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Wan2.1 14B I2V 480p v1.0:

Trained on 30 seconds of video comprised of 12 short clips (each clip captioned separately) of things being rotated 360 degrees. This was trained on the Wan.21 14B I2V 480p model.

The trigger word is: 'r0t4tion 360 degrees rotation'

See below for some prompt examples that worked well for me. You can also check the videos I've posted here for the captions that were used to generate them. For each video the input image is just the first frame.

Recommended Settings:

LoRA strength = 1.0

Embedded guidance scale = 6.0

Flow shift = 5.0

Here's a link to the Wan2.1 I2V LoRA inference workflow I used to generate these videos: https://huggingface.co/Remade/Squish/blob/main/workflow/wan_img2video_lora_workflow.json

This is a slight modification to Kijai's version, with the main difference being the addition of the WanVideo Lora Select node, connected to the 'lora' field of the WanVideo Lora Select node. Find Kijai's original workflow here:
https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/main/example_workflows/wanvideo_480p_I2V_example_02.json

Prompt Examples:

The video shows a man seated on a chair. The man and the chair performs a r0t4tion 360 degrees rotation.

The video features a Pomeranian puppy sitting on a gravel surface, and the puppy undergoes a r0t4tion 360 degrees rotation.

Let me know if there are any questions, I'll be happy to help!