Instructions to use nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08-i512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08-i512 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-base-ViTXL-n08-i512", 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
Remove conversion_metadata.json (internal conversion bookkeeping)
Browse files- conversion_metadata.json +0 -12
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-base",
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"decoder_checkpoint": "decoders/dinov2/wReg_base/ViTXL_n08_i512/model.pt",
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"stats_checkpoint": "stats/dinov2/wReg_base/imagenet1k_512/stat.pt",
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"variant": "ViTXL_n08_i512",
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"dataset_name": "imagenet1k_512",
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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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