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Remade-AI
/
Crane_up

Image-to-Video
Diffusers
English
text-to-image
lora
template:diffusion-lora
Model card Files Files and versions
xet
Community
1

Instructions to use Remade-AI/Crane_up with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use Remade-AI/Crane_up with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    from diffusers.utils import load_image, export_to_video
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.1-I2V-14B-480P,Wan-AI/Wan2.1-I2V-14B-480P-Diffusers", dtype=torch.bfloat16, device_map="cuda")
    pipe.load_lora_weights("Remade-AI/Crane_up")
    
    prompt = "A woman in a white dress is walking across a large, reflective body of water with mountains in the background. The words \"REMADE\" are etched into the surface near the water. The cr4n3 crane up effect is applied, smoothly lifting the camera's viewpoint higher, revealing more of the surrounding landscape, including the shoreline and the vast expanse of water ahead as the woman continues walking into the distance."
    input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png")
    
    image = pipe(image=input_image, prompt=prompt).frames[0]
    export_to_video(output, "output.mp4")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • Draw Things
Crane_up / workflow_I2V
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Remade
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  • wan_img2vid_lora_workflow.json
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  • workflow_screenshot.png
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    xet
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