#
Sharing pipelines and models

Share your pipeline or models and schedulers on the Hub with the PushToHubMixin class. This class:

1. creates a repository on the Hub
2. saves your model, scheduler, or pipeline files so they can be reloaded later
3. uploads folder containing these files to the Hub

This guide will show you how to upload your files to the Hub with the PushToHubMixin class.

Log in to your Hugging Face account with your access token.

notebook
hf CLI

    from huggingface_hub import notebook_login

    notebook_login()

##
Models

To push a model to the Hub, call push_to_hub() and specify the repository id of the model.

    from diffusers import ControlNetModel

    controlnet = ControlNetModel(
        block_out_channels=(32, 64),
        layers_per_block=2,
        in_channels=4,
        down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
        cross_attention_dim=32,
        conditioning_embedding_out_channels=(16, 32),
    )
    controlnet.push_to_hub("my-controlnet-model")

The push_to_hub() method saves the model’s `config.json` file and the weights are automatically saved as safetensors files.

Load the model again with from_pretrained().

    model = ControlNetModel.from_pretrained("your-namespace/my-controlnet-model")

##
Scheduler

To push a scheduler to the Hub, call push_to_hub() and specify the repository id of the scheduler.

    from diffusers import DDIMScheduler

    scheduler = DDIMScheduler(
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        clip_sample=False,
        set_alpha_to_one=False,
    )
    scheduler.push_to_hub("my-controlnet-scheduler")

The push_to_hub() function saves the scheduler’s `scheduler_config.json` file to the specified repository.

Load the scheduler again with from_pretrained().

    scheduler = DDIMScheduler.from_pretrained("your-namepsace/my-controlnet-scheduler")

##
Pipeline

To push a pipeline to the Hub, initialize the pipeline components with your desired parameters.

    from diffusers import (
        UNet2DConditionModel,
        AutoencoderKL,
        DDIMScheduler,
        StableDiffusionPipeline,
    )
    from transformers import CLIPTextModel, CLIPTextConfig, CLIPTokenizer

    unet = UNet2DConditionModel(
        block_out_channels=(32, 64),
        layers_per_block=2,
        sample_size=32,
        in_channels=4,
        out_channels=4,
        down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
        up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
        cross_attention_dim=32,
    )

    scheduler = DDIMScheduler(
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        clip_sample=False,
        set_alpha_to_one=False,
    )

    vae = AutoencoderKL(
        block_out_channels=[32, 64],
        in_channels=3,
        out_channels=3,
        down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
        up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
        latent_channels=4,
    )

    text_encoder_config = CLIPTextConfig(
        bos_token_id=0,
        eos_token_id=2,
        hidden_size=32,
        intermediate_size=37,
        layer_norm_eps=1e-05,
        num_attention_heads=4,
        num_hidden_layers=5,
        pad_token_id=1,
        vocab_size=1000,
    )
    text_encoder = CLIPTextModel(text_encoder_config)
    tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

Pass all components to the pipeline and call push_to_hub().

    components = {
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }

    pipeline = StableDiffusionPipeline(**components)
    pipeline.push_to_hub("my-pipeline")

The push_to_hub() method saves each component to a subfolder in the repository. Load the pipeline again with from_pretrained().

    pipeline = StableDiffusionPipeline.from_pretrained("your-namespace/my-pipeline")

##
Privacy

Set `private=True` in push_to_hub() to keep a model, scheduler, or pipeline files private.

    controlnet.push_to_hub("my-controlnet-model-private", private=True)

Private repositories are only visible to you. Other users won’t be able to clone the repository and it won’t appear in search results. Even if a user has the URL to your private repository, they’ll receive a `404 - Sorry, we can't find the page you are looking for`. You must be logged in to load a model from a private repository.

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