| import torch |
| from einops import rearrange |
| from torch import Tensor, nn |
| import torch.utils.checkpoint as ckpt |
| import math |
| from dataclasses import dataclass, field |
|
|
|
|
| @dataclass |
| class Flux2Params: |
| in_channels: int = 128 |
| context_in_dim: int = 15360 |
| hidden_size: int = 6144 |
| num_heads: int = 48 |
| depth: int = 8 |
| depth_single_blocks: int = 48 |
| axes_dim: list[int] = field(default_factory=lambda: [32, 32, 32, 32]) |
| theta: int = 2000 |
| mlp_ratio: float = 3.0 |
| use_guidance_embed: bool = True |
|
|
|
|
| @dataclass |
| class Klein9BParams: |
| in_channels: int = 128 |
| context_in_dim: int = 12288 |
| hidden_size: int = 4096 |
| num_heads: int = 32 |
| depth: int = 8 |
| depth_single_blocks: int = 24 |
| axes_dim: list[int] = field(default_factory=lambda: [32, 32, 32, 32]) |
| theta: int = 2000 |
| mlp_ratio: float = 3.0 |
| use_guidance_embed: bool = False |
|
|
|
|
| @dataclass |
| class Klein4BParams: |
| in_channels: int = 128 |
| context_in_dim: int = 7680 |
| hidden_size: int = 3072 |
| num_heads: int = 24 |
| depth: int = 5 |
| depth_single_blocks: int = 20 |
| axes_dim: list[int] = field(default_factory=lambda: [32, 32, 32, 32]) |
| theta: int = 2000 |
| mlp_ratio: float = 3.0 |
| use_guidance_embed: bool = False |
|
|
|
|
| class FakeConfig: |
| |
| def __init__(self): |
| self.patch_size = 1 |
|
|
|
|
| class Flux2(nn.Module): |
| def __init__(self, params: Flux2Params): |
| super().__init__() |
| self.config = FakeConfig() |
|
|
| self.in_channels = params.in_channels |
| self.out_channels = params.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=False) |
| self.time_in = MLPEmbedder( |
| in_dim=256, hidden_dim=self.hidden_size, disable_bias=True |
| ) |
| self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size, bias=False) |
|
|
| self.use_guidance_embed = params.use_guidance_embed |
| if self.use_guidance_embed: |
| self.guidance_in = MLPEmbedder( |
| in_dim=256, hidden_dim=self.hidden_size, disable_bias=True |
| ) |
|
|
| self.double_blocks = nn.ModuleList( |
| [ |
| DoubleStreamBlock( |
| self.hidden_size, |
| self.num_heads, |
| mlp_ratio=params.mlp_ratio, |
| ) |
| 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.double_stream_modulation_img = Modulation( |
| self.hidden_size, |
| double=True, |
| disable_bias=True, |
| ) |
| self.double_stream_modulation_txt = Modulation( |
| self.hidden_size, |
| double=True, |
| disable_bias=True, |
| ) |
| self.single_stream_modulation = Modulation( |
| self.hidden_size, double=False, disable_bias=True |
| ) |
|
|
| self.final_layer = LastLayer( |
| self.hidden_size, |
| self.out_channels, |
| ) |
|
|
| self.gradient_checkpointing = False |
|
|
| @property |
| def device(self): |
| return next(self.parameters()).device |
|
|
| @property |
| def dtype(self): |
| return next(self.parameters()).dtype |
|
|
| def enable_gradient_checkpointing(self): |
| self.gradient_checkpointing = True |
|
|
| def forward( |
| self, |
| x: Tensor, |
| x_ids: Tensor, |
| timesteps: Tensor, |
| ctx: Tensor, |
| ctx_ids: Tensor, |
| guidance: Tensor | None, |
| ): |
| num_txt_tokens = ctx.shape[1] |
|
|
| timestep_emb = timestep_embedding(timesteps, 256) |
| vec = self.time_in(timestep_emb) |
| if self.use_guidance_embed: |
| guidance_emb = timestep_embedding(guidance, 256) |
| vec = vec + self.guidance_in(guidance_emb) |
|
|
| double_block_mod_img = self.double_stream_modulation_img(vec) |
| double_block_mod_txt = self.double_stream_modulation_txt(vec) |
| single_block_mod, _ = self.single_stream_modulation(vec) |
|
|
| img = self.img_in(x) |
| txt = self.txt_in(ctx) |
|
|
| pe_x = self.pe_embedder(x_ids) |
| pe_ctx = self.pe_embedder(ctx_ids) |
|
|
| for block in self.double_blocks: |
| if torch.is_grad_enabled() and self.gradient_checkpointing: |
| img, txt = ckpt.checkpoint( |
| block, |
| img, |
| txt, |
| pe_x, |
| pe_ctx, |
| double_block_mod_img, |
| double_block_mod_txt, |
| use_reentrant=False, |
| ) |
| else: |
| img, txt = block( |
| img, |
| txt, |
| pe_x, |
| pe_ctx, |
| double_block_mod_img, |
| double_block_mod_txt, |
| ) |
|
|
| img = torch.cat((txt, img), dim=1) |
| pe = torch.cat((pe_ctx, pe_x), dim=2) |
|
|
| for i, block in enumerate(self.single_blocks): |
| if torch.is_grad_enabled() and self.gradient_checkpointing: |
| img = ckpt.checkpoint( |
| block, |
| img, |
| pe, |
| single_block_mod, |
| use_reentrant=False, |
| ) |
| else: |
| img = block( |
| img, |
| pe, |
| single_block_mod, |
| ) |
|
|
| img = img[:, num_txt_tokens:, ...] |
|
|
| img = self.final_layer(img, vec) |
| return img |
|
|
|
|
| class SelfAttention(nn.Module): |
| def __init__( |
| self, |
| dim: int, |
| num_heads: int = 8, |
| ): |
| super().__init__() |
| self.num_heads = num_heads |
| head_dim = dim // num_heads |
| self.qkv = nn.Linear(dim, dim * 3, bias=False) |
|
|
| self.norm = QKNorm(head_dim) |
| self.proj = nn.Linear(dim, dim, bias=False) |
|
|
|
|
| class SiLUActivation(nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.gate_fn = nn.SiLU() |
|
|
| def forward(self, x: Tensor) -> Tensor: |
| x1, x2 = x.chunk(2, dim=-1) |
| return self.gate_fn(x1) * x2 |
|
|
|
|
| class Modulation(nn.Module): |
| def __init__(self, dim: int, double: bool, disable_bias: bool = False): |
| super().__init__() |
| self.is_double = double |
| self.multiplier = 6 if double else 3 |
| self.lin = nn.Linear(dim, self.multiplier * dim, bias=not disable_bias) |
|
|
| def forward(self, vec: torch.Tensor): |
| out = self.lin(nn.functional.silu(vec)) |
| if out.ndim == 2: |
| out = out[:, None, :] |
| out = out.chunk(self.multiplier, dim=-1) |
| return out[:3], out[3:] if self.is_double else None |
|
|
|
|
| class LastLayer(nn.Module): |
| def __init__( |
| self, |
| hidden_size: int, |
| out_channels: int, |
| ): |
| super().__init__() |
| self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
| self.linear = nn.Linear(hidden_size, out_channels, bias=False) |
| self.adaLN_modulation = nn.Sequential( |
| nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=False) |
| ) |
|
|
| def forward(self, x: torch.Tensor, vec: torch.Tensor) -> torch.Tensor: |
| mod = self.adaLN_modulation(vec) |
| shift, scale = mod.chunk(2, dim=-1) |
| if shift.ndim == 2: |
| shift = shift[:, None, :] |
| scale = scale[:, None, :] |
| x = (1 + scale) * self.norm_final(x) + shift |
| x = self.linear(x) |
| return x |
|
|
|
|
| class SingleStreamBlock(nn.Module): |
| def __init__( |
| self, |
| hidden_size: int, |
| num_heads: int, |
| mlp_ratio: float = 4.0, |
| ): |
| super().__init__() |
|
|
| self.hidden_dim = hidden_size |
| self.num_heads = num_heads |
| head_dim = hidden_size // num_heads |
| self.scale = head_dim**-0.5 |
| self.mlp_hidden_dim = int(hidden_size * mlp_ratio) |
| self.mlp_mult_factor = 2 |
|
|
| self.linear1 = nn.Linear( |
| hidden_size, |
| hidden_size * 3 + self.mlp_hidden_dim * self.mlp_mult_factor, |
| bias=False, |
| ) |
|
|
| self.linear2 = nn.Linear( |
| hidden_size + self.mlp_hidden_dim, hidden_size, bias=False |
| ) |
|
|
| self.norm = QKNorm(head_dim) |
|
|
| self.hidden_size = hidden_size |
| self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
|
|
| self.mlp_act = SiLUActivation() |
|
|
| def forward( |
| self, |
| x: Tensor, |
| pe: Tensor, |
| mod: tuple[Tensor, Tensor], |
| ) -> Tensor: |
| mod_shift, mod_scale, mod_gate = mod |
| x_mod = (1 + mod_scale) * self.pre_norm(x) + mod_shift |
|
|
| qkv, mlp = torch.split( |
| self.linear1(x_mod), |
| [3 * self.hidden_size, self.mlp_hidden_dim * self.mlp_mult_factor], |
| dim=-1, |
| ) |
|
|
| q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) |
| q, k = self.norm(q, k, v) |
|
|
| attn = attention(q, k, v, pe) |
|
|
| |
| output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) |
| return x + mod_gate * output |
|
|
|
|
| class DoubleStreamBlock(nn.Module): |
| def __init__( |
| self, |
| hidden_size: int, |
| num_heads: int, |
| mlp_ratio: float, |
| ): |
| super().__init__() |
| mlp_hidden_dim = int(hidden_size * mlp_ratio) |
| self.num_heads = num_heads |
| assert hidden_size % num_heads == 0, ( |
| f"{hidden_size=} must be divisible by {num_heads=}" |
| ) |
|
|
| self.hidden_size = hidden_size |
| self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
| self.mlp_mult_factor = 2 |
|
|
| self.img_attn = SelfAttention( |
| dim=hidden_size, |
| num_heads=num_heads, |
| ) |
|
|
| self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
| self.img_mlp = nn.Sequential( |
| nn.Linear(hidden_size, mlp_hidden_dim * self.mlp_mult_factor, bias=False), |
| SiLUActivation(), |
| nn.Linear(mlp_hidden_dim, hidden_size, bias=False), |
| ) |
|
|
| self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
| self.txt_attn = SelfAttention( |
| dim=hidden_size, |
| num_heads=num_heads, |
| ) |
|
|
| self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
| self.txt_mlp = nn.Sequential( |
| nn.Linear( |
| hidden_size, |
| mlp_hidden_dim * self.mlp_mult_factor, |
| bias=False, |
| ), |
| SiLUActivation(), |
| nn.Linear(mlp_hidden_dim, hidden_size, bias=False), |
| ) |
|
|
| def forward( |
| self, |
| img: Tensor, |
| txt: Tensor, |
| pe: Tensor, |
| pe_ctx: Tensor, |
| mod_img: tuple[Tensor, Tensor], |
| mod_txt: tuple[Tensor, Tensor], |
| ) -> tuple[Tensor, Tensor]: |
| img_mod1, img_mod2 = mod_img |
| txt_mod1, txt_mod2 = mod_txt |
|
|
| img_mod1_shift, img_mod1_scale, img_mod1_gate = img_mod1 |
| img_mod2_shift, img_mod2_scale, img_mod2_gate = img_mod2 |
| txt_mod1_shift, txt_mod1_scale, txt_mod1_gate = txt_mod1 |
| txt_mod2_shift, txt_mod2_scale, txt_mod2_gate = txt_mod2 |
|
|
| |
| img_modulated = self.img_norm1(img) |
| img_modulated = (1 + img_mod1_scale) * img_modulated + img_mod1_shift |
|
|
| img_qkv = self.img_attn.qkv(img_modulated) |
| img_q, img_k, img_v = rearrange( |
| img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads |
| ) |
| img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) |
|
|
| |
| txt_modulated = self.txt_norm1(txt) |
| txt_modulated = (1 + txt_mod1_scale) * txt_modulated + txt_mod1_shift |
|
|
| txt_qkv = self.txt_attn.qkv(txt_modulated) |
| txt_q, txt_k, txt_v = rearrange( |
| txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads |
| ) |
| txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) |
|
|
| q = torch.cat((txt_q, img_q), dim=2) |
| k = torch.cat((txt_k, img_k), dim=2) |
| v = torch.cat((txt_v, img_v), dim=2) |
|
|
| pe = torch.cat((pe_ctx, pe), dim=2) |
| attn = attention(q, k, v, pe) |
| txt_attn, img_attn = attn[:, : txt_q.shape[2]], attn[:, txt_q.shape[2] :] |
|
|
| |
| img = img + img_mod1_gate * self.img_attn.proj(img_attn) |
| img = img + img_mod2_gate * self.img_mlp( |
| (1 + img_mod2_scale) * (self.img_norm2(img)) + img_mod2_shift |
| ) |
|
|
| |
| txt = txt + txt_mod1_gate * self.txt_attn.proj(txt_attn) |
| txt = txt + txt_mod2_gate * self.txt_mlp( |
| (1 + txt_mod2_scale) * (self.txt_norm2(txt)) + txt_mod2_shift |
| ) |
| return img, txt |
|
|
|
|
| class MLPEmbedder(nn.Module): |
| def __init__(self, in_dim: int, hidden_dim: int, disable_bias: bool = False): |
| super().__init__() |
| self.in_layer = nn.Linear(in_dim, hidden_dim, bias=not disable_bias) |
| self.silu = nn.SiLU() |
| self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=not disable_bias) |
|
|
| def forward(self, x: Tensor) -> Tensor: |
| return self.out_layer(self.silu(self.in_layer(x))) |
|
|
|
|
| class EmbedND(nn.Module): |
| def __init__(self, dim: int, theta: int, axes_dim: list[int]): |
| super().__init__() |
| self.dim = dim |
| self.theta = theta |
| self.axes_dim = axes_dim |
|
|
| def forward(self, ids: Tensor) -> Tensor: |
| emb = torch.cat( |
| [ |
| rope(ids[..., i], self.axes_dim[i], self.theta) |
| for i in range(len(self.axes_dim)) |
| ], |
| dim=-3, |
| ) |
|
|
| return emb.unsqueeze(1) |
|
|
|
|
| def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): |
| """ |
| Create sinusoidal timestep embeddings. |
| :param t: a 1-D Tensor of N indices, one per batch element. |
| These may be fractional. |
| :param dim: the dimension of the output. |
| :param max_period: controls the minimum frequency of the embeddings. |
| :return: an (N, D) Tensor of positional embeddings. |
| """ |
| t = time_factor * t |
| half = dim // 2 |
| freqs = torch.exp( |
| -math.log(max_period) |
| * torch.arange(start=0, end=half, device=t.device, dtype=torch.float32) |
| / half |
| ) |
|
|
| args = t[:, None].float() * freqs[None] |
| embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) |
| if dim % 2: |
| embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) |
| if torch.is_floating_point(t): |
| embedding = embedding.to(t) |
| return embedding |
|
|
|
|
| class RMSNorm(torch.nn.Module): |
| def __init__(self, dim: int): |
| super().__init__() |
| self.scale = nn.Parameter(torch.ones(dim)) |
|
|
| def forward(self, x: Tensor): |
| x_dtype = x.dtype |
| x = x.float() |
| rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6) |
| return (x * rrms).to(dtype=x_dtype) * self.scale |
|
|
|
|
| class QKNorm(torch.nn.Module): |
| def __init__(self, dim: int): |
| super().__init__() |
| self.query_norm = RMSNorm(dim) |
| self.key_norm = RMSNorm(dim) |
|
|
| def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]: |
| q = self.query_norm(q) |
| k = self.key_norm(k) |
| return q.to(v), k.to(v) |
|
|
|
|
| def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor: |
| q, k = apply_rope(q, k, pe) |
|
|
| x = torch.nn.functional.scaled_dot_product_attention(q, k, v) |
| x = rearrange(x, "B H L D -> B L (H D)") |
|
|
| return x |
|
|
|
|
| def rope(pos: Tensor, dim: int, theta: int) -> Tensor: |
| assert dim % 2 == 0 |
| scale = torch.arange(0, dim, 2, dtype=pos.dtype, device=pos.device) / dim |
| omega = 1.0 / (theta**scale) |
| out = torch.einsum("...n,d->...nd", pos, omega) |
| out = torch.stack( |
| [torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1 |
| ) |
| out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) |
| return out.float() |
|
|
|
|
| def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]: |
| xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) |
| xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) |
| xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] |
| xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] |
| return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) |
|
|
