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| import torch | |
| from .sd_unet import ResnetBlock, DownSampler | |
| from .sd_vae_decoder import VAEAttentionBlock | |
| from .tiler import TileWorker | |
| from einops import rearrange | |
| class SDVAEEncoder(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.scaling_factor = 0.18215 | |
| self.quant_conv = torch.nn.Conv2d(8, 8, kernel_size=1) | |
| self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1) | |
| self.blocks = torch.nn.ModuleList([ | |
| # DownEncoderBlock2D | |
| ResnetBlock(128, 128, eps=1e-6), | |
| ResnetBlock(128, 128, eps=1e-6), | |
| DownSampler(128, padding=0, extra_padding=True), | |
| # DownEncoderBlock2D | |
| ResnetBlock(128, 256, eps=1e-6), | |
| ResnetBlock(256, 256, eps=1e-6), | |
| DownSampler(256, padding=0, extra_padding=True), | |
| # DownEncoderBlock2D | |
| ResnetBlock(256, 512, eps=1e-6), | |
| ResnetBlock(512, 512, eps=1e-6), | |
| DownSampler(512, padding=0, extra_padding=True), | |
| # DownEncoderBlock2D | |
| ResnetBlock(512, 512, eps=1e-6), | |
| ResnetBlock(512, 512, eps=1e-6), | |
| # UNetMidBlock2D | |
| ResnetBlock(512, 512, eps=1e-6), | |
| VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), | |
| ResnetBlock(512, 512, eps=1e-6), | |
| ]) | |
| self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6) | |
| self.conv_act = torch.nn.SiLU() | |
| self.conv_out = torch.nn.Conv2d(512, 8, kernel_size=3, padding=1) | |
| def tiled_forward(self, sample, tile_size=64, tile_stride=32): | |
| hidden_states = TileWorker().tiled_forward( | |
| lambda x: self.forward(x), | |
| sample, | |
| tile_size, | |
| tile_stride, | |
| tile_device=sample.device, | |
| tile_dtype=sample.dtype | |
| ) | |
| return hidden_states | |
| def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): | |
| # For VAE Decoder, we do not need to apply the tiler on each layer. | |
| if tiled: | |
| return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) | |
| # 1. pre-process | |
| hidden_states = self.conv_in(sample) | |
| time_emb = None | |
| text_emb = None | |
| res_stack = 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 = self.quant_conv(hidden_states) | |
| hidden_states = hidden_states[:, :4] | |
| hidden_states *= self.scaling_factor | |
| return hidden_states | |
| def encode_video(self, sample, batch_size=8): | |
| B = sample.shape[0] | |
| hidden_states = [] | |
| for i in range(0, sample.shape[2], batch_size): | |
| j = min(i + batch_size, sample.shape[2]) | |
| sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W") | |
| hidden_states_batch = self(sample_batch) | |
| hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B) | |
| hidden_states.append(hidden_states_batch) | |
| hidden_states = torch.concat(hidden_states, dim=2) | |
| return hidden_states | |
| def state_dict_converter(self): | |
| return SDVAEEncoderStateDictConverter() | |
| class SDVAEEncoderStateDictConverter: | |
| def __init__(self): | |
| pass | |
| def from_diffusers(self, state_dict): | |
| # architecture | |
| block_types = [ | |
| 'ResnetBlock', 'ResnetBlock', 'DownSampler', | |
| 'ResnetBlock', 'ResnetBlock', 'DownSampler', | |
| 'ResnetBlock', 'ResnetBlock', 'DownSampler', | |
| 'ResnetBlock', 'ResnetBlock', | |
| 'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock' | |
| ] | |
| # Rename each parameter | |
| local_rename_dict = { | |
| "quant_conv": "quant_conv", | |
| "encoder.conv_in": "conv_in", | |
| "encoder.mid_block.attentions.0.group_norm": "blocks.12.norm", | |
| "encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q", | |
| "encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k", | |
| "encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v", | |
| "encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out", | |
| "encoder.mid_block.resnets.0.norm1": "blocks.11.norm1", | |
| "encoder.mid_block.resnets.0.conv1": "blocks.11.conv1", | |
| "encoder.mid_block.resnets.0.norm2": "blocks.11.norm2", | |
| "encoder.mid_block.resnets.0.conv2": "blocks.11.conv2", | |
| "encoder.mid_block.resnets.1.norm1": "blocks.13.norm1", | |
| "encoder.mid_block.resnets.1.conv1": "blocks.13.conv1", | |
| "encoder.mid_block.resnets.1.norm2": "blocks.13.norm2", | |
| "encoder.mid_block.resnets.1.conv2": "blocks.13.conv2", | |
| "encoder.conv_norm_out": "conv_norm_out", | |
| "encoder.conv_out": "conv_out", | |
| } | |
| name_list = sorted([name for name in state_dict]) | |
| rename_dict = {} | |
| block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1} | |
| last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""} | |
| for name in name_list: | |
| names = name.split(".") | |
| name_prefix = ".".join(names[:-1]) | |
| if name_prefix in local_rename_dict: | |
| rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1] | |
| elif name.startswith("encoder.down_blocks"): | |
| block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]] | |
| block_type_with_id = ".".join(names[:5]) | |
| if block_type_with_id != last_block_type_with_id[block_type]: | |
| block_id[block_type] += 1 | |
| last_block_type_with_id[block_type] = block_type_with_id | |
| while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: | |
| block_id[block_type] += 1 | |
| block_type_with_id = ".".join(names[:5]) | |
| names = ["blocks", str(block_id[block_type])] + names[5:] | |
| rename_dict[name] = ".".join(names) | |
| # Convert state_dict | |
| state_dict_ = {} | |
| for name, param in state_dict.items(): | |
| if name in rename_dict: | |
| state_dict_[rename_dict[name]] = param | |
| return state_dict_ | |
| def from_civitai(self, state_dict): | |
| rename_dict = { | |
| "first_stage_model.encoder.conv_in.bias": "conv_in.bias", | |
| "first_stage_model.encoder.conv_in.weight": "conv_in.weight", | |
| "first_stage_model.encoder.conv_out.bias": "conv_out.bias", | |
| "first_stage_model.encoder.conv_out.weight": "conv_out.weight", | |
| "first_stage_model.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", | |
| "first_stage_model.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", | |
| "first_stage_model.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", | |
| "first_stage_model.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", | |
| "first_stage_model.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", | |
| "first_stage_model.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", | |
| "first_stage_model.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", | |
| "first_stage_model.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", | |
| "first_stage_model.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", | |
| "first_stage_model.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", | |
| "first_stage_model.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", | |
| "first_stage_model.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", | |
| "first_stage_model.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", | |
| "first_stage_model.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", | |
| "first_stage_model.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", | |
| "first_stage_model.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", | |
| "first_stage_model.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", | |
| "first_stage_model.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", | |
| "first_stage_model.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", | |
| "first_stage_model.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", | |
| "first_stage_model.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", | |
| "first_stage_model.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", | |
| "first_stage_model.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", | |
| "first_stage_model.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", | |
| "first_stage_model.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", | |
| "first_stage_model.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", | |
| "first_stage_model.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", | |
| "first_stage_model.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", | |
| "first_stage_model.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", | |
| "first_stage_model.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", | |
| "first_stage_model.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", | |
| "first_stage_model.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", | |
| "first_stage_model.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", | |
| "first_stage_model.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", | |
| "first_stage_model.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", | |
| "first_stage_model.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", | |
| "first_stage_model.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", | |
| "first_stage_model.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", | |
| "first_stage_model.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", | |
| "first_stage_model.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", | |
| "first_stage_model.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", | |
| "first_stage_model.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", | |
| "first_stage_model.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", | |
| "first_stage_model.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", | |
| "first_stage_model.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", | |
| "first_stage_model.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", | |
| "first_stage_model.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", | |
| "first_stage_model.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", | |
| "first_stage_model.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", | |
| "first_stage_model.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", | |
| "first_stage_model.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", | |
| "first_stage_model.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", | |
| "first_stage_model.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", | |
| "first_stage_model.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", | |
| "first_stage_model.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", | |
| "first_stage_model.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", | |
| "first_stage_model.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", | |
| "first_stage_model.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", | |
| "first_stage_model.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", | |
| "first_stage_model.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", | |
| "first_stage_model.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", | |
| "first_stage_model.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", | |
| "first_stage_model.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", | |
| "first_stage_model.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", | |
| "first_stage_model.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", | |
| "first_stage_model.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", | |
| "first_stage_model.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", | |
| "first_stage_model.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", | |
| "first_stage_model.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", | |
| "first_stage_model.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", | |
| "first_stage_model.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", | |
| "first_stage_model.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", | |
| "first_stage_model.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", | |
| "first_stage_model.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", | |
| "first_stage_model.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", | |
| "first_stage_model.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", | |
| "first_stage_model.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", | |
| "first_stage_model.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", | |
| "first_stage_model.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", | |
| "first_stage_model.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", | |
| "first_stage_model.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", | |
| "first_stage_model.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", | |
| "first_stage_model.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", | |
| "first_stage_model.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", | |
| "first_stage_model.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", | |
| "first_stage_model.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", | |
| "first_stage_model.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", | |
| "first_stage_model.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", | |
| "first_stage_model.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", | |
| "first_stage_model.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", | |
| "first_stage_model.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", | |
| "first_stage_model.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", | |
| "first_stage_model.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", | |
| "first_stage_model.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", | |
| "first_stage_model.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", | |
| "first_stage_model.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", | |
| "first_stage_model.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", | |
| "first_stage_model.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", | |
| "first_stage_model.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", | |
| "first_stage_model.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", | |
| "first_stage_model.encoder.norm_out.bias": "conv_norm_out.bias", | |
| "first_stage_model.encoder.norm_out.weight": "conv_norm_out.weight", | |
| "first_stage_model.quant_conv.bias": "quant_conv.bias", | |
| "first_stage_model.quant_conv.weight": "quant_conv.weight", | |
| } | |
| state_dict_ = {} | |
| for name in state_dict: | |
| if name in rename_dict: | |
| param = state_dict[name] | |
| if "transformer_blocks" in rename_dict[name]: | |
| param = param.squeeze() | |
| state_dict_[rename_dict[name]] = param | |
| return state_dict_ | |