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| from dataclasses import dataclass | |
| import torch | |
| from torch import Tensor, nn | |
| from modules.layers import (DoubleStreamBlock, EmbedND, LastLayer, | |
| MLPEmbedder, SingleStreamBlock, | |
| timestep_embedding) | |
| class FluxParams: | |
| in_channels: int | |
| vec_in_dim: int | |
| context_in_dim: int | |
| hidden_size: int | |
| mlp_ratio: float | |
| num_heads: int | |
| depth: int | |
| depth_single_blocks: int | |
| axes_dim: list[int] | |
| theta: int | |
| qkv_bias: bool | |
| guidance_embed: bool | |
| class Flux(nn.Module): | |
| """ | |
| Transformer model for flow matching on sequences. | |
| """ | |
| def __init__(self, params: FluxParams): | |
| super().__init__() | |
| self.params = params | |
| self.in_channels = params.in_channels | |
| self.out_channels = self.in_channels | |
| if params.hidden_size % params.num_heads != 0: | |
| raise ValueError( | |
| f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" | |
| ) | |
| pe_dim = params.hidden_size // params.num_heads | |
| if sum(params.axes_dim) != pe_dim: | |
| raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") | |
| self.hidden_size = params.hidden_size | |
| self.num_heads = params.num_heads | |
| self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) | |
| self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) | |
| self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) | |
| self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) | |
| # self.guidance_in = ( | |
| # MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() | |
| # ) | |
| self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) | |
| self.double_blocks = nn.ModuleList( | |
| [ | |
| DoubleStreamBlock( | |
| self.hidden_size, | |
| self.num_heads, | |
| mlp_ratio=params.mlp_ratio, | |
| qkv_bias=params.qkv_bias, | |
| ) | |
| for _ in range(params.depth) | |
| ] | |
| ) | |
| self.single_blocks = nn.ModuleList( | |
| [ | |
| SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio) | |
| for _ in range(params.depth_single_blocks) | |
| ] | |
| ) | |
| self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) | |
| def forward( | |
| self, | |
| x: Tensor, | |
| img_ids: Tensor, | |
| txt: Tensor, | |
| txt_ids: Tensor, | |
| t: Tensor, | |
| y: Tensor, | |
| guidance: Tensor | None = None, | |
| ) -> Tensor: | |
| if x.ndim != 3 or txt.ndim != 3: | |
| raise ValueError("Input img and txt tensors must have 3 dimensions.") | |
| # running on sequences img | |
| img = self.img_in(x) | |
| vec = self.time_in(timestep_embedding(t, 256)) | |
| # if self.params.guidance_embed: | |
| # if guidance is None: | |
| # raise ValueError("Didn't get guidance strength for guidance distilled model.") | |
| # vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) | |
| vec = vec + self.vector_in(y) | |
| txt = self.txt_in(txt) | |
| ids = torch.cat((txt_ids, img_ids), dim=1) | |
| pe = self.pe_embedder(ids) | |
| for block in self.double_blocks: | |
| img, txt = block(img=img, txt=txt, vec=vec, pe=pe) | |
| img = torch.cat((txt, img), 1) | |
| for block in self.single_blocks: | |
| img = block(img, vec=vec, pe=pe) | |
| img = img[:, txt.shape[1] :, ...] | |
| img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) | |
| return img | |