import torch from .attention import Attention from .sd_unet import ResnetBlock, UpSampler from .tiler import TileWorker from einops import rearrange, repeat class VAEAttentionBlock(torch.nn.Module): def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5): super().__init__() inner_dim = num_attention_heads * attention_head_dim self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) self.transformer_blocks = torch.nn.ModuleList([ Attention( inner_dim, num_attention_heads, attention_head_dim, bias_q=True, bias_kv=True, bias_out=True ) for d in range(num_layers) ]) def forward(self, hidden_states, time_emb, text_emb, res_stack): batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) for block in self.transformer_blocks: hidden_states = block(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() hidden_states = hidden_states + residual return hidden_states, time_emb, text_emb, res_stack class TemporalResnetBlock(torch.nn.Module): def __init__(self, in_channels, out_channels, groups=32, eps=1e-5): super().__init__() self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True) self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0)) self.nonlinearity = torch.nn.SiLU() self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5])) def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs): x_spatial = hidden_states x = rearrange(hidden_states, "T C H W -> 1 C T H W") x = self.norm1(x) x = self.nonlinearity(x) x = self.conv1(x) x = self.norm2(x) x = self.nonlinearity(x) x = self.conv2(x) x_temporal = hidden_states + x[0].permute(1, 0, 2, 3) alpha = torch.sigmoid(self.mix_factor) hidden_states = alpha * x_temporal + (1 - alpha) * x_spatial return hidden_states, time_emb, text_emb, res_stack class SVDVAEDecoder(torch.nn.Module): def __init__(self): super().__init__() self.scaling_factor = 0.18215 self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1) self.blocks = torch.nn.ModuleList([ # UNetMidBlock ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), # UpDecoderBlock ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), UpSampler(512), # UpDecoderBlock ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), ResnetBlock(512, 512, eps=1e-6), TemporalResnetBlock(512, 512, eps=1e-6), UpSampler(512), # UpDecoderBlock ResnetBlock(512, 256, eps=1e-6), TemporalResnetBlock(256, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), TemporalResnetBlock(256, 256, eps=1e-6), ResnetBlock(256, 256, eps=1e-6), TemporalResnetBlock(256, 256, eps=1e-6), UpSampler(256), # UpDecoderBlock ResnetBlock(256, 128, eps=1e-6), TemporalResnetBlock(128, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), TemporalResnetBlock(128, 128, eps=1e-6), ResnetBlock(128, 128, eps=1e-6), TemporalResnetBlock(128, 128, eps=1e-6), ]) self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5) self.conv_act = torch.nn.SiLU() self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1) self.time_conv_out = torch.nn.Conv3d(3, 3, kernel_size=(3, 1, 1), padding=(1, 0, 0)) def forward(self, sample): # 1. pre-process hidden_states = rearrange(sample, "C T H W -> T C H W") hidden_states = hidden_states / self.scaling_factor hidden_states = self.conv_in(hidden_states) time_emb, text_emb, res_stack = None, None, None # 2. blocks for i, block in enumerate(self.blocks): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) # 3. output hidden_states = self.conv_norm_out(hidden_states) hidden_states = self.conv_act(hidden_states) hidden_states = self.conv_out(hidden_states) hidden_states = rearrange(hidden_states, "T C H W -> C T H W") hidden_states = self.time_conv_out(hidden_states) return hidden_states def build_mask(self, data, is_bound): _, T, H, W = data.shape t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W) h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W) w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W) border_width = (T + H + W) // 6 pad = torch.ones_like(t) * border_width mask = torch.stack([ pad if is_bound[0] else t + 1, pad if is_bound[1] else T - t, pad if is_bound[2] else h + 1, pad if is_bound[3] else H - h, pad if is_bound[4] else w + 1, pad if is_bound[5] else W - w ]).min(dim=0).values mask = mask.clip(1, border_width) mask = (mask / border_width).to(dtype=data.dtype, device=data.device) mask = rearrange(mask, "T H W -> 1 T H W") return mask def decode_video( self, sample, batch_time=8, batch_height=128, batch_width=128, stride_time=4, stride_height=32, stride_width=32, progress_bar=lambda x:x ): sample = sample.permute(1, 0, 2, 3) data_device = sample.device computation_device = self.conv_in.weight.device torch_dtype = sample.dtype _, T, H, W = sample.shape weight = torch.zeros((1, T, H*8, W*8), dtype=torch_dtype, device=data_device) values = torch.zeros((3, T, H*8, W*8), dtype=torch_dtype, device=data_device) # Split tasks tasks = [] for t in range(0, T, stride_time): for h in range(0, H, stride_height): for w in range(0, W, stride_width): if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\ or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\ or (w-stride_width >= 0 and w-stride_width+batch_width >= W): continue tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width)) # Run for tl, tr, hl, hr, wl, wr in progress_bar(tasks): sample_batch = sample[:, tl:tr, hl:hr, wl:wr].to(computation_device) sample_batch = self.forward(sample_batch).to(data_device) mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W)) values[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += sample_batch * mask weight[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += mask values /= weight return values @staticmethod def state_dict_converter(): return SVDVAEDecoderStateDictConverter() class SVDVAEDecoderStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): static_rename_dict = { "decoder.conv_in": "conv_in", "decoder.mid_block.attentions.0.group_norm": "blocks.2.norm", "decoder.mid_block.attentions.0.to_q": "blocks.2.transformer_blocks.0.to_q", "decoder.mid_block.attentions.0.to_k": "blocks.2.transformer_blocks.0.to_k", "decoder.mid_block.attentions.0.to_v": "blocks.2.transformer_blocks.0.to_v", "decoder.mid_block.attentions.0.to_out.0": "blocks.2.transformer_blocks.0.to_out", "decoder.up_blocks.0.upsamplers.0.conv": "blocks.11.conv", "decoder.up_blocks.1.upsamplers.0.conv": "blocks.18.conv", "decoder.up_blocks.2.upsamplers.0.conv": "blocks.25.conv", "decoder.conv_norm_out": "conv_norm_out", "decoder.conv_out": "conv_out", "decoder.time_conv_out": "time_conv_out" } prefix_rename_dict = { "decoder.mid_block.resnets.0.spatial_res_block": "blocks.0", "decoder.mid_block.resnets.0.temporal_res_block": "blocks.1", "decoder.mid_block.resnets.0.time_mixer": "blocks.1", "decoder.mid_block.resnets.1.spatial_res_block": "blocks.3", "decoder.mid_block.resnets.1.temporal_res_block": "blocks.4", "decoder.mid_block.resnets.1.time_mixer": "blocks.4", "decoder.up_blocks.0.resnets.0.spatial_res_block": "blocks.5", "decoder.up_blocks.0.resnets.0.temporal_res_block": "blocks.6", "decoder.up_blocks.0.resnets.0.time_mixer": "blocks.6", "decoder.up_blocks.0.resnets.1.spatial_res_block": "blocks.7", "decoder.up_blocks.0.resnets.1.temporal_res_block": "blocks.8", "decoder.up_blocks.0.resnets.1.time_mixer": "blocks.8", "decoder.up_blocks.0.resnets.2.spatial_res_block": "blocks.9", "decoder.up_blocks.0.resnets.2.temporal_res_block": "blocks.10", "decoder.up_blocks.0.resnets.2.time_mixer": "blocks.10", "decoder.up_blocks.1.resnets.0.spatial_res_block": "blocks.12", "decoder.up_blocks.1.resnets.0.temporal_res_block": "blocks.13", "decoder.up_blocks.1.resnets.0.time_mixer": "blocks.13", "decoder.up_blocks.1.resnets.1.spatial_res_block": "blocks.14", "decoder.up_blocks.1.resnets.1.temporal_res_block": "blocks.15", "decoder.up_blocks.1.resnets.1.time_mixer": "blocks.15", "decoder.up_blocks.1.resnets.2.spatial_res_block": "blocks.16", "decoder.up_blocks.1.resnets.2.temporal_res_block": "blocks.17", "decoder.up_blocks.1.resnets.2.time_mixer": "blocks.17", "decoder.up_blocks.2.resnets.0.spatial_res_block": "blocks.19", "decoder.up_blocks.2.resnets.0.temporal_res_block": "blocks.20", "decoder.up_blocks.2.resnets.0.time_mixer": "blocks.20", "decoder.up_blocks.2.resnets.1.spatial_res_block": "blocks.21", "decoder.up_blocks.2.resnets.1.temporal_res_block": "blocks.22", "decoder.up_blocks.2.resnets.1.time_mixer": "blocks.22", "decoder.up_blocks.2.resnets.2.spatial_res_block": "blocks.23", "decoder.up_blocks.2.resnets.2.temporal_res_block": "blocks.24", "decoder.up_blocks.2.resnets.2.time_mixer": "blocks.24", "decoder.up_blocks.3.resnets.0.spatial_res_block": "blocks.26", "decoder.up_blocks.3.resnets.0.temporal_res_block": "blocks.27", "decoder.up_blocks.3.resnets.0.time_mixer": "blocks.27", "decoder.up_blocks.3.resnets.1.spatial_res_block": "blocks.28", "decoder.up_blocks.3.resnets.1.temporal_res_block": "blocks.29", "decoder.up_blocks.3.resnets.1.time_mixer": "blocks.29", "decoder.up_blocks.3.resnets.2.spatial_res_block": "blocks.30", "decoder.up_blocks.3.resnets.2.temporal_res_block": "blocks.31", "decoder.up_blocks.3.resnets.2.time_mixer": "blocks.31", } suffix_rename_dict = { "norm1.weight": "norm1.weight", "conv1.weight": "conv1.weight", "norm2.weight": "norm2.weight", "conv2.weight": "conv2.weight", "conv_shortcut.weight": "conv_shortcut.weight", "norm1.bias": "norm1.bias", "conv1.bias": "conv1.bias", "norm2.bias": "norm2.bias", "conv2.bias": "conv2.bias", "conv_shortcut.bias": "conv_shortcut.bias", "mix_factor": "mix_factor", } state_dict_ = {} for name in static_rename_dict: state_dict_[static_rename_dict[name] + ".weight"] = state_dict[name + ".weight"] state_dict_[static_rename_dict[name] + ".bias"] = state_dict[name + ".bias"] for prefix_name in prefix_rename_dict: for suffix_name in suffix_rename_dict: name = prefix_name + "." + suffix_name name_ = prefix_rename_dict[prefix_name] + "." + suffix_rename_dict[suffix_name] if name in state_dict: state_dict_[name_] = state_dict[name] return state_dict_ def from_civitai(self, state_dict): rename_dict = { "first_stage_model.decoder.conv_in.bias": "conv_in.bias", "first_stage_model.decoder.conv_in.weight": "conv_in.weight", "first_stage_model.decoder.conv_out.bias": "conv_out.bias", "first_stage_model.decoder.conv_out.time_mix_conv.bias": "time_conv_out.bias", "first_stage_model.decoder.conv_out.time_mix_conv.weight": "time_conv_out.weight", "first_stage_model.decoder.conv_out.weight": "conv_out.weight", "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.2.transformer_blocks.0.to_k.bias", "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.2.transformer_blocks.0.to_k.weight", "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.2.norm.bias", "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.2.norm.weight", "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.2.transformer_blocks.0.to_out.bias", "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.2.transformer_blocks.0.to_out.weight", "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.2.transformer_blocks.0.to_q.bias", "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.2.transformer_blocks.0.to_q.weight", "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.2.transformer_blocks.0.to_v.bias", "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.2.transformer_blocks.0.to_v.weight", "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias", "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight", "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias", "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight", "first_stage_model.decoder.mid.block_1.mix_factor": "blocks.1.mix_factor", "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias", "first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight", "first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias", "first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight", "first_stage_model.decoder.mid.block_1.time_stack.in_layers.0.bias": "blocks.1.norm1.bias", "first_stage_model.decoder.mid.block_1.time_stack.in_layers.0.weight": "blocks.1.norm1.weight", "first_stage_model.decoder.mid.block_1.time_stack.in_layers.2.bias": "blocks.1.conv1.bias", "first_stage_model.decoder.mid.block_1.time_stack.in_layers.2.weight": "blocks.1.conv1.weight", "first_stage_model.decoder.mid.block_1.time_stack.out_layers.0.bias": "blocks.1.norm2.bias", "first_stage_model.decoder.mid.block_1.time_stack.out_layers.0.weight": "blocks.1.norm2.weight", "first_stage_model.decoder.mid.block_1.time_stack.out_layers.3.bias": "blocks.1.conv2.bias", "first_stage_model.decoder.mid.block_1.time_stack.out_layers.3.weight": "blocks.1.conv2.weight", "first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.3.conv1.bias", "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.3.conv1.weight", "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.3.conv2.bias", "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.3.conv2.weight", "first_stage_model.decoder.mid.block_2.mix_factor": "blocks.4.mix_factor", "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.3.norm1.bias", "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.3.norm1.weight", "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.3.norm2.bias", "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.3.norm2.weight", "first_stage_model.decoder.mid.block_2.time_stack.in_layers.0.bias": "blocks.4.norm1.bias", "first_stage_model.decoder.mid.block_2.time_stack.in_layers.0.weight": "blocks.4.norm1.weight", "first_stage_model.decoder.mid.block_2.time_stack.in_layers.2.bias": "blocks.4.conv1.bias", "first_stage_model.decoder.mid.block_2.time_stack.in_layers.2.weight": "blocks.4.conv1.weight", "first_stage_model.decoder.mid.block_2.time_stack.out_layers.0.bias": "blocks.4.norm2.bias", "first_stage_model.decoder.mid.block_2.time_stack.out_layers.0.weight": "blocks.4.norm2.weight", "first_stage_model.decoder.mid.block_2.time_stack.out_layers.3.bias": "blocks.4.conv2.bias", "first_stage_model.decoder.mid.block_2.time_stack.out_layers.3.weight": "blocks.4.conv2.weight", "first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias", "first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight", "first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.26.conv1.bias", "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.26.conv1.weight", "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.26.conv2.bias", "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.26.conv2.weight", "first_stage_model.decoder.up.0.block.0.mix_factor": "blocks.27.mix_factor", "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.26.conv_shortcut.bias", "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.26.conv_shortcut.weight", "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.26.norm1.bias", "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.26.norm1.weight", "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.26.norm2.bias", "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.26.norm2.weight", "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.0.bias": "blocks.27.norm1.bias", "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.0.weight": "blocks.27.norm1.weight", "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.2.bias": "blocks.27.conv1.bias", "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.2.weight": "blocks.27.conv1.weight", "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.0.bias": "blocks.27.norm2.bias", "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.0.weight": "blocks.27.norm2.weight", "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.bias": "blocks.27.conv2.bias", "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.weight": "blocks.27.conv2.weight", "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.28.conv1.bias", "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.28.conv1.weight", "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.28.conv2.bias", "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.28.conv2.weight", "first_stage_model.decoder.up.0.block.1.mix_factor": "blocks.29.mix_factor", "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.28.norm1.bias", "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.28.norm1.weight", "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.28.norm2.bias", "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.28.norm2.weight", "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.0.bias": "blocks.29.norm1.bias", "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.0.weight": "blocks.29.norm1.weight", "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.2.bias": "blocks.29.conv1.bias", "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.2.weight": "blocks.29.conv1.weight", "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.0.bias": "blocks.29.norm2.bias", "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.0.weight": "blocks.29.norm2.weight", "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.3.bias": "blocks.29.conv2.bias", "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.3.weight": "blocks.29.conv2.weight", "first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.30.conv1.bias", 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"first_stage_model.decoder.up.3.block.0.conv1.bias": "blocks.5.conv1.bias", "first_stage_model.decoder.up.3.block.0.conv1.weight": "blocks.5.conv1.weight", "first_stage_model.decoder.up.3.block.0.conv2.bias": "blocks.5.conv2.bias", "first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.5.conv2.weight", "first_stage_model.decoder.up.3.block.0.mix_factor": "blocks.6.mix_factor", "first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.5.norm1.bias", "first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.5.norm1.weight", "first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.5.norm2.bias", "first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.5.norm2.weight", "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.0.bias": "blocks.6.norm1.bias", "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.0.weight": "blocks.6.norm1.weight", "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.2.bias": "blocks.6.conv1.bias", 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"blocks.7.norm1.weight", "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.7.norm2.bias", "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.7.norm2.weight", "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.bias": "blocks.8.norm1.bias", "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.weight": "blocks.8.norm1.weight", "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.bias": "blocks.8.conv1.bias", "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.weight": "blocks.8.conv1.weight", "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.bias": "blocks.8.norm2.bias", "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.weight": "blocks.8.norm2.weight", "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.bias": "blocks.8.conv2.bias", "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.weight": "blocks.8.conv2.weight", "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.9.conv1.bias", "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.9.conv1.weight", "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.9.conv2.bias", "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.9.conv2.weight", "first_stage_model.decoder.up.3.block.2.mix_factor": "blocks.10.mix_factor", "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.9.norm1.bias", "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.9.norm1.weight", "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.9.norm2.bias", "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.9.norm2.weight", "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.bias": "blocks.10.norm1.bias", "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.weight": "blocks.10.norm1.weight", "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.bias": "blocks.10.conv1.bias", "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.weight": "blocks.10.conv1.weight", "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.bias": "blocks.10.norm2.bias", "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.weight": "blocks.10.norm2.weight", "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.bias": "blocks.10.conv2.bias", "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.weight": "blocks.10.conv2.weight", "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.11.conv.bias", "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.11.conv.weight", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if "blocks.2.transformer_blocks.0" in rename_dict[name]: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_