| Name | pruna JSON |
| Version |
0.2.9
JSON |
| download |
| home_page | None |
| Summary | Smash your AI models |
| upload_time | 2025-08-13 14:24:33 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.9 |
| license | Copyright 2025 - Pruna AI GmbH. All rights reserved.
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| keywords |
ai
machine learning
model optimization
pruning
|
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<div align="center">
<img src="./docs/assets/images/logo.png" alt="Pruna AI Logo" width=400></img>
<img src="./docs/assets/images/element.png" alt="Element" width=10></img>
**Simply make AI models faster, cheaper, smaller, greener!**
<img src="./docs/assets/images/element.png" alt="Element" width=10></img>
<br>
[][documentation]
<br>
[][website]
[][x]
[][devto]
[][reddit]
[][discord]
[][huggingface]
[][replicate]
<br>
<img src="./docs/assets/images/triple_line.png" alt="Pruna AI Logo" width=600, height=30></img>
</div>
## <img src="./docs/assets/images/pruna_cool.png" alt="Pruna Cool" width=20></img> Introduction
Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead. It provides a comprehensive suite of compression algorithms including [caching](https://docs.pruna.ai/en/stable/compression.html#cachers), [quantization](https://docs.pruna.ai/en/stable/compression.html#quantizers), [pruning](https://docs.pruna.ai/en/stable/compression.html#pruners), [distillation](https://docs.pruna.ai/en/stable/compression.html#distillers) and [compilation](https://docs.pruna.ai/en/stable/compression.html#compilers) techniques to make your models:
- **Faster**: Accelerate inference times through advanced optimization techniques
- **Smaller**: Reduce model size while maintaining quality
- **Cheaper**: Lower computational costs and resource requirements
- **Greener**: Decrease energy consumption and environmental impact
The toolkit is designed with simplicity in mind - requiring just a few lines of code to optimize your models. It supports various model types including LLMs, Diffusion and Flow Matching Models, Vision Transformers, Speech Recognition Models and more.
<img align="left" width="40" src="docs/assets/images/highlight.png" alt="Pruna Pro"/>
**To move at top speed**, we offer [Pruna Pro](https://docs.pruna.ai/en/stable/docs_pruna_pro/user_manual/pruna_pro.html), our enterprise solution that unlocks advanced optimization features, our `OptimizationAgent`, priority support, and much more.
<br clear="left"/>
## <img src="./docs/assets/images/pruna_cool.png" alt="Pruna Cool" width=20></img> Installation
Pruna is currently available for installation on Linux, MacOS and Windows. However, some algorithms impose restrictions on the operating system and might not be available on all platforms.
Before installing, ensure you have:
- Python 3.9 or higher
- Optional: [CUDA toolkit](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/) for GPU support
#### Option 1: Install Pruna using pip
Pruna is available on PyPI, so you can [install it using pip](https://docs.pruna.ai/en/stable/setup/install.html):
```bash
pip install pruna
```
#### Option 2: Install Pruna from source
You can also install Pruna directly from source by cloning the repository and installing the package in editable mode:
```bash
git clone https://github.com/pruna-ai/pruna.git
cd pruna
pip install -e .
```
## <img src="./docs/assets/images/pruna_cool.png" alt="Pruna Cool" width=20></img> Quick Start
Getting started with Pruna is easy-peasy pruna-squeezy!
First, load any pre-trained model. Here's an example using Stable Diffusion:
```python
from diffusers import StableDiffusionPipeline
base_model = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
```
Then, use Pruna's `smash` function to optimize your model. Pruna provides a variety of different optimization algorithms, allowing you to combine different algorithms to get the best possible results. You can customize the optimization process using `SmashConfig`:
```python
from pruna import smash, SmashConfig
# Create and smash your model
smash_config = SmashConfig()
smash_config["cacher"] = "deepcache"
smash_config["compiler"] = "stable_fast"
smashed_model = smash(model=base_model, smash_config=smash_config)
```
Your model is now optimized and you can use it as you would use the original model:
```python
smashed_model("An image of a cute prune.").images[0]
```
<br>
You can then use our evaluation interface to measure the performance of your model:
```python
from pruna.evaluation.task import Task
from pruna.evaluation.evaluation_agent import EvaluationAgent
from pruna.data.pruna_datamodule import PrunaDataModule
datamodule = PrunaDataModule.from_string("LAION256")
datamodule.limit_datasets(10)
task = Task("image_generation_quality", datamodule=datamodule)
eval_agent = EvaluationAgent(task)
eval_agent.evaluate(smashed_model)
```
This was the minimal example, but you are looking for the maximal example? You can check out our [documentation][documentation] for an overview of all supported [algorithms][docs-algorithms] as well as our tutorials for more use-cases and examples.
## <img src="./docs/assets/images/pruna_heart.png" alt="Pruna Heart" width=20></img> Pruna Pro
Pruna has everything you need to get started on optimizing your own models. To push the efficiency of your models even further, we offer Pruna Pro. To give you a glimpse of what is possible with Pruna Pro, let us consider three of the most widely used diffusers pipelines and see how much smaller and faster we can make them. In addition to popular open-source algorithms, we use our proprietary Auto Caching algorithm. We compare the fidelity of the compressed models. Fidelity measures the similarity between the images of the compressed models and the images of the original model.
### Stable Diffusion XL
For [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), we compare Auto Caching with [DeepCache](https://github.com/horseee/DeepCache) (available with Pruna). We combine these caching algorithms with torch.compile to get an additional **9%** reduction in inference latency, and we use [HQQ](https://github.com/mobiusml/hqq) 8-bit quantization to reduce the size of the model from **8.8GB** to **6.7GB**.
<img src="./docs/assets/plots/benchmark_sdxl.svg" alt="SDXL Benchmark"/>
### FLUX [dev]
For [FLUX [dev]](https://huggingface.co/black-forest-labs/FLUX.1-dev), we compare Auto Caching with the popular [TeaCache](https://github.com/ali-vilab/TeaCache) algorithm. In this case, we used [Stable Fast](https://github.com/chengzeyi/stable-fast) to reduce the latency of Auto Caching by additional **13%**, and [HQQ](https://github.com/mobiusml/hqq) with 8-bit reduced the size of FLUX from **33GB** to **23GB**.
<img src="./docs/assets/plots/benchmark_flux.svg" alt="FLUX [dev] Benchmark"/>
### HunyuanVideo
For [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo), we compare Auto Caching with [TeaCache](https://github.com/ali-vilab/TeaCache). Applying [HQQ](https://github.com/mobiusml/hqq) 8-bit quantization to the model reduced the size from **41GB** to **29GB**.
<img src="./docs/assets/plots/benchmark_hunyuan.svg" alt="HunyuanVideo Benchmark"/>
## <img src="./docs/assets/images/pruna_cool.png" alt="Pruna Cool" width=20></img> Algorithm Overview
Since Pruna offers a broad range of optimization algorithms, the following table provides a high-level overview of all methods available in Pruna. For a detailed description of each algorithm, have a look at our [documentation](https://docs.pruna.ai/en/stable/).
| Technique | Description | Speed | Memory | Quality |
|--------------|-----------------------------------------------------------------------------------------------|:-----:|:------:|:-------:|
| `batcher` | Groups multiple inputs together to be processed simultaneously, improving computational efficiency and reducing processing time. | ✅ | ❌ | ➖ |
| `cacher` | Stores intermediate results of computations to speed up subsequent operations. | ✅ | ➖ | ➖ |
| `compiler` | Optimises the model with instructions for specific hardware. | ✅ | ➖ | ➖ |
| `distiller` | Trains a smaller, simpler model to mimic a larger, more complex model. | ✅ | ✅ | ❌ |
| `quantizer` | Reduces the precision of weights and activations, lowering memory requirements. | ✅ | ✅ | ❌ |
| `pruner` | Removes less important or redundant connections and neurons, resulting in a sparser, more efficient network. | ✅ | ✅ | ❌ |
| `recoverer` | Restores the performance of a model after compression. | ➖ | ➖ | ✅ |
| `factorizer` | Factorization batches several small matrix multiplications into one large fused operation. | ✅ | ➖ | ➖ |
| `enhancer` | Enhances the model output by applying post-processing algorithms such as denoising or upscaling. | ❌ | ➖ | ✅ |
| `distributer` | Distributes the inference, the model or certain calculations across multiple devices. | ✅ | ❌ | ➖ |
| `kernel` | Kernels are specialized GPU routines that speed up parts of the computation. | ✅ | ➖ | ➖ |
✅ (improves), ➖ (approx. the same), ❌ (worsens)
<br><br>
<p align="center"><img src="./docs/assets/images/single_line.png" alt="Pruna AI Logo" width=600, height=30></img></p>
<br>
## <img src="./docs/assets/images/pruna_sad.png" alt="Pruna Sad" width=20></img> FAQ and Troubleshooting
If you can not find an answer to your question or problem in our [documentation][documentation], in our [FAQs][docs-faq] or in an existing issue, we are happy to help you! You can either get help from the Pruna community on [Discord][discord], join our [Office Hours][docs-office-hours] or open an issue on GitHub.
## <img src="./docs/assets/images/pruna_heart.png" alt="Pruna Heart" width=20></img> Contributors
The Pruna package was made with 💜 by the Pruna AI team and our amazing contributors. [Contribute to the repository][docs-contributing] to become part of the Pruna family!
[](https://github.com/PrunaAI/pruna/graphs/contributors)
## <img src="./docs/assets/images/pruna_emotional.png" alt="Pruna Emotional" width=20></img> Citation
If you use Pruna in your research, feel free to cite the project! 💜
```
@misc{pruna,
title = {Efficient Machine Learning with Pruna},
year = {2023},
note = {Software available from pruna.ai},
url={https://www.pruna.ai/}
}
```
<br>
<p align="center"><img src="./docs/assets/images/triple_line.png" alt="Pruna AI Logo" width=600, height=30></img></p>
[discord]: https://discord.gg/Tun8YgzxZ9
[reddit]: https://www.reddit.com/r/PrunaAI/
[x]: https://x.com/PrunaAI
[devto]: https://dev.to/pruna-ai
[website]: https://pruna.ai
[huggingface]: https://huggingface.co/PrunaAI
[replicate]: https://replicate.com/prunaai
[documentation]: https://docs.pruna.ai/en/stable
[docs-algorithms]: https://docs.pruna.ai/en/stable/compression.html
[docs-faq]: https://docs.pruna.ai/en/stable/resources/faq.html
[docs-office-hours]: https://docs.pruna.ai/en/stable/resources/office_hours.html
[docs-contributing]: https://docs.pruna.ai/en/stable/docs_pruna/contributions/how_to_contribute.html
Raw data
{
"_id": null,
"home_page": null,
"name": "pruna",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.13,>=3.9",
"maintainer_email": null,
"keywords": "AI, machine learning, model optimization, pruning",
"author": null,
"author_email": "Pruna AI <hello@pruna.ai>",
"download_url": null,
"platform": null,
"description": "<div align=\"center\">\n\n<img src=\"./docs/assets/images/logo.png\" alt=\"Pruna AI Logo\" width=400></img>\n\n\n <img src=\"./docs/assets/images/element.png\" alt=\"Element\" width=10></img>\n **Simply make AI models faster, cheaper, smaller, greener!**\n <img src=\"./docs/assets/images/element.png\" alt=\"Element\" width=10></img>\n\n<br>\n\n[][documentation]\n\n<br>\n\n\n\n\n\n\n\n\n\n[][website]\n[][x]\n[][devto]\n[][reddit]\n[][discord]\n[][huggingface]\n[][replicate]\n\n<br>\n\n<img src=\"./docs/assets/images/triple_line.png\" alt=\"Pruna AI Logo\" width=600, height=30></img>\n\n</div>\n\n## <img src=\"./docs/assets/images/pruna_cool.png\" alt=\"Pruna Cool\" width=20></img> Introduction\n\nPruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead. It provides a comprehensive suite of compression algorithms including [caching](https://docs.pruna.ai/en/stable/compression.html#cachers), [quantization](https://docs.pruna.ai/en/stable/compression.html#quantizers), [pruning](https://docs.pruna.ai/en/stable/compression.html#pruners), [distillation](https://docs.pruna.ai/en/stable/compression.html#distillers) and [compilation](https://docs.pruna.ai/en/stable/compression.html#compilers) techniques to make your models:\n\n- **Faster**: Accelerate inference times through advanced optimization techniques\n- **Smaller**: Reduce model size while maintaining quality\n- **Cheaper**: Lower computational costs and resource requirements\n- **Greener**: Decrease energy consumption and environmental impact\n\nThe toolkit is designed with simplicity in mind - requiring just a few lines of code to optimize your models. It supports various model types including LLMs, Diffusion and Flow Matching Models, Vision Transformers, Speech Recognition Models and more.\n\n\n<img align=\"left\" width=\"40\" src=\"docs/assets/images/highlight.png\" alt=\"Pruna Pro\"/>\n\n**To move at top speed**, we offer [Pruna Pro](https://docs.pruna.ai/en/stable/docs_pruna_pro/user_manual/pruna_pro.html), our enterprise solution that unlocks advanced optimization features, our `OptimizationAgent`, priority support, and much more.\n<br clear=\"left\"/>\n\n\n## <img src=\"./docs/assets/images/pruna_cool.png\" alt=\"Pruna Cool\" width=20></img> Installation\n\nPruna is currently available for installation on Linux, MacOS and Windows. However, some algorithms impose restrictions on the operating system and might not be available on all platforms.\n\nBefore installing, ensure you have:\n- Python 3.9 or higher\n- Optional: [CUDA toolkit](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/) for GPU support\n\n#### Option 1: Install Pruna using pip\n\nPruna is available on PyPI, so you can [install it using pip](https://docs.pruna.ai/en/stable/setup/install.html):\n\n```bash\npip install pruna\n```\n\n#### Option 2: Install Pruna from source\n\nYou can also install Pruna directly from source by cloning the repository and installing the package in editable mode:\n\n```bash\ngit clone https://github.com/pruna-ai/pruna.git\ncd pruna\npip install -e .\n```\n\n## <img src=\"./docs/assets/images/pruna_cool.png\" alt=\"Pruna Cool\" width=20></img> Quick Start\n\n\nGetting started with Pruna is easy-peasy pruna-squeezy!\n\nFirst, load any pre-trained model. Here's an example using Stable Diffusion:\n\n```python\nfrom diffusers import StableDiffusionPipeline\nbase_model = StableDiffusionPipeline.from_pretrained(\"stable-diffusion-v1-5/stable-diffusion-v1-5\")\n```\n\nThen, use Pruna's `smash` function to optimize your model. Pruna provides a variety of different optimization algorithms, allowing you to combine different algorithms to get the best possible results. You can customize the optimization process using `SmashConfig`:\n\n```python\nfrom pruna import smash, SmashConfig\n\n# Create and smash your model\nsmash_config = SmashConfig()\nsmash_config[\"cacher\"] = \"deepcache\"\nsmash_config[\"compiler\"] = \"stable_fast\"\nsmashed_model = smash(model=base_model, smash_config=smash_config)\n```\n\nYour model is now optimized and you can use it as you would use the original model:\n\n```python\nsmashed_model(\"An image of a cute prune.\").images[0]\n```\n\n<br>\n\nYou can then use our evaluation interface to measure the performance of your model:\n\n```python\nfrom pruna.evaluation.task import Task\nfrom pruna.evaluation.evaluation_agent import EvaluationAgent\nfrom pruna.data.pruna_datamodule import PrunaDataModule\n\ndatamodule = PrunaDataModule.from_string(\"LAION256\")\ndatamodule.limit_datasets(10)\ntask = Task(\"image_generation_quality\", datamodule=datamodule)\neval_agent = EvaluationAgent(task)\neval_agent.evaluate(smashed_model)\n```\n\nThis was the minimal example, but you are looking for the maximal example? You can check out our [documentation][documentation] for an overview of all supported [algorithms][docs-algorithms] as well as our tutorials for more use-cases and examples.\n\n## <img src=\"./docs/assets/images/pruna_heart.png\" alt=\"Pruna Heart\" width=20></img> Pruna Pro\n\nPruna has everything you need to get started on optimizing your own models. To push the efficiency of your models even further, we offer Pruna Pro. To give you a glimpse of what is possible with Pruna Pro, let us consider three of the most widely used diffusers pipelines and see how much smaller and faster we can make them. In addition to popular open-source algorithms, we use our proprietary Auto Caching algorithm. We compare the fidelity of the compressed models. Fidelity measures the similarity between the images of the compressed models and the images of the original model.\n\n### Stable Diffusion XL\n\nFor [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), we compare Auto Caching with [DeepCache](https://github.com/horseee/DeepCache) (available with Pruna). We combine these caching algorithms with torch.compile to get an additional **9%** reduction in inference latency, and we use [HQQ](https://github.com/mobiusml/hqq) 8-bit quantization to reduce the size of the model from **8.8GB** to **6.7GB**.\n\n<img src=\"./docs/assets/plots/benchmark_sdxl.svg\" alt=\"SDXL Benchmark\"/>\n\n### FLUX [dev]\nFor [FLUX [dev]](https://huggingface.co/black-forest-labs/FLUX.1-dev), we compare Auto Caching with the popular [TeaCache](https://github.com/ali-vilab/TeaCache) algorithm. In this case, we used [Stable Fast](https://github.com/chengzeyi/stable-fast) to reduce the latency of Auto Caching by additional **13%**, and [HQQ](https://github.com/mobiusml/hqq) with 8-bit reduced the size of FLUX from **33GB** to **23GB**.\n\n<img src=\"./docs/assets/plots/benchmark_flux.svg\" alt=\"FLUX [dev] Benchmark\"/>\n\n### HunyuanVideo\nFor [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo), we compare Auto Caching with [TeaCache](https://github.com/ali-vilab/TeaCache). Applying [HQQ](https://github.com/mobiusml/hqq) 8-bit quantization to the model reduced the size from **41GB** to **29GB**.\n\n<img src=\"./docs/assets/plots/benchmark_hunyuan.svg\" alt=\"HunyuanVideo Benchmark\"/>\n\n\n\n## <img src=\"./docs/assets/images/pruna_cool.png\" alt=\"Pruna Cool\" width=20></img> Algorithm Overview\n\nSince Pruna offers a broad range of optimization algorithms, the following table provides a high-level overview of all methods available in Pruna. For a detailed description of each algorithm, have a look at our [documentation](https://docs.pruna.ai/en/stable/).\n\n| Technique | Description | Speed | Memory | Quality |\n|--------------|-----------------------------------------------------------------------------------------------|:-----:|:------:|:-------:|\n| `batcher` | Groups multiple inputs together to be processed simultaneously, improving computational efficiency and reducing processing time. | \u2705 | \u274c | \u2796 |\n| `cacher` | Stores intermediate results of computations to speed up subsequent operations. | \u2705 | \u2796 | \u2796 |\n| `compiler` | Optimises the model with instructions for specific hardware. | \u2705 | \u2796 | \u2796 |\n| `distiller` | Trains a smaller, simpler model to mimic a larger, more complex model. | \u2705 | \u2705 | \u274c |\n| `quantizer` | Reduces the precision of weights and activations, lowering memory requirements. | \u2705 | \u2705 | \u274c |\n| `pruner` | Removes less important or redundant connections and neurons, resulting in a sparser, more efficient network. | \u2705 | \u2705 | \u274c |\n| `recoverer` | Restores the performance of a model after compression. | \u2796 | \u2796 | \u2705 |\n| `factorizer` | Factorization batches several small matrix multiplications into one large fused operation. | \u2705 | \u2796 | \u2796 |\n| `enhancer` | Enhances the model output by applying post-processing algorithms such as denoising or upscaling. | \u274c | \u2796 | \u2705 |\n| `distributer` | Distributes the inference, the model or certain calculations across multiple devices. | \u2705 | \u274c | \u2796 |\n| `kernel` | Kernels are specialized GPU routines that speed up parts of the computation. | \u2705 | \u2796 | \u2796 |\n\n\u2705 (improves), \u2796 (approx. the same), \u274c (worsens)\n\n<br><br>\n\n<p align=\"center\"><img src=\"./docs/assets/images/single_line.png\" alt=\"Pruna AI Logo\" width=600, height=30></img></p>\n\n<br>\n\n## <img src=\"./docs/assets/images/pruna_sad.png\" alt=\"Pruna Sad\" width=20></img> FAQ and Troubleshooting\n\nIf you can not find an answer to your question or problem in our [documentation][documentation], in our [FAQs][docs-faq] or in an existing issue, we are happy to help you! You can either get help from the Pruna community on [Discord][discord], join our [Office Hours][docs-office-hours] or open an issue on GitHub.\n\n## <img src=\"./docs/assets/images/pruna_heart.png\" alt=\"Pruna Heart\" width=20></img> Contributors\n\n\nThe Pruna package was made with \ud83d\udc9c by the Pruna AI team and our amazing contributors. [Contribute to the repository][docs-contributing] to become part of the Pruna family!\n\n[](https://github.com/PrunaAI/pruna/graphs/contributors)\n\n## <img src=\"./docs/assets/images/pruna_emotional.png\" alt=\"Pruna Emotional\" width=20></img> Citation\n\nIf you use Pruna in your research, feel free to cite the project! \ud83d\udc9c\n\n```\n@misc{pruna,\n title = {Efficient Machine Learning with Pruna},\n year = {2023},\n note = {Software available from pruna.ai},\n url={https://www.pruna.ai/}\n}\n```\n\n<br>\n\n<p align=\"center\"><img src=\"./docs/assets/images/triple_line.png\" alt=\"Pruna AI Logo\" width=600, height=30></img></p>\n\n[discord]: https://discord.gg/Tun8YgzxZ9\n[reddit]: https://www.reddit.com/r/PrunaAI/\n[x]: https://x.com/PrunaAI\n[devto]: https://dev.to/pruna-ai\n[website]: https://pruna.ai\n[huggingface]: https://huggingface.co/PrunaAI\n[replicate]: https://replicate.com/prunaai\n[documentation]: https://docs.pruna.ai/en/stable\n[docs-algorithms]: https://docs.pruna.ai/en/stable/compression.html\n[docs-faq]: https://docs.pruna.ai/en/stable/resources/faq.html\n[docs-office-hours]: https://docs.pruna.ai/en/stable/resources/office_hours.html\n[docs-contributing]: https://docs.pruna.ai/en/stable/docs_pruna/contributions/how_to_contribute.html\n",
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