<h3 id="2.2">2.2</h3>For I2V this motion helper node is extremely useful:<a target="_blank" rel="ugc" href="https://github.com/princepainter/ComfyUI-PainterI2V">https://github.com/princepainter/ComfyUI-PainterI2V</a>10/30 High lora was further refined.New I2V 1022 versions are out. They have by far the best prompt following / motion quality yet. (The lora key warning is fine, it just contains extra modulation keys that comfyui does not use. It does not matter.)<a target="_blank" rel="ugc" href="https://github.com/VraethrDalkr/ComfyUI-TripleKSampler">https://github.com/VraethrDalkr/ComfyUI-TripleKSampler</a>T2V versions just got updated 09/28. Probably still best to use a step or two with cfg / without the lora to establish motion with high noise as usual like:2 steps high noise without the low-step lora at 3.5 CFG2 steps high noise with lora and 1 CFG2-4 steps low noise with lora and 1 CFGIts definitely a big improvement either way.T2V:Using their full 'dyno' model for your high model seems best."On Sep 28, 2025, we released two models, Wan2.2-T2V-A14B-4steps-lora-250928 and Wan2.2-T2V-A14B-4steps-250928-dyno. The two models share the same low-noise weight. Wan2.2-T2V-A14B-4steps-250928-dyno delivers superior motion rendering and camera response, with object movement speeds that closely match those of the base model. For projects requiring highly dynamic visuals, we strongly recommend using Wan2.2-T2V-A14B-4steps-250928-dyno. Below, you will find some showcases for reference."<a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Wan2.2-Lightning/blob/main/Wan2.2-T2V-A14B-4steps-250928-dyno/Wan2.2-T2V-A14B-4steps-250928-dyno-high-lightx2v.safetensors">https://huggingface.co/lightx2v/Wan2.2-Lightning/blob/main/Wan2.2-T2V-A14B-4steps-250928-dyno/Wan2.2-T2V-A14B-4steps-250928-dyno-high-lightx2v.safetensors</a><hr /><h3 id="2.1">2.1</h3>7/15 update: I added the new I2V lora, seems to have much better motion than using the old text to video lora on a image to video model. Example is 4 steps, 1 CFG, LCM sampler, 8 shift. I uploaded the new version of the T2V one also.I'm also putting up the rank 128 versions extracted by Kijai, they are double the size but are slightly better quality.I suggest using it with the Pusa V1 lora as well, it seems to improve movement even more: <a target="_blank" rel="ugc" href="https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Pusa">https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Pusa</a> No need for 2 sampler WF anymore IMO. Just plug it into your normal WF with 1 CFG and 4 steps or so. Could prob sharpen it with another pass if you wanted but it no longer hurts the movement like before for image to video.Full Image to video Lightx2V model: <a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-StepDistill-CfgDistill-Lightx2v/tree/main/distill_models">https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-StepDistill-CfgDistill-Lightx2v/tree/main/distill_models</a><hr />Old: <a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill">lightx2v</a> made a 14B self forcing model that is a massive improvement compared to Causvid / Accvid. Kijai extracted it as a <a target="_blank" rel="ugc" href="https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors">lora</a>. Example above was generated in about 35 seconds on a 4090 using 4 steps, lcm, 1 cfg, 8 shift, still playing with settings to see what is best.<hr />Please don't send me buzz or anything, give the <a target="_blank" rel="ugc" href="https://github.com/ModelTC/lightx2v">lightx2v</a> team or <a target="_blank" rel="ugc" href="https://www.myminifactory.com/users/kijai">kijai</a> support if anyone.

Lightning Lora, massive speed up for Wan2.1 / Wan2.2 made by Lightx2v / Kijai

Wan21_CausVid_14B_T2V_lora_rank32.safetensors

Wan21_CausVid_14B_T2V_lora_rank32_v2.safetensors

Self-Forcing / CausVid / Accvid Lora, massive speed up for Wan2.1 made by Kijai

Causvid 14B V2

2.2-Lightning-I2V-1030-H

2.2-Lightning-I2V-1022-L

2.2-Lightning-I2V-1022-H

2.2 Lightning I2V H 2?

2.2-Lightning-T2V-0928-L

2.2-Lightning-T2V-0928-H

2.2 Lightning I2V L

2.2 Lightning I2V H

2.2 Lightning T2V L

2.2 Lightning T2V H

Self-Forcing 14B T2V R128

Self-Forcing 14B I2V R128

Self-Forcing 14B T2V

Self-Forcing 14B I2V

Self-Forcing 14B T2V V1

Accvid 14B I2V

Accvid 14B

Causvid 1.3B

Wan2.1-T2V-CausVid is an advanced text-to-video generation model built upon the Wan2.1-T2V foundation, enhanced with CausVid's causal diffusion approach. This integration enables the model to generate high-quality, temporally consistent videos from text prompts. By leveraging causal diffusion, our model excels at producing coherent long-form videos through an autoregressive generation process, addressing the temporal consistency limitations commonly found in traditional diffusion models. This approach also allows the model to generate videos with significantly fewer inference steps, substantially reducing video generation time while maintaining high quality outputs.

Wan21 CausVid LoRA

further pruned version with only attention layers and no first block, fixes flashing and retains motion better, needs more steps and can also benefit from cfg.

14B_V2

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adamshuo/Wan2.2-CausVid-10Steps-LoRA

v1.0

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