--- 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: >- A lone tree stands silhouetted against the backdrop of a wildfire consuming a vast forest, the sky filled with smoke. output: url: example_videos/fire1.mp4 - text: >- A wildfire rages across a field, with flames consuming the dry grass and smoke filling the sky above. output: url: example_videos/fire2.mp4 - text: >- A close-up view of a burning gas station at night, with flames engulfing the pumps and a chaotic scene unfolding. output: url: example_videos/fire3.mp4 - text: >- A small town is on [r3al_f1re] with many houses burning, smoke filling the air, and the sky glowing orange. output: url: example_videos/fire4.mp4 ---
This LoRA is trained on the Wan2.1 14B T2V model and allows you to generate videos of realistic fires!
The key trigger phrase is: [r3al_f1re]
For prompting, check out the example prompts; this way of prompting seems to work very well. Including the key trigger phrase is not required, as long as the fire and the scene are described 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!