Instructions to use nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Update config for diffusers AutoencoderRAE refactor
#2
by kashif HF Staff - opened
- config.json +2 -2
- conversion_metadata.json +0 -12
config.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:1fcdf538f99f7c0ba6ae2655ceb6635852d29bbe048c4f17a8a0efeb6e6b0465
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size 15541689
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conversion_metadata.json
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{
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"source": "/data/boyang/models",
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"encoder_cls": "dinov2",
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"encoder_name_or_path": "facebook/dinov2-with-registers-large",
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"decoder_checkpoint": "decoders/dinov2/wReg_large/ViTXL_n08/model.pt",
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"stats_checkpoint": "stats/dinov2/wReg_large/imagenet1k/stat.pt",
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"variant": "ViTXL_n08",
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"dataset_name": "imagenet1k",
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"decoder_config_name": "ViTXL",
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"missing_decoder_keys": [],
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"unexpected_decoder_keys": []
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}
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