# Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict, Optional, Tuple, Union import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict from ...configuration_utils import ConfigMixin, flax_register_to_config from ...utils import BaseOutput from ..embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps from ..modeling_flax_utils import FlaxModelMixin from .unet_2d_blocks_flax import ( FlaxCrossAttnDownBlock2D, FlaxCrossAttnUpBlock2D, FlaxDownBlock2D, FlaxUNetMidBlock2DCrossAttn, FlaxUpBlock2D, ) @flax.struct.dataclass class FlaxUNet2DConditionOutput(BaseOutput): """ The output of [`FlaxUNet2DConditionModel`]. Args: sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. """ sample: jnp.ndarray @flax_register_to_config class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin): r""" A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample shaped output. This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matters related to its general usage and behavior. Inherent JAX features such as the following are supported: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) 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 `("FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D")`): The tuple of downsample blocks to use. up_block_types (`Tuple[str]`, *optional*, defaults to `("FlaxUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D")`): The tuple of upsample blocks to use. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`. If `None`, the mid block layer is skipped. 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. num_attention_heads (`int` or `Tuple[int]`, *optional*): The number of 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. use_memory_efficient_attention (`bool`, *optional*, defaults to `False`): Enable memory efficient attention as described [here](https://arxiv.org/abs/2112.05682). split_head_dim (`bool`, *optional*, defaults to `False`): Whether to split the head dimension into a new axis for the self-attention computation. In most cases, enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL. """ sample_size: int = 32 in_channels: int = 4 out_channels: int = 4 down_block_types: Tuple[str, ...] = ( "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ) up_block_types: Tuple[str, ...] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D") mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn" only_cross_attention: Union[bool, Tuple[bool]] = False block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280) layers_per_block: int = 2 attention_head_dim: Union[int, Tuple[int, ...]] = 8 num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None cross_attention_dim: int = 1280 dropout: float = 0.0 use_linear_projection: bool = False dtype: jnp.dtype = jnp.float32 flip_sin_to_cos: bool = True freq_shift: int = 0 use_memory_efficient_attention: bool = False split_head_dim: bool = False transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1 addition_embed_type: Optional[str] = None addition_time_embed_dim: Optional[int] = None addition_embed_type_num_heads: int = 64 projection_class_embeddings_input_dim: Optional[int] = None def init_weights(self, rng: jax.Array) -> FrozenDict: # init input tensors sample_shape = (1, self.in_channels, self.sample_size, self.sample_size) sample = jnp.zeros(sample_shape, dtype=jnp.float32) timesteps = jnp.ones((1,), dtype=jnp.int32) encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} added_cond_kwargs = None if self.addition_embed_type == "text_time": # we retrieve the expected `text_embeds_dim` by first checking if the architecture is a refiner # or non-refiner architecture and then by "reverse-computing" from `projection_class_embeddings_input_dim` is_refiner = ( 5 * self.config.addition_time_embed_dim + self.config.cross_attention_dim == self.config.projection_class_embeddings_input_dim ) num_micro_conditions = 5 if is_refiner else 6 text_embeds_dim = self.config.projection_class_embeddings_input_dim - ( num_micro_conditions * self.config.addition_time_embed_dim ) time_ids_channels = self.projection_class_embeddings_input_dim - text_embeds_dim time_ids_dims = time_ids_channels // self.addition_time_embed_dim added_cond_kwargs = { "text_embeds": jnp.zeros((1, text_embeds_dim), dtype=jnp.float32), "time_ids": jnp.zeros((1, time_ids_dims), dtype=jnp.float32), } return self.init(rngs, sample, timesteps, encoder_hidden_states, added_cond_kwargs)["params"] def setup(self) -> None: block_out_channels = self.block_out_channels time_embed_dim = block_out_channels[0] * 4 if self.num_attention_heads is not None: raise ValueError( "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." ) # If `num_attention_heads` is not defined (which is the case for most models) # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. # The reason for this behavior is to correct for incorrectly named variables that were introduced # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking # which is why we correct for the naming here. num_attention_heads = self.num_attention_heads or self.attention_head_dim # input self.conv_in = nn.Conv( block_out_channels[0], kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) # time self.time_proj = FlaxTimesteps( block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift ) self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype) only_cross_attention = self.only_cross_attention if isinstance(only_cross_attention, bool): only_cross_attention = (only_cross_attention,) * len(self.down_block_types) if isinstance(num_attention_heads, int): num_attention_heads = (num_attention_heads,) * len(self.down_block_types) # transformer layers per block transformer_layers_per_block = self.transformer_layers_per_block if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * len(self.down_block_types) # addition embed types if self.addition_embed_type is None: self.add_embedding = None elif self.addition_embed_type == "text_time": if self.addition_time_embed_dim is None: raise ValueError( f"addition_embed_type {self.addition_embed_type} requires `addition_time_embed_dim` to not be None" ) self.add_time_proj = FlaxTimesteps(self.addition_time_embed_dim, self.flip_sin_to_cos, self.freq_shift) self.add_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype) else: raise ValueError(f"addition_embed_type: {self.addition_embed_type} must be None or `text_time`.") # down down_blocks = [] output_channel = block_out_channels[0] for i, down_block_type in enumerate(self.down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 if down_block_type == "CrossAttnDownBlock2D": down_block = FlaxCrossAttnDownBlock2D( in_channels=input_channel, out_channels=output_channel, dropout=self.dropout, num_layers=self.layers_per_block, transformer_layers_per_block=transformer_layers_per_block[i], num_attention_heads=num_attention_heads[i], add_downsample=not is_final_block, use_linear_projection=self.use_linear_projection, only_cross_attention=only_cross_attention[i], use_memory_efficient_attention=self.use_memory_efficient_attention, split_head_dim=self.split_head_dim, dtype=self.dtype, ) else: down_block = FlaxDownBlock2D( in_channels=input_channel, out_channels=output_channel, dropout=self.dropout, num_layers=self.layers_per_block, add_downsample=not is_final_block, dtype=self.dtype, ) down_blocks.append(down_block) self.down_blocks = down_blocks # mid if self.config.mid_block_type == "UNetMidBlock2DCrossAttn": self.mid_block = FlaxUNetMidBlock2DCrossAttn( in_channels=block_out_channels[-1], dropout=self.dropout, num_attention_heads=num_attention_heads[-1], transformer_layers_per_block=transformer_layers_per_block[-1], use_linear_projection=self.use_linear_projection, use_memory_efficient_attention=self.use_memory_efficient_attention, split_head_dim=self.split_head_dim, dtype=self.dtype, ) elif self.config.mid_block_type is None: self.mid_block = None else: raise ValueError(f"Unexpected mid_block_type {self.config.mid_block_type}") # up up_blocks = [] reversed_block_out_channels = list(reversed(block_out_channels)) reversed_num_attention_heads = list(reversed(num_attention_heads)) only_cross_attention = list(reversed(only_cross_attention)) output_channel = reversed_block_out_channels[0] reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) for i, up_block_type in enumerate(self.up_block_types): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] is_final_block = i == len(block_out_channels) - 1 if up_block_type == "CrossAttnUpBlock2D": up_block = FlaxCrossAttnUpBlock2D( in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, num_layers=self.layers_per_block + 1, transformer_layers_per_block=reversed_transformer_layers_per_block[i], num_attention_heads=reversed_num_attention_heads[i], add_upsample=not is_final_block, dropout=self.dropout, use_linear_projection=self.use_linear_projection, only_cross_attention=only_cross_attention[i], use_memory_efficient_attention=self.use_memory_efficient_attention, split_head_dim=self.split_head_dim, dtype=self.dtype, ) else: up_block = FlaxUpBlock2D( in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, num_layers=self.layers_per_block + 1, add_upsample=not is_final_block, dropout=self.dropout, dtype=self.dtype, ) up_blocks.append(up_block) prev_output_channel = output_channel self.up_blocks = up_blocks # out self.conv_norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-5) self.conv_out = nn.Conv( self.out_channels, kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, ) def __call__( self, sample: jnp.ndarray, timesteps: Union[jnp.ndarray, float, int], encoder_hidden_states: jnp.ndarray, added_cond_kwargs: Optional[Union[Dict, FrozenDict]] = None, down_block_additional_residuals: Optional[Tuple[jnp.ndarray, ...]] = None, mid_block_additional_residual: Optional[jnp.ndarray] = None, return_dict: bool = True, train: bool = False, ) -> Union[FlaxUNet2DConditionOutput, Tuple[jnp.ndarray]]: r""" Args: sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor timestep (`jnp.ndarray` or `float` or `int`): timesteps encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states added_cond_kwargs: (`dict`, *optional*): A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that are passed along to the UNet blocks. down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): A tuple of tensors that if specified are added to the residuals of down unet blocks. mid_block_additional_residual: (`torch.Tensor`, *optional*): A tensor that if specified is added to the residual of the middle unet block. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a plain tuple. train (`bool`, *optional*, defaults to `False`): Use deterministic functions and disable dropout when not training. Returns: [`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`: [`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. """ # 1. time if not isinstance(timesteps, jnp.ndarray): timesteps = jnp.array([timesteps], dtype=jnp.int32) elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0: timesteps = timesteps.astype(dtype=jnp.float32) timesteps = jnp.expand_dims(timesteps, 0) t_emb = self.time_proj(timesteps) t_emb = self.time_embedding(t_emb) # additional embeddings aug_emb = None if self.addition_embed_type == "text_time": if added_cond_kwargs is None: raise ValueError( f"Need to provide argument `added_cond_kwargs` for {self.__class__} when using `addition_embed_type={self.addition_embed_type}`" ) text_embeds = added_cond_kwargs.get("text_embeds") if text_embeds is None: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" ) time_ids = added_cond_kwargs.get("time_ids") if time_ids is None: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" ) # compute time embeds time_embeds = self.add_time_proj(jnp.ravel(time_ids)) # (1, 6) => (6,) => (6, 256) time_embeds = jnp.reshape(time_embeds, (text_embeds.shape[0], -1)) add_embeds = jnp.concatenate([text_embeds, time_embeds], axis=-1) aug_emb = self.add_embedding(add_embeds) t_emb = t_emb + aug_emb if aug_emb is not None else t_emb # 2. pre-process sample = jnp.transpose(sample, (0, 2, 3, 1)) sample = self.conv_in(sample) # 3. down down_block_res_samples = (sample,) for down_block in self.down_blocks: if isinstance(down_block, FlaxCrossAttnDownBlock2D): sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train) else: sample, res_samples = down_block(sample, t_emb, deterministic=not train) down_block_res_samples += res_samples if down_block_additional_residuals is not None: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample += down_block_additional_residual new_down_block_res_samples += (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid if self.mid_block is not None: sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train) if mid_block_additional_residual is not None: sample += mid_block_additional_residual # 5. up for up_block in self.up_blocks: res_samples = down_block_res_samples[-(self.layers_per_block + 1) :] down_block_res_samples = down_block_res_samples[: -(self.layers_per_block + 1)] if isinstance(up_block, FlaxCrossAttnUpBlock2D): sample = up_block( sample, temb=t_emb, encoder_hidden_states=encoder_hidden_states, res_hidden_states_tuple=res_samples, deterministic=not train, ) else: sample = up_block(sample, temb=t_emb, res_hidden_states_tuple=res_samples, deterministic=not train) # 6. post-process sample = self.conv_norm_out(sample) sample = nn.silu(sample) sample = self.conv_out(sample) sample = jnp.transpose(sample, (0, 3, 1, 2)) if not return_dict: return (sample,) return FlaxUNet2DConditionOutput(sample=sample)