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Can be used to overwrite load and saveable variables (for example the pipeline components of the |
specific pipeline class). The overwritten components are directly passed to the pipelines __init__ |
method. See example below for more information. Instantiate a AutoencoderKL from pretrained ControlNet weights saved in the original .ckpt or |
.safetensors format. The pipeline is set in evaluation mode (model.eval()) by default. Make sure to pass both image_size and scaling_factor to from_single_file() if you’re loading |
a VAE from SDXL or a Stable Diffusion v2 model or higher. Examples: Copied from diffusers import AutoencoderKL |
url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors" # can also be local file |
model = AutoencoderKL.from_single_file(url) FromOriginalControlnetMixin class diffusers.loaders.FromOriginalControlNetMixin < source > ( ) Load pretrained ControlNet weights saved in the .ckpt or .safetensors format into a ControlNetModel. from_single_file < source > ( pretrained_model_link_or_path **kwargs ) Parameters pretrained_model_link_or_path (str or os.PathLike, optional) — |
Can be either: |
A link to the .ckpt file (for example |
"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt") on the Hub. |
A path to a file containing all pipeline weights. |
torch_dtype (str or torch.dtype, optional) — |
Override the default torch.dtype and load the model with another dtype. If "auto" is passed, the |
dtype is automatically derived from the model’s weights. force_download (bool, optional, defaults to False) — |
Whether or not to force the (re-)download of the model weights and configuration files, overriding the |
cached versions if they exist. cache_dir (Union[str, os.PathLike], optional) — |
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache |
is not used. resume_download (bool, optional, defaults to False) — |
Whether or not to resume downloading the model weights and configuration files. If set to False, any |
incompletely downloaded files are deleted. proxies (Dict[str, str], optional) — |
A dictionary of proxy servers to use by protocol or endpoint, for example, {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. local_files_only (bool, optional, defaults to False) — |
Whether to only load local model weights and configuration files or not. If set to True, the model |
won’t be downloaded from the Hub. token (str or bool, optional) — |
The token to use as HTTP bearer authorization for remote files. If True, the token generated from |
diffusers-cli login (stored in ~/.huggingface) is used. revision (str, optional, defaults to "main") — |
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier |
allowed by Git. use_safetensors (bool, optional, defaults to None) — |
If set to None, the safetensors weights are downloaded if they’re available and if the |
safetensors library is installed. If set to True, the model is forcibly loaded from safetensors |
weights. If set to False, safetensors weights are not loaded. image_size (int, optional, defaults to 512) — |
The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable |
Diffusion v2 base model. Use 768 for Stable Diffusion v2. upcast_attention (bool, optional, defaults to None) — |
Whether the attention computation should always be upcasted. kwargs (remaining dictionary of keyword arguments, optional) — |
Can be used to overwrite load and saveable variables (for example the pipeline components of the |
specific pipeline class). The overwritten components are directly passed to the pipelines __init__ |
method. See example below for more information. Instantiate a ControlNetModel from pretrained ControlNet weights saved in the original .ckpt or |
.safetensors format. The pipeline is set in evaluation mode (model.eval()) by default. Examples: Copied from diffusers import StableDiffusionControlNetPipeline, ControlNetModel |
url = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth" # can also be a local path |
model = ControlNetModel.from_single_file(url) |
url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors" # can also be a local path |
pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet) |
Quicktour |
Get up and running with 🧨 Diffusers quickly! |
Whether you’re a developer or an everyday user, this quick tour will help you get started and show you how to use DiffusionPipeline for inference. |
Before you begin, make sure you have all the necessary libraries installed: |
Copied |
pip install --upgrade diffusers accelerate transformers |
accelerate speeds up model loading for inference and training |
transformers is required to run the most popular diffusion models, such as Stable Diffusion |
DiffusionPipeline |
The DiffusionPipeline is the easiest way to use a pre-trained diffusion system for inference. You can use the DiffusionPipeline out-of-the-box for many tasks across different modalities. Take a look at the table below for some supported tasks: |
Task |
Description |
Pipeline |
Unconditional Image Generation |
generate an image from gaussian noise |
unconditional_image_generation |
Text-Guided Image Generation |
generate an image given a text prompt |
conditional_image_generation |
Text-Guided Image-to-Image Translation |
adapt an image guided by a text prompt |
img2img |
Text-Guided Image-Inpainting |
fill the masked part of an image given the image, the mask and a text prompt |
inpaint |
Text-Guided Depth-to-Image Translation |
adapt parts of an image guided by a text prompt while preserving structure via depth estimation |
depth2image |
For more in-detail information on how diffusion pipelines function for the different tasks, please have a look at the Using Diffusers section. |
As an example, start by creating an instance of DiffusionPipeline and specify which pipeline checkpoint you would like to download. |
You can use the DiffusionPipeline for any Diffusers’ checkpoint. |
In this guide though, you’ll use DiffusionPipeline for text-to-image generation with Stable Diffusion. |
For Stable Diffusion, please carefully read its license before running the model. |
This is due to the improved image generation capabilities of the model and the potentially harmful content that could be produced with it. |
Please, head over to your stable diffusion model of choice, e.g. runwayml/stable-diffusion-v1-5, and read the license. |
You can load the model as follows: |
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>>> from diffusers import DiffusionPipeline |
>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") |
The DiffusionPipeline downloads and caches all modeling, tokenization, and scheduling components. |
Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on GPU. |
You can move the generator object to GPU, just like you would in PyTorch. |
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>>> pipeline.to("cuda") |
Now you can use the pipeline on your text prompt: |
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