Instructions to use BestWishYsh/ConsisID-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BestWishYsh/ConsisID-preview with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BestWishYsh/ConsisID-preview", 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
Upload folder using huggingface_hub
Browse files
data_process/step1_yolov8_face.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:37396ac6a9601ab9f5177e4231b09d81cf6f65a7f22db99ec3b36ab63f674e71
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size 6247065
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data_process/step1_yolov8_head_big.pt
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oid sha256:ed729429a2d68f14adf1d93ec14fe6ba759ecba099fb0781750fa535b67f1640
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size 136728947
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data_process/step1_yolov8l-pose.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:4be6ab6c6eca601cb1b7356751cd93af42990abde92cab02c79652159f1dcf42
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size 89396617
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data_process/step1_yolov8l-worldv2.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:276b618c2f04e6e08e32f085b3c0e8baf2dbffa46af5119b888423e859a6ee01
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size 94224632
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