# Copyright 2023 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 dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import torch from torch import nn from torch.nn import functional as F from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.loaders import FromOriginalControlnetMixin from diffusers.utils import BaseOutput, logging from diffusers.models.attention_processor import ( ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor, ) from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps from diffusers.models.modeling_utils import ModelMixin from diffusers.models.unet_3d_blocks import ( get_down_block, get_up_block,UNetMidBlockSpatioTemporal, ) from diffusers.models import UNetSpatioTemporalConditionModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class ControlNetOutput(BaseOutput): """ The output of [`ControlNetModel`]. Args: down_block_res_samples (`tuple[torch.Tensor]`): A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be used to condition the original UNet's downsampling activations. mid_down_block_re_sample (`torch.Tensor`): The activation of the midde block (the lowest sample resolution). Each tensor should be of shape `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`. Output can be used to condition the original UNet's middle block activation. """ down_block_res_samples: Tuple[torch.Tensor] mid_block_res_sample: torch.Tensor class ControlNetConditioningEmbeddingSVD(nn.Module): """ Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full model) to encode image-space conditions ... into feature maps ..." """ def __init__( self, conditioning_embedding_channels: int, conditioning_channels: int = 3, block_out_channels: Tuple[int, ...] = (16, 32, 96, 256), ): super().__init__() self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1) self.blocks = nn.ModuleList([]) for i in range(len(block_out_channels) - 1): channel_in = block_out_channels[i] channel_out = block_out_channels[i + 1] self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1)) self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)) self.conv_out = zero_module( nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1) ) def forward(self, conditioning): #this seeems appropriate? idk if i should be applying a more complex setup to handle the frames #combine batch and frames dimensions batch_size, frames, channels, height, width = conditioning.size() conditioning = conditioning.view(batch_size * frames, channels, height, width) embedding = self.conv_in(conditioning) embedding = F.silu(embedding) for block in self.blocks: embedding = block(embedding) embedding = F.silu(embedding) embedding = self.conv_out(embedding) #split them apart again #actually not needed #new_channels, new_height, new_width = embedding.shape[1], embedding.shape[2], embedding.shape[3] #embedding = embedding.view(batch_size, frames, new_channels, new_height, new_width) return embedding class ControlNetSDVModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin): r""" A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). 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 8): Number of channels in the input sample. out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`): The tuple of downsample blocks to use. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`): The tuple of upsample blocks to use. block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. addition_time_embed_dim: (`int`, defaults to 256): Dimension to to encode the additional time ids. projection_class_embeddings_input_dim (`int`, defaults to 768): The dimension of the projection of encoded `added_time_ids`. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): The dimension of the cross attention features. transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`], [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`]. num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`): The number of attention heads. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, sample_size: Optional[int] = None, in_channels: int = 8, out_channels: int = 4, down_block_types: Tuple[str] = ( "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal", ), up_block_types: Tuple[str] = ( "UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", ), block_out_channels: Tuple[int] = (320, 640, 1280, 1280), addition_time_embed_dim: int = 256, projection_class_embeddings_input_dim: int = 768, layers_per_block: Union[int, Tuple[int]] = 2, cross_attention_dim: Union[int, Tuple[int]] = 1024, transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20), num_frames: int = 25, conditioning_channels: int = 3, conditioning_embedding_out_channels : Optional[Tuple[int, ...]] = (16, 32, 96, 256), ): super().__init__() self.sample_size = sample_size print("layers per block is", layers_per_block) # 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`. `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`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): raise ValueError( f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." ) if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): raise ValueError( f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." ) if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): raise ValueError( f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." ) # input self.conv_in = nn.Conv2d( in_channels, block_out_channels[0], kernel_size=3, padding=1, ) # time time_embed_dim = block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0) self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) self.down_blocks = nn.ModuleList([]) self.controlnet_down_blocks = nn.ModuleList([]) if isinstance(num_attention_heads, int): num_attention_heads = (num_attention_heads,) * len(down_block_types) if isinstance(cross_attention_dim, int): cross_attention_dim = (cross_attention_dim,) * len(down_block_types) if isinstance(layers_per_block, int): layers_per_block = [layers_per_block] * len(down_block_types) if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) blocks_time_embed_dim = time_embed_dim self.controlnet_cond_embedding = ControlNetConditioningEmbeddingSVD( conditioning_embedding_channels=block_out_channels[0], block_out_channels=conditioning_embedding_out_channels, conditioning_channels=conditioning_channels, ) # down output_channel = block_out_channels[0] controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) controlnet_block = zero_module(controlnet_block) self.controlnet_down_blocks.append(controlnet_block) 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[i], transformer_layers_per_block=transformer_layers_per_block[i], in_channels=input_channel, out_channels=output_channel, temb_channels=blocks_time_embed_dim, add_downsample=not is_final_block, resnet_eps=1e-5, cross_attention_dim=cross_attention_dim[i], num_attention_heads=num_attention_heads[i], resnet_act_fn="silu", ) self.down_blocks.append(down_block) for _ in range(layers_per_block[i]): controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) controlnet_block = zero_module(controlnet_block) self.controlnet_down_blocks.append(controlnet_block) if not is_final_block: controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) controlnet_block = zero_module(controlnet_block) self.controlnet_down_blocks.append(controlnet_block) # mid mid_block_channel = block_out_channels[-1] controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1) controlnet_block = zero_module(controlnet_block) self.controlnet_mid_block = controlnet_block self.mid_block = UNetMidBlockSpatioTemporal( block_out_channels[-1], temb_channels=blocks_time_embed_dim, transformer_layers_per_block=transformer_layers_per_block[-1], cross_attention_dim=cross_attention_dim[-1], num_attention_heads=num_attention_heads[-1], ) # out #self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5) #self.conv_act = nn.SiLU() #self.conv_out = nn.Conv2d( # block_out_channels[0], # out_channels, # kernel_size=3, # padding=1, #) @property def attn_processors(self) -> Dict[str, AttentionProcessor]: 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, AttentionProcessor], ): if hasattr(module, "get_processor"): processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) 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[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `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_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnProcessor() else: raise ValueError( f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" ) self.set_attn_processor(processor) def _set_gradient_checkpointing(self, module, value=False): if hasattr(module, "gradient_checkpointing"): module.gradient_checkpointing = value # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None: """ Sets the attention processor to use [feed forward chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). Parameters: chunk_size (`int`, *optional*): The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually over each tensor of dim=`dim`. dim (`int`, *optional*, defaults to `0`): The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) or dim=1 (sequence length). """ if dim not in [0, 1]: raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}") # By default chunk size is 1 chunk_size = chunk_size or 1 def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): if hasattr(module, "set_chunk_feed_forward"): module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) for child in module.children(): fn_recursive_feed_forward(child, chunk_size, dim) for module in self.children(): fn_recursive_feed_forward(module, chunk_size, dim) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, added_time_ids: torch.Tensor, controlnet_cond: torch.FloatTensor = None, image_only_indicator: Optional[torch.Tensor] = None, return_dict: bool = True, guess_mode: bool = False, conditioning_scale: float = 1.0, ) -> Union[ControlNetOutput, Tuple]: r""" The [`UNetSpatioTemporalConditionModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`. timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. encoder_hidden_states (`torch.FloatTensor`): The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`. added_time_ids: (`torch.FloatTensor`): The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal embeddings and added to the time embeddings. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain tuple. Returns: [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`: If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise a `tuple` is returned where the first element is the sample tensor. """ # 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 batch_size, num_frames = sample.shape[:2] timesteps = timesteps.expand(batch_size) 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=sample.dtype) # print(t_emb.dtype) emb = self.time_embedding(t_emb) time_embeds = self.add_time_proj(added_time_ids.flatten()) time_embeds = time_embeds.reshape((batch_size, -1)) time_embeds = time_embeds.to(emb.dtype) aug_emb = self.add_embedding(time_embeds) emb = emb + aug_emb # Flatten the batch and frames dimensions # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width] sample = sample.flatten(0, 1) # Repeat the embeddings num_video_frames times # emb: [batch, channels] -> [batch * frames, channels] emb = emb.repeat_interleave(num_frames, dim=0) # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels] encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0) # 2. pre-process sample = self.conv_in(sample) #controlnet cond if controlnet_cond != None: controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) sample = sample + controlnet_cond image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device) 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, image_only_indicator=image_only_indicator, ) else: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, image_only_indicator=image_only_indicator, ) down_block_res_samples += res_samples # 4. mid sample = self.mid_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, image_only_indicator=image_only_indicator, ) controlnet_down_block_res_samples = () for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks): down_block_res_sample = controlnet_block(down_block_res_sample) controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,) down_block_res_samples = controlnet_down_block_res_samples mid_block_res_sample = self.controlnet_mid_block(sample) # 6. scaling down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples] mid_block_res_sample = mid_block_res_sample * conditioning_scale if not return_dict: return (down_block_res_samples, mid_block_res_sample) return ControlNetOutput( down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample ) @classmethod def from_unet( cls, unet: UNetSpatioTemporalConditionModel, controlnet_conditioning_channel_order: str = "rgb", conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), load_weights_from_unet: bool = True, conditioning_channels: int = 3, ): r""" Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`]. Parameters: unet (`UNet2DConditionModel`): The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied where applicable. """ transformer_layers_per_block = ( unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1 ) encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None addition_time_embed_dim = ( unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None ) print(unet.config) controlnet = cls( in_channels=unet.config.in_channels, down_block_types=unet.config.down_block_types, block_out_channels=unet.config.block_out_channels, addition_time_embed_dim=unet.config.addition_time_embed_dim, transformer_layers_per_block=unet.config.transformer_layers_per_block, cross_attention_dim=unet.config.cross_attention_dim, num_attention_heads=unet.config.num_attention_heads, num_frames=unet.config.num_frames, sample_size=unet.config.sample_size, # Added based on the dict layers_per_block=unet.config.layers_per_block, projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim, conditioning_channels = conditioning_channels, conditioning_embedding_out_channels = conditioning_embedding_out_channels, ) #controlnet rgb channel order ignored, set to not makea difference by default if load_weights_from_unet: controlnet.conv_in.load_state_dict(unet.conv_in.state_dict()) controlnet.time_proj.load_state_dict(unet.time_proj.state_dict()) controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict()) # if controlnet.class_embedding: # controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict()) controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict()) controlnet.mid_block.load_state_dict(unet.mid_block.state_dict()) return controlnet @property # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors def attn_processors(self) -> Dict[str, AttentionProcessor]: 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, AttentionProcessor]): if hasattr(module, "get_processor"): processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) 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 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor def set_attn_processor( self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False ): r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `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, _remove_lora=_remove_lora) else: module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora) 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) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnAddedKVProcessor() elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnProcessor() else: raise ValueError( f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" ) self.set_attn_processor(processor, _remove_lora=True) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None: r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. This is useful for saving some memory in exchange for a small decrease in speed. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If `"max"`, maximum amount of memory is 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_sliceable_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_sliceable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_sliceable_dims(module) num_sliceable_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_sliceable_layers * [1] slice_size = num_sliceable_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: bool = False) -> None: # if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)): # module.gradient_checkpointing = value def zero_module(module): for p in module.parameters(): nn.init.zeros_(p) return module