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from dataclasses import dataclass |
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import torch |
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from torch import Tensor, nn |
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from flux.modules.layers import ( |
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DoubleStreamBlock, |
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EmbedND, |
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LastLayer, |
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MLPEmbedder, |
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SingleStreamBlock, |
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timestep_embedding, |
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) |
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@dataclass |
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class FluxParams: |
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in_channels: int |
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vec_in_dim: int |
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context_in_dim: int |
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hidden_size: int |
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mlp_ratio: float |
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num_heads: int |
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depth: int |
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depth_single_blocks: int |
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axes_dim: list[int] |
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theta: int |
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qkv_bias: bool |
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guidance_embed: bool |
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class Flux(nn.Module): |
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""" |
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Transformer model for flow matching on sequences. |
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""" |
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def __init__(self, params: FluxParams): |
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super().__init__() |
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self.params = params |
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self.in_channels = params.in_channels |
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self.out_channels = self.in_channels |
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if params.hidden_size % params.num_heads != 0: |
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raise ValueError( |
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" |
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) |
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pe_dim = params.hidden_size // params.num_heads |
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if sum(params.axes_dim) != pe_dim: |
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raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") |
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self.hidden_size = params.hidden_size |
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self.num_heads = params.num_heads |
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self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) |
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self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) |
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) |
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self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) |
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self.guidance_in = ( |
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MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() |
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) |
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self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) |
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self.double_blocks = nn.ModuleList( |
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[ |
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DoubleStreamBlock( |
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self.hidden_size, |
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self.num_heads, |
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mlp_ratio=params.mlp_ratio, |
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qkv_bias=params.qkv_bias, |
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) |
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for _ in range(params.depth) |
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] |
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) |
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self.single_blocks = nn.ModuleList( |
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[ |
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SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio) |
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for _ in range(params.depth_single_blocks) |
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] |
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) |
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self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) |
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self.pulid_ca = None |
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self.pulid_double_interval = 2 |
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self.pulid_single_interval = 4 |
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def forward( |
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self, |
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img: Tensor, |
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img_ids: Tensor, |
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txt: Tensor, |
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txt_ids: Tensor, |
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timesteps: Tensor, |
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y: Tensor, |
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guidance: Tensor = None, |
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id: Tensor = None, |
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id_weight: float = 1.0, |
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) -> Tensor: |
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if img.ndim != 3 or txt.ndim != 3: |
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raise ValueError("Input img and txt tensors must have 3 dimensions.") |
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img = self.img_in(img) |
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vec = self.time_in(timestep_embedding(timesteps, 256)) |
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if self.params.guidance_embed: |
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if guidance is None: |
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raise ValueError("Didn't get guidance strength for guidance distilled model.") |
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vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) |
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vec = vec + self.vector_in(y) |
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txt = self.txt_in(txt) |
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ids = torch.cat((txt_ids, img_ids), dim=1) |
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pe = self.pe_embedder(ids) |
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ca_idx = 0 |
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for i, block in enumerate(self.double_blocks): |
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img, txt = block(img=img, txt=txt, vec=vec, pe=pe) |
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if i % self.pulid_double_interval == 0 and id is not None: |
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img = img + id_weight * self.pulid_ca[ca_idx](id, img) |
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ca_idx += 1 |
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img = torch.cat((txt, img), 1) |
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for i, block in enumerate(self.single_blocks): |
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x = block(img, vec=vec, pe=pe) |
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real_img, txt = x[:, txt.shape[1]:, ...], x[:, :txt.shape[1], ...] |
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if i % self.pulid_single_interval == 0 and id is not None: |
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real_img = real_img + id_weight * self.pulid_ca[ca_idx](id, real_img) |
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ca_idx += 1 |
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img = torch.cat((txt, real_img), 1) |
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img = img[:, txt.shape[1] :, ...] |
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img = self.final_layer(img, vec) |
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return img |
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