Crying LoRA for Wan2.1 14B I2V 480p

Overview

This LoRA is trained on the Wan2.1 14B I2V 480p model and allows you to make yourself or your friends cry!

Features

• Trained on the Wan2.1 14B 480p I2V base model
• Consistent results across different object types
• Simple prompt structure that's easy to adapt

Community

• Discord: Join our community to generate videos with this LoRA for free
• Request LoRAs: We're training and open-sourcing Wan2.1 LoRAs for free - join our Discord to make requests!

Prompt

The video starts with a man, appearing with a sad expression. Then a tear rolls down his cheek, as he is cr471ng crying.

Prompt

The video starts with a man with a solemn expression. Then a tear rolls down his cheek, as he is cr471ng crying.

Prompt

The video starts with a woman smiling at the camera, but her face quickly turns to an expression of deep cr471ng crying as tears well in her eyes. She covers her face with both hands, her watery eyes and sad expression are clearly visible as she continues the cr471ng crying.

Model File and Inference Workflow

📥 Download Links:

• crying_20_epochs.safetensors - LoRA Model File
• wan_img2vid_lora_workflow.json - Wan I2V with LoRA Workflow for ComfyUI

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Recommended Settings

• LoRA Strength: 1.0
• Embedded Guidance Scale: 6.0
• Flow Shift: 5.0

Trigger Words

The key trigger phrase is: cr471ng crying

Prompt Template

For prompting, check out the example prompts; this way of prompting seems to work very well.

ComfyUI Workflow

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.

Model Information

The model weights are available in Safetensors format. See the Downloads section above.

Training Details

• Base Model: Wan2.1 14B I2V 480p
• Training Data: Trained on 50 seconds of video comprised of 10 short clips (each clip captioned separately) of people crying.
• Epochs: 20

Additional Information

Training was done using Diffusion Pipe for Training

Acknowledgments

Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!