Vintage Cinematic Color. A wide, static shot of a lone ballerina mid-spin in a vast, empty dance studio. The polished wood floor reflects her form. Dramatic shafts of volumetric light cut through the dusty air from high windows, illuminating her movement. The scene is composed entirely of brown tones, from the deep mahogany floor to the pale, dusty light. The detailed texture of the wood and the dancer's focused grace lend a melancholic, arthouse film aesthetic.
Prompt
Vintage Cinematic Color. A cinematic shot from inside a moving car at night, focused on the passenger window. Raindrops streak down the glass, blurring the streetlights into an abstract bokeh of amber and brown. The passenger's reflection is vaguely visible. The entire color palette is a moody mix of deep shadows and warm, diffuse brown and amber lights. A contemplative and hyper-realistic scene with the aesthetic of an independent film.
Prompt
Vintage Cinematic Color. An intimate close-up on a woman's face as she looks directly into the lens. She offers a subtle, genuine smile while soft, directional light from a window illuminates one side of her face, creating delicate shadows. The background is a soft-focus blur of earthy browns. The high sharpness and rich brown tones create a powerful and emotive portrait with the quiet depth of an independent arthouse film.
Prompt
Vintage Cinematic Color. An intimate medium shot of a woman in her sun-drenched kitchen, her face smudged with flour, laughing genuinely at someone off-camera. In the soft-focus background, the silhouettes of children are playing. Steam rises from a pot on the stove, catching the golden morning light. The entire scene is bathed in a warm brown palette, from the dark wood cabinets to her light beige apron. Shot on 16mm film, with high-sharpness, capturing a candid, nostalgic moment with an arthouse aesthetic.
Prompt
Vintage Cinematic Color. A medium shot of a cellist, eyes closed in concentration, drawing the bow slowly across the strings. Warm, soft light from a single source models the rich, polished brown wood of the cello and catches the rosin dust floating in the air. The background is a dark, out-of-focus study with bookshelves. The hyper-detailed shot captures the grain of the wood and the focused emotion on the musician's face, creating a moment of pure, warm, auditory and visual art.
Prompt
Vintage Cinematic Color. An extreme close-up, hyper-detailed shot of a turntable needle gently landing on a spinning black vinyl record. The scene is lit by the warm, low light of a nearby lamp. The turntable's wood base and the soft glow from a vintage amplifier fill the atmosphere with a sense of analog warmth and brown tones. A tactile and nostalgic moment, full of anticipation.
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 imparts a vintage cinematic style characterized by rich brown tones, dramatic lighting, and an arthouse film aesthetic. The style creates melancholic, contemplative, and nostalgic scenes with a focus on texture, shadow, and warm color palettes.
This version is specifically adapted for use with
Wan-AI
models, like
Wan2.2-T2V-A14B
.
Trigger Words
The trigger phrase is
Vintage Cinematic Color
.
For best results, describe a scene with dramatic lighting, rich textures, and warm brown tones, and the model will render it in this unique aesthetic.
Setup for ComfyUI
To use this LoRA in ComfyUI, simply place the file in your
loras
directory:
Download
Vintage-Cinematic-Redmond-WAN22.safetensors
and place it 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.
Load your base
Wan
model.
Connect the output of the model loader to the
model
input of the
Load LoRA
node.
Select
Vintage-Cinematic-Redmond-WAN22.safetensors
in the Load LoRA node.
Connect the
MODEL
output of the LoRA node to your KSampler.
Adjust the
strength_model
as needed (0.8-1.0 is a good starting point).
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