Push files to the Hub¶

🤗 Diffusers provides a PushToHubMixin for uploading your model, scheduler, or pipeline to the Hub. It is an easy way to store your files on the Hub, and also allows you to share your work with others. Under the hood, the PushToHubMixin:

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

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

You'll need to log in to your Hub account with your access token first:

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 to be stored on the Hub:

from mindone.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")

For models, you can also specify the variant of the weights to push to the Hub. For example, to push fp16 weights:

controlnet.push_to_hub("my-controlnet-model", variant="fp16")

The push_to_hub function saves the model's config.json file and the weights are automatically saved in the safetensors format.

Now you can reload the model from your repository on the Hub:

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 to be stored on the Hub:

from mindone.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.

Now you can reload the scheduler from your repository on the Hub:

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

You can also push an entire pipeline with all it's components to the Hub. For example, initialize the components of a StableDiffusionPipeline with the parameters you want:

from mindone.diffusers import (
    UNet2DConditionModel,
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionPipeline,
)
from mindone.transformers import CLIPTextModel
from transformers import 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 of the components to the StableDiffusionPipeline and call push_to_hub to push the pipeline to the 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 function saves each component to a subfolder in the repository. Now you can reload the pipeline from your repository on the Hub:

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

Privacy¶

Set private=True in the push_to_hub function to keep your model, scheduler, or pipeline files private:

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

Private repositories are only visible to you, and other users won't be able to clone the repository and your repository 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.