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---
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
}
}
```
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