Metalcification LoRA for Wan

Prompt
Metalcification

Special Thanks:
This project was made possible thanks to generous sponsorship and GPU time provided by reDMOND Ai.
We are grateful for their support in training this LoRA.


Model Description

This LoRA specializes in creating mesmerizing metalcification effects - the transformation of objects into metallic forms. Watch as everyday materials gradually transform into stunning metallic surfaces with realistic textures, reflections, and properties. The model captures the gradual process of metallization with beautiful visual effects.

This version is specifically adapted for use with Wan-AI models, like Wan2.2-T2V-A14B.

Trigger Words

The trigger phrase is Metalcification.

For best results, use "Metalcification" in your prompt when describing a scene. The model will create a transformation sequence where objects gradually turn into metallic versions of themselves.

Setup for ComfyUI

To use this LoRA in ComfyUI, simply place the files in your loras directory:

  • Download both [WAN2.2]Metalcification_Redmond_low_noise.safetensors and [WAN2.2]Metalcification_Redmond_high_noise.safetensors and place them in ComfyUI/models/loras/

Ensure your ComfyUI environment is already configured to run Wan models.

Example Workflow (ComfyUI)

In your ComfyUI workflow, add a Load LoRA node after your base model loader.

  1. Load your base Wan model.
  2. Connect the output of the model loader to the model input of the Load LoRA node.
  3. Select either [WAN2.2]Metalcification_Redmond_low_noise.safetensors for cleaner results or [WAN2.2]Metalcification_Redmond_high_noise.safetensors for more dramatic effects in the Load LoRA node.
  4. Connect the MODEL output of the LoRA node to your KSampler.
  5. Adjust the strength_model as needed (0.8-1.0 is a good starting point).

References


🌐 Website

You can find more of my models, projects, and information on my official website:

πŸ’– Support My Work

If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.

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