Instructions to use Bitset/person with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Bitset/person with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Bitset/person", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
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Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#3)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (f4f73cb46befb8ee806aa89d62326b041373bd25)65bbecf
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Upload concept_images with huggingface_hub
b882351 -
Upload with huggingface_hub
570f1fa -
Upload the concept person embeds and token
7fcca92 -
initial commit
b92abb2