RoPE as unified soft guidance. To mitigate synthetic-data bias in detailed regions (e.g. fingers), we propose Bias-Aware DPO for post-training refinement. Extensive experiments demonstrate that SCAIL-2 substantially outperforms existing state-of-the-art approaches across all tasks, while unlocking emerging zero-shot capabilities such as animal-driven animation and mesh-based control.

Paper: (coming soon)

Code: https://github.com/zai-org/SCAIL-2

Model: https://huggingface.co/zai-org/SCAIL-2

Repackaged Models for ComfyUI: https://huggingface.co/Comfy-Org/SCAIL-2/tree/main/diffusion_models

Project Page: https://teal024.github.io/SCAIL-2/