Add initial support for FLUX.2

extensions_built_in/diffusion_models/flux2/src/autoencoder.py

@@ -0,0 +1,370 @@
+from dataclasses import dataclass, field
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+import math
+
+
+@dataclass
+class AutoEncoderParams:
+    resolution: int = 256
+    in_channels: int = 3
+    ch: int = 128
+    out_ch: int = 3
+    ch_mult: list[int] = field(default_factory=lambda: [1, 2, 4, 4])
+    num_res_blocks: int = 2
+    z_channels: int = 32
+
+
+def swish(x: Tensor) -> Tensor:
+    return x * torch.sigmoid(x)
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = nn.GroupNorm(
+            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+        )
+
+        self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+
+    def attention(self, h_: Tensor) -> Tensor:
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        b, c, h, w = q.shape
+        q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
+        k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
+        v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
+        h_ = nn.functional.scaled_dot_product_attention(q, k, v)
+
+        return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x + self.proj_out(self.attention(x))
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = nn.GroupNorm(
+            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+        )
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        self.norm2 = nn.GroupNorm(
+            num_groups=32, num_channels=out_channels, eps=1e-6, affine=True
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = nn.Conv2d(
+                in_channels, out_channels, kernel_size=1, stride=1, padding=0
+            )
+
+    def forward(self, x):
+        h = x
+        h = self.norm1(h)
+        h = swish(h)
+        h = self.conv1(h)
+
+        h = self.norm2(h)
+        h = swish(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        # no asymmetric padding in torch conv, must do it ourselves
+        self.conv = nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=2, padding=0
+        )
+
+    def forward(self, x: Tensor):
+        pad = (0, 1, 0, 1)
+        x = nn.functional.pad(x, pad, mode="constant", value=0)
+        x = self.conv(x)
+        return x
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_channels, in_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x: Tensor):
+        x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        x = self.conv(x)
+        return x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        resolution: int,
+        in_channels: int,
+        ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.quant_conv = torch.nn.Conv2d(2 * z_channels, 2 * z_channels, 1)
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        # downsampling
+        self.conv_in = nn.Conv2d(
+            in_channels, self.ch, kernel_size=3, stride=1, padding=1
+        )
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        block_in = self.ch
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+
+        # end
+        self.norm_out = nn.GroupNorm(
+            num_groups=32, num_channels=block_in, eps=1e-6, affine=True
+        )
+        self.conv_out = nn.Conv2d(
+            block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1])
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions - 1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        # end
+        h = self.norm_out(h)
+        h = swish(h)
+        h = self.conv_out(h)
+        h = self.quant_conv(h)
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        ch: int,
+        out_ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        in_channels: int,
+        resolution: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.ffactor = 2 ** (self.num_resolutions - 1)
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+
+        # z to block_in
+        self.conv_in = nn.Conv2d(
+            z_channels, block_in, kernel_size=3, stride=1, padding=1
+        )
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = nn.GroupNorm(
+            num_groups=32, num_channels=block_in, eps=1e-6, affine=True
+        )
+        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, z: Tensor) -> Tensor:
+        z = self.post_quant_conv(z)
+
+        # get dtype for proper tracing
+        upscale_dtype = next(self.up.parameters()).dtype
+
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+
+        # cast to proper dtype
+        h = h.to(upscale_dtype)
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = swish(h)
+        h = self.conv_out(h)
+        return h
+
+
+class AutoEncoder(nn.Module):
+    def __init__(self, params: AutoEncoderParams):
+        super().__init__()
+        self.params = params
+        self.encoder = Encoder(
+            resolution=params.resolution,
+            in_channels=params.in_channels,
+            ch=params.ch,
+            ch_mult=params.ch_mult,
+            num_res_blocks=params.num_res_blocks,
+            z_channels=params.z_channels,
+        )
+        self.decoder = Decoder(
+            resolution=params.resolution,
+            in_channels=params.in_channels,
+            ch=params.ch,
+            out_ch=params.out_ch,
+            ch_mult=params.ch_mult,
+            num_res_blocks=params.num_res_blocks,
+            z_channels=params.z_channels,
+        )
+
+        self.bn_eps = 1e-4
+        self.bn_momentum = 0.1
+        self.ps = [2, 2]
+        self.bn = torch.nn.BatchNorm2d(
+            math.prod(self.ps) * params.z_channels,
+            eps=self.bn_eps,
+            momentum=self.bn_momentum,
+            affine=False,
+            track_running_stats=True,
+        )
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def normalize(self, z):
+        self.bn.eval()
+        return self.bn(z)
+
+    def inv_normalize(self, z):
+        self.bn.eval()
+        s = torch.sqrt(self.bn.running_var.view(1, -1, 1, 1) + self.bn_eps)
+        m = self.bn.running_mean.view(1, -1, 1, 1)
+        return z * s + m
+
+    def encode(self, x: Tensor) -> Tensor:
+        moments = self.encoder(x)
+        mean = torch.chunk(moments, 2, dim=1)[0]
+
+        z = rearrange(
+            mean,
+            "... c (i pi) (j pj)  -> ... (c pi pj) i j",
+            pi=self.ps[0],
+            pj=self.ps[1],
+        )
+        z = self.normalize(z)
+        return z
+
+    def decode(self, z: Tensor) -> Tensor:
+        z = self.inv_normalize(z)
+        z = rearrange(
+            z,
+            "... (c pi pj) i j -> ... c (i pi) (j pj)",
+            pi=self.ps[0],
+            pj=self.ps[1],
+        )
+        dec = self.decoder(z)
+        return dec

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)

extensions_built_in/diffusion_models/flux2/src/pipeline.py

@@ -0,0 +1,358 @@
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import PIL.Image
+from dataclasses import dataclass
+from typing import List, Union
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import (
+    logging,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+
+from diffusers.utils import BaseOutput
+
+from .autoencoder import AutoEncoder
+from .model import Flux2
+
+from einops import rearrange
+
+from transformers import AutoProcessor, Mistral3ForConditionalGeneration
+
+from .sampling import (
+    get_schedule,
+    batched_prc_img,
+    batched_prc_txt,
+    encode_image_refs,
+    scatter_ids,
+)
+
+
+@dataclass
+class Flux2ImagePipelineOutput(BaseOutput):
+    images: Union[List[PIL.Image.Image], np.ndarray]
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+SYSTEM_MESSAGE = """You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object
+attribution and actions without speculation."""
+OUTPUT_LAYERS = [10, 20, 30]
+MAX_LENGTH = 512
+
+
+class Flux2Pipeline(DiffusionPipeline):
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoEncoder,
+        text_encoder: Mistral3ForConditionalGeneration,
+        tokenizer: AutoProcessor,
+        transformer: Flux2,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 16  # 8x plus 2x pixel shuffle
+        self.num_channels_latents = 128
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.default_sample_size = 64
+
+    def format_input(
+        self,
+        txt: list[str],
+    ) -> list[list[dict]]:
+        # Remove [IMG] tokens from prompts to avoid Pixtral validation issues
+        # when truncation is enabled. The processor counts [IMG] tokens and fails
+        # if the count changes after truncation.
+        cleaned_txt = [prompt.replace("[IMG]", "") for prompt in txt]
+
+        return [
+            [
+                {
+                    "role": "system",
+                    "content": [{"type": "text", "text": SYSTEM_MESSAGE}],
+                },
+                {"role": "user", "content": [{"type": "text", "text": prompt}]},
+            ]
+            for prompt in cleaned_txt
+        ]
+
+    def _get_mistral_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        max_sequence_length: int = 512,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        if not isinstance(prompt, list):
+            prompt = [prompt]
+
+        # Format input messages
+        messages_batch = self.format_input(txt=prompt)
+
+        # Process all messages at once
+        # with image processing a too short max length can throw an error in here.
+        try:
+            inputs = self.tokenizer.apply_chat_template(
+                messages_batch,
+                add_generation_prompt=False,
+                tokenize=True,
+                return_dict=True,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=max_sequence_length,
+            )
+        except ValueError as e:
+            print(
+                f"Error processing input: {e}, your max length is probably too short, when you have images in the input."
+            )
+            raise e
+
+        # Move to device
+        input_ids = inputs["input_ids"].to(device)
+        attention_mask = inputs["attention_mask"].to(device)
+
+        # Forward pass through the model
+        output = self.text_encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            use_cache=False,
+        )
+
+        out = torch.stack([output.hidden_states[k] for k in OUTPUT_LAYERS], dim=1)
+        prompt_embeds = rearrange(out, "b c l d -> b l (c d)")
+
+        # they don't return attention mask, so we create it here
+        return prompt_embeds, None
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 512,
+    ):
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt) if prompt_embeds is None else prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_mistral_prompt_embeds(
+                prompt, device, max_sequence_length=max_sequence_length
+            )
+
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(
+            batch_size * num_images_per_prompt, seq_len, -1
+        )
+
+        return prompt_embeds, prompt_embeds_mask
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = int(height) // self.vae_scale_factor
+        width = int(width) // self.vae_scale_factor
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: Optional[float] = None,
+        num_images_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        max_sequence_length: int = 512,
+        control_img_list: Optional[List[PIL.Image.Image]] = None,
+    ):
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        self._guidance_scale = guidance_scale
+        self._current_timestep = None
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Encode the prompt
+
+        prompt_embeds, _ = self.encode_prompt(
+            prompt=prompt,
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+        )
+
+        txt, txt_ids = batched_prc_txt(prompt_embeds)
+
+        # 4. Prepare latent variables\
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            self.num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        packed_latents, img_ids = batched_prc_img(latents)
+
+        timesteps = get_schedule(num_inference_steps, packed_latents.shape[1])
+
+        self._num_timesteps = len(timesteps)
+
+        guidance_vec = torch.full(
+            (packed_latents.shape[0],),
+            guidance_scale,
+            device=packed_latents.device,
+            dtype=packed_latents.dtype,
+        )
+
+        if control_img_list is not None and len(control_img_list) > 0:
+            img_cond_seq, img_cond_seq_ids = encode_image_refs(
+                self.vae, control_img_list
+            )
+        else:
+            img_cond_seq, img_cond_seq_ids = None, None
+
+        # 6. Denoising loop
+        i = 0
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for t_curr, t_prev in zip(timesteps[:-1], timesteps[1:]):
+                if self.interrupt:
+                    continue
+                t_vec = torch.full(
+                    (packed_latents.shape[0],),
+                    t_curr,
+                    dtype=packed_latents.dtype,
+                    device=packed_latents.device,
+                )
+
+                self._current_timestep = t_curr
+                img_input = packed_latents
+                img_input_ids = img_ids
+
+                if img_cond_seq is not None:
+                    assert img_cond_seq_ids is not None, (
+                        "You need to provide either both or neither of the sequence conditioning"
+                    )
+                    img_input = torch.cat((img_input, img_cond_seq), dim=1)
+                    img_input_ids = torch.cat((img_input_ids, img_cond_seq_ids), dim=1)
+
+                pred = self.transformer(
+                    x=img_input,
+                    x_ids=img_input_ids,
+                    timesteps=t_vec,
+                    ctx=txt,
+                    ctx_ids=txt_ids,
+                    guidance=guidance_vec,
+                )
+
+                if img_cond_seq is not None:
+                    pred = pred[:, : packed_latents.shape[1]]
+
+                packed_latents = packed_latents + (t_prev - t_curr) * pred
+                i += 1
+                progress_bar.update(1)
+
+        self._current_timestep = None
+
+        # 7. Post-processing
+        latents = torch.cat(scatter_ids(packed_latents, img_ids)).squeeze(2)
+
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = latents.to(self.vae.dtype)
+            image = self.vae.decode(latents).float()
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return Flux2ImagePipelineOutput(images=image)

extensions_built_in/diffusion_models/flux2/src/sampling.py

@@ -0,0 +1,365 @@
+import math
+from typing import Callable, Union
+
+import torch
+from einops import rearrange
+from PIL import Image
+from torch import Tensor
+
+from .model import Flux2
+import torchvision
+
+
+def compress_time(t_ids: Tensor) -> Tensor:
+    assert t_ids.ndim == 1
+    t_ids_max = torch.max(t_ids)
+    t_remap = torch.zeros((t_ids_max + 1,), device=t_ids.device, dtype=t_ids.dtype)
+    t_unique_sorted_ids = torch.unique(t_ids, sorted=True)
+    t_remap[t_unique_sorted_ids] = torch.arange(
+        len(t_unique_sorted_ids), device=t_ids.device, dtype=t_ids.dtype
+    )
+    t_ids_compressed = t_remap[t_ids]
+    return t_ids_compressed
+
+
+def scatter_ids(x: Tensor, x_ids: Tensor) -> list[Tensor]:
+    """
+    using position ids to scatter tokens into place
+    """
+    x_list = []
+    t_coords = []
+    for data, pos in zip(x, x_ids):
+        _, ch = data.shape  # noqa: F841
+        t_ids = pos[:, 0].to(torch.int64)
+        h_ids = pos[:, 1].to(torch.int64)
+        w_ids = pos[:, 2].to(torch.int64)
+
+        t_ids_cmpr = compress_time(t_ids)
+
+        t = torch.max(t_ids_cmpr) + 1
+        h = torch.max(h_ids) + 1
+        w = torch.max(w_ids) + 1
+
+        flat_ids = t_ids_cmpr * w * h + h_ids * w + w_ids
+
+        out = torch.zeros((t * h * w, ch), device=data.device, dtype=data.dtype)
+        out.scatter_(0, flat_ids.unsqueeze(1).expand(-1, ch), data)
+
+        x_list.append(rearrange(out, "(t h w) c -> 1 c t h w", t=t, h=h, w=w))
+        t_coords.append(torch.unique(t_ids, sorted=True))
+    return x_list
+
+
+def encode_image_refs(
+    ae,
+    img_ctx: Union[list[Image.Image], list[torch.Tensor]],
+    scale=10,
+    limit_pixels=1024**2,
+):
+    if not img_ctx:
+        return None, None
+
+    img_ctx_prep = default_prep(img=img_ctx, limit_pixels=limit_pixels)
+    if not isinstance(img_ctx_prep, list):
+        img_ctx_prep = [img_ctx_prep]
+
+    # Encode each reference image
+    encoded_refs = []
+    for img in img_ctx_prep:
+        if img.ndim == 3:
+            img = img.unsqueeze(0)
+        encoded = ae.encode(img.to(ae.device, ae.dtype))[0]
+        encoded_refs.append(encoded)
+
+    # Create time offsets for each reference
+    t_off = [scale + scale * t for t in torch.arange(0, len(encoded_refs))]
+    t_off = [t.view(-1) for t in t_off]
+
+    # Process with position IDs
+    ref_tokens, ref_ids = listed_prc_img(encoded_refs, t_coord=t_off)
+
+    # Concatenate all references along sequence dimension
+    ref_tokens = torch.cat(ref_tokens, dim=0)  # (total_ref_tokens, C)
+    ref_ids = torch.cat(ref_ids, dim=0)  # (total_ref_tokens, 4)
+
+    # Add batch dimension
+    ref_tokens = ref_tokens.unsqueeze(0)  # (1, total_ref_tokens, C)
+    ref_ids = ref_ids.unsqueeze(0)  # (1, total_ref_tokens, 4)
+
+    return ref_tokens.to(torch.bfloat16), ref_ids
+
+
+def prc_txt(
+    x: Tensor, t_coord: Tensor | None = None, l_coord: Tensor | None = None
+) -> tuple[Tensor, Tensor]:
+    assert l_coord is None, "l_coord not supported for txts"
+
+    _l, _ = x.shape  # noqa: F841
+
+    coords = {
+        "t": torch.arange(1) if t_coord is None else t_coord,
+        "h": torch.arange(1),  # dummy dimension
+        "w": torch.arange(1),  # dummy dimension
+        "l": torch.arange(_l),
+    }
+    x_ids = torch.cartesian_prod(coords["t"], coords["h"], coords["w"], coords["l"])
+    return x, x_ids.to(x.device)
+
+
+def batched_wrapper(fn):
+    def batched_prc(
+        x: Tensor, t_coord: Tensor | None = None, l_coord: Tensor | None = None
+    ) -> tuple[Tensor, Tensor]:
+        results = []
+        for i in range(len(x)):
+            results.append(
+                fn(
+                    x[i],
+                    t_coord[i] if t_coord is not None else None,
+                    l_coord[i] if l_coord is not None else None,
+                )
+            )
+        x, x_ids = zip(*results)
+        return torch.stack(x), torch.stack(x_ids)
+
+    return batched_prc
+
+
+def listed_wrapper(fn):
+    def listed_prc(
+        x: list[Tensor],
+        t_coord: list[Tensor] | None = None,
+        l_coord: list[Tensor] | None = None,
+    ) -> tuple[list[Tensor], list[Tensor]]:
+        results = []
+        for i in range(len(x)):
+            results.append(
+                fn(
+                    x[i],
+                    t_coord[i] if t_coord is not None else None,
+                    l_coord[i] if l_coord is not None else None,
+                )
+            )
+        x, x_ids = zip(*results)
+        return list(x), list(x_ids)
+
+    return listed_prc
+
+
+def prc_img(
+    x: Tensor, t_coord: Tensor | None = None, l_coord: Tensor | None = None
+) -> tuple[Tensor, Tensor]:
+    c, h, w = x.shape  # noqa: F841
+    x_coords = {
+        "t": torch.arange(1) if t_coord is None else t_coord,
+        "h": torch.arange(h),
+        "w": torch.arange(w),
+        "l": torch.arange(1) if l_coord is None else l_coord,
+    }
+    x_ids = torch.cartesian_prod(
+        x_coords["t"], x_coords["h"], x_coords["w"], x_coords["l"]
+    )
+    x = rearrange(x, "c h w -> (h w) c")
+    return x, x_ids.to(x.device)
+
+
+listed_prc_img = listed_wrapper(prc_img)
+batched_prc_img = batched_wrapper(prc_img)
+batched_prc_txt = batched_wrapper(prc_txt)
+
+
+def center_crop_to_multiple_of_x(
+    img: Image.Image | list[Image.Image] | torch.Tensor | list[torch.Tensor], x: int
+) -> Image.Image | list[Image.Image] | torch.Tensor | list[torch.Tensor]:
+    if isinstance(img, list):
+        return [center_crop_to_multiple_of_x(_img, x) for _img in img]  # type: ignore
+
+    if isinstance(img, torch.Tensor):
+        h, w = img.shape[-2], img.shape[-1]
+    else:
+        w, h = img.size
+    new_w = (w // x) * x
+    new_h = (h // x) * x
+
+    left = (w - new_w) // 2
+    top = (h - new_h) // 2
+    right = left + new_w
+    bottom = top + new_h
+
+    if isinstance(img, torch.Tensor):
+        return img[..., top:bottom, left:right]
+    resized = img.crop((left, top, right, bottom))
+    return resized
+
+
+def cap_pixels(
+    img: Image.Image | list[Image.Image] | torch.Tensor | list[torch.Tensor], k
+):
+    if isinstance(img, list):
+        return [cap_pixels(_img, k) for _img in img]
+    if isinstance(img, torch.Tensor):
+        h, w = img.shape[-2], img.shape[-1]
+    else:
+        w, h = img.size
+    pixel_count = w * h
+
+    if pixel_count <= k:
+        return img
+
+    # Scaling factor to reduce total pixels below K
+    scale = math.sqrt(k / pixel_count)
+    new_w = int(w * scale)
+    new_h = int(h * scale)
+
+    if isinstance(img, torch.Tensor):
+        did_expand = False
+        if img.ndim == 3:
+            img = img.unsqueeze(0)
+            did_expand = True
+        img = torch.nn.functional.interpolate(
+            img,
+            size=(new_h, new_w),
+            mode="bicubic",
+            align_corners=False,
+        )
+        if did_expand:
+            img = img.squeeze(0)
+        return img
+    return img.resize((new_w, new_h), Image.Resampling.LANCZOS)
+
+
+def cap_min_pixels(
+    img: Image.Image | list[Image.Image] | torch.Tensor | list[torch.Tensor],
+    max_ar=8,
+    min_sidelength=64,
+):
+    if isinstance(img, list):
+        return [
+            cap_min_pixels(_img, max_ar=max_ar, min_sidelength=min_sidelength)
+            for _img in img
+        ]
+    if isinstance(img, torch.Tensor):
+        h, w = img.shape[-2], img.shape[-1]
+    else:
+        w, h = img.size
+    if w < min_sidelength or h < min_sidelength:
+        raise ValueError(
+            f"Skipping due to minimal sidelength underschritten h {h} w {w}"
+        )
+    if w / h > max_ar or h / w > max_ar:
+        raise ValueError(f"Skipping due to maximal ar overschritten h {h} w {w}")
+    return img
+
+
+def to_rgb(
+    img: Image.Image | list[Image.Image] | torch.Tensor | list[torch.Tensor],
+) -> Image.Image | list[Image.Image] | torch.Tensor | list[torch.Tensor]:
+    if isinstance(img, list):
+        return [
+            to_rgb(
+                _img,
+            )
+            for _img in img
+        ]
+    if isinstance(img, torch.Tensor):
+        return img  # assume already in tensor format
+    return img.convert("RGB")
+
+
+def default_images_prep(
+    x: Image.Image | list[Image.Image] | torch.Tensor | list[torch.Tensor],
+) -> torch.Tensor | list[torch.Tensor]:
+    if isinstance(x, list):
+        return [default_images_prep(e) for e in x]  # type: ignore
+    if isinstance(x, torch.Tensor):
+        return x  # assume already in tensor format
+    x_tensor = torchvision.transforms.ToTensor()(x)
+    return 2 * x_tensor - 1
+
+
+def default_prep(
+    img: Image.Image | list[Image.Image] | torch.Tensor | list[torch.Tensor],
+    limit_pixels: int,
+    ensure_multiple: int = 16,
+) -> torch.Tensor | list[torch.Tensor]:
+    # if passing a tensor, assume it is -1 to 1 already
+    img_rgb = to_rgb(img)
+    img_min = cap_min_pixels(img_rgb)  # type: ignore
+    img_cap = cap_pixels(img_min, limit_pixels)  # type: ignore
+    img_crop = center_crop_to_multiple_of_x(img_cap, ensure_multiple)  # type: ignore
+    img_tensor = default_images_prep(img_crop)
+    return img_tensor
+
+
+def time_shift(mu: float, sigma: float, t: Tensor):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def get_lin_function(
+    x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15
+) -> Callable[[float], float]:
+    m = (y2 - y1) / (x2 - x1)
+    b = y1 - m * x1
+    return lambda x: m * x + b
+
+
+def get_schedule(
+    num_steps: int,
+    image_seq_len: int,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+    shift: bool = True,
+) -> list[float]:
+    # extra step for zero
+    timesteps = torch.linspace(1, 0, num_steps + 1)
+
+    # shifting the schedule to favor high timesteps for higher signal images
+    if shift:
+        # estimate mu based on linear estimation between two points
+        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
+        timesteps = time_shift(mu, 1.0, timesteps)
+
+    return timesteps.tolist()
+
+
+def denoise(
+    model: Flux2,
+    # model input
+    img: Tensor,
+    img_ids: Tensor,
+    txt: Tensor,
+    txt_ids: Tensor,
+    # sampling parameters
+    timesteps: list[float],
+    guidance: float,
+    # extra img tokens (sequence-wise)
+    img_cond_seq: Tensor | None = None,
+    img_cond_seq_ids: Tensor | None = None,
+):
+    guidance_vec = torch.full(
+        (img.shape[0],), guidance, device=img.device, dtype=img.dtype
+    )
+    for t_curr, t_prev in zip(timesteps[:-1], timesteps[1:]):
+        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
+        img_input = img
+        img_input_ids = img_ids
+        if img_cond_seq is not None:
+            assert img_cond_seq_ids is not None, (
+                "You need to provide either both or neither of the sequence conditioning"
+            )
+            img_input = torch.cat((img_input, img_cond_seq), dim=1)
+            img_input_ids = torch.cat((img_input_ids, img_cond_seq_ids), dim=1)
+        pred = model(
+            x=img_input,
+            x_ids=img_input_ids,
+            timesteps=t_vec,
+            ctx=txt,
+            ctx_ids=txt_ids,
+            guidance=guidance_vec,
+        )
+        if img_input_ids is not None:
+            pred = pred[:, : img.shape[1]]
+
+        img = img + (t_prev - t_curr) * pred
+
+    return img