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revision (str, optional, defaults to "main") — |
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a |
git-based system for storing models and other artifacts on huggingface.co, so revision can be any |
identifier allowed by git. |
subfolder (str, optional, defaults to "") — |
In case the relevant files are located inside a subfolder of the model repo (either remote in |
huggingface.co or downloaded locally), you can specify the folder name here. |
Instantiate a Python class from a config dictionary |
It is required to be logged in (huggingface-cli login) when you want to use private or gated |
models. |
Activate the special “offline-mode” to |
use this method in a firewalled environment. |
save_config |
< |
source |
> |
( |
save_directory: typing.Union[str, os.PathLike] |
push_to_hub: bool = False |
**kwargs |
) |
Parameters |
save_directory (str or os.PathLike) — |
Directory where the configuration JSON file will be saved (will be created if it does not exist). |
Save a configuration object to the directory save_directory, so that it can be re-loaded using the |
from_config() class method. |
to_json_file |
< |
source |
> |
( |
json_file_path: typing.Union[str, os.PathLike] |
) |
Parameters |
json_file_path (str or os.PathLike) — |
Path to the JSON file in which this configuration instance’s parameters will be saved. |
Save this instance to a JSON file. |
to_json_string |
< |
source |
> |
( |
) |
→ |
str |
Returns |
str |
String containing all the attributes that make up this configuration instance in JSON format. |
Serializes this instance to a JSON string. |
Under further construction 🚧, open a PR if you want to contribute! |
Denoising diffusion probabilistic models (DDPM) |
Overview |
Denoising Diffusion Probabilistic Models |
(DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes the diffusion based model of the same name, but in the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline. |
The abstract of the paper is the following: |
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion prob... |
The original paper can be found here. |
DDPMScheduler |
class diffusers.DDPMScheduler |
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