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: Adaptive Prompt-Tailored Pruning of T2I Diffusion Models
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[](https://github.com/rezashkv/diffusion_pruning)
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The implementation of the paper ["Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models"]()
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# APTP: Adaptive Prompt-Tailored Pruning of T2I Diffusion Models
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[](https://arxiv.org/abs/2406.12042)
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[](https://github.com/rezashkv/diffusion_pruning)
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The implementation of the paper ["Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models"]()
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