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