--- license: apache-2.0 language: - en base_model: - Wan-AI/Wan2.1-I2V-14B-480P - Wan-AI/Wan2.1-I2V-14B-480P-Diffusers pipeline_tag: image-to-video tags: - text-to-image - lora - diffusers - template:diffusion-lora - image-to-video widget: - text: >- The video begins with an image of purple Thanos from Marvel. Then the v1p red carpet transformation appears. Purple Thanos is shown wearing a black dress, with gold jewelry around his neck and ears. The image is again of purple Thanos looking straight at the camera against a more lighted gray background. The v1p red carpet transformation continues, purple Thanos is now on the red carpet with photographers taking pictures and other people behind a barricade to the sides. Purple Thanos is wearing the same black dress and jewelry, in focus at the center of the frame. output: url: example_videos/thanos_vip.mp4 - text: >- The video begins with an image of a man. Then the v1p red carpet transformation appears. He is shown wearing a black dress, with gold jewelry around his neck and ears. The image is again of him looking straight at the camera against a more lighted gray background. The v1p red carpet transformation continues, he is now on the red carpet with photographers taking pictures and other people behind a barricade to the sides. He is wearing the same black dress and jewelry, in focus at the center of the frame. output: url: example_videos/man_vip.mp4 ---
This LoRA is trained on the Wan2.1 14B I2V 480p model and allows you to make any person/object in an image become a female VIP version of themselves!
The key trigger phrase is: v1p red carpet transformation
For best results, try following the structure of the prompt examples above. These worked well for me.
This LoRA works with a modified version of Kijai's Wan Video Wrapper workflow. The main modification is adding a Wan LoRA node connected to the base model.
See the Downloads section above for the modified workflow.
The model weights are available in Safetensors format. See the Downloads section above.
Training was done using Diffusion Pipe for Training
Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!