Instructions to use yugkha3/avatar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yugkha3/avatar with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yugkha3/avatar", 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`. (684927ec1a1167f02973fc6dee48dbb09a6575c6)81de560
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Add `scale_factor` to vae config. (#1)
- Add `scale_factor` to vae config. (1775b00b55395a2ebf7ddf9f0e1779911bfebfad) Co-authored-by: Suraj Patil <valhalla@users.noreply.huggingface.co>212f04a
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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 Avatar embeds and token
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initial commit
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