| import torch |
| from .attention import Attention |
| from .sd_unet import ResnetBlock, UpSampler |
| from .tiler import TileWorker |
|
|
|
|
| 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 SDVAEDecoder(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.scaling_factor = 0.18215 |
| self.post_quant_conv = torch.nn.Conv2d(4, 4, kernel_size=1) |
| self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1) |
|
|
| self.blocks = torch.nn.ModuleList([ |
| |
| ResnetBlock(512, 512, eps=1e-6), |
| VAEAttentionBlock(1, 512, 512, 1, eps=1e-6), |
| ResnetBlock(512, 512, eps=1e-6), |
| |
| ResnetBlock(512, 512, eps=1e-6), |
| ResnetBlock(512, 512, eps=1e-6), |
| ResnetBlock(512, 512, eps=1e-6), |
| UpSampler(512), |
| |
| ResnetBlock(512, 512, eps=1e-6), |
| ResnetBlock(512, 512, eps=1e-6), |
| ResnetBlock(512, 512, eps=1e-6), |
| UpSampler(512), |
| |
| ResnetBlock(512, 256, eps=1e-6), |
| ResnetBlock(256, 256, eps=1e-6), |
| ResnetBlock(256, 256, eps=1e-6), |
| UpSampler(256), |
| |
| ResnetBlock(256, 128, eps=1e-6), |
| ResnetBlock(128, 128, eps=1e-6), |
| ResnetBlock(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) |
| |
| 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): |
| |
| if tiled: |
| return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride) |
|
|
| |
| sample = sample / self.scaling_factor |
| hidden_states = self.post_quant_conv(sample) |
| hidden_states = self.conv_in(hidden_states) |
| time_emb = None |
| text_emb = None |
| res_stack = None |
|
|
| |
| for i, block in enumerate(self.blocks): |
| hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) |
| |
| |
| hidden_states = self.conv_norm_out(hidden_states) |
| hidden_states = self.conv_act(hidden_states) |
| hidden_states = self.conv_out(hidden_states) |
|
|
| return hidden_states |
| |
| def state_dict_converter(self): |
| return SDVAEDecoderStateDictConverter() |
| |
|
|
| class SDVAEDecoderStateDictConverter: |
| def __init__(self): |
| pass |
|
|
| def from_diffusers(self, state_dict): |
| |
| block_types = [ |
| 'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock', |
| 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', |
| 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', |
| 'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler', |
| 'ResnetBlock', 'ResnetBlock', 'ResnetBlock' |
| ] |
|
|
| |
| local_rename_dict = { |
| "post_quant_conv": "post_quant_conv", |
| "decoder.conv_in": "conv_in", |
| "decoder.mid_block.attentions.0.group_norm": "blocks.1.norm", |
| "decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q", |
| "decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k", |
| "decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v", |
| "decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out", |
| "decoder.mid_block.resnets.0.norm1": "blocks.0.norm1", |
| "decoder.mid_block.resnets.0.conv1": "blocks.0.conv1", |
| "decoder.mid_block.resnets.0.norm2": "blocks.0.norm2", |
| "decoder.mid_block.resnets.0.conv2": "blocks.0.conv2", |
| "decoder.mid_block.resnets.1.norm1": "blocks.2.norm1", |
| "decoder.mid_block.resnets.1.conv1": "blocks.2.conv1", |
| "decoder.mid_block.resnets.1.norm2": "blocks.2.norm2", |
| "decoder.mid_block.resnets.1.conv2": "blocks.2.conv2", |
| "decoder.conv_norm_out": "conv_norm_out", |
| "decoder.conv_out": "conv_out", |
| } |
| name_list = sorted([name for name in state_dict]) |
| rename_dict = {} |
| block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2} |
| 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("decoder.up_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) |
|
|
| |
| 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.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.weight": "conv_out.weight", |
| "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias", |
| "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight", |
| "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias", |
| "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight", |
| "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias", |
| "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight", |
| "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias", |
| "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight", |
| "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias", |
| "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.1.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.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_2.conv1.bias": "blocks.2.conv1.bias", |
| "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight", |
| "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias", |
| "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight", |
| "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias", |
| "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight", |
| "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias", |
| "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.2.norm2.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.15.conv1.bias", |
| "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight", |
| "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias", |
| "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight", |
| "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias", |
| "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight", |
| "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias", |
| "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight", |
| "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias", |
| "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight", |
| "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias", |
| "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight", |
| "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias", |
| "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight", |
| "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias", |
| "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight", |
| "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias", |
| "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight", |
| "first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias", |
| "first_stage_model.decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight", |
| "first_stage_model.decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias", |
| "first_stage_model.decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight", |
| "first_stage_model.decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias", |
| "first_stage_model.decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight", |
| "first_stage_model.decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias", |
| "first_stage_model.decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight", |
| "first_stage_model.decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias", |
| "first_stage_model.decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight", |
| "first_stage_model.decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias", |
| "first_stage_model.decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight", |
| "first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias", |
| "first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight", |
| "first_stage_model.decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias", |
| "first_stage_model.decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight", |
| "first_stage_model.decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias", |
| "first_stage_model.decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight", |
| "first_stage_model.decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias", |
| "first_stage_model.decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight", |
| "first_stage_model.decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias", |
| "first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight", |
| "first_stage_model.decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias", |
| "first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight", |
| "first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias", |
| "first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight", |
| "first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias", |
| "first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight", |
| "first_stage_model.decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias", |
| "first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight", |
| "first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias", |
| "first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight", |
| "first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias", |
| "first_stage_model.decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight", |
| "first_stage_model.decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias", |
| "first_stage_model.decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight", |
| "first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias", |
| "first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight", |
| "first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias", |
| "first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight", |
| "first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias", |
| "first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight", |
| "first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias", |
| "first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight", |
| "first_stage_model.decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias", |
| "first_stage_model.decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight", |
| "first_stage_model.decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias", |
| "first_stage_model.decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight", |
| "first_stage_model.decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias", |
| "first_stage_model.decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight", |
| "first_stage_model.decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias", |
| "first_stage_model.decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight", |
| "first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias", |
| "first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight", |
| "first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias", |
| "first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight", |
| "first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias", |
| "first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight", |
| "first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias", |
| "first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight", |
| "first_stage_model.decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias", |
| "first_stage_model.decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight", |
| "first_stage_model.decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias", |
| "first_stage_model.decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight", |
| "first_stage_model.decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias", |
| "first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight", |
| "first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias", |
| "first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight", |
| "first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias", |
| "first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight", |
| "first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias", |
| "first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight", |
| "first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias", |
| "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight", |
| "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias", |
| "first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight", |
| "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias", |
| "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight", |
| "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias", |
| "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight", |
| "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias", |
| "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight", |
| "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias", |
| "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight", |
| "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias", |
| "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight", |
| "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias", |
| "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight", |
| "first_stage_model.post_quant_conv.bias": "post_quant_conv.bias", |
| "first_stage_model.post_quant_conv.weight": "post_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_ |
|
|
