Instructions to use Spiltcokesf/samantha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Spiltcokesf/samantha with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Spiltcokesf/samantha", 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`. (a976416ed2deb7572da66e5bd524323b7f2a8c42)4e2ed25
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Upload sample_images with huggingface_hub
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Upload model_index.json with huggingface_hub
851961c -
Upload vae with huggingface_hub
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Upload unet with huggingface_hub
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Upload feature_extractor with huggingface_hub
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Upload the concept samantha embeds and token
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initial commit
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