README.md

---
library_name: diffusers
tags:
- pruna_pro-ai
- pruna-ai
- safetensors
---

# Model Card for pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro

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_pro
```

You can [use the diffusers library to load the model](https://huggingface.co/pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro?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_pro import PrunaProModel

loaded_model = PrunaProModel.from_pretrained(
    "pruna-test/test-save-tiny-stable-diffusion-pipe-smashed-pro"
)
# 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/hf-internal-testing/tiny-stable-diffusion-pipe?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
{
    "adaptive": false,
    "auto": false,
    "awq": false,
    "bottleneck": false,
    "c_generate": false,
    "c_translate": false,
    "c_whisper": false,
    "deepcache": false,
    "diffusers_higgs": false,
    "diffusers_int8": false,
    "fastercache": false,
    "flash_attn3": false,
    "flux_caching": false,
    "fora": false,
    "fp4": false,
    "fp8": false,
    "gptq": false,
    "half": false,
    "higgs": false,
    "hqq": false,
    "hqq_diffusers": false,
    "hyper": false,
    "ifw": false,
    "img2img_denoise": false,
    "ipex_llm": false,
    "llm_int8": false,
    "pab": false,
    "padding_pruning": false,
    "periodic": false,
    "prores": false,
    "qkv_diffusers": false,
    "quanto": false,
    "realesrgan_upscale": false,
    "reduce_noe": false,
    "ring_attn": false,
    "sage_attn": false,
    "stable_fast": false,
    "taylor": false,
    "taylor_auto": false,
    "text_to_image_distillation_inplace_perp": false,
    "text_to_image_distillation_lora": false,
    "text_to_image_distillation_perp": false,
    "text_to_image_inplace_perp": false,
    "text_to_image_lora": false,
    "text_to_image_perp": false,
    "text_to_text_inplace_perp": false,
    "text_to_text_lora": false,
    "text_to_text_perp": false,
    "torch_compile": false,
    "torch_dynamic": false,
    "torch_structured": false,
    "torch_unstructured": false,
    "torchao": false,
    "torchao_autoquant": false,
    "whisper_s2t": false,
    "x_fast": false,
    "zipar": false,
    "batch_size": 1,
    "device": "cpu",
    "device_map": null,
    "save_fns": [],
    "save_artifacts_fns": [],
    "load_fns": [
        "diffusers"
    ],
    "load_artifacts_fns": [],
    "reapply_after_load": {}
}
```

## 🌍 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/)