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on
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Building
on
Zero
| import math | |
| from typing import List, Optional, Tuple | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from einops import rearrange | |
| from torch import Tensor | |
| from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch | |
| class ControlNetEmbedder(nn.Module): | |
| def __init__( | |
| self, | |
| img_size: int, | |
| patch_size: int, | |
| in_chans: int, | |
| attention_head_dim: int, | |
| num_attention_heads: int, | |
| adm_in_channels: int, | |
| num_layers: int, | |
| main_model_double: int, | |
| double_y_emb: bool, | |
| device: torch.device, | |
| dtype: torch.dtype, | |
| pos_embed_max_size: Optional[int] = None, | |
| operations = None, | |
| ): | |
| super().__init__() | |
| self.main_model_double = main_model_double | |
| self.dtype = dtype | |
| self.hidden_size = num_attention_heads * attention_head_dim | |
| self.patch_size = patch_size | |
| self.x_embedder = PatchEmbed( | |
| img_size=img_size, | |
| patch_size=patch_size, | |
| in_chans=in_chans, | |
| embed_dim=self.hidden_size, | |
| strict_img_size=pos_embed_max_size is None, | |
| device=device, | |
| dtype=dtype, | |
| operations=operations, | |
| ) | |
| self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) | |
| self.double_y_emb = double_y_emb | |
| if self.double_y_emb: | |
| self.orig_y_embedder = VectorEmbedder( | |
| adm_in_channels, self.hidden_size, dtype, device, operations=operations | |
| ) | |
| self.y_embedder = VectorEmbedder( | |
| self.hidden_size, self.hidden_size, dtype, device, operations=operations | |
| ) | |
| else: | |
| self.y_embedder = VectorEmbedder( | |
| adm_in_channels, self.hidden_size, dtype, device, operations=operations | |
| ) | |
| self.transformer_blocks = nn.ModuleList( | |
| DismantledBlock( | |
| hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True, | |
| dtype=dtype, device=device, operations=operations | |
| ) | |
| for _ in range(num_layers) | |
| ) | |
| # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features | |
| # TODO double check this logic when 8b | |
| self.use_y_embedder = True | |
| self.controlnet_blocks = nn.ModuleList([]) | |
| for _ in range(len(self.transformer_blocks)): | |
| controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) | |
| self.controlnet_blocks.append(controlnet_block) | |
| self.pos_embed_input = PatchEmbed( | |
| img_size=img_size, | |
| patch_size=patch_size, | |
| in_chans=in_chans, | |
| embed_dim=self.hidden_size, | |
| strict_img_size=False, | |
| device=device, | |
| dtype=dtype, | |
| operations=operations, | |
| ) | |
| def forward( | |
| self, | |
| x: torch.Tensor, | |
| timesteps: torch.Tensor, | |
| y: Optional[torch.Tensor] = None, | |
| context: Optional[torch.Tensor] = None, | |
| hint = None, | |
| ) -> Tuple[Tensor, List[Tensor]]: | |
| x_shape = list(x.shape) | |
| x = self.x_embedder(x) | |
| if not self.double_y_emb: | |
| h = (x_shape[-2] + 1) // self.patch_size | |
| w = (x_shape[-1] + 1) // self.patch_size | |
| x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device) | |
| c = self.t_embedder(timesteps, dtype=x.dtype) | |
| if y is not None and self.y_embedder is not None: | |
| if self.double_y_emb: | |
| y = self.orig_y_embedder(y) | |
| y = self.y_embedder(y) | |
| c = c + y | |
| x = x + self.pos_embed_input(hint) | |
| block_out = () | |
| repeat = math.ceil(self.main_model_double / len(self.transformer_blocks)) | |
| for i in range(len(self.transformer_blocks)): | |
| out = self.transformer_blocks[i](x, c) | |
| if not self.double_y_emb: | |
| x = out | |
| block_out += (self.controlnet_blocks[i](out),) * repeat | |
| return {"output": block_out} | |