diff --git a/api/onnx_web/convert/diffusion/control.py b/api/onnx_web/convert/diffusion/control.py index 7e96801b..7a304871 100644 --- a/api/onnx_web/convert/diffusion/control.py +++ b/api/onnx_web/convert/diffusion/control.py @@ -58,7 +58,6 @@ def convert_diffusion_control( "timestep", "encoder_hidden_states", "controlnet_cond", - "return_dict", ], output_names=[ "down_block_res_samples", diff --git a/api/onnx_web/convert/diffusion/diffusion.py b/api/onnx_web/convert/diffusion/diffusion.py index 2587440b..2f1899a5 100644 --- a/api/onnx_web/convert/diffusion/diffusion.py +++ b/api/onnx_web/convert/diffusion/diffusion.py @@ -351,7 +351,7 @@ def convert_diffusion_diffusers( logger.debug("extracting SD checkpoint to Torch models: %s", source) torch_source = convert_extract_checkpoint( conversion, - source, + cache_path, f"{name}-torch", is_inpainting=is_inpainting, config_file=config, diff --git a/api/onnx_web/diffusers/pipelines/controlnet.py b/api/onnx_web/diffusers/pipelines/controlnet.py index be16265f..5d7bad58 100644 --- a/api/onnx_web/diffusers/pipelines/controlnet.py +++ b/api/onnx_web/diffusers/pipelines/controlnet.py @@ -416,9 +416,9 @@ class OnnxStableDiffusionControlNetPipeline(DiffusionPipeline): timestep = np.array([t], dtype=timestep_dtype) blocksamples = self.controlnet( - sample=latent_model_input, + sample=latent_model_input.astype(np.float32), timestep=timestep, - encoder_hidden_states=prompt_embeds, + encoder_hidden_states=prompt_embeds.astype(np.float32), controlnet_cond=image, ) diff --git a/api/onnx_web/models/cnet.py b/api/onnx_web/models/cnet.py index 71c4b4d0..6243587d 100644 --- a/api/onnx_web/models/cnet.py +++ b/api/onnx_web/models/cnet.py @@ -16,729 +16,48 @@ # See the License for the specific language governing permissions and # limitations under the License. -from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple, Union +from logging import getLogger +from typing import Optional, Tuple, Union import torch -import torch.nn as nn import torch.utils.checkpoint -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.loaders import UNet2DConditionLoadersMixin -from diffusers.models.attention_processor import AttnProcessor -from diffusers.models.embeddings import ( - GaussianFourierProjection, - TimestepEmbedding, - Timesteps, -) -from diffusers.models.modeling_utils import ModelMixin -from diffusers.models.unet_2d_blocks import ( - CrossAttnDownBlock2D, - CrossAttnUpBlock2D, - DownBlock2D, - UNetMidBlock2DCrossAttn, - UNetMidBlock2DSimpleCrossAttn, - UpBlock2D, - get_down_block, - get_up_block, -) -from diffusers.utils import BaseOutput, logging +from diffusers import UNet2DConditionModel +from diffusers.models.unet_2d_condition import UNet2DConditionOutput -logger = logging.get_logger(__name__) # pylint: disable=invalid-name +logger = getLogger(__name__) -@dataclass -class UNet2DConditionOutput(BaseOutput): - """ - Args: - 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. - """ - - sample: torch.FloatTensor - - -class UNet2DConditionModel_CNet(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): - r""" - 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.) - 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 - [`~models.attention.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 [`~models.resnet.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`. - """ - - _supports_gradient_checkpointing = True - - @register_to_config - def __init__( - self, - sample_size: 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: Tuple[str] = ( - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D", - ), - mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", - up_block_types: Tuple[str] = ( - "UpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - ), - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: 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: Optional[int] = 32, - norm_eps: float = 1e-5, - cross_attention_dim: int = 1280, - attention_head_dim: Union[int, Tuple[int]] = 8, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - class_embed_type: Optional[str] = None, - num_class_embeds: Optional[int] = None, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - time_embedding_type: str = "positional", - timestep_post_act: Optional[str] = None, - time_cond_proj_dim: Optional[int] = None, - conv_in_kernel: int = 3, - conv_out_kernel: int = 3, - projection_class_embeddings_input_dim: Optional[int] = None, - ): - super().__init__() - - self.sample_size = sample_size - - # Check inputs - if len(down_block_types) != len(up_block_types): - raise ValueError( - f"Must provide the same number of `down_block_types` as `up_block_types`." - f"`down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." - ) - - if len(block_out_channels) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `block_out_channels` as `down_block_types`." - f"`block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(only_cross_attention, bool) and len( - only_cross_attention - ) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `only_cross_attention` as `down_block_types`." - f"`only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len( - down_block_types - ): - raise ValueError( - f"Must provide the same number of `attention_head_dim` as `down_block_types`." - f"`attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." - ) - - # input - conv_in_padding = (conv_in_kernel - 1) // 2 - self.conv_in = nn.Conv2d( - in_channels, - block_out_channels[0], - kernel_size=conv_in_kernel, - padding=conv_in_padding, - ) - - # time - if time_embedding_type == "fourier": - time_embed_dim = block_out_channels[0] * 2 - if time_embed_dim % 2 != 0: - raise ValueError( - f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}." - ) - self.time_proj = GaussianFourierProjection( - time_embed_dim // 2, - set_W_to_weight=False, - log=False, - flip_sin_to_cos=flip_sin_to_cos, - ) - timestep_input_dim = time_embed_dim - elif time_embedding_type == "positional": - time_embed_dim = block_out_channels[0] * 4 - - self.time_proj = Timesteps( - block_out_channels[0], flip_sin_to_cos, freq_shift - ) - timestep_input_dim = block_out_channels[0] - else: - raise ValueError( - f"{time_embedding_type} does not exist. Pleaes make sure to use one of `fourier` or `positional`." - ) - - self.time_embedding = TimestepEmbedding( - timestep_input_dim, - time_embed_dim, - act_fn=act_fn, - post_act_fn=timestep_post_act, - cond_proj_dim=time_cond_proj_dim, - ) - - # class embedding - if class_embed_type is None and num_class_embeds is not None: - self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) - elif class_embed_type == "timestep": - self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) - elif class_embed_type == "identity": - self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) - elif class_embed_type == "projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" - ) - # The projection `class_embed_type` is the same as the timestep `class_embed_type` except - # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings - # 2. it projects from an arbitrary input dimension. - # - # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. - # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. - # As a result, `TimestepEmbedding` can be passed arbitrary vectors. - self.class_embedding = TimestepEmbedding( - projection_class_embeddings_input_dim, time_embed_dim - ) - else: - self.class_embedding = None - - self.down_blocks = nn.ModuleList([]) - self.up_blocks = nn.ModuleList([]) - - if isinstance(only_cross_attention, bool): - only_cross_attention = [only_cross_attention] * len(down_block_types) - - if isinstance(attention_head_dim, int): - attention_head_dim = (attention_head_dim,) * len(down_block_types) - - # down - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block, - in_channels=input_channel, - out_channels=output_channel, - temb_channels=time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim, - num_attention_heads=attention_head_dim[i], - downsample_padding=downsample_padding, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - self.down_blocks.append(down_block) - - # mid - if mid_block_type == "UNetMidBlock2DCrossAttn": - self.mid_block = UNetMidBlock2DCrossAttn( - in_channels=block_out_channels[-1], - temb_channels=time_embed_dim, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - output_scale_factor=mid_block_scale_factor, - resnet_time_scale_shift=resnet_time_scale_shift, - cross_attention_dim=cross_attention_dim, - num_attention_heads=attention_head_dim[-1], - resnet_groups=norm_num_groups, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - ) - elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": - self.mid_block = UNetMidBlock2DSimpleCrossAttn( - in_channels=block_out_channels[-1], - temb_channels=time_embed_dim, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - output_scale_factor=mid_block_scale_factor, - cross_attention_dim=cross_attention_dim, - num_attention_heads=attention_head_dim[-1], - resnet_groups=norm_num_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif mid_block_type is None: - self.mid_block = None - else: - raise ValueError(f"unknown mid_block_type : {mid_block_type}") - - # count how many layers upsample the images - self.num_upsamplers = 0 - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - reversed_attention_head_dim = list(reversed(attention_head_dim)) - only_cross_attention = list(reversed(only_cross_attention)) - - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - is_final_block = i == len(block_out_channels) - 1 - - 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) - ] - - # add upsample block for all BUT final layer - if not is_final_block: - add_upsample = True - self.num_upsamplers += 1 - else: - add_upsample = False - - up_block = get_up_block( - up_block_type, - num_layers=layers_per_block + 1, - in_channels=input_channel, - out_channels=output_channel, - prev_output_channel=prev_output_channel, - temb_channels=time_embed_dim, - add_upsample=add_upsample, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim, - num_attention_heads=reversed_attention_head_dim[i], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - if norm_num_groups is not None: - self.conv_norm_out = nn.GroupNorm( - num_channels=block_out_channels[0], - num_groups=norm_num_groups, - eps=norm_eps, - ) - self.conv_act = nn.SiLU() - else: - self.conv_norm_out = None - self.conv_act = None - - conv_out_padding = (conv_out_kernel - 1) // 2 - self.conv_out = nn.Conv2d( - block_out_channels[0], - out_channels, - kernel_size=conv_out_kernel, - padding=conv_out_padding, - ) - - @property - def attn_processors(self) -> Dict[str, AttnProcessor]: - r""" - Returns: - `dict` of attention processors: A dictionary containing all attention processors used in the model with - indexed by its weight name. - """ - # set recursively - processors = {} - - def fn_recursive_add_processors( - name: str, module: torch.nn.Module, processors: Dict[str, AttnProcessor] - ): - if hasattr(module, "set_processor"): - processors[f"{name}.processor"] = module.processor - - for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) - - return processors - - for name, module in self.named_children(): - fn_recursive_add_processors(name, module, processors) - - return processors - - def set_attn_processor( - self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]] - ): - r""" - 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 trainable attention processors. - """ - count = len(self.attn_processors.keys()) - - if isinstance(processor, dict) and len(processor) != count: - raise ValueError( - f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) - - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): - if hasattr(module, "set_processor"): - if not isinstance(processor, dict): - module.set_processor(processor) - else: - module.set_processor(processor.pop(f"{name}.processor")) - - for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) - - for name, module in self.named_children(): - fn_recursive_attn_processor(name, module, processor) - - def set_attention_slice(self, slice_size): - r""" - 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. - Args: - 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`. - """ - sliceable_head_dims = [] - - def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): - if hasattr(module, "set_attention_slice"): - sliceable_head_dims.append(module.sliceable_head_dim) - - for child in module.children(): - fn_recursive_retrieve_slicable_dims(child) - - # retrieve number of attention layers - for module in self.children(): - fn_recursive_retrieve_slicable_dims(module) - - num_slicable_layers = len(sliceable_head_dims) - - if slice_size == "auto": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = [dim // 2 for dim in sliceable_head_dims] - elif slice_size == "max": - # make smallest slice possible - slice_size = num_slicable_layers * [1] - - slice_size = ( - num_slicable_layers * [slice_size] - if not isinstance(slice_size, list) - else slice_size - ) - - if len(slice_size) != len(sliceable_head_dims): - raise ValueError( - f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" - f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." - ) - - for i in range(len(slice_size)): - size = slice_size[i] - dim = sliceable_head_dims[i] - if size is not None and size > dim: - raise ValueError(f"size {size} has to be smaller or equal to {dim}.") - - # Recursively walk through all the children. - # Any children which exposes the set_attention_slice method - # gets the message - def fn_recursive_set_attention_slice( - module: torch.nn.Module, slice_size: List[int] - ): - if hasattr(module, "set_attention_slice"): - module.set_attention_slice(slice_size.pop()) - - for child in module.children(): - fn_recursive_set_attention_slice(child, slice_size) - - reversed_slice_size = list(reversed(slice_size)) - for module in self.children(): - fn_recursive_set_attention_slice(module, reversed_slice_size) - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance( - module, (CrossAttnDownBlock2D, DownBlock2D, CrossAttnUpBlock2D, UpBlock2D) - ): - module.gradient_checkpointing = value - +class UNet2DConditionModel_Cnet(UNet2DConditionModel): def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, - # class_labels: Optional[torch.Tensor] = None, - # timestep_cond: Optional[torch.Tensor] = None, - # attention_mask: Optional[torch.Tensor] = None, - # cross_attention_kwargs: Optional[Dict[str, Any]] = None, - down_block_additional_residuals0: Optional[torch.Tensor] = None, - down_block_additional_residuals1: Optional[torch.Tensor] = None, - down_block_additional_residuals2: Optional[torch.Tensor] = None, - down_block_additional_residuals3: Optional[torch.Tensor] = None, - down_block_additional_residuals4: Optional[torch.Tensor] = None, - down_block_additional_residuals5: Optional[torch.Tensor] = None, - down_block_additional_residuals6: Optional[torch.Tensor] = None, - down_block_additional_residuals7: Optional[torch.Tensor] = None, - down_block_additional_residuals8: Optional[torch.Tensor] = None, - down_block_additional_residuals9: Optional[torch.Tensor] = None, - down_block_additional_residuals10: Optional[torch.Tensor] = None, - down_block_additional_residuals11: Optional[torch.Tensor] = None, + down_block_add_res00: Optional[torch.Tensor] = None, + down_block_add_res01: Optional[torch.Tensor] = None, + down_block_add_res02: Optional[torch.Tensor] = None, + down_block_add_res03: Optional[torch.Tensor] = None, + down_block_add_res04: Optional[torch.Tensor] = None, + down_block_add_res05: Optional[torch.Tensor] = None, + down_block_add_res06: Optional[torch.Tensor] = None, + down_block_add_res07: Optional[torch.Tensor] = None, + down_block_add_res08: Optional[torch.Tensor] = None, + down_block_add_res09: Optional[torch.Tensor] = None, + down_block_add_res10: Optional[torch.Tensor] = None, + down_block_add_res11: Optional[torch.Tensor] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, return_dict: bool = False, ) -> Union[UNet2DConditionOutput, Tuple]: - r""" - Args: - 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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). - Returns: - [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: - [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When - returning a tuple, the first element is the sample tensor. - """ - # By default samples have to be AT least a multiple of the overall upsampling factor. - # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). - # However, the upsampling interpolation output size can be forced to fit any upsampling size - # on the fly if necessary. - - class_labels = None - timestep_cond = None - attention_mask = None - cross_attention_kwargs = None - - down_block_additional_residuals = ( - down_block_additional_residuals0, - down_block_additional_residuals1, - down_block_additional_residuals2, - down_block_additional_residuals3, - down_block_additional_residuals4, - down_block_additional_residuals5, - down_block_additional_residuals6, - down_block_additional_residuals7, - down_block_additional_residuals8, - down_block_additional_residuals9, - down_block_additional_residuals10, - down_block_additional_residuals11, + down_block_add_res = ( + down_block_add_res00, down_block_add_res01, down_block_add_res02, + down_block_add_res03, down_block_add_res04, down_block_add_res05, + down_block_add_res06, down_block_add_res07, down_block_add_res08, + down_block_add_res09, down_block_add_res10, down_block_add_res11) + return super().forward( + sample = sample, + timestep = timestep, + encoder_hidden_states = encoder_hidden_states, + down_block_additional_residuals = down_block_add_res, + mid_block_additional_residual = mid_block_additional_residual, + return_dict = return_dict ) - default_overall_up_factor = 2**self.num_upsamplers - - # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` - forward_upsample_size = False - upsample_size = None - - if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): - logger.info("Forward upsample size to force interpolation output size.") - forward_upsample_size = True - - # prepare attention_mask - if attention_mask is not None: - attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 - attention_mask = attention_mask.unsqueeze(1) - - # 0. center input if necessary - if self.config.center_input_sample: - sample = 2 * sample - 1.0 - - # 1. time - timesteps = timestep - if not torch.is_tensor(timesteps): - # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can - # This would be a good case for the `match` statement (Python 3.10+) - is_mps = sample.device.type == "mps" - if isinstance(timestep, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(sample.shape[0]) - - t_emb = self.time_proj(timesteps) - - # timesteps does not contain any weights and will always return f32 tensors - # but time_embedding might actually be running in fp16. so we need to cast here. - # there might be better ways to encapsulate this. - t_emb = t_emb.to(dtype=self.dtype) - - emb = self.time_embedding(t_emb, timestep_cond) - - if self.class_embedding is not None: - if class_labels is None: - raise ValueError( - "class_labels should be provided when num_class_embeds > 0" - ) - - if self.config.class_embed_type == "timestep": - class_labels = self.time_proj(class_labels) - - class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) - emb = emb + class_emb - - # 2. pre-process - sample = self.conv_in(sample) - - # 3. down - down_block_res_samples = (sample,) - for downsample_block in self.down_blocks: - if ( - hasattr(downsample_block, "has_cross_attention") - and downsample_block.has_cross_attention - ): - sample, res_samples = downsample_block( - hidden_states=sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - ) - else: - sample, res_samples = downsample_block(hidden_states=sample, temb=emb) - - 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, - emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - ) - - if mid_block_additional_residual is not None: - sample += mid_block_additional_residual - - # 5. up - for i, upsample_block in enumerate(self.up_blocks): - is_final_block = i == len(self.up_blocks) - 1 - - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[ - : -len(upsample_block.resnets) - ] - - # if we have not reached the final block and need to forward the - # upsample size, we do it here - if not is_final_block and forward_upsample_size: - upsample_size = down_block_res_samples[-1].shape[2:] - - if ( - hasattr(upsample_block, "has_cross_attention") - and upsample_block.has_cross_attention - ): - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - upsample_size=upsample_size, - attention_mask=attention_mask, - ) - else: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - upsample_size=upsample_size, - ) - - # 6. post-process - if self.conv_norm_out: - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if not return_dict: - return (sample,) - - return UNet2DConditionOutput(sample=sample)