--- 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: >- p0v_dr1v1n6, video shows a person driving a car through a burning hellscape. The driver is holding the steering wheel with both hands. Rivers of lava flow on both sides of the cracked road, and firestorms rage in the distance. The driver is looking straight ahead. The car has a digital dashboard and a touchscreen display flickering with demonic symbols. output: url: example_videos/pov1.mp4 - text: >- p0v_dr1v1n6 through a sandstorm in the desert, visibility dropping as golden dust engulfs the horizon, digital dashboard displaying emergency alerts, the car struggling against the powerful winds. output: url: example_videos/pov2.mp4 - text: >- dr1v12ng POV Driving. The video shows the interior of a car driving down a city street at night. The driver's hands are visible on the steering wheel. The city lights are reflecting in the windshield. output: url: example_videos/pov3.mp4 - text: >- p0v_dr1v1n6, video shows a person driving a car on the surface of the Moon. The driver is holding the steering wheel with both hands. The road is covered in lunar dust, and Earth glows brightly in the sky. The driver is looking straight ahead. The car has a digital dashboard and a touchscreen display output: url: example_videos/pov4.mp4 ---
This LoRA is trained on the Wan2.1 14B T2V model and allows you to generate POV driving videos in any scene or landscape you desire!
The key trigger phrase is: p0v_dr1v1n6
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!