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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
{
"awq": false,
"c_generate": false,
"c_translate": false,
"c_whisper": false,
"deepcache": false,
"diffusers_int8": false,
"fastercache": false,
"flash_attn3": false,
"fora": false,
"gptq": false,
"half": false,
"hqq": false,
"hqq_diffusers": true,
"hyper": false,
"ifw": false,
"img2img_denoise": false,
"ipex_llm": false,
"llm_int8": false,
"pab": false,
"padding_pruning": false,
"qkv_diffusers": false,
"quanto": false,
"realesrgan_upscale": false,
"reduce_noe": false,
"ring_attn": false,
"sage_attn": false,
"stable_fast": 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,
"whisper_s2t": false,
"x_fast": false,
"zipar": false,
"hqq_diffusers_backend": "torchao_int4",
"hqq_diffusers_group_size": 64,
"hqq_diffusers_target_modules": null,
"hqq_diffusers_weight_bits": 8,
"batch_size": 1,
"device": "cuda:0",
"device_map": null,
"save_fns": [
"hqq_diffusers"
],
"save_artifacts_fns": [],
"load_fns": [
"hqq_diffusers"
],
"load_artifacts_fns": [],
"reapply_after_load": {
"hqq_diffusers": false
}
}
```
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