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---
library_name: diffusers
tags:
- pruna-ai
---
# Model Card for PrunaAI/FLUX.1-dev-smashed-test
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/FLUX.1-dev-smashed-test?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_hub(
"PrunaAI/FLUX.1-dev-smashed-test"
)
```
After loading the model, you can use the inference methods of the original model. Take a look at the [documentation](https://pruna.readthedocs.io/en/latest/index.html) for more usage 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": "fora",
"compiler": null,
"factorizer": "qkv_diffusers",
"pruner": null,
"quantizer": "torchao",
"fora_interval": 2,
"fora_start_step": 2,
"torchao_excluded_modules": "norm+embedding",
"torchao_quant_type": "int8dq",
"batch_size": 1,
"device": "cuda",
"save_fns": [
"save_before_apply",
"save_before_apply"
],
"load_fns": [
"diffusers"
],
"reapply_after_load": {
"factorizer": "qkv_diffusers",
"pruner": null,
"quantizer": "torchao",
"cacher": "fora",
"compiler": null,
"batcher": null
}
}
```
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