from dataclasses import dataclass import torch from torch import Tensor, nn from modules.layers import (DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock, timestep_embedding) @dataclass 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