--- license: apache-2.0 language: - en base_model: - Wan-AI/Wan2.1-T2V-14B pipeline_tag: text-to-video tags: - text-to-video - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- d48g dog video A beagle puppy is sitting in a basket on a bicycle, ears flapping in the wind as the bike moves forward. The dog looks excited, glancing side to side at the passing scenery. The road is lined with trees, their leaves bright green. output: url: example_videos/dog1.mp4 - text: >- d48g dog video A Shiba Inu wearing a superhero cape is standing proudly on a hill, looking off into the distance. The cape flutters slightly in the breeze as the dog stands with its chest puffed out. The sky is bright blue with a few clouds, making it look like the perfect day for a hero. output: url: example_videos/dog2.mp4 - text: >- d48g dog video A Boston Terrier wearing a tiny suit and bow tie is sitting at a small dinner table. A candle flickers on the table as the dog stares at a fancy plate of steak in front of it. The background is softly lit, creating the feeling of a romantic dinner for one. output: url: example_videos/dog3.mp4 - text: >- d48g dog video A Pomeranian dressed as a tiny chef is sitting on a kitchen counter, staring at a bowl of cookie dough. It wears a little white chef's hat and apron while its fluffy tail wags. A wooden spoon rests next to the bowl, as if the dog is about to start baking. output: url: example_videos/dog4.mp4 ---
This LoRA is trained on the Wan2.1 14B T2V model and allows you to generate awesome dog videos!
The key trigger phrase is: d48g dog video
For prompting, check out the example prompts; this way of prompting seems to work very well.
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!