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import torch |
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from .sd_unet import ResnetBlock, DownSampler |
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from .sd_vae_decoder import VAEAttentionBlock |
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from .tiler import TileWorker |
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class SDVAEEncoder(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.quant_conv = torch.nn.Conv2d(8, 8, kernel_size=1) |
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self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1) |
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self.blocks = torch.nn.ModuleList([ |
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ResnetBlock(128, 128, eps=1e-6), |
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ResnetBlock(128, 128, eps=1e-6), |
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DownSampler(128, padding=0, extra_padding=True), |
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ResnetBlock(128, 256, eps=1e-6), |
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ResnetBlock(256, 256, eps=1e-6), |
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DownSampler(256, padding=0, extra_padding=True), |
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ResnetBlock(256, 512, eps=1e-6), |
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ResnetBlock(512, 512, eps=1e-6), |
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DownSampler(512, padding=0, extra_padding=True), |
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ResnetBlock(512, 512, eps=1e-6), |
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ResnetBlock(512, 512, eps=1e-6), |
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ResnetBlock(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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]) |
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self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6) |
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self.conv_act = torch.nn.SiLU() |
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self.conv_out = torch.nn.Conv2d(512, 8, kernel_size=3, padding=1) |
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def tiled_forward(self, sample, tile_size=64, tile_stride=32): |
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hidden_states = TileWorker().tiled_forward( |
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lambda x: self.forward(x), |
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sample, |
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tile_size, |
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tile_stride, |
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tile_device=sample.device, |
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tile_dtype=sample.dtype |
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) |
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return hidden_states |
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def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs): |
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if tiled: |
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return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) |
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hidden_states = self.conv_in(sample) |
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time_emb = None |
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text_emb = None |
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res_stack = 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 = self.quant_conv(hidden_states) |
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hidden_states = hidden_states[:, :4] |
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hidden_states *= self.scaling_factor |
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return hidden_states |
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def state_dict_converter(self): |
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return SDVAEEncoderStateDictConverter() |
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class SDVAEEncoderStateDictConverter: |
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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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block_types = [ |
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'ResnetBlock', 'ResnetBlock', 'DownSampler', |
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'ResnetBlock', 'ResnetBlock', 'DownSampler', |
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'ResnetBlock', 'ResnetBlock', 'DownSampler', |
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'ResnetBlock', 'ResnetBlock', |
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'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock' |
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] |
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local_rename_dict = { |
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"quant_conv": "quant_conv", |
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"encoder.conv_in": "conv_in", |
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"encoder.mid_block.attentions.0.group_norm": "blocks.12.norm", |
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"encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q", |
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"encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k", |
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"encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v", |
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"encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out", |
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"encoder.mid_block.resnets.0.norm1": "blocks.11.norm1", |
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"encoder.mid_block.resnets.0.conv1": "blocks.11.conv1", |
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"encoder.mid_block.resnets.0.norm2": "blocks.11.norm2", |
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"encoder.mid_block.resnets.0.conv2": "blocks.11.conv2", |
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"encoder.mid_block.resnets.1.norm1": "blocks.13.norm1", |
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"encoder.mid_block.resnets.1.conv1": "blocks.13.conv1", |
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"encoder.mid_block.resnets.1.norm2": "blocks.13.norm2", |
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"encoder.mid_block.resnets.1.conv2": "blocks.13.conv2", |
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"encoder.conv_norm_out": "conv_norm_out", |
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"encoder.conv_out": "conv_out", |
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} |
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name_list = sorted([name for name in state_dict]) |
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rename_dict = {} |
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block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1} |
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last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""} |
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for name in name_list: |
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names = name.split(".") |
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name_prefix = ".".join(names[:-1]) |
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if name_prefix in local_rename_dict: |
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rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1] |
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elif name.startswith("encoder.down_blocks"): |
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block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]] |
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block_type_with_id = ".".join(names[:5]) |
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if block_type_with_id != last_block_type_with_id[block_type]: |
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block_id[block_type] += 1 |
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last_block_type_with_id[block_type] = block_type_with_id |
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while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: |
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block_id[block_type] += 1 |
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block_type_with_id = ".".join(names[:5]) |
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names = ["blocks", str(block_id[block_type])] + names[5:] |
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rename_dict[name] = ".".join(names) |
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state_dict_ = {} |
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for name, param in state_dict.items(): |
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if name in rename_dict: |
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state_dict_[rename_dict[name]] = param |
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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.encoder.conv_in.bias": "conv_in.bias", |
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"first_stage_model.encoder.conv_in.weight": "conv_in.weight", |
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"first_stage_model.encoder.conv_out.bias": "conv_out.bias", |
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"first_stage_model.encoder.conv_out.weight": "conv_out.weight", |
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"first_stage_model.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias", |
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"first_stage_model.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight", |
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"first_stage_model.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias", |
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"first_stage_model.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight", |
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"first_stage_model.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias", |
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"first_stage_model.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight", |
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"first_stage_model.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias", |
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"first_stage_model.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight", |
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"first_stage_model.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias", |
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"first_stage_model.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight", |
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"first_stage_model.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias", |
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"first_stage_model.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight", |
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"first_stage_model.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias", |
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"first_stage_model.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight", |
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"first_stage_model.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias", |
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"first_stage_model.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight", |
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"first_stage_model.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias", |
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"first_stage_model.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight", |
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"first_stage_model.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias", |
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"first_stage_model.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight", |
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"first_stage_model.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias", |
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"first_stage_model.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight", |
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"first_stage_model.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias", |
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"first_stage_model.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight", |
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"first_stage_model.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias", |
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"first_stage_model.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight", |
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"first_stage_model.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias", |
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"first_stage_model.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight", |
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"first_stage_model.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias", |
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"first_stage_model.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight", |
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"first_stage_model.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias", |
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"first_stage_model.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight", |
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"first_stage_model.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias", |
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"first_stage_model.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight", |
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"first_stage_model.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias", |
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"first_stage_model.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight", |
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"first_stage_model.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias", |
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"first_stage_model.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight", |
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"first_stage_model.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias", |
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"first_stage_model.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight", |
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"first_stage_model.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias", |
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"first_stage_model.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight", |
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"first_stage_model.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias", |
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"first_stage_model.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight", |
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"first_stage_model.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias", |
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"first_stage_model.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight", |
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"first_stage_model.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias", |
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"first_stage_model.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight", |
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"first_stage_model.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias", |
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"first_stage_model.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight", |
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"first_stage_model.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias", |
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"first_stage_model.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight", |
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"first_stage_model.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias", |
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"first_stage_model.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight", |
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"first_stage_model.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias", |
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"first_stage_model.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight", |
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"first_stage_model.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias", |
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"first_stage_model.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight", |
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"first_stage_model.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias", |
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"first_stage_model.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight", |
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"first_stage_model.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias", |
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"first_stage_model.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight", |
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"first_stage_model.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias", |
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"first_stage_model.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight", |
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"first_stage_model.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias", |
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"first_stage_model.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight", |
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"first_stage_model.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias", |
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"first_stage_model.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight", |
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"first_stage_model.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias", |
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"first_stage_model.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight", |
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"first_stage_model.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias", |
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"first_stage_model.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight", |
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"first_stage_model.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias", |
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"first_stage_model.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight", |
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"first_stage_model.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias", |
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"first_stage_model.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight", |
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"first_stage_model.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias", |
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"first_stage_model.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight", |
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"first_stage_model.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias", |
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"first_stage_model.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight", |
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"first_stage_model.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias", |
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"first_stage_model.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight", |
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"first_stage_model.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias", |
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"first_stage_model.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight", |
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"first_stage_model.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias", |
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"first_stage_model.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight", |
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"first_stage_model.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias", |
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"first_stage_model.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight", |
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"first_stage_model.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias", |
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"first_stage_model.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight", |
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"first_stage_model.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias", |
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"first_stage_model.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight", |
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"first_stage_model.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias", |
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"first_stage_model.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight", |
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"first_stage_model.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias", |
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"first_stage_model.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight", |
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"first_stage_model.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias", |
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"first_stage_model.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight", |
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"first_stage_model.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias", |
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"first_stage_model.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight", |
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"first_stage_model.encoder.norm_out.bias": "conv_norm_out.bias", |
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"first_stage_model.encoder.norm_out.weight": "conv_norm_out.weight", |
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"first_stage_model.quant_conv.bias": "quant_conv.bias", |
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"first_stage_model.quant_conv.weight": "quant_conv.weight", |
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} |
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state_dict_ = {} |
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for name in state_dict: |
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if name in rename_dict: |
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param = state_dict[name] |
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if "transformer_blocks" in rename_dict[name]: |
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param = param.squeeze() |
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state_dict_[rename_dict[name]] = param |
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return state_dict_ |
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