Hugging Face's logo

LenSch
/
caching_only

Text-to-Image
Diffusers
Safetensors
Pruna AI
English
FluxPipeline
Model card Files
xet
Community
caching_only
File size: 2,864 Bytes 
af4524a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
 
---
language:
- en
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
tags:
- safetensors
- pruna-ai
extra_gated_prompt: By clicking "Agree", you agree to the [FluxDev Non-Commercial
  License Agreement](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)
  and acknowledge the [Acceptable Use Policy](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/POLICY.md).
---

# Model Card for LenSch/caching_only

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 library_name library to load the model](https://huggingface.co/LenSch/caching_only?library=library_name) 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(
    "LenSch/caching_only"
)
# we can then run inference using the methods supported by the base model
```

 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": "fora",
    "compiler": "stable_fast",
    "factorizer": null,
    "kernel": null,
    "pruner": null,
    "quantizer": null,
    "fora_interval": 4,
    "fora_start_step": 1,
    "batch_size": 1,
    "device": "cuda:0",
    "device_map": null,
    "save_fns": [
        "save_before_apply"
    ],
    "load_fns": [
        "diffusers"
    ],
    "reapply_after_load": {
        "factorizer": null,
        "pruner": null,
        "quantizer": null,
        "kernel": null,
        "cacher": "fora",
        "compiler": "stable_fast",
        "batcher": null
    }
}
```

## 🌍 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/)