FangSen9000
commented
6 days ago After a few hours of hard work, if anyone has the same problem as me, just replace 'MusePose/src/models/unet_3d.py' with the following:
Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py
from collections import OrderedDict from dataclasses import dataclass from os import PathLike from pathlib import Path from typing import Dict, List, Optional, Tuple, Union
import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.attention_processor import AttentionProcessor from diffusers.models.embeddings import TimestepEmbedding, Timesteps try: from diffusers.modeling_utils import ModelMixin except: from diffusers.models.modeling_utils import ModelMixin from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging from safetensors.torch import load_file
from .resnet import InflatedConv3d, InflatedGroupNorm from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block
logger = logging.get_logger(name) # pylint: disable=invalid-name
@dataclass class UNet3DConditionOutput(BaseOutput): sample: torch.FloatTensor
class UNet3DConditionModel(ModelMixin, ConfigMixin): _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] = ( "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D", ), mid_block_type: str = "UNetMidBlock3DCrossAttn", up_block_types: Tuple[str] = ( "UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", ), 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_inflated_groupnorm=False, # Additional use_motion_module=False, motion_module_resolutions=(1, 2, 4, 8), motion_module_mid_block=False, motion_module_decoder_only=False, motion_module_type=None, motion_module_kwargs={}, unet_use_cross_frame_attention=None, unet_use_temporal_attention=None, mode=None, task_type="action", ): super().__init__() self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 # Add this line in the __init__ method self.pose_projector = nn.Conv3d(4, 320, kernel_size=1) # 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): if task_type == "action": name_index, mid_name = None, None else: name_index, mid_name = i, "MidBlock" res = 2**i 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, unet_use_cross_frame_attention=unet_use_cross_frame_attention, unet_use_temporal_attention=unet_use_temporal_attention, use_inflated_groupnorm=use_inflated_groupnorm, use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only), motion_module_type=motion_module_type, motion_module_kwargs=motion_module_kwargs, name_index=name_index, ) self.down_blocks.append(down_block) # mid if mid_block_type == "UNetMidBlock3DCrossAttn": self.mid_block = UNetMidBlock3DCrossAttn( 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, unet_use_cross_frame_attention=unet_use_cross_frame_attention, unet_use_temporal_attention=unet_use_temporal_attention, use_inflated_groupnorm=use_inflated_groupnorm, use_motion_module=use_motion_module and motion_module_mid_block, motion_module_type=motion_module_type, motion_module_kwargs=motion_module_kwargs, name=mid_name, ) 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): res = 2 ** (3 - i) is_final_block = i == len(block_out_channels) - 1 if task_type == "action": name_index = None else: name_index = i 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, unet_use_cross_frame_attention=unet_use_cross_frame_attention, unet_use_temporal_attention=unet_use_temporal_attention, use_inflated_groupnorm=use_inflated_groupnorm, use_motion_module=use_motion_module and (res in motion_module_resolutions), motion_module_type=motion_module_type, motion_module_kwargs=motion_module_kwargs, name_index=name_index, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out if use_inflated_groupnorm: self.conv_norm_out = InflatedGroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps, ) else: 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 ) self.mode = mode @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, "set_processor"): processors[f"{name}.processor"] = module.processor for sub_name, child in module.named_children(): if "temporal_transformer" not in sub_name: fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): if "temporal_transformer" not in name: fn_recursive_add_processors(name, module, processors) return processors 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 hasattr(module, "gradient_checkpointing"): module.gradient_checkpointing = value # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor 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(): if "temporal_transformer" not in sub_name: fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): if "temporal_transformer" not in name: fn_recursive_attn_processor(name, module, processor) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, class_labels: Optional[torch.Tensor] = None, pose_cond_fea: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, return_dict: bool = True, self_attention_additional_feats = None, ) -> Union[UNet3DConditionOutput, 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): # 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 # 1. Check the shapes of both tensors print(f"sample shape: {sample.shape}") print(f"pose_cond_fea shape: {pose_cond_fea.shape}") # pre-process #sample = self.conv_in(sample) #if pose_cond_fea is not None: # sample = sample + pose_cond_fea # pre-process if sample is not None: # Store original height of sample original_height = sample.shape[3] # Project sample to match pose_cond_fea's channel dimension sample = self.pose_projector(sample) # Adjust height of pose_cond_fea to match original sample height pose_cond_fea = F.interpolate(pose_cond_fea, size=(pose_cond_fea.shape[2], original_height, pose_cond_fea.shape[4]), mode='trilinear', align_corners=False) # Add debug prints print(f"Adjusted sample shape: {sample.shape}") print(f"Adjusted pose_cond_fea shape: {pose_cond_fea.shape}") # Ensure the shapes match before adding assert pose_cond_fea.shape == sample.shape, "Shapes still don't match after adjustment" sample = sample + pose_cond_fea # 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, self_attention_additional_feats=self_attention_additional_feats, mode=self.mode, ) else: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, ) 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_res_sample + down_block_additional_residual ) new_down_block_res_samples += (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # mid sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, self_attention_additional_feats=self_attention_additional_feats, mode=self.mode, ) if mid_block_additional_residual is not None: sample = sample + mid_block_additional_residual # 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, self_attention_additional_feats=self_attention_additional_feats, mode=self.mode, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states, ) # 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 UNet3DConditionOutput(sample=sample) @classmethod def from_pretrained_2d( cls, pretrained_model_path: PathLike, motion_module_path: PathLike, subfolder=None, unet_additional_kwargs=None, mm_zero_proj_out=False, ): pretrained_model_path = Path(pretrained_model_path) motion_module_path = Path(motion_module_path) if subfolder is not None: pretrained_model_path = pretrained_model_path.joinpath(subfolder) logger.info( f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..." ) config_file = pretrained_model_path / "config.json" if not (config_file.exists() and config_file.is_file()): raise RuntimeError(f"{config_file} does not exist or is not a file") unet_config = cls.load_config(config_file) unet_config["_class_name"] = cls.__name__ unet_config["down_block_types"] = [ "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D", ] unet_config["up_block_types"] = [ "UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", ] unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn" model = cls.from_config(unet_config, **unet_additional_kwargs) # load the vanilla weights if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists(): logger.debug( f"loading safeTensors weights from {pretrained_model_path} ..." ) state_dict = load_file( pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu" ) elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists(): logger.debug(f"loading weights from {pretrained_model_path} ...") state_dict = torch.load( pretrained_model_path.joinpath(WEIGHTS_NAME), map_location="cpu", weights_only=True, ) else: raise FileNotFoundError(f"no weights file found in {pretrained_model_path}") # load the motion module weights if motion_module_path.exists() and motion_module_path.is_file(): if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]: logger.info(f"Load motion module params from {motion_module_path}") motion_state_dict = torch.load( motion_module_path, map_location="cpu", weights_only=True ) elif motion_module_path.suffix.lower() == ".safetensors": motion_state_dict = load_file(motion_module_path, device="cpu") else: raise RuntimeError( f"unknown file format for motion module weights: {motion_module_path.suffix}" ) if mm_zero_proj_out: logger.info(f"Zero initialize proj_out layers in motion module...") new_motion_state_dict = OrderedDict() for k in motion_state_dict: if "proj_out" in k: continue new_motion_state_dict[k] = motion_state_dict[k] motion_state_dict = new_motion_state_dict # merge the state dicts state_dict.update(motion_state_dict) # load the weights into the model m, u = model.load_state_dict(state_dict, strict=False) logger.debug(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};") params = [ p.numel() if "temporal" in n else 0 for n, p in model.named_parameters() ] logger.info(f"Loaded {sum(params) / 1e6}M-parameter motion module") return model
~/# accelerate launch train_stage_2.py --config configs/train/stage2.yaml The following values were not passed to
accelerate launchand had defaults used instead:--num_processeswas set to a value of1--num_machineswas set to a value of1--mixed_precisionwas set to a value of'no'--dynamo_backendwas set to a value of'no'To avoid this warning pass in values for each of the problematic parameters or runaccelerate config. 10/12/2024 09:16:18 - INFO - main - Distributed environment: NO Num processes: 1 Process index: 0 Local process index: 0 Device: cudaMixed precision type: fp16
{'scaling_factor', 'force_upcast'} was not found in config. Values will be initialized to default values. {'addition_time_embed_dim', 'time_embedding_type', 'num_class_embeds', 'encoder_hid_dim', 'encoder_hid_dim_type', 'addition_embed_type_num_heads', 'addition_embed_type', 'dual_cross_attention', 'dropout', 'resnet_out_scale_factor', 'attention_type', 'reverse_transformer_layers_per_block', 'projection_class_embeddings_input_dim', 'mid_block_type', 'conv_out_kernel', 'resnet_skip_time_act', 'use_linear_projection', 'class_embeddings_concat', 'time_embedding_dim', 'timestep_post_act', 'resnet_time_scale_shift', 'only_cross_attention', 'transformer_layers_per_block', 'class_embed_type', 'conv_in_kernel', 'time_cond_proj_dim', 'time_embedding_act_fn', 'mid_block_only_cross_attention', 'num_attention_heads', 'upcast_attention', 'cross_attention_norm'} was not found in config. Values will be initialized to default values. Some weights of the model checkpoint were not used when initializing UNet2DConditionModel: ['conv_norm_out.weight, conv_norm_out.bias, conv_out.weight, conv_out.bias'] 10/12/2024 09:16:25 - INFO - src.models.unet_3d - loaded temporal unet's pretrained weights from pretrained_weights/stable-diffusion-v1-5/unet ... {'dual_cross_attention', 'use_linear_projection', 'num_class_embeds', 'upcast_attention', 'mode', 'task_type', 'resnet_time_scale_shift', 'only_cross_attention', 'class_embed_type'} was not found in config. Values will be initialized to default values. 10/12/2024 09:16:38 - INFO - src.models.unet_3d - Load motion module params from pretrained_weights/mm_sd_v15_v2.ckpt 10/12/2024 09:16:39 - INFO - src.models.unet_3d - Loaded 453.20928M-parameter motion module 10/12/2024 09:16:44 - INFO - main - Total trainable params 546 10/12/2024 09:16:45 - INFO - main - Running training 10/12/2024 09:16:45 - INFO - main - Num examples = 7755 10/12/2024 09:16:45 - INFO - main - Num Epochs = 2 10/12/2024 09:16:45 - INFO - main - Instantaneous batch size per device = 1 10/12/2024 09:16:45 - INFO - main - Total train batch size (w. parallel, distributed & accumulation) = 1 10/12/2024 09:16:45 - INFO - main - Gradient Accumulation steps = 1 10/12/2024 09:16:45 - INFO - main - Total optimization steps = 10000 Steps: 0%| | 0/10000 [00:00<?, ?it/s]10/12/2024 09:16:50 - INFO - src.models.unet_3d - Forward upsample size to force interpolation output size. Traceback (most recent call last): File "/root/MusePose/train_stage_2.py", line 773, in
main(config)
File "/root/MusePose/train_stage_2.py", line 602, in main
model_pred = net(
File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/utils/operations.py", line 825, in forward
return model_forward(*args, *kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/utils/operations.py", line 813, in call
return convert_to_fp32(self.model_forward(args, kwargs))
File "/root/miniconda3/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast
return func(*args, *kwargs)
File "/root/MusePose/train_stage_2.py", line 96, in forward
model_pred = self.denoising_unet(
File "/root/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(args, *kwargs)
File "/root//src/models/unet_3d.py", line 505, in forward
sample = sample + pose_cond_fea
RuntimeError: The size of tensor a (22) must match the size of tensor b (23) at non-singleton dimension 3
Steps: 0%| | 0/10000 [00:06<?, ?it/s]
Traceback (most recent call last):
File "/root/miniconda3/bin/accelerate", line 8, in
sys.exit(main())
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 46, in main
args.func(args)
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1075, in launch_command
simple_launcher(args)
File "/root/miniconda3/lib/python3.10/site-packages/accelerate/commands/launch.py", line 681, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['/root/miniconda3/bin/python', 'train_stage_2.py', '--config', 'configs/train/stage2.yaml']' returned non-zero exit status 1.