Add initial support for FLUX.2

extensions_built_in/diffusion_models/flux2/src/model.py

@@ -0,0 +1,520 @@
+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
+
+
+class FakeConfig:
+    # for diffusers compatability
+    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.guidance_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.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,
+    ):
+        num_txt_tokens = ctx.shape[1]
+
+        timestep_emb = timestep_embedding(timesteps, 256)
+        vec = self.time_in(timestep_emb)
+        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.requires_grad_(True)
+                txt.requires_grad_(True)
+                img, txt = ckpt.checkpoint(
+                    block,
+                    img,
+                    txt,
+                    pe_x,
+                    pe_ctx,
+                    double_block_mod_img,
+                    double_block_mod_txt,
+                )
+            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.requires_grad_(True)
+                img = ckpt.checkpoint(
+                    block,
+                    img,
+                    pe,
+                    single_block_mod,
+                )
+            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)
+
+        # compute activation in mlp stream, cat again and run second linear layer
+        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
+
+        # prepare image for attention
+        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)
+
+        # prepare txt for attention
+        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] :]
+
+        # calculate the img blocks
+        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
+        )
+
+        # calculate the txt blocks
+        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)