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| 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 | |
| def state_dict_converter(self): | |
| 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", | |
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| "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.weight": "blocks.27.conv2.weight", | |
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| "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.0.bias": "blocks.31.norm2.bias", | |
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| "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.3.bias": "blocks.31.conv2.bias", | |
| "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.3.weight": "blocks.31.conv2.weight", | |
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| "first_stage_model.decoder.up.1.block.0.mix_factor": "blocks.20.mix_factor", | |
| "first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.19.conv_shortcut.bias", | |
| "first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.19.conv_shortcut.weight", | |
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| "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.0.weight": "blocks.20.norm1.weight", | |
| "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.2.bias": "blocks.20.conv1.bias", | |
| "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.2.weight": "blocks.20.conv1.weight", | |
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| "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.3.weight": "blocks.20.conv2.weight", | |
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| "first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.21.conv2.weight", | |
| "first_stage_model.decoder.up.1.block.1.mix_factor": "blocks.22.mix_factor", | |
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| "first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.21.norm1.weight", | |
| "first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.21.norm2.bias", | |
| "first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.21.norm2.weight", | |
| "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.0.bias": "blocks.22.norm1.bias", | |
| "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.0.weight": "blocks.22.norm1.weight", | |
| "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.2.bias": "blocks.22.conv1.bias", | |
| "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.2.weight": "blocks.22.conv1.weight", | |
| "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.0.bias": "blocks.22.norm2.bias", | |
| "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.0.weight": "blocks.22.norm2.weight", | |
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| "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.3.weight": "blocks.22.conv2.weight", | |
| "first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.23.conv1.bias", | |
| "first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.23.conv1.weight", | |
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| "first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.23.conv2.weight", | |
| "first_stage_model.decoder.up.1.block.2.mix_factor": "blocks.24.mix_factor", | |
| "first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.23.norm1.bias", | |
| "first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.23.norm1.weight", | |
| "first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.23.norm2.bias", | |
| "first_stage_model.decoder.up.1.block.2.norm2.weight": "blocks.23.norm2.weight", | |
| "first_stage_model.decoder.up.1.block.2.time_stack.in_layers.0.bias": "blocks.24.norm1.bias", | |
| "first_stage_model.decoder.up.1.block.2.time_stack.in_layers.0.weight": "blocks.24.norm1.weight", | |
| "first_stage_model.decoder.up.1.block.2.time_stack.in_layers.2.bias": "blocks.24.conv1.bias", | |
| "first_stage_model.decoder.up.1.block.2.time_stack.in_layers.2.weight": "blocks.24.conv1.weight", | |
| "first_stage_model.decoder.up.1.block.2.time_stack.out_layers.0.bias": "blocks.24.norm2.bias", | |
| "first_stage_model.decoder.up.1.block.2.time_stack.out_layers.0.weight": "blocks.24.norm2.weight", | |
| "first_stage_model.decoder.up.1.block.2.time_stack.out_layers.3.bias": "blocks.24.conv2.bias", | |
| "first_stage_model.decoder.up.1.block.2.time_stack.out_layers.3.weight": "blocks.24.conv2.weight", | |
| "first_stage_model.decoder.up.1.upsample.conv.bias": "blocks.25.conv.bias", | |
| "first_stage_model.decoder.up.1.upsample.conv.weight": "blocks.25.conv.weight", | |
| "first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.12.conv1.bias", | |
| "first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.12.conv1.weight", | |
| "first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.12.conv2.bias", | |
| "first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.12.conv2.weight", | |
| "first_stage_model.decoder.up.2.block.0.mix_factor": "blocks.13.mix_factor", | |
| "first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.12.norm1.bias", | |
| "first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.12.norm1.weight", | |
| "first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.12.norm2.bias", | |
| "first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.12.norm2.weight", | |
| "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.0.bias": "blocks.13.norm1.bias", | |
| "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.0.weight": "blocks.13.norm1.weight", | |
| "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.2.bias": "blocks.13.conv1.bias", | |
| "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.2.weight": "blocks.13.conv1.weight", | |
| "first_stage_model.decoder.up.2.block.0.time_stack.out_layers.0.bias": "blocks.13.norm2.bias", | |
| "first_stage_model.decoder.up.2.block.0.time_stack.out_layers.0.weight": "blocks.13.norm2.weight", | |
| "first_stage_model.decoder.up.2.block.0.time_stack.out_layers.3.bias": "blocks.13.conv2.bias", | |
| "first_stage_model.decoder.up.2.block.0.time_stack.out_layers.3.weight": "blocks.13.conv2.weight", | |
| "first_stage_model.decoder.up.2.block.1.conv1.bias": "blocks.14.conv1.bias", | |
| "first_stage_model.decoder.up.2.block.1.conv1.weight": "blocks.14.conv1.weight", | |
| "first_stage_model.decoder.up.2.block.1.conv2.bias": "blocks.14.conv2.bias", | |
| "first_stage_model.decoder.up.2.block.1.conv2.weight": "blocks.14.conv2.weight", | |
| "first_stage_model.decoder.up.2.block.1.mix_factor": "blocks.15.mix_factor", | |
| "first_stage_model.decoder.up.2.block.1.norm1.bias": "blocks.14.norm1.bias", | |
| "first_stage_model.decoder.up.2.block.1.norm1.weight": "blocks.14.norm1.weight", | |
| "first_stage_model.decoder.up.2.block.1.norm2.bias": "blocks.14.norm2.bias", | |
| "first_stage_model.decoder.up.2.block.1.norm2.weight": "blocks.14.norm2.weight", | |
| "first_stage_model.decoder.up.2.block.1.time_stack.in_layers.0.bias": "blocks.15.norm1.bias", | |
| "first_stage_model.decoder.up.2.block.1.time_stack.in_layers.0.weight": "blocks.15.norm1.weight", | |
| "first_stage_model.decoder.up.2.block.1.time_stack.in_layers.2.bias": "blocks.15.conv1.bias", | |
| "first_stage_model.decoder.up.2.block.1.time_stack.in_layers.2.weight": "blocks.15.conv1.weight", | |
| "first_stage_model.decoder.up.2.block.1.time_stack.out_layers.0.bias": "blocks.15.norm2.bias", | |
| "first_stage_model.decoder.up.2.block.1.time_stack.out_layers.0.weight": "blocks.15.norm2.weight", | |
| "first_stage_model.decoder.up.2.block.1.time_stack.out_layers.3.bias": "blocks.15.conv2.bias", | |
| "first_stage_model.decoder.up.2.block.1.time_stack.out_layers.3.weight": "blocks.15.conv2.weight", | |
| "first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.16.conv1.bias", | |
| "first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.16.conv1.weight", | |
| "first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.16.conv2.bias", | |
| "first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.16.conv2.weight", | |
| "first_stage_model.decoder.up.2.block.2.mix_factor": "blocks.17.mix_factor", | |
| "first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.16.norm1.bias", | |
| "first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.16.norm1.weight", | |
| "first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.16.norm2.bias", | |
| "first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.16.norm2.weight", | |
| "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.0.bias": "blocks.17.norm1.bias", | |
| "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.0.weight": "blocks.17.norm1.weight", | |
| "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.2.bias": "blocks.17.conv1.bias", | |
| "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.2.weight": "blocks.17.conv1.weight", | |
| "first_stage_model.decoder.up.2.block.2.time_stack.out_layers.0.bias": "blocks.17.norm2.bias", | |
| "first_stage_model.decoder.up.2.block.2.time_stack.out_layers.0.weight": "blocks.17.norm2.weight", | |
| "first_stage_model.decoder.up.2.block.2.time_stack.out_layers.3.bias": "blocks.17.conv2.bias", | |
| "first_stage_model.decoder.up.2.block.2.time_stack.out_layers.3.weight": "blocks.17.conv2.weight", | |
| "first_stage_model.decoder.up.2.upsample.conv.bias": "blocks.18.conv.bias", | |
| "first_stage_model.decoder.up.2.upsample.conv.weight": "blocks.18.conv.weight", | |
| "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", | |
| "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.2.weight": "blocks.6.conv1.weight", | |
| "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.0.bias": "blocks.6.norm2.bias", | |
| "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.0.weight": "blocks.6.norm2.weight", | |
| "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.3.bias": "blocks.6.conv2.bias", | |
| "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.3.weight": "blocks.6.conv2.weight", | |
| "first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.7.conv1.bias", | |
| "first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.7.conv1.weight", | |
| "first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.7.conv2.bias", | |
| "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.7.conv2.weight", | |
| "first_stage_model.decoder.up.3.block.1.mix_factor": "blocks.8.mix_factor", | |
| "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.7.norm1.bias", | |
| "first_stage_model.decoder.up.3.block.1.norm1.weight": "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_ | |