Instructions to use duja1/catollage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use duja1/catollage with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("duja1/catollage", dtype=torch.bfloat16, device_map="cuda") prompt = "catollage123" 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`. (d3da50b5f4754947d02ec86bae7d39541dd5dbc6)25e4aef
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Delete concept_images
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Update README.md
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Upload concept_images with huggingface_hub
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Upload with huggingface_hub
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Upload the model Catollage
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initial commit
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