Autouppspelning
Kör lokalt
[WAN2.2]WindowSeat_Redmond.zip
459 MB · Diffusers
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Om denna modell
WAN · LoRA
by artificialguybr - [WAN2.2] WindowSeat - Redmond - T2V-14B
Skaparens anteckningar
💖 Support My Work & Explore More
If this model is useful to you, your support helps me keep publishing free models and open-source AI tools.
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Discover more of my models, projects, and practical AI resources:
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Portfolio: — models, projects, and experiments.
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AI tools directory: — a curated directory of useful AI tools.
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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.
Window Seat LoRA for Wan
Model Description
This LoRA captures the contemplative, surreal experience of looking through windows while traveling. Each scene presents a view from buses, trains, planes, and trams that reveals extraordinary moments beyond ordinary reality - from floating islands above clouds to orchestras performing in wheat fields, from breathing forests to submerged cities. The style combines cinematic framing with surreal elements, creating dreamlike journeys that transform the mundane act of looking out a window into a portal to other worlds.
Trigger Words
The trigger phrase is Window Seat.
For best results, start your prompt with “Window Seat” followed by a description of a view through a vehicle window that reveals something surreal or extraordinary. The model will render these scenes with a cinematic, contemplative quality.
Setup for ComfyUI
To use this LoRA in ComfyUI, simply place the file in your loras directory:
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Download [WAN2.2]WindowSeat_Redmond_high_noise.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.
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Load your base Wan model.
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Connect the output of the model loader to the model input of the Load LoRA node.
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Select [WAN2.2]WindowSeat_Redmond_high_noise.safetensors in the Load LoRA node.
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Connect the MODEL output of the LoRA node to your KSampler.
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Adjust the strength_model as needed (0.8-1.0 is a good starting point).
Example Prompts
Here are some example prompts to get you started with Window Seat:
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Window Seat. A handheld wide shot from inside a moving bus, looking through dirty windows at a one-room schoolhouse that sits isolated in the middle of a vast sunflower field in late summer.
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Window Seat. From a plane window during turbulence: a vast desert below turns into a sea of mirror-like salt flats reflecting storm clouds. A lone horse gallops across the reflection.
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Window Seat. Through airplane window at cruising altitude: floating islands drift above cloud layer, connected by hanging bridges made of ropes and lanterns.
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Window Seat. A locked-off shot from the interior of a moving subway captures the window view of an old theater stage where a single spotlight illuminates a giraffe standing center stage.
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Window Seat. Fixed side view from within a moving train, the window perfectly framing a surreal orchestral moment in an empty wheat field under stormy grey skies.
Triggerord
Window Seat
SHA256
E8EB3B532C53772BDE39D829EDE406234AB586D71C0E27A1F61CD9242E6FB6A3
ModelTrends.ai modell-ID
#18271
Rapportera innehåll
Exempelrenderingar
Prompter och inställningar delade av personerna som gjorde dessa renderingar på Civitai. Välj en rendering för att se hur den gjordes.
Inställningar
Storlek
1440x1920
Prompt
Window Seat, side view from within a rapidly moving vintage train with two leather seat and a table in between, under the sea. a woman sitting on one side on the seat, while sipping on a cup of coffee. fishes swimming, view of sunken ships and wreckages. a moment later a shark swim pass the window and startle the woman.
AI-modeller för bild-, video- och textgenerering
windowseat wan är en LoRA för familjen Wan, listad på ModelTrends.ai, en skrivskyddad katalog över AI-modeller med öppen källkod för bild, video och text. Jämför den med andra Wan-modeller, se dess hettapoäng och prova den på PirateDiffusion eller BitVector.
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