Instructions to use hassanblend/evamendes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hassanblend/evamendes with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hassanblend/evamendes", 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`. (#3)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (eb2127a25bb7b32841ac2a36990d77403e8bcb64) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>170f774
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Add `scale_factor` to vae config. (#2)
- Add `scale_factor` to vae config. (f47fdb9b241cccbec6bdaef7dd52a10f1da1f468) Co-authored-by: Suraj Patil <valhalla@users.noreply.huggingface.co>4ccabd9
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Add `clip_sample=False` to scheduler to make model compatible with DDIM. (#1)
- Add `clip_sample=False` to scheduler to make model compatible with DDIM. (b2aac6b072cb75bd1e92dc6572828936dbb8c7cb) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>7d230d4
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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 EvaMendes embeds and token
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initial commit
Hassaneabf0d6