<h3 id="ooooh-minimax!">Ooooh, MiniMax! </h3>Same dataset as my earlier Wans, just trained on MiniMax H3! Disclaimer: I'm not overly impressed with T2V. Perhaps it needs to train more... I'll play around with it. Works great for I2V though._____<h3 id="wan-2.2!">Wan 2.2!</h3>Re-trained the dataset on Wan 2.2, make sure to grab BOTH Loras (High and Low)! Getting great results with @K3NK's workflow - <a target="_blank" rel="ugc" href="https://civarchive.com/models/1824027/wan-22-t2v-i2v4-stepskijais-wrapper-workflowk3nk">https://civarchive.com/models/1824027/wan-22-t2v-i2v4-stepskijais-wrapper-workflowk3nk</a> Update 6.17I realized while trying to improve this LoRA that I never uploaded a T2V version, so... here it is. Enjoy!-- -- --My second LoRA... I've made some drastic changes to my captioning approach, and the results show some nice improvement. Please share anything you make with this, I'd love to see some better prompts! This was trained on WAN 14B I2V, on 45 videos normalized to 480P / 24FPS and trimmed to 3 seconds using diffusion-pipe. However, it works okay with the T2V model, and I've included some examples from that. My captioning approach:<ol><li>45 videos, resized to 480P, 3 seconds, 24FPS.</li><li>Ran each one through <a target="_blank" rel="ugc" href="https://github.com/IuvenisSapiens/ComfyUI_Qwen2-VL-Instruct">ComfyUI_Qwen2-VL-Instruct</a> to generate a base video description, but this unfortunately doesn't pick up on any NSFW bits. This usually took a few tries on the same image, since the LLM almost seemed "disgusted" by the suggestion. :D</li><li>Grabbed my "favorite" frame and ran that through Joy Caption 2, then I manually combined the Qwen description and the Joy Caption Two caption to make the final .txt file.</li></ol>

BBC Ride

Behold, the T2V version that I never quite got around to baking all the way! Surprised how great it looks at 40 epochs, so I'm uploading. Enjoy!

Files with SHA256: e1932a184a34a0c9e2a57a3c1a0101cd9611be9550f34cf95271111d6f92a50d