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Sana 600M ControlNet HED Smashed

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.

Sourcehuggingface.co/AINovice2005/Sana_600M_ControlNet_HED-smashed/tree/e874c4487f1bffb28df57e5dbfb18b0b50a91ac9
KindOtherBase modelEfficient-Large-Model/Sana_600M_512pxVersionRevision e874c44Bgrade

Model source

Kind
Other
File size
230 MB–4.65 GB
Base model
Efficient-Large-Model/Sana_600M_512px
Version
vRevision e874c44
Updated
Oct 7, 2026
Downloads
5
Source
Hugging Face

Source description

Model Card for AINovice2005/Sana_600M_ControlNet_HED-smashed

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

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 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 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.

{
    "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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Sources

1 source
Hugging FacePrimaryVersion e874c4487f1bffb28df57e5dbfb18b0b50a91ac9
huggingface.co/AINovice2005/Sana_600M_ControlNet_HED-smashed/tree/e874c4487f1bffb28df57e5dbfb18b0b50a91ac9

Verified Oct 7

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Model artifacts

4 artifacts

backbone_quantized/qmodel.pt

pt · 851 MB · SHA-256 eaf139ed5499…d8f9 · Hugging Face

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text_encoder/model-00001-of-00002.safetensors

safetensors · 4.65 GB · SHA-256 890e888bd3ed…a704 · Hugging Face

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text_encoder/model-00002-of-00002.safetensors

safetensors · 230 MB · SHA-256 d2c702a2a0d0…082a · Hugging Face

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vae/diffusion_pytorch_model.safetensors

safetensors · 596 MB · SHA-256 d9955ff0c149…15b7 · Hugging Face

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Source excerpts

3 excerpts
ReadmeJul 30huggingface.co/AINovice2005/Sana_600M_ControlNet_HED-smashed

--- 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 } } ``` ## 🌍 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.com/invite/rskEr...

NoteOct 7huggingface.co/AINovice2005/Sana_600M_ControlNet_HED-smashed

Source context: 5 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo AINovice2005/Sana_600M_ControlNet_HED-smashed

NoteJul 30huggingface.co/AINovice2005/Sana_600M_ControlNet_HED-smashed

Source context: 1 downloads · 0 likes · Pipeline text-to-image · Library diffusers · Repo AINovice2005/Sana_600M_ControlNet_HED-smashed