🔥 SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer

💡 The paper introduces SANA-Video, a small diffusion model designed for efficient video generation. The problem addressed is the high cost and slow speed of existing video generation models. To solve this, the authors propose two core designs: Linear DiT, which leverages linear attention as the core operation, and a constant-memory KV cache for block linear attention. This cache provides global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient long video generation.

The method used is a block-wise autoregressive approach for long video generation, which employs a constant-memory state derived from the cumulative properties of linear attention. The authors also explore effective data filters and model training strategies, which narrow the training cost to 12 days on 64 H100 GPUs, a significant reduction compared to other models.

The results show that SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models, while being 16 times faster in measured latency. The model can generate high-resolution, high-quality videos up to 720x1280 resolution and minute-length duration at a remarkably fast speed. Additionally, SANA-Video can be deployed on RTX 5090 GPUs, accelerating the inference speed of generating a 5-second 720p video from 71 seconds to 29 seconds, a 2.4 times speedup. Overall, SANA-Video enables low-cost, high-quality video generation, making it a significant contribution to the field of video generation.

📅 Published on Sep 29, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2509.24695
• PDF: https://arxiv.org/pdf/2509.24695
• Project Page: https://nvlabs.github.io/Sana/Video

🤖 Models citing this paper:
• https://huggingface.co/Efficient-Large-Model/SANA-Video_2B_720p
• https://huggingface.co/Efficient-Large-Model/SANA-Video_2B_480p
• https://huggingface.co/Efficient-Large-Model/SANA-Video_2B_480p_diffusers

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/helenai/check-optimum-intel-support

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📢 By: https://xn--r1a.website/PaperNexus

#VideoGenerationModels #DiffusionTransformer #BlockLinearAttention #EfficientVideoProcessing #AutoregressiveVideoGeneration

🔥 LoomVideo: Unifying Multimodal Inputs into Video Generation and Editing

💡 The paper presents LoomVideo, a unified architecture for video generation and editing that efficiently handles multimodal inputs. The problem addressed is that existing unified frameworks for video generation and editing rely on large models with high computational overhead, typically requiring 13 billion parameters or more. These models often incorporate source video conditions for editing by concatenating sequence tokens, which doubles the sequence length and quadruples the computational complexity of the self-attention mechanism.

To address this issue, LoomVideo introduces a novel 5 billion parameter architecture that replaces the standard text encoder with a Multimodal Large Language Model and employs a Deepstack injection mechanism to align multi-layer features with the Diffusion Transformer. The key contribution is the introduction of a zero-overhead Scale-and-Add conditioning approach for video editing, which eliminates the need for token concatenation and reduces computational cost. This approach scales and directly adds the clean source video latent to the noised target latent, allowing for complex and non-rigid edits.

Additionally, the model incorporates a Negative Temporal RoPE strategy to handle multiple reference images. The results demonstrate that LoomVideo achieves state-of-the-art or highly competitive performance across comprehensive benchmarks, with exceptional performance in e-commerce and fashion generation scenarios. The zero-overhead conditioning mechanism enables LoomVideo to achieve at least a 5.41x acceleration in inference speed compared to models of similar capabilities, making it a highly practical and efficient video foundation model. Overall, LoomVideo presents a significant advancement in unified video generation and editing models, offering a more efficient and effective solution for handling multimodal inputs.

📅 Published on Jun 4

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.06042
• PDF: https://arxiv.org/pdf/2606.06042
• Project Page: https://msalab-pku.github.io/projects/LoomVideo/index.html

🤖 Models citing this paper:
• https://huggingface.co/MSALab/LoomVideo

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📢 By: https://xn--r1a.website/PaperNexus

#MultimodalVideoGeneration #VideoEditingArchitecture #UnifiedVideoFrameworks #MultimodalLargeLanguageModels #EfficientVideoProcessing