---
datasets:
- zzliang/GRIT
- wanng/midjourney-v5-202304-clean
library_name: diffusers
license: apache-2.0
tags:
- pruna-ai
- safetensors
pinned: true
---

# Model Card for PrunaAI/Segmind-Vega-smashed

This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.

## Usage

First things first, you need to install the pruna library:

```bash
pip install pruna
```

You can [use the diffusers library to load the model](https://huggingface.co/PrunaAI/Segmind-Vega-smashed?library=diffusers) but this might not include all optimizations by default.

To ensure that all optimizations are applied, use the pruna library to load the model using the following code:

```python
from pruna import PrunaModel

loaded_model = PrunaModel.from_pretrained(
    "PrunaAI/Segmind-Vega-smashed"
)
# we can then run inference using the methods supported by the base model
```


For inference, you can use the inference methods of the original model like shown in [the original model card](https://huggingface.co/segmind/Segmind-Vega?library=diffusers).
 Alternatively, you can visit [the Pruna documentation](https://docs.pruna.ai/en/stable/) for more information.

## Smash Configuration

The compression configuration of the model is stored in the `smash_config.json` file, which describes the optimization methods that were applied to the model.

```bash
{
    "batcher": null,
    "cacher": null,
    "compiler": null,
    "factorizer": null,
    "kernel": null,
    "pruner": null,
    "quantizer": "hqq_diffusers",
    "hqq_diffusers_backend": "torchao_int4",
    "hqq_diffusers_group_size": 64,
    "hqq_diffusers_weight_bits": 8,
    "batch_size": 1,
    "device": "cuda",
    "device_map": null,
    "save_fns": [
        "hqq_diffusers"
    ],
    "load_fns": [
        "hqq_diffusers"
    ],
    "reapply_after_load": {
        "factorizer": null,
        "pruner": null,
        "quantizer": null,
        "kernel": null,
        "cacher": null,
        "compiler": null,
        "batcher": null
    }
}
```

## 🌍 Join the Pruna AI community!

[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/JFQmtFKCjd)
[![Reddit](https://img.shields.io/reddit/subreddit-subscribers/PrunaAI?style=social)](https://www.reddit.com/r/PrunaAI/)