Sharing pipelines and models

Share your pipelines, models, and schedulers on the Hub with PushToHubMixin. This mixin:

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

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

from huggingface_hub import notebook_login

notebook_login()

Push to your user namespace with a short id ("my-controlnet-model") or to an org with "your-org/my-controlnet-model".

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 ControlNetModel.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-ddim-scheduler")

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

Load the scheduler again with from_pretrained().

scheduler = DDIMScheduler.from_pretrained("your-namespace/my-ddim-scheduler")

Pipeline

To push a pipeline to the Hub, load it with from_pretrained(), then call push_to_hub() with a repository id.

import torch
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "Qwen/Qwen-Image", dtype=torch.bfloat16, device_map="cuda"  # or "mps", "xpu", "cpu"
)
pipeline.push_to_hub("your-namespace/my-qwen-image")

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

pipeline = DiffusionPipeline.from_pretrained("your-namespace/my-qwen-image")

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)

Pass create_pr=True to open a pull request on an existing Hub repository instead of pushing straight to the default branch.

Models and pipelines also accept variant= on push when you want a named weight file such as fp16. Schedulers do not use variant.

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.