Instructions to use rezashkv/diffusion_pruning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rezashkv/diffusion_pruning with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rezashkv/diffusion_pruning", 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
- Draw Things
- DiffusionBee
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APTP is released under the MIT License. Please see the [LICENSE](LICENSE) file for details.
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### Model Sources
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For local or self-hosted use, follow the instructions in the [Github Repository](https://github.com/rezashkv/diffusion_pruning)
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## Training Dataset
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APTP is released under the MIT License. Please see the [LICENSE](LICENSE) file for details.
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## Training Dataset
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