#
Models

Diffusers contains pretrained models for popular algorithms and modules for creating the next set of diffusion models. The primary function of these models is to denoise an input sample, by modeling the distribution $p\theta(\mathbf{x}{t-1}|\mathbf{x}_t)$. The models are built on the base class [‘ModelMixin’] that is a `torch.nn.module` with basic functionality for saving and loading models both locally and from the HuggingFace hub.

##
ModelMixin

###
class diffusers.ModelMixin

< source >

( )

Base class for all models.

ModelMixin takes care of storing the configuration of the models and handles methods for loading, downloading and saving models.

- config_name (`str`) — A filename under which the model should be stored when calling save_pretrained().

####
disable_gradient_checkpointing

< source >

( )

Deactivates gradient checkpointing for the current model.

Note that in other frameworks this feature can be referred to as “activation checkpointing” or “checkpoint activations”.

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention as implemented in xformers.

####
enable_gradient_checkpointing

< source >

( )

Activates gradient checkpointing for the current model.

Note that in other frameworks this feature can be referred to as “activation checkpointing” or “checkpoint activations”.

####
enable_xformers_memory_efficient_attention

< source >

( attention_op: typing.Optional[typing.Callable] = None )

Parameters

- attention_op (`Callable`, optional) — Override the default `None` operator for use as `op` argument to the `memory_efficient_attention()` function of xFormers.

Enable memory efficient attention as implemented in xformers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference time. Speed up at training time is not guaranteed.

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

Examples:

    >>> import torch
    >>> from diffusers import UNet2DConditionModel
    >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

    >>> model = UNet2DConditionModel.from_pretrained(
    ...     "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16
    ... )
    >>> model = model.to("cuda")
    >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)

####
from_pretrained

< source >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`, optional) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
  - A path to a directory containing model weights saved using `~ModelMixin.save_config`, e.g., `./my_model_directory/`.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- torch_dtype (`str` or `torch.dtype`, optional) — Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype will be 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.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (i.e., do not try to download the model).
- use_auth_token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `diffusers-cli login` (stored in `~/.huggingface`).
- 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.
- from_flax (`bool`, optional, defaults to `False`) — Load the model weights from a Flax checkpoint save file.
- 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.
- mirror (`str`, optional) — Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information.
- device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, optional) — A map that specifies where each submodule should go. It doesn’t need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device.

  To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For more information about each option see designing a device map.

- low_cpu_mem_usage (`bool`, optional, defaults to `True` if torch version >= 1.9.0 else `False`) — Speed up model loading by not initializing the weights and only loading the pre-trained weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch, setting this argument to `True` will raise an error.
- variant (`str`, optional) — If specified load weights from `variant` filename, e.g. pytorch_model..bin. `variant` is ignored when using `from_flax`.

Instantiate a pretrained pytorch model from a pre-trained model configuration.

The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`.

The warning Weights from XXX not initialized from pretrained model means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task.

The warning Weights from XXX not used in YYY means that the layer XXX is not used by YYY, therefore those weights are discarded.

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.

####
num_parameters

< source >

( only_trainable: bool = False exclude_embeddings: bool = False ) → `int`

Parameters

- only_trainable (`bool`, optional, defaults to `False`) — Whether or not to return only the number of trainable parameters
- exclude_embeddings (`bool`, optional, defaults to `False`) — Whether or not to return only the number of non-embeddings parameters

Returns

`int`

The number of parameters.

Get number of (optionally, trainable or non-embeddings) parameters in the module.

####
save_pretrained

< source >

( save_directory: typing.Union[str, os.PathLike] is_main_process: bool = True save_function: typing.Callable = None safe_serialization: bool = False variant: typing.Optional[str] = None )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to which to save. Will be created if it doesn’t exist.
- is_main_process (`bool`, optional, defaults to `True`) — Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on the main process to avoid race conditions.
- save_function (`Callable`) — The function to use to save the state dictionary. Useful on distributed training like TPUs when one need to replace `torch.save` by another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`.
- safe_serialization (`bool`, optional, defaults to `False`) — Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
- variant (`str`, optional) — If specified, weights are saved in the format pytorch_model..bin.

Save a model and its configuration file to a directory, so that it can be re-loaded using the `[from_pretrained()](/docs/diffusers/main/en/api/models#diffusers.ModelMixin.from_pretrained)` class method.

##
UNet2DOutput

###
class diffusers.models.unet_2d.UNet2DOutput

< source >

( sample: FloatTensor )

Parameters

- sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Hidden states output. Output of last layer of model.

##
UNet2DModel

###
class diffusers.UNet2DModel

< source >

( sample_size: typing.Union[int, typing.Tuple[int, int], NoneType] = None in_channels: int = 3 out_channels: int = 3 center_input_sample: bool = False time_embedding_type: str = 'positional' freq_shift: int = 0 flip_sin_to_cos: bool = True down_block_types: typing.Tuple[str] = ('DownBlock2D', 'AttnDownBlock2D', 'AttnDownBlock2D', 'AttnDownBlock2D') up_block_types: typing.Tuple[str] = ('AttnUpBlock2D', 'AttnUpBlock2D', 'AttnUpBlock2D', 'UpBlock2D') block_out_channels: typing.Tuple[int] = (224, 448, 672, 896) layers_per_block: int = 2 mid_block_scale_factor: float = 1 downsample_padding: int = 1 act_fn: str = 'silu' attention_head_dim: typing.Optional[int] = 8 norm_num_groups: int = 32 norm_eps: float = 1e-05 resnet_time_scale_shift: str = 'default' add_attention: bool = True class_embed_type: typing.Optional[str] = None num_class_embeds: typing.Optional[int] = None )

Parameters

- sample_size (`int` or `Tuple[int, int]`, optional, defaults to `None`) — Height and width of input/output sample.
- in_channels (`int`, optional, defaults to 3) — Number of channels in the input image.
- out_channels (`int`, optional, defaults to 3) — Number of channels in the output.
- center_input_sample (`bool`, optional, defaults to `False`) — Whether to center the input sample.
- time_embedding_type (`str`, optional, defaults to `"positional"`) — Type of time embedding to use.
- freq_shift (`int`, optional, defaults to 0) — Frequency shift for fourier time embedding.
- flip_sin_to_cos (`bool`, optional, defaults to — obj:`True`): Whether to flip sin to cos for fourier time embedding.
- down_block_types (`Tuple[str]`, optional, defaults to — obj:`("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block types.
- mid_block_type (`str`, optional, defaults to `"UNetMidBlock2D"`) — The mid block type. Choose from `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.
- up_block_types (`Tuple[str]`, optional, defaults to — obj:`("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, optional, defaults to — obj:`(224, 448, 672, 896)`): Tuple of block output channels.
- layers_per_block (`int`, optional, defaults to `2`) — The number of layers per block.
- mid_block_scale_factor (`float`, optional, defaults to `1`) — The scale factor for the mid block.
- downsample_padding (`int`, optional, defaults to `1`) — The padding for the downsample convolution.
- act_fn (`str`, optional, defaults to `"silu"`) — The activation function to use.
- attention_head_dim (`int`, optional, defaults to `8`) — The attention head dimension.
- norm_num_groups (`int`, optional, defaults to `32`) — The number of groups for the normalization.
- norm_eps (`float`, optional, defaults to `1e-5`) — The epsilon for the normalization.
- resnet_time_scale_shift (`str`, optional, defaults to `"default"`) — Time scale shift config for resnet blocks, see `ResnetBlock2D`. Choose from `default` or `scale_shift`.
- class_embed_type (`str`, optional, defaults to None) — The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, `"timestep"`, or `"identity"`.
- num_class_embeds (`int`, optional, defaults to None) — Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing class conditioning with `class_embed_type` equal to `None`.

UNet2DModel is a 2D UNet model that takes in a noisy sample and a timestep and returns sample shaped output.

This model inherits from ModelMixin. Check the superclass documentation for the generic methods the library implements for all the model (such as downloading or saving, etc.)

####
forward

< source >

( sample: FloatTensor timestep: typing.Union[torch.Tensor, float, int] class_labels: typing.Optional[torch.Tensor] = None return_dict: bool = True ) → UNet2DOutput or `tuple`

Parameters

- sample (`torch.FloatTensor`) — (batch, channel, height, width) noisy inputs tensor
- timestep (`torch.FloatTensor` or `float` or `int) — (batch) timesteps
- class_labels (`torch.FloatTensor`, optional, defaults to `None`) — Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a UNet2DOutput instead of a plain tuple.

Returns

UNet2DOutput or `tuple`

UNet2DOutput if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.

##
UNet1DOutput

###
class diffusers.models.unet_1d.UNet1DOutput

< source >

( sample: FloatTensor )

Parameters

- sample (`torch.FloatTensor` of shape `(batch_size, num_channels, sample_size)`) — Hidden states output. Output of last layer of model.

##
UNet1DModel

###
class diffusers.UNet1DModel

< source >

( sample_size: int = 65536 sample_rate: typing.Optional[int] = None in_channels: int = 2 out_channels: int = 2 extra_in_channels: int = 0 time_embedding_type: str = 'fourier' flip_sin_to_cos: bool = True use_timestep_embedding: bool = False freq_shift: float = 0.0 down_block_types: typing.Tuple[str] = ('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') up_block_types: typing.Tuple[str] = ('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') mid_block_type: typing.Tuple[str] = 'UNetMidBlock1D' out_block_type: str = None block_out_channels: typing.Tuple[int] = (32, 32, 64) act_fn: str = None norm_num_groups: int = 8 layers_per_block: int = 1 downsample_each_block: bool = False )

Parameters

- sample_size (`int`, optional) — Default length of sample. Should be adaptable at runtime.
- in_channels (`int`, optional, defaults to 2) — Number of channels in the input sample.
- out_channels (`int`, optional, defaults to 2) — Number of channels in the output.
- time_embedding_type (`str`, optional, defaults to `"fourier"`) — Type of time embedding to use.
- freq_shift (`float`, optional, defaults to 0.0) — Frequency shift for fourier time embedding.
- flip_sin_to_cos (`bool`, optional, defaults to — obj:`False`): Whether to flip sin to cos for fourier time embedding.
- down_block_types (`Tuple[str]`, optional, defaults to — obj:`("DownBlock1D", "DownBlock1DNoSkip", "AttnDownBlock1D")`): Tuple of downsample block types.
- up_block_types (`Tuple[str]`, optional, defaults to — obj:`("UpBlock1D", "UpBlock1DNoSkip", "AttnUpBlock1D")`): Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, optional, defaults to — obj:`(32, 32, 64)`): Tuple of block output channels.
- mid_block_type (`str`, optional, defaults to “UNetMidBlock1D”) — block type for middle of UNet.
- out_block_type (`str`, optional, defaults to `None`) — optional output processing of UNet.
- act_fn (`str`, optional, defaults to None) — optional activitation function in UNet blocks.
- norm_num_groups (`int`, optional, defaults to 8) — group norm member count in UNet blocks.
- layers_per_block (`int`, optional, defaults to 1) — added number of layers in a UNet block.
- downsample_each_block (`int`, optional, defaults to False — experimental feature for using a UNet without upsampling.

UNet1DModel is a 1D UNet model that takes in a noisy sample and a timestep and returns sample shaped output.

This model inherits from ModelMixin. Check the superclass documentation for the generic methods the library implements for all the model (such as downloading or saving, etc.)

####
forward

< source >

( sample: FloatTensor timestep: typing.Union[torch.Tensor, float, int] return_dict: bool = True ) → UNet1DOutput or `tuple`

Parameters

- sample (`torch.FloatTensor`) — `(batch_size, sample_size, num_channels)` noisy inputs tensor
- timestep (`torch.FloatTensor` or `float` or `int) — (batch) timesteps
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a UNet1DOutput instead of a plain tuple.

Returns

UNet1DOutput or `tuple`

UNet1DOutput if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.

##
UNet2DConditionOutput

###
class diffusers.models.unet_2d_condition.UNet2DConditionOutput

< source >

( sample: FloatTensor )

Parameters

- sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.

##
UNet2DConditionModel

###
class diffusers.UNet2DConditionModel

< source >

( sample_size: typing.Optional[int] = None in_channels: int = 4 out_channels: int = 4 center_input_sample: bool = False flip_sin_to_cos: bool = True freq_shift: int = 0 down_block_types: typing.Tuple[str] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D') mid_block_type: typing.Optional[str] = 'UNetMidBlock2DCrossAttn' up_block_types: typing.Tuple[str] = ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D') only_cross_attention: typing.Union[bool, typing.Tuple[bool]] = False block_out_channels: typing.Tuple[int] = (320, 640, 1280, 1280) layers_per_block: int = 2 downsample_padding: int = 1 mid_block_scale_factor: float = 1 act_fn: str = 'silu' norm_num_groups: typing.Optional[int] = 32 norm_eps: float = 1e-05 cross_attention_dim: int = 1280 attention_head_dim: typing.Union[int, typing.Tuple[int]] = 8 dual_cross_attention: bool = False use_linear_projection: bool = False class_embed_type: typing.Optional[str] = None num_class_embeds: typing.Optional[int] = None upcast_attention: bool = False resnet_time_scale_shift: str = 'default' time_embedding_type: str = 'positional' timestep_post_act: typing.Optional[str] = None time_cond_proj_dim: typing.Optional[int] = None conv_in_kernel: int = 3 conv_out_kernel: int = 3 projection_class_embeddings_input_dim: typing.Optional[int] = None )

Parameters

- sample_size (`int` or `Tuple[int, int]`, optional, defaults to `None`) — Height and width of input/output sample.
- in_channels (`int`, optional, defaults to 4) — The number of channels in the input sample.
- out_channels (`int`, optional, defaults to 4) — The number of channels in the output.
- center_input_sample (`bool`, optional, defaults to `False`) — Whether to center the input sample.
- flip_sin_to_cos (`bool`, optional, defaults to `False`) — Whether to flip the sin to cos in the time embedding.
- freq_shift (`int`, optional, defaults to 0) — The frequency shift to apply to the time embedding.
- down_block_types (`Tuple[str]`, optional, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`) — The tuple of downsample blocks to use.
- mid_block_type (`str`, optional, defaults to `"UNetMidBlock2DCrossAttn"`) — The mid block type. Choose from `UNetMidBlock2DCrossAttn` or `UNetMidBlock2DSimpleCrossAttn`, will skip the mid block layer if `None`.
- up_block_types (`Tuple[str]`, optional, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`) — The tuple of upsample blocks to use.
- only_cross_attention(`bool` or `Tuple[bool]`, optional, default to `False`) — Whether to include self-attention in the basic transformer blocks, see `BasicTransformerBlock`.
- block_out_channels (`Tuple[int]`, optional, defaults to `(320, 640, 1280, 1280)`) — The tuple of output channels for each block.
- layers_per_block (`int`, optional, defaults to 2) — The number of layers per block.
- downsample_padding (`int`, optional, defaults to 1) — The padding to use for the downsampling convolution.
- mid_block_scale_factor (`float`, optional, defaults to 1.0) — The scale factor to use for the mid block.
- act_fn (`str`, optional, defaults to `"silu"`) — The activation function to use.
- norm_num_groups (`int`, optional, defaults to 32) — The number of groups to use for the normalization. If `None`, it will skip the normalization and activation layers in post-processing
- norm_eps (`float`, optional, defaults to 1e-5) — The epsilon to use for the normalization.
- cross_attention_dim (`int`, optional, defaults to 1280) — The dimension of the cross attention features.
- attention_head_dim (`int`, optional, defaults to 8) — The dimension of the attention heads.
- resnet_time_scale_shift (`str`, optional, defaults to `"default"`) — Time scale shift config for resnet blocks, see `ResnetBlock2D`. Choose from `default` or `scale_shift`.
- class_embed_type (`str`, optional, defaults to None) — The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, `"timestep"`, `"identity"`, or `"projection"`.
- num_class_embeds (`int`, optional, defaults to None) — Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing class conditioning with `class_embed_type` equal to `None`.
- time_embedding_type (`str`, optional, default to `positional`) — The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
- timestep_post_act (`str, *optional*, default to` None`) -- The second activation function to use in timestep embedding. Choose from` silu`,` mish`and`gelu`.
- time_cond_proj_dim (`int`, optional, default to `None`) — The dimension of `cond_proj` layer in timestep embedding.
- conv_in_kernel (`int`, optional, default to `3`) — The kernel size of `conv_in` layer.
- conv_out_kernel (`int`, optional, default to `3`) — The kernel size of `conv_out` layer.
- projection_class_embeddings_input_dim (`int`, optional) — The dimension of the `class_labels` input when using the “projection” `class_embed_type`. Required when using the “projection” `class_embed_type`.

UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output.

This model inherits from ModelMixin. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.)

####
forward

< source >

( sample: FloatTensor timestep: typing.Union[torch.Tensor, float, int] encoder_hidden_states: Tensor class_labels: typing.Optional[torch.Tensor] = None timestep_cond: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None return_dict: bool = True ) → UNet2DConditionOutput or `tuple`

Parameters

- sample (`torch.FloatTensor`) — (batch, channel, height, width) noisy inputs tensor
- timestep (`torch.FloatTensor` or `float` or `int`) — (batch) timesteps
- encoder_hidden_states (`torch.FloatTensor`) — (batch, sequence_length, feature_dim) encoder hidden states
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a models.unet_2d_condition.UNet2DConditionOutput instead of a plain tuple.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under `self.processor` in diffusers.cross_attention.

Returns

UNet2DConditionOutput or `tuple`

UNet2DConditionOutput if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.

####
set_attention_slice

< source >

( slice_size )

Parameters

- slice_size (`str` or `int` or `list(int)`, optional, defaults to `"auto"`) — When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation.

When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

####
set_attn_processor

< source >

( processor: typing.Union[diffusers.models.cross_attention.CrossAttnProcessor, diffusers.models.cross_attention.XFormersCrossAttnProcessor, diffusers.models.cross_attention.SlicedAttnProcessor, diffusers.models.cross_attention.CrossAttnAddedKVProcessor, diffusers.models.cross_attention.SlicedAttnAddedKVProcessor, diffusers.models.cross_attention.LoRACrossAttnProcessor, diffusers.models.cross_attention.LoRAXFormersCrossAttnProcessor, typing.Dict[str, typing.Union[diffusers.models.cross_attention.CrossAttnProcessor, diffusers.models.cross_attention.XFormersCrossAttnProcessor, diffusers.models.cross_attention.SlicedAttnProcessor, diffusers.models.cross_attention.CrossAttnAddedKVProcessor, diffusers.models.cross_attention.SlicedAttnAddedKVProcessor, diffusers.models.cross_attention.LoRACrossAttnProcessor, diffusers.models.cross_attention.LoRAXFormersCrossAttnProcessor]]] )

Parameters

- `processor (`dict` of `AttnProcessor` or `AttnProcessor`) — The instantiated processor class or a dictionary of processor classes that will be set as the processor of all `CrossAttention` layers.
- In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors. —

##
DecoderOutput

###
class diffusers.models.vae.DecoderOutput

< source >

( sample: FloatTensor )

Parameters

- sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Decoded output sample of the model. Output of the last layer of the model.

Output of decoding method.

##
VQEncoderOutput

###
class diffusers.models.vq_model.VQEncoderOutput

< source >

( latents: FloatTensor )

Parameters

- latents (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Encoded output sample of the model. Output of the last layer of the model.

Output of VQModel encoding method.

##
VQModel

###
class diffusers.VQModel

< source >

( in_channels: int = 3 out_channels: int = 3 down_block_types: typing.Tuple[str] = ('DownEncoderBlock2D',) up_block_types: typing.Tuple[str] = ('UpDecoderBlock2D',) block_out_channels: typing.Tuple[int] = (64,) layers_per_block: int = 1 act_fn: str = 'silu' latent_channels: int = 3 sample_size: int = 32 num_vq_embeddings: int = 256 norm_num_groups: int = 32 vq_embed_dim: typing.Optional[int] = None scaling_factor: float = 0.18215 )

Parameters

- in_channels (int, optional, defaults to 3) — Number of channels in the input image.
- out_channels (int, optional, defaults to 3) — Number of channels in the output.
- down_block_types (`Tuple[str]`, optional, defaults to — obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
- up_block_types (`Tuple[str]`, optional, defaults to — obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, optional, defaults to — obj:`(64,)`): Tuple of block output channels.
- act_fn (`str`, optional, defaults to `"silu"`) — The activation function to use.
- latent_channels (`int`, optional, defaults to `3`) — Number of channels in the latent space.
- sample_size (`int`, optional, defaults to `32`) — TODO
- num_vq_embeddings (`int`, optional, defaults to `256`) — Number of codebook vectors in the VQ-VAE.
- vq_embed_dim (`int`, optional) — Hidden dim of codebook vectors in the VQ-VAE.
- scaling_factor (`float`, optional, defaults to `0.18215`) — The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the High-Resolution Image Synthesis with Latent Diffusion Models paper.

VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray Kavukcuoglu.

This model inherits from ModelMixin. Check the superclass documentation for the generic methods the library implements for all the model (such as downloading or saving, etc.)

####
forward

< source >

( sample: FloatTensor return_dict: bool = True )

Parameters

- sample (`torch.FloatTensor`) — Input sample.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `DecoderOutput` instead of a plain tuple.

##
AutoencoderKLOutput

###
class diffusers.models.autoencoder_kl.AutoencoderKLOutput

< source >

( latent_dist: DiagonalGaussianDistribution )

Parameters

- latent_dist (`DiagonalGaussianDistribution`) — Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`. `DiagonalGaussianDistribution` allows for sampling latents from the distribution.

Output of AutoencoderKL encoding method.

##
AutoencoderKL

###
class diffusers.AutoencoderKL

< source >

( in_channels: int = 3 out_channels: int = 3 down_block_types: typing.Tuple[str] = ('DownEncoderBlock2D',) up_block_types: typing.Tuple[str] = ('UpDecoderBlock2D',) block_out_channels: typing.Tuple[int] = (64,) layers_per_block: int = 1 act_fn: str = 'silu' latent_channels: int = 4 norm_num_groups: int = 32 sample_size: int = 32 scaling_factor: float = 0.18215 )

Parameters

- in_channels (int, optional, defaults to 3) — Number of channels in the input image.
- out_channels (int, optional, defaults to 3) — Number of channels in the output.
- down_block_types (`Tuple[str]`, optional, defaults to — obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
- up_block_types (`Tuple[str]`, optional, defaults to — obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, optional, defaults to — obj:`(64,)`): Tuple of block output channels.
- act_fn (`str`, optional, defaults to `"silu"`) — The activation function to use.
- latent_channels (`int`, optional, defaults to 4) — Number of channels in the latent space.
- sample_size (`int`, optional, defaults to `32`) — TODO
- scaling_factor (`float`, optional, defaults to 0.18215) — The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the High-Resolution Image Synthesis with Latent Diffusion Models paper.

Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling.

This model inherits from ModelMixin. Check the superclass documentation for the generic methods the library implements for all the model (such as downloading or saving, etc.)

####
disable_slicing

< source >

( )

Disable sliced VAE decoding. If `enable_slicing` was previously invoked, this method will go back to computing decoding in one step.

####
enable_slicing

< source >

( )

Enable sliced VAE decoding.

When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

####
forward

< source >

( sample: FloatTensor sample_posterior: bool = False return_dict: bool = True generator: typing.Optional[torch._C.Generator] = None )

Parameters

- sample (`torch.FloatTensor`) — Input sample.
- sample_posterior (`bool`, optional, defaults to `False`) — Whether to sample from the posterior.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `DecoderOutput` instead of a plain tuple.

##
Transformer2DModel

###
class diffusers.Transformer2DModel

< source >

( num_attention_heads: int = 16 attention_head_dim: int = 88 in_channels: typing.Optional[int] = None out_channels: typing.Optional[int] = None num_layers: int = 1 dropout: float = 0.0 norm_num_groups: int = 32 cross_attention_dim: typing.Optional[int] = None attention_bias: bool = False sample_size: typing.Optional[int] = None num_vector_embeds: typing.Optional[int] = None patch_size: typing.Optional[int] = None activation_fn: str = 'geglu' num_embeds_ada_norm: typing.Optional[int] = None use_linear_projection: bool = False only_cross_attention: bool = False upcast_attention: bool = False norm_type: str = 'layer_norm' norm_elementwise_affine: bool = True )

Parameters

- num_attention_heads (`int`, optional, defaults to 16) — The number of heads to use for multi-head attention.
- attention_head_dim (`int`, optional, defaults to 88) — The number of channels in each head.
- in_channels (`int`, optional) — Pass if the input is continuous. The number of channels in the input and output.
- num_layers (`int`, optional, defaults to 1) — The number of layers of Transformer blocks to use.
- dropout (`float`, optional, defaults to 0.0) — The dropout probability to use.
- cross_attention_dim (`int`, optional) — The number of encoder_hidden_states dimensions to use.
- sample_size (`int`, optional) — Pass if the input is discrete. The width of the latent images. Note that this is fixed at training time as it is used for learning a number of position embeddings. See `ImagePositionalEmbeddings`.
- num_vector_embeds (`int`, optional) — Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels. Includes the class for the masked latent pixel.
- activation_fn (`str`, optional, defaults to `"geglu"`) — Activation function to be used in feed-forward.
- num_embeds_ada_norm ( `int`, optional) — Pass if at least one of the norm_layers is `AdaLayerNorm`. The number of diffusion steps used during training. Note that this is fixed at training time as it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more than steps than `num_embeds_ada_norm`.
- attention_bias (`bool`, optional) — Configure if the TransformerBlocks’ attention should contain a bias parameter.

Transformer model for image-like data. Takes either discrete (classes of vector embeddings) or continuous (actual embeddings) inputs.

When input is continuous: First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image.

When input is discrete: First, input (classes of latent pixels) is converted to embeddings and has positional embeddings applied, see `ImagePositionalEmbeddings`. Then apply standard transformer action. Finally, predict classes of unnoised image.

Note that it is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image do not contain a prediction for the masked pixel as the unnoised image cannot be masked.

####
forward

< source >

( hidden_states encoder_hidden_states = None timestep = None class_labels = None cross_attention_kwargs = None return_dict: bool = True ) → Transformer2DModelOutput or `tuple`

Parameters

- hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`. — When continous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input hidden_states
- encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, optional) — Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention.
- timestep ( `torch.long`, optional) — Optional timestep to be applied as an embedding in AdaLayerNorm’s. Used to indicate denoising step.
- class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, optional) — Optional class labels to be applied as an embedding in AdaLayerZeroNorm. Used to indicate class labels conditioning.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a models.unet_2d_condition.UNet2DConditionOutput instead of a plain tuple.

Returns

Transformer2DModelOutput or `tuple`

Transformer2DModelOutput if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.

##
Transformer2DModelOutput

###
class diffusers.models.transformer_2d.Transformer2DModelOutput

< source >

( sample: FloatTensor )

Parameters

- sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if Transformer2DModel is discrete) — Hidden states conditioned on `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels.

##
PriorTransformer

###
class diffusers.PriorTransformer

< source >

( num_attention_heads: int = 32 attention_head_dim: int = 64 num_layers: int = 20 embedding_dim: int = 768 num_embeddings = 77 additional_embeddings = 4 dropout: float = 0.0 )

Parameters

- num_attention_heads (`int`, optional, defaults to 32) — The number of heads to use for multi-head attention.
- attention_head_dim (`int`, optional, defaults to 64) — The number of channels in each head.
- num_layers (`int`, optional, defaults to 20) — The number of layers of Transformer blocks to use.
- embedding_dim (`int`, optional, defaults to 768) — The dimension of the CLIP embeddings. Note that CLIP image embeddings and text embeddings are both the same dimension.
- num_embeddings (`int`, optional, defaults to 77) — The max number of clip embeddings allowed. I.e. the length of the prompt after it has been tokenized.
- additional_embeddings (`int`, optional, defaults to 4) — The number of additional tokens appended to the projected hidden_states. The actual length of the used hidden_states is `num_embeddings + additional_embeddings`.
- dropout (`float`, optional, defaults to 0.0) — The dropout probability to use.

The prior transformer from unCLIP is used to predict CLIP image embeddings from CLIP text embeddings. Note that the transformer predicts the image embeddings through a denoising diffusion process.

This model inherits from ModelMixin. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.)

For more details, see the original paper: https://arxiv.org/abs/2204.06125

####
forward

< source >

( hidden_states timestep: typing.Union[torch.Tensor, float, int] proj_embedding: FloatTensor encoder_hidden_states: FloatTensor attention_mask: typing.Optional[torch.BoolTensor] = None return_dict: bool = True ) → PriorTransformerOutput or `tuple`

Parameters

- hidden_states (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`) — x_t, the currently predicted image embeddings.
- timestep (`torch.long`) — Current denoising step.
- proj_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`) — Projected embedding vector the denoising process is conditioned on.
- encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_embeddings, embedding_dim)`) — Hidden states of the text embeddings the denoising process is conditioned on.
- attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`) — Text mask for the text embeddings.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a models.prior_transformer.PriorTransformerOutput instead of a plain tuple.

Returns

PriorTransformerOutput or `tuple`

PriorTransformerOutput if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.

##
PriorTransformerOutput

###
class diffusers.models.prior_transformer.PriorTransformerOutput

< source >

( predicted_image_embedding: FloatTensor )

Parameters

- predicted_image_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`) — The predicted CLIP image embedding conditioned on the CLIP text embedding input.

##
FlaxModelMixin

###
class diffusers.FlaxModelMixin

< source >

( )

Base class for all flax models.

FlaxModelMixin takes care of storing the configuration of the models and handles methods for loading, downloading and saving models.

####
from_pretrained

< source >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike] dtype: dtype = <class 'jax.numpy.float32'> *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids are namespaced under a user or organization name, like `runwayml/stable-diffusion-v1-5`.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
- dtype (`jax.numpy.dtype`, optional, defaults to `jax.numpy.float32`) — The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs).

  This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`.

  Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.

  If you wish to change the dtype of the model parameters, see `~ModelMixin.to_fp16` and `~ModelMixin.to_bf16`.

- model_args (sequence of positional arguments, optional) — All remaining positional arguments will be passed to the underlying model’s `__init__` method.
- cache_dir (`Union[str, os.PathLike]`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- 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.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
- 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.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file.
- kwargs (remaining dictionary of keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_config()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate a pretrained flax model from a pre-trained model configuration.

The warning Weights from XXX not initialized from pretrained model means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task.

The warning Weights from XXX not used in YYY means that the layer XXX is not used by YYY, therefore those weights are discarded.

Examples:

    >>> from diffusers import FlaxUNet2DConditionModel

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
    >>> model, params = FlaxUNet2DConditionModel.from_pretrained("./test/saved_model/")

####
save_pretrained

< source >

( save_directory: typing.Union[str, os.PathLike] params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] is_main_process: bool = True )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory to which to save. Will be created if it doesn’t exist.
- params (`Union[Dict, FrozenDict]`) — A `PyTree` of model parameters.
- is_main_process (`bool`, optional, defaults to `True`) — Whether the process calling this is the main process or not. Useful when in distributed training like TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on the main process to avoid race conditions.

Save a model and its configuration file to a directory, so that it can be re-loaded using the `[from_pretrained()](/docs/diffusers/main/en/api/models#diffusers.FlaxModelMixin.from_pretrained)` class method

####
to_bf16

< source >

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

- params (`Union[Dict, FrozenDict]`) — A `PyTree` of model parameters.
- mask (`Union[Dict, FrozenDict]`) — A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params you want to cast, and should be `False` for those you want to skip.

Cast the floating-point `params` to `jax.numpy.bfloat16`. This returns a new `params` tree and does not cast the `params` in place.

This method can be used on TPU to explicitly convert the model parameters to bfloat16 precision to do full half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed.

Examples:

    >>> from diffusers import FlaxUNet2DConditionModel

    >>> # load model
    >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision
    >>> params = model.to_bf16(params)
    >>> # If you don't want to cast certain parameters (for example layer norm bias and scale)
    >>> # then pass the mask as follows
    >>> from flax import traverse_util

    >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> flat_params = traverse_util.flatten_dict(params)
    >>> mask = {
    ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
    ...     for path in flat_params
    ... }
    >>> mask = traverse_util.unflatten_dict(mask)
    >>> params = model.to_bf16(params, mask)

####
to_fp16

< source >

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

- params (`Union[Dict, FrozenDict]`) — A `PyTree` of model parameters.
- mask (`Union[Dict, FrozenDict]`) — A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params you want to cast, and should be `False` for those you want to skip

Cast the floating-point `params` to `jax.numpy.float16`. This returns a new `params` tree and does not cast the `params` in place.

This method can be used on GPU to explicitly convert the model parameters to float16 precision to do full half-precision training or to save weights in float16 for inference in order to save memory and improve speed.

Examples:

    >>> from diffusers import FlaxUNet2DConditionModel

    >>> # load model
    >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> # By default, the model params will be in fp32, to cast these to float16
    >>> params = model.to_fp16(params)
    >>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)
    >>> # then pass the mask as follows
    >>> from flax import traverse_util

    >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> flat_params = traverse_util.flatten_dict(params)
    >>> mask = {
    ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
    ...     for path in flat_params
    ... }
    >>> mask = traverse_util.unflatten_dict(mask)
    >>> params = model.to_fp16(params, mask)

####
to_fp32

< source >

( params: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict] mask: typing.Any = None )

Parameters

- params (`Union[Dict, FrozenDict]`) — A `PyTree` of model parameters.
- mask (`Union[Dict, FrozenDict]`) — A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params you want to cast, and should be `False` for those you want to skip

Cast the floating-point `params` to `jax.numpy.float32`. This method can be used to explicitly convert the model parameters to fp32 precision. This returns a new `params` tree and does not cast the `params` in place.

Examples:

    >>> from diffusers import FlaxUNet2DConditionModel

    >>> # Download model and configuration from huggingface.co
    >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
    >>> # By default, the model params will be in fp32, to illustrate the use of this method,
    >>> # we'll first cast to fp16 and back to fp32
    >>> params = model.to_f16(params)
    >>> # now cast back to fp32
    >>> params = model.to_fp32(params)

##
FlaxUNet2DConditionOutput

###
class diffusers.models.unet_2d_condition_flax.FlaxUNet2DConditionOutput

< source >

( sample: ndarray )

Parameters

- sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`) — Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.

####
replace

< source >

( **updates )

“Returns a new object replacing the specified fields with new values.

##
FlaxUNet2DConditionModel

###
class diffusers.FlaxUNet2DConditionModel

< source >

( sample_size: int = 32 in_channels: int = 4 out_channels: int = 4 down_block_types: typing.Tuple[str] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D') up_block_types: typing.Tuple[str] = ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D') only_cross_attention: typing.Union[bool, typing.Tuple[bool]] = False block_out_channels: typing.Tuple[int] = (320, 640, 1280, 1280) layers_per_block: int = 2 attention_head_dim: typing.Union[int, typing.Tuple[int]] = 8 cross_attention_dim: int = 1280 dropout: float = 0.0 use_linear_projection: bool = False dtype: dtype = <class 'jax.numpy.float32'> flip_sin_to_cos: bool = True freq_shift: int = 0 parent: typing.Union[typing.Type[flax.linen.module.Module], typing.Type[flax.core.scope.Scope], typing.Type[flax.linen.module._Sentinel], NoneType] = <flax.linen.module._Sentinel object at 0x7fc51a4c1a30> name: str = None )

Parameters

- sample_size (`int`, optional) — The size of the input sample.
- in_channels (`int`, optional, defaults to 4) — The number of channels in the input sample.
- out_channels (`int`, optional, defaults to 4) — The number of channels in the output.
- down_block_types (`Tuple[str]`, optional, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`) — The tuple of downsample blocks to use. The corresponding class names will be: “FlaxCrossAttnDownBlock2D”, “FlaxCrossAttnDownBlock2D”, “FlaxCrossAttnDownBlock2D”, “FlaxDownBlock2D”
- up_block_types (`Tuple[str]`, optional, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`) — The tuple of upsample blocks to use. The corresponding class names will be: “FlaxUpBlock2D”, “FlaxCrossAttnUpBlock2D”, “FlaxCrossAttnUpBlock2D”, “FlaxCrossAttnUpBlock2D”
- block_out_channels (`Tuple[int]`, optional, defaults to `(320, 640, 1280, 1280)`) — The tuple of output channels for each block.
- layers_per_block (`int`, optional, defaults to 2) — The number of layers per block.
- attention_head_dim (`int` or `Tuple[int]`, optional, defaults to 8) — The dimension of the attention heads.
- cross_attention_dim (`int`, optional, defaults to 768) — The dimension of the cross attention features.
- dropout (`float`, optional, defaults to 0) — Dropout probability for down, up and bottleneck blocks.
- flip_sin_to_cos (`bool`, optional, defaults to `True`) — Whether to flip the sin to cos in the time embedding.
- freq_shift (`int`, optional, defaults to 0) — The frequency shift to apply to the time embedding.

FlaxUNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output.

This model inherits from FlaxModelMixin. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.)

Also, this model is a Flax Linen flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- Just-In-Time (JIT) compilation
- Automatic Differentiation
- Vectorization
- Parallelization

##
FlaxDecoderOutput

###
class diffusers.models.vae_flax.FlaxDecoderOutput

< source >

( sample: ndarray )

Parameters

- sample (jnp.ndarray of shape (batch_size, num_channels, height, width)) — Decoded output sample of the model. Output of the last layer of the model.
- dtype (`jnp.dtype`, optional, defaults to jnp.float32) — Parameters dtype

Output of decoding method.

####
replace

< source >

( **updates )

“Returns a new object replacing the specified fields with new values.

##
FlaxAutoencoderKLOutput

###
class diffusers.models.vae_flax.FlaxAutoencoderKLOutput

< source >

( latent_dist: FlaxDiagonalGaussianDistribution )

Parameters

- latent_dist (`FlaxDiagonalGaussianDistribution`) — Encoded outputs of `Encoder` represented as the mean and logvar of `FlaxDiagonalGaussianDistribution`. `FlaxDiagonalGaussianDistribution` allows for sampling latents from the distribution.

Output of AutoencoderKL encoding method.

####
replace

< source >

( **updates )

“Returns a new object replacing the specified fields with new values.

##
FlaxAutoencoderKL

###
class diffusers.FlaxAutoencoderKL

< source >

( in_channels: int = 3 out_channels: int = 3 down_block_types: typing.Tuple[str] = ('DownEncoderBlock2D',) up_block_types: typing.Tuple[str] = ('UpDecoderBlock2D',) block_out_channels: typing.Tuple[int] = (64,) layers_per_block: int = 1 act_fn: str = 'silu' latent_channels: int = 4 norm_num_groups: int = 32 sample_size: int = 32 scaling_factor: float = 0.18215 dtype: dtype = <class 'jax.numpy.float32'> parent: typing.Union[typing.Type[flax.linen.module.Module], typing.Type[flax.core.scope.Scope], typing.Type[flax.linen.module._Sentinel], NoneType] = <flax.linen.module._Sentinel object at 0x7fc51a4c1a30> name: str = None )

Parameters

- in_channels (`int`, optional, defaults to 3) — Input channels
- out_channels (`int`, optional, defaults to 3) — Output channels
- down_block_types (`Tuple[str]`, optional, defaults to (DownEncoderBlock2D)) — DownEncoder block type
- up_block_types (`Tuple[str]`, optional, defaults to (UpDecoderBlock2D)) — UpDecoder block type
- block_out_channels (`Tuple[str]`, optional, defaults to (64,)) — Tuple containing the number of output channels for each block
- layers_per_block (`int`, optional, defaults to 2) — Number of Resnet layer for each block
- act_fn (`str`, optional, defaults to silu) — Activation function
- latent_channels (`int`, optional, defaults to 4) — Latent space channels
- norm_num_groups (`int`, optional, defaults to 32) — Norm num group
- sample_size (`int`, optional, defaults to 32) — Sample input size
- scaling_factor (float, optional, defaults to 0.18215) — The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula z = z scaling_factor before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: z = 1 / scaling_factor z. For more details, refer to sections 4.3.2 and D.1 of the High-Resolution Image Synthesis with Latent Diffusion Models paper.
- dtype (`jnp.dtype`, optional, defaults to jnp.float32) — parameters dtype

Flax Implementation of Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling.

This model is a Flax Linen flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- Just-In-Time (JIT) compilation
- Automatic Differentiation
- Vectorization
- Parallelization