Instructions to use duja1/tjmo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use duja1/tjmo 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/tjmo", 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`.
Hey duja1 👋, Your model repository seems to contain logic to load a feature extractor that is deprecated, which you should notice by seeing the warning: ``` transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead. warnings.warn( ``` when running `pipe = DiffusionPipeline.from_pretrained(duja1/tjmo)`.This PR makes sure that the warning does not show anymore by replacing `CLIPFeatureExtractor` with `CLIPImageProcessor`. This will certainly not change or break your checkpoint, but onlymake sure that everything is up to date. Best, the 🧨 Diffusers team.7490616
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Update vae/config.json
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Update README.md
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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 tjmo
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initial commit
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