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A config dictionary from which the Python class is instantiated. Make sure to only load configuration |
files of compatible classes. return_unused_kwargs (bool, optional, defaults to False) — |
Whether kwargs that are not consumed by the Python class should be returned or not. kwargs (remaining dictionary of keyword arguments, optional) — |
Can be used to update the configuration object (after it is loaded) and initiate the Python class. |
**kwargs are passed directly to the underlying scheduler/model’s __init__ method and eventually |
overwrite the same named arguments in config. Returns |
ModelMixin or SchedulerMixin |
A model or scheduler object instantiated from a config dictionary. |
Instantiate a Python class from a config dictionary. Examples: Copied >>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler |
>>> # Download scheduler from huggingface.co and cache. |
>>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32") |
>>> # Instantiate DDIM scheduler class with same config as DDPM |
>>> scheduler = DDIMScheduler.from_config(scheduler.config) |
>>> # Instantiate PNDM scheduler class with same config as DDPM |
>>> scheduler = PNDMScheduler.from_config(scheduler.config) save_config < source > ( save_directory: Union push_to_hub: bool = False **kwargs ) Parameters save_directory (str or os.PathLike) — |
Directory where the configuration JSON file is saved (will be created if it does not exist). push_to_hub (bool, optional, defaults to False) — |
Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the |
repository you want to push to with repo_id (will default to the name of save_directory in your |
namespace). kwargs (Dict[str, Any], optional) — |
Additional keyword arguments passed along to the push_to_hub() method. Save a configuration object to the directory specified in save_directory so that it can be reloaded using the |
from_config() class method. to_json_file < source > ( json_file_path: Union ) Parameters json_file_path (str or os.PathLike) — |
Path to the JSON file to save a configuration instance’s parameters. Save the configuration instance’s parameters to a JSON file. to_json_string < source > ( ) → str Returns |
str |
String containing all the attributes that make up the configuration instance in JSON format. |
Serializes the configuration instance to a JSON string. |
Latent Diffusion |
Overview |
Latent Diffusion was proposed in High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. |
The abstract of the paper is the following: |
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. Howev... |
The original codebase can be found here. |
Tips: |
Available Pipelines: |
Pipeline |
Tasks |
Colab |
pipeline_latent_diffusion.py |
Text-to-Image Generation |
- |
pipeline_latent_diffusion_superresolution.py |
Super Resolution |
- |
Examples: |
LDMTextToImagePipeline |
class diffusers.LDMTextToImagePipeline |
< |
source |
> |
( |
vqvae: typing.Union[diffusers.models.vq_model.VQModel, diffusers.models.autoencoder_kl.AutoencoderKL] |
bert: PreTrainedModel |
tokenizer: PreTrainedTokenizer |
unet: typing.Union[diffusers.models.unet_2d.UNet2DModel, diffusers.models.unet_2d_condition.UNet2DConditionModel] |
scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler] |
) |
Parameters |
vqvae (VQModel) — |
Vector-quantized (VQ) Model to encode and decode images to and from latent representations. |
bert (LDMBertModel) — |
Text-encoder model based on BERT architecture. |
tokenizer (transformers.BertTokenizer) — |
Tokenizer of class |
BertTokenizer. |
unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents. |
scheduler (SchedulerMixin) — |
A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of |
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. |
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