Image-to-Image
Diffusers
Safetensors
ZoomLDMPipeline
zoomldm
remote-sensing
naip
latent-diffusion
custom-pipeline
arxiv:2411.16969
Instructions to use BiliSakura/ZoomLDM-naip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/ZoomLDM-naip with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/ZoomLDM-naip", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add files using upload-large-folder tool
Browse files
README.md
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url: demo_images/output.jpeg
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# BiliSakura/ZoomLDM-naip
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Diffusers-format **NAIP** variant of ZoomLDM with a bundled custom pipeline and local `ldm` modules.
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url: demo_images/output.jpeg
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> [!WARNING] we do not have a full checkpoint conversion validation, if you encounter pipeline loading failure and unsidered output, please contact me via bili_sakura@zju.edu.cn
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# BiliSakura/ZoomLDM-naip
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Diffusers-format **NAIP** variant of ZoomLDM with a bundled custom pipeline and local `ldm` modules.
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