Modelscope: https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511

Lightning Model: https://huggingface.co/lightx2v/Qwen-Image-Edit-2511-Lightning

GGUF: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF

Abstract

Visual storytelling requires generating multi-shot videos with cinematic quality and long-range consistency. Inspired by human memory, we propose StoryMem, a paradigm that reformulates long-form video storytelling as iterative shot synthesis conditioned on explicit visual memory, transforming pre-trained single-shot video diffusion models into multi-shot storytellers. This is achieved by a novel Memory-to-Video (M2V) design, which maintains a compact and dynamically updated memory bank of keyframes from historical generated shots. The stored memory is then injected into single-shot video diffusion models via latent concatenation and negative RoPE shifts with only LoRA fine-tuning. A semantic keyframe selection strategy, together with aesthetic preference filtering, further ensures informative and stable memory throughout generation. Moreover, the proposed framework naturally accommodates smooth shot transitions and customized story generation applications. To facilitate evaluation, we introduce ST-Bench, a diverse benchmark for multi-shot video storytelling. Extensive experiments demonstrate that StoryMem achieves superior cross-shot consistency over previous methods while preserving high aesthetic quality and prompt adherence, marking a significant step toward coherent minute-long video storytelling.

Paper: https://arxiv.org/abs/2512.19539

Code: https://github.com/Kevin-thu/StoryMem

Model: https://huggingface.co/Kevin-thu/StoryMem

Project Page: https://kevin-thu.github.io/StoryMem/

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

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

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

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