Instructions to use tagachi/kara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tagachi/kara with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tagachi/kara", 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`. (#2)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (c5bb19f3d69ac861f3a496e5c17a64c678719c62)5e9e1dd
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Add `scale_factor` to vae config. (#1)
- Add `scale_factor` to vae config. (fd13a4aeda3409ef3d2e71a362fc4d0291a9af0b) Co-authored-by: Suraj Patil <valhalla@users.noreply.huggingface.co>eebc081
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Update README.md
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Upload sample_images with huggingface_hub
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Upload model_index.json with huggingface_hub
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Upload vae with huggingface_hub
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Upload unet with huggingface_hub
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Upload tokenizer with huggingface_hub
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Upload text_encoder with huggingface_hub
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Upload scheduler with huggingface_hub
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Upload safety_checker with huggingface_hub
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Upload feature_extractor with huggingface_hub
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Upload the concept kara embeds and token
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initial commit
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