# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py import os, json from dataclasses import dataclass from typing import List, 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.modeling_utils import ModelMixin from diffusers.utils import BaseOutput, logging from diffusers.models.embeddings import TimestepEmbedding, Timesteps from .unet_2d_blocks import ( CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UNetMidBlock2DCrossAttn, # UNetMidBlock2DSimpleCrossAttn, UpBlock2D, get_down_block, get_up_block, ) from .resnet_2d import InflatedConv3d logger = logging.get_logger(__name__) # pylint: disable=invalid-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(ModelMixin, ConfigMixin): 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`. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`): 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. 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. 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"`, or `"identity"`. """ _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: 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: 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", use_sc_attn: bool = False, use_st_attn: bool = False, st_attn_idx: int = None, ): super().__init__() self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 # input self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) # time self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_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) else: self.class_embedding = None self.down_blocks = nn.ModuleList([]) self.mid_block = None 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, attn_num_head_channels=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, use_sc_attn=use_sc_attn, # idx range from 0 to 2, i.e., ['CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D'] use_st_attn=True if (use_st_attn and i == st_attn_idx) else False, ) 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, attn_num_head_channels=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, use_sc_attn=use_sc_attn, use_st_attn=use_st_attn, ) else: raise ValueError(f"unknown mid_block_type : {mid_block_type}") # count how many layers upsample the videos 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, attn_num_head_channels=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, use_sc_attn=use_sc_attn, # idx range from 0 to 2, i.e., ['UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D'] use_st_attn=True if (use_st_attn and i-1 == st_attn_idx) else False, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out 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() self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1) 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 def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, class_labels: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, return_dict: bool = True, normal_infer: 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. 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. 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) # center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 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) 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 # pre-process sample = self.conv_in(sample) # 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, normal_infer=normal_infer, ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb) down_block_res_samples += res_samples # mid sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, normal_infer=normal_infer, ) # 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, upsample_size=upsample_size, attention_mask=attention_mask, normal_infer=normal_infer, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size ) # post-process 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) @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs): r""" for gradio demo """ import diffusers __version__ = diffusers.__version__ from diffusers.utils import ( CONFIG_NAME, DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, is_accelerate_available, is_safetensors_available, is_torch_version, logging, ) if is_torch_version(">=", "1.9.0"): _LOW_CPU_MEM_USAGE_DEFAULT = True else: _LOW_CPU_MEM_USAGE_DEFAULT = False if is_accelerate_available(): import accelerate from accelerate.utils import set_module_tensor_to_device from accelerate.utils.versions import is_torch_version if is_safetensors_available(): import safetensors from diffusers.modeling_utils import load_state_dict cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) output_loading_info = kwargs.pop("output_loading_info", False) local_files_only = kwargs.pop("local_files_only", False) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) torch_dtype = kwargs.pop("torch_dtype", None) subfolder = kwargs.pop("subfolder", None) device_map = kwargs.pop("device_map", None) low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) # custom arg use_sc_attn = kwargs.pop("use_sc_attn", True) use_st_attn = kwargs.pop("use_st_attn", True) st_attn_idx = kwargs.pop("st_attn_idx", 0) if low_cpu_mem_usage and not is_accelerate_available(): low_cpu_mem_usage = False logger.warning( "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" " install accelerate\n```\n." ) if device_map is not None and not is_accelerate_available(): raise NotImplementedError( "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set" " `device_map=None`. You can install accelerate with `pip install accelerate`." ) # Check if we can handle device_map and dispatching the weights if device_map is not None and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" " `device_map=None`." ) if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" " `low_cpu_mem_usage=False`." ) if low_cpu_mem_usage is False and device_map is not None: raise ValueError( f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and" " dispatching. Please make sure to set `low_cpu_mem_usage=True`." ) user_agent = { "diffusers": __version__, "file_type": "model", "framework": "pytorch", } # Load config if we don't provide a configuration config_path = pretrained_model_name_or_path # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the # Load model model_file = None if is_safetensors_available(): try: model_file = cls._get_model_file( pretrained_model_name_or_path, weights_name=SAFETENSORS_WEIGHTS_NAME, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) except: pass if model_file is None: model_file = cls._get_model_file( pretrained_model_name_or_path, weights_name=WEIGHTS_NAME, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, user_agent=user_agent, ) if low_cpu_mem_usage: # Instantiate model with empty weights with accelerate.init_empty_weights(): config, unused_kwargs = cls.load_config( config_path, cache_dir=cache_dir, return_unused_kwargs=True, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, device_map=device_map, **kwargs, ) # custom arg config['use_sc_attn'] = use_sc_attn config['use_st_attn'] = use_st_attn config['st_attn_idx'] = st_attn_idx model = cls.from_config(config, **unused_kwargs) # if device_map is Non,e load the state dict on move the params from meta device to the cpu if device_map is None: param_device = "cpu" state_dict = load_state_dict(model_file) # move the parms from meta device to cpu for param_name, param in state_dict.items(): set_module_tensor_to_device(model, param_name, param_device, value=param) else: # else let accelerate handle loading and dispatching. # Load weights and dispatch according to the device_map # by deafult the device_map is None and the weights are loaded on the CPU accelerate.load_checkpoint_and_dispatch(model, model_file, device_map) loading_info = { "missing_keys": [], "unexpected_keys": [], "mismatched_keys": [], "error_msgs": [], } else: config, unused_kwargs = cls.load_config( config_path, cache_dir=cache_dir, return_unused_kwargs=True, force_download=force_download, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, device_map=device_map, **kwargs, ) # custom arg config['use_sc_attn'] = use_sc_attn config['use_st_attn'] = use_st_attn config['st_attn_idx'] = st_attn_idx model = cls.from_config(config, **unused_kwargs) state_dict = load_state_dict(model_file) dtype = set(v.dtype for v in state_dict.values()) if len(dtype) > 1 and torch.float32 not in dtype: raise ValueError( f"The weights of the model file {model_file} have a mixture of incompatible dtypes {dtype}. Please" f" make sure that {model_file} weights have only one dtype." ) elif len(dtype) > 1 and torch.float32 in dtype: dtype = torch.float32 else: dtype = dtype.pop() # move model to correct dtype model = model.to(dtype) model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model( model, state_dict, model_file, pretrained_model_name_or_path, ignore_mismatched_sizes=ignore_mismatched_sizes, ) loading_info = { "missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "mismatched_keys": mismatched_keys, "error_msgs": error_msgs, } if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype): raise ValueError( f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}." ) elif torch_dtype is not None: model = model.to(torch_dtype) model.register_to_config(_name_or_path=pretrained_model_name_or_path) # Set model in evaluation mode to deactivate DropOut modules by default model.eval() if output_loading_info: return model, loading_info return model