Instructions to use warp-ai/wuerstchen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use warp-ai/wuerstchen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("warp-ai/wuerstchen", 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 (#9)
Browse files- Update README.md (cda2de19b83468ebc8924b201cc298c46bd7ef9d)
Co-authored-by: Richter <MLRichter@users.noreply.huggingface.co>
README.md
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@@ -70,8 +70,8 @@ The left figure shows inference times (using torch > 2.0), whereas the right fig
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- **Cite as:**
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@misc{pernias2023wuerstchen,
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title={Wuerstchen: Efficient
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author={Pablo Pernias and Dominic Rampas and Mats L. Richter and Christopher Pal and Marc Aubreville},
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year={2023},
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eprint={2306.00637},
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archivePrefix={arXiv},
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- **Cite as:**
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@misc{pernias2023wuerstchen,
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title={Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models},
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author={Pablo Pernias and Dominic Rampas and Mats L. Richter and Christopher J. Pal and Marc Aubreville},
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year={2023},
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eprint={2306.00637},
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archivePrefix={arXiv},
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