FineView 欢视 | VIDEO H3&LTX

本页面右侧下载列表13.31G Safetensors 格式，workflow 在 Trainning data 压缩包内In the download list on the right, there are 4 files, the fourth of which is a compressed file called "training data". In CivitAI, if you really don't want to throw sample images or videos into ComfyUI to obtain workflows, you can download this compressed file and get the component nodes and sample workflows synchronized with Github.This is a T2I model, CausVid DMD2 FT for WAN 14B,which can be FAST generated in just 6-16 steps.also supports input images as reference frames也就是说，一个模型通吃所有生成场景！That is to say, one model can handle all generated scenarios![ VACE technology adapted to T2V model, And VACE also supports input images as reference frames, whether at the beginning or ending frame ][The adaptability test results of WAN1.2 LoRAs for VACE show that about 75% of I2V/T2V LoRA weights can take effect, but the sensitivity is reduced ( try to increase the LoRA weight ,more than 100% Sometimes it can be helpful ) ]---这是一个T2I模型，适用于WAN 14B的CausVid DMD2 FT，只需6-16步即可快速生成。[ VACE技术适用于T2V型号，VACE还支持输入图像作为参考帧，无论是在开始帧还是结束帧 ]---5/15 REDCausVid-Wan2-1-14B-DMD2-FP8 Uploaded8-15 steps CFG 1 Best Adaptation for WAN-VACE5/15 <a target="_blank" rel="ugc" href="https://civitai.com/user/Aiwood/models">Aiwood</a> WAN-ACE Fully functional workflow Uploaded5/15 ComfyUI <a target="_blank" rel="ugc" href="https://github.com/kijai/ComfyUI-WanVideoWrapper">KJ-WanVideoWrapper</a> have been updated5/14 autoregressive_checkpoint.pt 1.3b Uploaded5/14 bidirectional_checkpoint2.pt 1.3b Uploaded---NEW workflow<a target="_blank" rel="ugc" href="https://civitai.com/user/Aiwood/models"> Aiwood</a> WAN-ACE Fully functional workflowsource: <a target="_blank" rel="ugc" href="https://www.bilibili.com/video/BV1FGE6zGEDK">https://www.bilibili.com/video/BV1FGE6zGEDK</a> ⭐ leave a star⭐---NEW Sampler Flowmatch_causvid in KJ-WanVideoWrapperReleases from：<a target="_blank" rel="ugc" href="https://github.com/kijai/ComfyUI-WanVideoWrapper">kijai/ComfyUI-WanVideoWrapper </a>⭐ leave a star⭐---项目页 <a target="_blank" rel="ugc" href="https://causvid.github.io/">https://causvid.github.io/</a> 🔥 多图警告！AbstractCurrent video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. The generation of a single frame requires the model to process the entire sequence, including the future. We address this limitation by adapting a pretrained bidirectional diffusion transformer to an autoregressive transformer that generates frames on-the-fly. To further reduce latency, we extend distribution matching distillation (DMD) to videos, distilling 50-step diffusion model into a 4-step generator. To enable stable and high-quality distillation, we introduce a student initialization scheme based on teacher's ODE trajectories, as well as an asymmetric distillation strategy that supervises a causal student model with a bidirectional teacher. This approach effectively mitigates error accumulation in autoregressive generation, allowing long-duration video synthesis despite training on short clips. Our model achieves a total score of 84.27 on the VBench-Long benchmark, surpassing all previous video generation models. It enables fast streaming generation of high-quality videos at 9.4 FPS on a single GPU thanks to KV caching. Our approach also enables streaming video-to-video translation, image-to-video, and dynamic prompting in a zero-shot manner.⚠️ This repo is a work in progress. Expect frequent updates in the coming weeks.@inproceedings{yin2025causvid,title={From Slow Bidirectional to Fast Autoregressive Video Diffusion Models},author={Yin, Tianwei and Zhang, Qiang and Zhang, Richard and Freeman, William T and Durand, Fredo and Shechtman, Eli and Huang, Xun},booktitle={CVPR},year={2025}}@inproceedings{yin2024improved,title={Improved Distribution Matching Distillation for Fast Image Synthesis},author={Yin, Tianwei and Gharbi, Micha{\"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, William T},booktitle={NeurIPS},year={2024}}@inproceedings{yin2024onestep,title={One-step Diffusion with Distribution Matching Distillation},author={Yin, Tianwei and Gharbi, Micha{\"e}l and Zhang, Richard and Shechtman, Eli and Durand, Fr{\'e}do and Freeman, William T and Park, Taesung},booktitle={CVPR},year={2024}}Acknowledgments<a target="_blank" rel="ugc" href="https://huggingface.co/tianweiy/CausVid">CausVid</a> implementation is largely based on the <a target="_blank" rel="ugc" href="https://github.com/Wan-Video/Wan2.1">Wan</a> model suite.⭐ leave a star⭐

onTHEFLYWanAIWan21VideoModel_causvid13bOnTheFly_trainingData.zip

fineviewVIDEOH3LTX_causvid14bVACE_full_fp16.pt

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