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", torch_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
update readme.md to add artist attribution
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README.md
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- text-to-image
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inference: true
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Readers of the Wall Street Journal (WSJ) are familiar with the distinctive style used to create portaits of their writers and subjects. This is a fine-tuned stable
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diffusion model that can be used to create hedcut-styled images using the prompt **_wsj hedcut of \<subject\>_**. This model can also be used in a DreamBooth environment
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to train a face or other subject for custom and unique hedcuts.
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```python
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#!pip install diffusers transformers scipy torch
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from diffusers import StableDiffusionPipeline
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- text-to-image
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inference: true
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# Wall Street Journal Hedcut Style for Stable Diffusion
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Readers of the Wall Street Journal (WSJ) are familiar with the distinctive style used to create portaits of their writers and subjects. This is a fine-tuned stable
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diffusion model that can be used to create hedcut-styled images using the prompt **_wsj hedcut of \<subject\>_**. This model can also be used in a DreamBooth environment
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to train a face or other subject for custom and unique hedcuts.
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#### *If you use this model, please make an effort to attribute the principal artist of the training images, Noli Novak, along with the other disclosures and restrictions required by the license.
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```python
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#!pip install diffusers transformers scipy torch
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from diffusers import StableDiffusionPipeline
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