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Wan2.2 BladeRunner Cinematography Style & Theme

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Updated: Sep 10, 2025

stylethemewan2.2

Verified:

SafeTensor

Type

LoRA

Stats

537

0

Reviews

Published

Sep 10, 2025

Base Model

Wan Video 2.2 T2V-A14B

Training

Steps: 5,400
Epochs: 15

Usage Tips

Strength: 1

Hash

AutoV2
BB3D6AF7D3

Features

  • Cinematography Style and Theme from BladeRunner 2042

  • Yellow, Dark, and Orange colored themes

  • Compatible with both T2V & I2V Wan2.2 Models

  • This LoRA was designed to provide a subtle influence on background, clothing, and scenes

Tips

  • Play around with LoRA strength below 1.0 if the LoRA is not 'working'.

  • SPEED LoRAs destroy dark scenes. I was only able to get really dark scenes by not using the popular low step speed LoRAs.

  • HIGHLY recommend reviewing and understanding how to correctly split steps for Wan2.2 models: https://www.reddit.com/r/StableDiffusion/comments/1mkv9c6/wan22_schedulers_steps_shift_and_noise/?tl=fr

  • Workflow that correctly splits High / Low noise steps: https://github.com/stduhpf/ComfyUI-WanMoeKSampler

  • Example prompts. These prompts were used during training for the image/video text captions 'The scene is shrouded in darkness, with only the occasional glow from distant lights piercing the fog', 'a dimly lit room, surrounded by various objects such as a desk, a table, a chair, and a lamp', 'a man in a long coat standing in a dusty, yellow-toned environment. He appears to be observing something off-camera'

  • Use negative prompts to remove items that keep appearing in the scene. For example, 'tires' when trying to generate a futuristic car that keeps having tires on it! lol

My Wan2.2 Cover Videos

  • These videos have the ComfyUI metadata saved in them.

Training

Dataset images and video contained scenes without people facing the camera because of Civitai's real person terms of service policy.

I'm testing a fork of musubi-tuner that allows me to run validation tests against a dataset that is not in the training set. This should prevent over-fitting during training. Why validation testing is important: https://github.com/spacepxl/demystifying-sd-finetuning