1058 lines
38 KiB
Python
1058 lines
38 KiB
Python
from typing import Any, List, Optional, Union, Dict
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import torch
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from torch.optim.lr_scheduler import LambdaLR
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from safetensors.torch import load_file as load_safetensors
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from ..sgm.modules.diffusionmodules.openaimodel import *
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from ..sgm.modules.video_attention import SpatialVideoTransformer
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from ..sgm.util import default, instantiate_from_config, get_obj_from_str
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from ..sgm.modules.diffusionmodules.util import zero_module
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from ..sgm.modules.diffusionmodules.video_model import VideoResBlock, VideoUNet
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from ..sgm.models.diffusion import DiffusionEngine
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from ..sgm.util import instantiate_from_config, get_obj_from_str
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import comfy.ops
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ops = comfy.ops.manual_cast
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class ControlledVideoUNet(VideoUNet):
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def __init__(
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self,
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in_channels: int,
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model_channels: int,
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out_channels: int,
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num_res_blocks: int,
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attention_resolutions: int,
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dropout: float = 0.0,
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channel_mult: List[int] = (1, 2, 4, 8),
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conv_resample: bool = True,
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dims: int = 2,
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num_classes: Optional[int] = None,
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use_checkpoint: bool = False,
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num_heads: int = -1,
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num_head_channels: int = -1,
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num_heads_upsample: int = -1,
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use_scale_shift_norm: bool = False,
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resblock_updown: bool = False,
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transformer_depth: Union[List[int], int] = 1,
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transformer_depth_middle: Optional[int] = None,
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context_dim: Optional[int] = None,
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time_downup: bool = False,
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time_context_dim: Optional[int] = None,
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extra_ff_mix_layer: bool = False,
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use_spatial_context: bool = False,
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merge_strategy: str = "fixed",
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merge_factor: float = 0.5,
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spatial_transformer_attn_type: str = "softmax",
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temporal_attn_type = None,
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spatial_self_attn_type: str = None,
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conv3d_type: str = None,
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video_kernel_size: Union[int, List[int]] = 3,
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use_linear_in_transformer: bool = False,
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adm_in_channels: Optional[int] = None,
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disable_temporal_crossattention: bool = False,
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max_ddpm_temb_period: int = 10000,
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trainable_layers = None,
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):
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nn.Module.__init__(self)
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assert context_dim is not None
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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if num_heads == -1:
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assert num_head_channels != -1
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if num_head_channels == -1:
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assert num_heads != -1
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self.trainable_layers = trainable_layers
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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if isinstance(transformer_depth, int):
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transformer_depth = len(channel_mult) * [transformer_depth]
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transformer_depth_middle = default(
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transformer_depth_middle, transformer_depth[-1]
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)
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self.num_res_blocks = num_res_blocks
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.num_classes = num_classes
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self.use_checkpoint = use_checkpoint
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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self.temporal_attn_type = temporal_attn_type
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self.spatial_self_attn_type = spatial_self_attn_type
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self.conv3d_type = conv3d_type
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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if self.num_classes is not None:
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if isinstance(self.num_classes, int):
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self.label_emb = nn.Embedding(num_classes, time_embed_dim)
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elif self.num_classes == "continuous":
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print("setting up linear c_adm embedding layer")
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self.label_emb = ops.Conv2d(1, time_embed_dim)
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elif self.num_classes == "timestep":
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self.label_emb = nn.Sequential(
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Timestep(model_channels),
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nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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),
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)
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elif self.num_classes == "sequential":
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assert adm_in_channels is not None
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self.label_emb = nn.Sequential(
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nn.Sequential(
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linear(adm_in_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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)
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else:
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raise ValueError()
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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)
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]
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)
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self._feature_size = model_channels
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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def get_attention_layer(
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ch,
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num_heads,
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dim_head,
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depth=1,
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context_dim=None,
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use_checkpoint=False,
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disabled_sa=False,
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):
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return SpatialVideoTransformer(
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ch,
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num_heads,
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dim_head,
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depth=depth,
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context_dim=context_dim,
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time_context_dim=time_context_dim,
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dropout=dropout,
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ff_in=extra_ff_mix_layer,
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use_spatial_context=use_spatial_context,
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merge_strategy=merge_strategy,
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merge_factor=merge_factor,
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checkpoint=use_checkpoint,
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use_linear=use_linear_in_transformer,
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attn_mode=spatial_transformer_attn_type,
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temporal_attn_mode=get_obj_from_str(temporal_attn_type) \
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if temporal_attn_type is not None else None,
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disable_self_attn=disabled_sa,
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disable_temporal_crossattention=disable_temporal_crossattention,
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max_time_embed_period=max_ddpm_temb_period,
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spatial_self_attn_type=get_obj_from_str(spatial_self_attn_type) \
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if spatial_self_attn_type is not None else None,
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)
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def get_resblock(
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merge_factor,
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merge_strategy,
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video_kernel_size,
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ch,
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time_embed_dim,
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dropout,
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out_ch,
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dims,
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use_checkpoint,
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use_scale_shift_norm,
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down=False,
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up=False,
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):
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return VideoResBlock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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channels=ch,
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emb_channels=time_embed_dim,
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dropout=dropout,
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out_channels=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=down,
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up=up,
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temporal_conv=get_obj_from_str(conv3d_type) \
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if conv3d_type is not None else None,
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)
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for level, mult in enumerate(channel_mult):
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for _ in range(num_res_blocks):
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layers = [
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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out_ch=mult * model_channels,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = mult * model_channels
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if ds in attention_resolutions:
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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layers.append(
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get_attention_layer(
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ch,
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num_heads,
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dim_head,
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depth=transformer_depth[level],
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context_dim=context_dim,
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use_checkpoint=use_checkpoint,
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disabled_sa=False,
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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ds *= 2
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out_ch = ch
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self.input_blocks.append(
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TimestepEmbedSequential(
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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out_ch=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True,
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)
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if resblock_updown
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else Downsample(
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ch,
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conv_resample,
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dims=dims,
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out_channels=out_ch,
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third_down=time_downup,
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)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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self._feature_size += ch
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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self.middle_block = TimestepEmbedSequential(
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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time_embed_dim=time_embed_dim,
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out_ch=None,
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dropout=dropout,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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get_attention_layer(
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ch,
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num_heads,
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dim_head,
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depth=transformer_depth_middle,
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context_dim=context_dim,
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use_checkpoint=use_checkpoint,
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),
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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out_ch=None,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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)
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self._feature_size += ch
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self.output_blocks = nn.ModuleList([])
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for level, mult in list(enumerate(channel_mult))[::-1]:
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for i in range(num_res_blocks + 1):
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ich = input_block_chans.pop()
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layers = [
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch + ich,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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out_ch=model_channels * mult,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = model_channels * mult
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if ds in attention_resolutions:
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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layers.append(
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get_attention_layer(
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ch,
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num_heads,
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dim_head,
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depth=transformer_depth[level],
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context_dim=context_dim,
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use_checkpoint=use_checkpoint,
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disabled_sa=False,
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)
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)
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if level and i == num_res_blocks:
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out_ch = ch
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ds //= 2
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layers.append(
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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out_ch=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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up=True,
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)
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if resblock_updown
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else Upsample(
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ch,
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conv_resample,
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dims=dims,
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out_channels=out_ch,
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third_up=time_downup,
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)
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)
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self.output_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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self.out = nn.Sequential(
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normalization(ch),
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nn.SiLU(),
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zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
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)
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def forward(
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self,
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x: th.Tensor,
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timesteps: th.Tensor,
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context: Optional[th.Tensor] = None,
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y: Optional[th.Tensor] = None,
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time_context: Optional[th.Tensor] = None,
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control: Optional[th.Tensor] = None,
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num_video_frames: Optional[int] = None,
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image_only_indicator: Optional[th.Tensor] = None,
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):
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assert (y is not None) == (
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self.num_classes is not None
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), "must specify y if and only if the model is class-conditional -> no, relax this TODO"
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hs = []
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
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emb = self.time_embed(t_emb)
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if self.num_classes is not None:
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assert y.shape[0] == x.shape[0]
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emb = emb + self.label_emb(y)
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h = x
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for module in self.input_blocks:
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h = module(
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h,
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emb,
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context=context,
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image_only_indicator=image_only_indicator,
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time_context=time_context,
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num_video_frames=num_video_frames,
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)
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hs.append(h)
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h = self.middle_block(
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h,
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emb,
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context=context,
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image_only_indicator=image_only_indicator,
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time_context=time_context,
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num_video_frames=num_video_frames,
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)
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if control is not None:
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h += control.pop()
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for module in self.output_blocks:
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if control is None:
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h = th.cat([h, hs.pop()], dim=1)
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else:
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h = th.cat([h, hs.pop() + control.pop()], dim=1)
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h = module(
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h,
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emb,
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context=context,
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image_only_indicator=image_only_indicator,
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time_context=time_context,
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num_video_frames=num_video_frames,
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)
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h = h.type(x.dtype)
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return self.out(h)
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|
|
|
def set_parameters_requires_grad(self):
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self.requires_grad_(False)
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|
|
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if self.trainable_layers is not None:
|
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for name, module in self.named_modules():
|
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if module.__class__.__name__ in self.trainable_layers:
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module.requires_grad_(True)
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a = 0
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|
|
|
def get_trainable_parameters(self):
|
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params = []
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for p in self.parameters():
|
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if p.requires_grad:
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params += [p]
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return params
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|
|
|
def get_state_dict(self):
|
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dicts = []
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for name, param in self.named_parameters():
|
|
if param.requires_grad:
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dicts.append( f'model.diffusion_model.{name}' )
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return dicts
|
|
|
|
|
|
class ControlNet(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
model_channels: int,
|
|
hint_channels: int,
|
|
num_res_blocks: int,
|
|
attention_resolutions: int,
|
|
dropout: float = 0.0,
|
|
channel_mult: List[int] = (1, 2, 4, 8),
|
|
conv_resample: bool = True,
|
|
dims: int = 2,
|
|
num_classes: Optional[int] = None,
|
|
use_checkpoint: bool = False,
|
|
num_heads: int = -1,
|
|
num_head_channels: int = -1,
|
|
num_heads_upsample: int = -1,
|
|
use_scale_shift_norm: bool = False,
|
|
resblock_updown: bool = False,
|
|
transformer_depth: Union[List[int], int] = 1,
|
|
transformer_depth_middle: Optional[int] = None,
|
|
context_dim: Optional[int] = None,
|
|
time_downup: bool = False,
|
|
time_context_dim: Optional[int] = None,
|
|
extra_ff_mix_layer: bool = False,
|
|
use_spatial_context: bool = False,
|
|
merge_strategy: str = "fixed",
|
|
merge_factor: float = 0.5,
|
|
spatial_transformer_attn_type: str = "softmax",
|
|
temporal_attn_type: str = None,
|
|
spatial_self_attn_type: str = None,
|
|
conv3d_type: str = None,
|
|
video_kernel_size: Union[int, List[int]] = 3,
|
|
use_linear_in_transformer: bool = False,
|
|
adm_in_channels: Optional[int] = None,
|
|
disable_temporal_crossattention: bool = False,
|
|
max_ddpm_temb_period: int = 10000,
|
|
):
|
|
super().__init__()
|
|
assert context_dim is not None
|
|
|
|
if num_heads_upsample == -1:
|
|
num_heads_upsample = num_heads
|
|
|
|
if num_heads == -1:
|
|
assert num_head_channels != -1
|
|
|
|
if num_head_channels == -1:
|
|
assert num_heads != -1
|
|
|
|
self.dims = dims
|
|
self.in_channels = in_channels
|
|
self.model_channels = model_channels
|
|
self.hint_channels = hint_channels
|
|
if isinstance(transformer_depth, int):
|
|
transformer_depth = len(channel_mult) * [transformer_depth]
|
|
transformer_depth_middle = default(
|
|
transformer_depth_middle, transformer_depth[-1]
|
|
)
|
|
|
|
self.num_res_blocks = num_res_blocks
|
|
self.attention_resolutions = attention_resolutions
|
|
self.dropout = dropout
|
|
self.channel_mult = channel_mult
|
|
self.conv_resample = conv_resample
|
|
self.num_classes = num_classes
|
|
self.use_checkpoint = use_checkpoint
|
|
self.num_heads = num_heads
|
|
self.num_head_channels = num_head_channels
|
|
self.num_heads_upsample = num_heads_upsample
|
|
self.context_dim = context_dim
|
|
self.adm_in_channels = adm_in_channels
|
|
|
|
self.temporal_attn_type = temporal_attn_type
|
|
self.spatial_self_attn_type = spatial_self_attn_type
|
|
self.conv3d_type = conv3d_type
|
|
|
|
time_embed_dim = model_channels * 4
|
|
self.time_embed = nn.Sequential(
|
|
linear(model_channels, time_embed_dim),
|
|
nn.SiLU(),
|
|
linear(time_embed_dim, time_embed_dim),
|
|
)
|
|
|
|
if self.num_classes is not None:
|
|
if isinstance(self.num_classes, int):
|
|
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
|
|
elif self.num_classes == "continuous":
|
|
print("setting up linear c_adm embedding layer")
|
|
self.label_emb = ops.Conv2d(1, time_embed_dim)
|
|
elif self.num_classes == "timestep":
|
|
self.label_emb = nn.Sequential(
|
|
Timestep(model_channels),
|
|
nn.Sequential(
|
|
linear(model_channels, time_embed_dim),
|
|
nn.SiLU(),
|
|
linear(time_embed_dim, time_embed_dim),
|
|
),
|
|
)
|
|
|
|
elif self.num_classes == "sequential":
|
|
assert adm_in_channels is not None
|
|
self.label_emb = nn.Sequential(
|
|
nn.Sequential(
|
|
linear(adm_in_channels, time_embed_dim),
|
|
nn.SiLU(),
|
|
linear(time_embed_dim, time_embed_dim),
|
|
)
|
|
)
|
|
else:
|
|
raise ValueError()
|
|
|
|
self.input_blocks = nn.ModuleList(
|
|
[
|
|
TimestepEmbedSequential(
|
|
conv_nd(dims, in_channels, model_channels, 3, padding=1)
|
|
)
|
|
]
|
|
)
|
|
|
|
self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
|
|
|
|
self.input_hint_block = TimestepEmbedSequential(
|
|
conv_nd(dims, hint_channels, 16, 3, padding=1),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 16, 16, 3, padding=1),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 16, 32, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 32, 32, 3, padding=1),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 32, 96, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 96, 96, 3, padding=1),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 96, 256, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
|
|
)
|
|
|
|
self._feature_size = model_channels
|
|
input_block_chans = [model_channels]
|
|
ch = model_channels
|
|
ds = 1
|
|
|
|
def get_attention_layer(
|
|
ch,
|
|
num_heads,
|
|
dim_head,
|
|
depth=1,
|
|
context_dim=None,
|
|
use_checkpoint=False,
|
|
disabled_sa=False,
|
|
):
|
|
return SpatialVideoTransformer(
|
|
ch,
|
|
num_heads,
|
|
dim_head,
|
|
depth=depth,
|
|
context_dim=context_dim,
|
|
time_context_dim=time_context_dim,
|
|
dropout=dropout,
|
|
ff_in=extra_ff_mix_layer,
|
|
use_spatial_context=use_spatial_context,
|
|
merge_strategy=merge_strategy,
|
|
merge_factor=merge_factor,
|
|
checkpoint=use_checkpoint,
|
|
use_linear=use_linear_in_transformer,
|
|
attn_mode=spatial_transformer_attn_type,
|
|
temporal_attn_mode=get_obj_from_str(temporal_attn_type) \
|
|
if temporal_attn_type is not None else None,
|
|
disable_self_attn=disabled_sa,
|
|
disable_temporal_crossattention=disable_temporal_crossattention,
|
|
max_time_embed_period=max_ddpm_temb_period,
|
|
spatial_self_attn_type=get_obj_from_str(spatial_self_attn_type) \
|
|
if spatial_self_attn_type is not None else None,
|
|
)
|
|
|
|
def get_resblock(
|
|
merge_factor,
|
|
merge_strategy,
|
|
video_kernel_size,
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
out_ch,
|
|
dims,
|
|
use_checkpoint,
|
|
use_scale_shift_norm,
|
|
down=False,
|
|
up=False,
|
|
):
|
|
return VideoResBlock(
|
|
merge_factor=merge_factor,
|
|
merge_strategy=merge_strategy,
|
|
video_kernel_size=video_kernel_size,
|
|
channels=ch,
|
|
emb_channels=time_embed_dim,
|
|
dropout=dropout,
|
|
out_channels=out_ch,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
down=down,
|
|
up=up,
|
|
temporal_conv=get_obj_from_str(conv3d_type) \
|
|
if conv3d_type is not None else None,
|
|
)
|
|
|
|
for level, mult in enumerate(channel_mult):
|
|
for _ in range(num_res_blocks):
|
|
layers = [
|
|
get_resblock(
|
|
merge_factor=merge_factor,
|
|
merge_strategy=merge_strategy,
|
|
video_kernel_size=video_kernel_size,
|
|
ch=ch,
|
|
time_embed_dim=time_embed_dim,
|
|
dropout=dropout,
|
|
out_ch=mult * model_channels,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
)
|
|
]
|
|
ch = mult * model_channels
|
|
if ds in attention_resolutions:
|
|
if num_head_channels == -1:
|
|
dim_head = ch // num_heads
|
|
else:
|
|
num_heads = ch // num_head_channels
|
|
dim_head = num_head_channels
|
|
|
|
layers.append(
|
|
get_attention_layer(
|
|
ch,
|
|
num_heads,
|
|
dim_head,
|
|
depth=transformer_depth[level],
|
|
context_dim=context_dim,
|
|
use_checkpoint=use_checkpoint,
|
|
disabled_sa=False,
|
|
)
|
|
)
|
|
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
|
self.zero_convs.append(self.make_zero_conv(ch))
|
|
self._feature_size += ch
|
|
input_block_chans.append(ch)
|
|
if level != len(channel_mult) - 1:
|
|
ds *= 2
|
|
out_ch = ch
|
|
self.input_blocks.append(
|
|
TimestepEmbedSequential(
|
|
get_resblock(
|
|
merge_factor=merge_factor,
|
|
merge_strategy=merge_strategy,
|
|
video_kernel_size=video_kernel_size,
|
|
ch=ch,
|
|
time_embed_dim=time_embed_dim,
|
|
dropout=dropout,
|
|
out_ch=out_ch,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
down=True,
|
|
)
|
|
if resblock_updown
|
|
else Downsample(
|
|
ch,
|
|
conv_resample,
|
|
dims=dims,
|
|
out_channels=out_ch,
|
|
third_down=time_downup,
|
|
)
|
|
)
|
|
)
|
|
ch = out_ch
|
|
input_block_chans.append(ch)
|
|
self.zero_convs.append(self.make_zero_conv(ch))
|
|
|
|
self._feature_size += ch
|
|
|
|
if num_head_channels == -1:
|
|
dim_head = ch // num_heads
|
|
else:
|
|
num_heads = ch // num_head_channels
|
|
dim_head = num_head_channels
|
|
|
|
self.middle_block = TimestepEmbedSequential(
|
|
get_resblock(
|
|
merge_factor=merge_factor,
|
|
merge_strategy=merge_strategy,
|
|
video_kernel_size=video_kernel_size,
|
|
ch=ch,
|
|
time_embed_dim=time_embed_dim,
|
|
out_ch=None,
|
|
dropout=dropout,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
),
|
|
get_attention_layer(
|
|
ch,
|
|
num_heads,
|
|
dim_head,
|
|
depth=transformer_depth_middle,
|
|
context_dim=context_dim,
|
|
use_checkpoint=use_checkpoint,
|
|
),
|
|
get_resblock(
|
|
merge_factor=merge_factor,
|
|
merge_strategy=merge_strategy,
|
|
video_kernel_size=video_kernel_size,
|
|
ch=ch,
|
|
out_ch=None,
|
|
time_embed_dim=time_embed_dim,
|
|
dropout=dropout,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
),
|
|
)
|
|
self.middle_block_out = self.make_zero_conv(ch)
|
|
self._feature_size += ch
|
|
|
|
def make_zero_conv(self, channels):
|
|
return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
|
|
|
|
def forward(
|
|
self,
|
|
x: th.Tensor,
|
|
hint: th.Tensor,
|
|
timesteps: th.Tensor,
|
|
context: Optional[th.Tensor] = None,
|
|
y: Optional[th.Tensor] = None,
|
|
time_context: Optional[th.Tensor] = None,
|
|
num_video_frames: Optional[int] = None,
|
|
image_only_indicator: Optional[th.Tensor] = None,
|
|
):
|
|
assert (y is not None) == (
|
|
self.num_classes is not None
|
|
), "must specify y if and only if the model is class-conditional -> no, relax this TODO"
|
|
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
|
|
emb = self.time_embed(t_emb)
|
|
|
|
if self.num_classes is not None:
|
|
assert y.shape[0] == x.shape[0]
|
|
emb = emb + self.label_emb(y)
|
|
|
|
guided_hint = self.input_hint_block(hint, emb, context)
|
|
|
|
outs = []
|
|
h = x
|
|
for module, zero_conv in zip(self.input_blocks, self.zero_convs):
|
|
h = module(
|
|
h,
|
|
emb,
|
|
context=context,
|
|
image_only_indicator=image_only_indicator,
|
|
time_context=time_context,
|
|
num_video_frames=num_video_frames,
|
|
)
|
|
if guided_hint is not None:
|
|
h += guided_hint
|
|
guided_hint = None
|
|
outs.append(zero_conv(h, emb, context))
|
|
|
|
h = self.middle_block(
|
|
h,
|
|
emb,
|
|
context=context,
|
|
image_only_indicator=image_only_indicator,
|
|
time_context=time_context,
|
|
num_video_frames=num_video_frames,
|
|
)
|
|
outs.append(self.middle_block_out(h, emb, context))
|
|
|
|
return outs
|
|
|
|
def init_from_ckpt(
|
|
self,
|
|
path: str,
|
|
) -> None:
|
|
if path.endswith("ckpt"):
|
|
sd = torch.load(path, map_location="cpu")["state_dict"]
|
|
elif path.endswith("safetensors"):
|
|
sd = load_safetensors(path)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
missing, unexpected = self.load_state_dict(sd, strict=False)
|
|
#print(
|
|
# f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
|
|
#)
|
|
# if len(missing) > 0:
|
|
# print(f"Missing Keys: {missing}")
|
|
# if len(unexpected) > 0:
|
|
# print(f"Unexpected Keys: {unexpected}")
|
|
|
|
def set_parameters_requires_grad(self):
|
|
self.requires_grad_(True)
|
|
|
|
def get_trainable_parameters(self):
|
|
params = []
|
|
for p in self.parameters():
|
|
if p.requires_grad:
|
|
params += [p]
|
|
return params
|
|
|
|
def get_state_dict(self):
|
|
dicts = []
|
|
for name, param in self.named_parameters():
|
|
if param.requires_grad:
|
|
dicts.append( f'control_model.{name}' )
|
|
return dicts
|
|
|
|
|
|
|
|
class VideoDiffusionEngine(DiffusionEngine):
|
|
def __init__(
|
|
self,
|
|
controlnet_config,
|
|
control_model_path=None,
|
|
init_from_unet=False,
|
|
global_average_pooling=False,
|
|
sd_locked=True,
|
|
drop_first_stage_model=False,
|
|
*args, **kwargs
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
self.sd_locked = sd_locked
|
|
self.control_model = instantiate_from_config(controlnet_config)
|
|
self.control_scales = [1.0] * 13
|
|
self.global_average_pooling = global_average_pooling
|
|
|
|
self.conditioner.embedders[0].eval()
|
|
self.conditioner.embedders[0].requires_grad_(False)
|
|
self.conditioner.embedders[3].eval()
|
|
self.conditioner.embedders[3].requires_grad_(False)
|
|
self.first_stage_model.eval()
|
|
self.first_stage_model.requires_grad_(False)
|
|
if self.sd_locked:
|
|
self.model.diffusion_model.eval()
|
|
self.model.diffusion_model.requires_grad_(False)
|
|
else:
|
|
self.model.diffusion_model.train()
|
|
self.model.diffusion_model.set_parameters_requires_grad()
|
|
self.control_model.train()
|
|
self.control_model.set_parameters_requires_grad()
|
|
|
|
if init_from_unet:
|
|
missing, unexpected = self.control_model.load_state_dict( self.model.diffusion_model.state_dict(), strict=False )
|
|
#print(f"Restored from UNet {len(missing)} missing and {len(unexpected)} unexpected keys")
|
|
|
|
# Expand K, V layers for TripleAttention
|
|
for name, module in self.named_modules():
|
|
if '.time_stack' not in name and name[-5:] == 'attn1' and hasattr(module, 'expand'):
|
|
module.expand()
|
|
|
|
if control_model_path is not None:
|
|
self.init_from_ckpt(control_model_path)
|
|
if drop_first_stage_model:
|
|
del self.first_stage_model
|
|
|
|
def forward(self, x, batch):
|
|
loss = self.loss_fn(self.apply_model, self.denoiser, self.conditioner, x, batch)
|
|
loss_mean = loss.mean()
|
|
loss_dict = {"loss": loss_mean}
|
|
return loss_mean, loss_dict
|
|
|
|
def shared_step(self, batch: Dict) -> Any:
|
|
x = self.get_input(batch)
|
|
x.requires_grad_(True)
|
|
if x.shape[1] == 3:
|
|
x = self.encode_first_stage(x)
|
|
batch["global_step"] = self.global_step
|
|
loss, loss_dict = self(x, batch)
|
|
return loss, loss_dict
|
|
|
|
def apply_model(
|
|
self,
|
|
x: th.Tensor,
|
|
timesteps: th.Tensor,
|
|
cond: Dict,
|
|
time_context: Optional[th.Tensor] = None,
|
|
num_video_frames: Optional[int] = None,
|
|
image_only_indicator: Optional[th.Tensor] = None,
|
|
):
|
|
B, _, _, _ = x.shape
|
|
|
|
context = cond.get('crossattn', None)
|
|
if 'crossattn_scale' in cond.keys():
|
|
context = context * cond['crossattn_scale']
|
|
|
|
y = cond.get('vector', None)
|
|
|
|
control_hint = cond.get('control_hint', None)
|
|
|
|
cond_concat = cond.get("concat", torch.Tensor([]))
|
|
cond_concat = cond_concat.type_as(x)
|
|
if 'concat_scale' in cond.keys():
|
|
cond_concat = cond_concat * cond['concat_scale']
|
|
input_x = torch.cat([x, cond_concat], dim=1)
|
|
|
|
if control_hint is not None:
|
|
controls = self.control_model(
|
|
x=input_x,
|
|
hint=control_hint,
|
|
timesteps=timesteps,
|
|
context=context,
|
|
y=y,
|
|
time_context=time_context,
|
|
num_video_frames=num_video_frames,
|
|
image_only_indicator=image_only_indicator,
|
|
)
|
|
controls = [c * scale for c, scale in zip(controls, self.control_scales)]
|
|
if self.global_average_pooling:
|
|
controls = [torch.mean(c, dim=(2, 3), keepdim=True) for c in controls]
|
|
else:
|
|
controls = None
|
|
|
|
out = self.model.diffusion_model(
|
|
x=input_x,
|
|
timesteps=timesteps,
|
|
context=context,
|
|
y=y,
|
|
time_context=time_context,
|
|
control=controls,
|
|
num_video_frames=num_video_frames,
|
|
image_only_indicator=image_only_indicator,
|
|
)
|
|
|
|
return out
|
|
|
|
def configure_optimizers(self):
|
|
lr = self.learning_rate
|
|
params = self.control_model.get_trainable_parameters()
|
|
if not self.sd_locked:
|
|
params += self.model.diffusion_model.get_trainable_parameters()
|
|
for embedder in self.conditioner.embedders:
|
|
if embedder.is_trainable:
|
|
params = params + list(embedder.parameters())
|
|
opt = self.instantiate_optimizer_from_config(params, lr, self.optimizer_config)
|
|
if self.scheduler_config is not None:
|
|
scheduler = instantiate_from_config(self.scheduler_config)
|
|
print("Setting up LambdaLR scheduler...")
|
|
scheduler = [
|
|
{
|
|
"scheduler": LambdaLR(opt, lr_lambda=scheduler.schedule),
|
|
"interval": "step",
|
|
"frequency": 1,
|
|
}
|
|
]
|
|
return [opt], scheduler
|
|
return opt
|
|
|
|
def on_save_checkpoint(self, checkpoint):
|
|
save_dict = self.control_model.get_state_dict() + self.model.diffusion_model.get_state_dict()
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keys = list( checkpoint['state_dict'].keys() )
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for k in keys:
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if k not in save_dict:
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del checkpoint['state_dict'][k]
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def on_load_checkpoint(self, checkpoint):
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load_keys = list( checkpoint['state_dict'].keys() )
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# Change learning rate when resume
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#checkpoint['optimizer_states'][0]['param_groups'][0]['lr'] = 1.0e-5
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|
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self_state_dict = self.state_dict()
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keys = list( self_state_dict.keys() )
|
|
for k in keys:
|
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if k not in load_keys:
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checkpoint['state_dict'][k] = self_state_dict[k]
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