pruna-test / test-save-tiny-stable-diffusion-pipe-smashed-pro

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

This model was created using the 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:

pip install pruna_pro

You can use the diffusers library to load the model 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:

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 . Alternatively, you can visit the Pruna documentation 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.

{
    "batcher": null,
    "cacher": null,
    "compiler": null,
    "decoder": null,
    "distiller": null,
    "distributer": null,
    "enhancer": null,
    "factorizer": null,
    "kernel": null,
    "pruner": null,
    "quantizer": null,
    "recoverer": null,
    "resampler": null,
    "batch_size": 1,
    "device": "cpu",
    "device_map": null,
    "save_fns": [],
    "load_fns": [
        "diffusers"
    ],
    "reapply_after_load": {
        "factorizer": null,
        "pruner": null,
        "quantizer": null,
        "distiller": null,
        "kernel": null,
        "cacher": null,
        "resampler": null,
        "recoverer": null,
        "distributer": null,
        "compiler": null,
        "batcher": null,
        "enhancer": null,
        "decoder": null
    }
}

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