Instructions to use dmillar/wsj-hedcut-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dmillar/wsj-hedcut-v1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dmillar/wsj-hedcut-v1", 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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update readme.md to add artist attribution
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Add `scale_factor` to vae config. (#2)
- Add `scale_factor` to vae config. (f0b5f04daee45bd1d2509198d3ff4973f45fa044) Co-authored-by: Suraj Patil <valhalla@users.noreply.huggingface.co>63821d0
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Updated some sample images with generation details
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added images to README (#1)
- added images to README (43904851cb65ff0622d8b9f584df9c8b3bf0480b)5548c6c -
sample images
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added python snippet
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added initial description of model
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update license, tags, and inference
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Update README.md
update licensea0febca
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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 wsj-hedcut-v1 embeds and token
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initial commit
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