Video: https://www.youtube.com/watch?v=3CdyGxvYeHo

Models: https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1

Code: https://github.com/aigc-apps/VideoX-Fun

Abstract

Recent visual generative models often struggle with consistency during image editing due to the entangled nature of raster images, where all visual content is fused into a single canvas. In contrast, professional design tools employ layered representations, allowing isolated edits while preserving consistency. Motivated by this, we propose \textbf{Qwen-Image-Layered}, an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers, enabling \textbf{inherent editability}, where each RGBA layer can be independently manipulated without affecting other content. To support variable-length decomposition, we introduce three key components: (1) an RGBA-VAE to unify the latent representations of RGB and RGBA images; (2) a VLD-MMDiT (Variable Layers Decomposition MMDiT) architecture capable of decomposing a variable number of image layers; and (3) a Multi-stage Training strategy to adapt a pretrained image generation model into a multilayer image decomposer. Furthermore, to address the scarcity of high-quality multilayer training images, we build a pipeline to extract and annotate multilayer images from Photoshop documents (PSD). Experiments demonstrate that our method significantly surpasses existing approaches in decomposition quality and establishes a new paradigm for consistent image editing. Our code and models are released on this https URL

Paper: https://arxiv.org/abs/2512.15603

Code: https://github.com/QwenLM/Qwen-Image-Layered

Blog: https://qwenlm.github.io/blog/qwen-image-layered/

Hugging Face: https://huggingface.co/Qwen/Qwen-Image-Layered

Demo: https://huggingface.co/spaces/Qwen/Qwen-Image-Layered

Modelscope: https://modelscope.cn/models/Qwen/Qwen-Image-Layered

Comfy-Org files: https://huggingface.co/Comfy-Org/Qwen-Image-Layered_ComfyUI/tree/main

GGUFs: https://huggingface.co/QuantStack/Qwen-Image-Layered-GGUF/tree/main

NewBie image Exp0.1 is a 3.5B parameter DiT model developed through research on the Lumina architecture. Building on these insights, it adopts Next-DiT as the foundation to design a new NewBie architecture tailored for text-to-image generation. The NewBie image Exp0.1 model is trained within this newly constructed system, representing the first experimental release of the NewBie text-to-image generation framework. Text Encoder

We use Gemma3-4B-it as the primary text encoder, conditioning on its penultimate-layer token hidden states. We also extract pooled text features from Jina CLIP v2, project them, and fuse them into the time/AdaLN conditioning pathway. Together, Gemma3-4B-it and Jina CLIP v2 provide strong prompt understanding and improved instruction adherence. VAE

Use the FLUX.1-dev 16channel VAE to encode images into latents, delivering richer, smoother color rendering and finer texture detail helping safeguard the stunning visual quality of NewBie image Exp0.1.

Checkpoint: https://huggingface.co/NewBie-AI/NewBie-image-Exp0.1

Lora Trainer: https://github.com/NewBieAI-Lab/NewbieLoraTrainer

Abstract

We introduce TurboDiffusion, a video generation acceleration framework that can speed up end-to-end diffusion generation by 100-200x while maintaining video quality. TurboDiffusion mainly relies on several components for acceleration: (1) Attention acceleration: TurboDiffusion uses low-bit SageAttention and trainable Sparse-Linear Attention (SLA) to speed up attention computation. (2) Step distillation: TurboDiffusion adopts rCM for efficient step distillation. (3) W8A8 quantization: TurboDiffusion quantizes model parameters and activations to 8 bits to accelerate linear layers and compress the model. In addition, TurboDiffusion incorporates several other engineering optimizations. We conduct experiments on the Wan2.2-I2V-14B-720P, Wan2.1-T2V-1.3B-480P, Wan2.1-T2V-14B-720P, and Wan2.1-T2V-14B-480P models. Experimental results show that TurboDiffusion achieves 100-200x speedup for video generation even on a single RTX 5090 GPU, while maintaining comparable video quality. The GitHub repository, which includes model checkpoints and easy-to-use code, is available at this https URL.

Paper: https://arxiv.org/pdf/2512.16093

Code: https://github.com/thu-ml/TurboDiffusion

Models: https://huggingface.co/TurboDiffusion

This model is a LoRA designed to repair the acceleration capability of Z-Image Turbo LoRA.

LoRAs directly trained on Z-Image Turbo lose their acceleration ability, resulting in blurry images when generated under accelerated settings (steps=8, cfg=1), while the images generated under non-accelerated settings (steps=30, cfg=2) are normal.

Github: https://github.com/modelscope/DiffSynth-Studio/blob/main/docs/en/Model_Details/Z-Image.md

Civitai Page: https://civitai.com/models/2213075/amazing-z-comics-workflow

Abstract

Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation. These models are typically built upon multi-step frameworks like diffusion and flow matching, which inherently limits their inference efficiency (requiring 40-100 Number of Function Evaluations (NFEs)). While various few-step methods aim to accelerate the inference, existing solutions have clear limitations. Prominent distillation-based methods, such as progressive and consistency distillation, either require an iterative distillation procedure or show significant degradation at very few steps (< 4-NFE). Meanwhile, integrating adversarial training into distillation (e.g., DMD/DMD2 and SANA-Sprint) to enhance performance introduces training instability, added complexity, and high GPU memory overhead due to the auxiliary trained models. To this end, we propose TwinFlow, a simple yet effective framework for training 1-step generative models that bypasses the need of fixed pretrained teacher models and avoids standard adversarial networks during training, making it ideal for building large-scale, efficient models. On text-to-image tasks, our method achieves a GenEval score of 0.83 in 1-NFE, outperforming strong baselines like SANA-Sprint (a GAN loss-based framework) and RCGM (a consistency-based framework). Notably, we demonstrate the scalability of TwinFlow by full-parameter training on Qwen-Image-20B and transform it into an efficient few-step generator. With just 1-NFE, our approach matches the performance of the original 100-NFE model on both the GenEval and DPG-Bench benchmarks, reducing computational cost by x100 with minor quality degradation. Project page is available at this https URL.

They are also working on Z-Image-Turbo to make it faster.

Paper: https://arxiv.org/abs/2512.05150

Code: https://github.com/inclusionAI/TwinFlow

Project Page: https://zhenglin-cheng.com/twinflow