Instructions to use NoxiusEngine/Vivid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NoxiusEngine/Vivid with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NoxiusEngine/Vivid", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit History
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Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#2)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (371527c62a9b4eee43652902e5635111397bab73)3198f17
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Upload diffusion_pytorch_model.bin
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Upload pytorch_model.bin
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Create config.json
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Create scheduler_config.json
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Create README.md
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Create model_index.json
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Delete .gitattributes
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Upload Vivid.ckpt
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initial commit
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