README.md

---
library_name: diffusers
tags:
- pruna-ai
license: apache-2.0
base_model:
- Efficient-Large-Model/Sana_600M_512px
---

# Model Card for AINovice2005/Sana_600M_ControlNet_HED-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/AINovice2005/Sana_600M_ControlNet_HED-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_hub(
    "AINovice2005/Sana_600M_ControlNet_HED-smashed"
)
```

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": null,
    "compiler": null,
    "factorizer": 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,
        "cacher": null,
        "compiler": null,
        "batcher": null
    }
}
```

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