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jit.py

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+# --------------------------------------------------------
+# Adapted from JiT: https://github.com/LTH14/JiT/blob/main/model_jit.py
+# References: SiT, Lightning-DiT (see upstream repo)
+#
+# Unconditional variant: no class labels; conditioning is time t only.
+# In-context tokens use learnable positional embeddings only (no label embedding).
+# --------------------------------------------------------
+from __future__ import annotations
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    from .jit_model_util import RMSNorm, VisionRotaryEmbeddingFast, get_2d_sincos_pos_embed
+except ImportError:
+    from jit_model_util import RMSNorm, VisionRotaryEmbeddingFast, get_2d_sincos_pos_embed
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+class BottleneckPatchEmbed(nn.Module):
+    """Image to patch embedding."""
+
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        pca_dim=768,
+        embed_dim=768,
+        bias=True,
+    ):
+        super().__init__()
+        img_size = (img_size, img_size)
+        patch_size = (patch_size, patch_size)
+        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj1 = nn.Conv2d(in_chans, pca_dim, kernel_size=patch_size, stride=patch_size, bias=False)
+        self.proj2 = nn.Conv2d(pca_dim, embed_dim, kernel_size=1, stride=1, bias=bias)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        assert h == self.img_size[0] and w == self.img_size[1], (
+            f"Input image size ({h}*{w}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        )
+
+        x = self.proj2(self.proj1(x)).flatten(2).transpose(1, 2)
+        return x
+
+
+class TimestepEmbedder(nn.Module):
+    """Embeds scalar timesteps into vector representations."""
+
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / 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)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        return self.mlp(t_freq)
+
+
+def scaled_dot_product_attention(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    dropout_p: float = 0.0,
+    training: bool = True,
+) -> torch.Tensor:
+    scale_factor = 1 / math.sqrt(query.size(-1))
+    with torch.cuda.amp.autocast(enabled=False):
+        attn_weight = query.float() @ key.float().transpose(-2, -1) * scale_factor
+    attn_weight = torch.softmax(attn_weight, dim=-1)
+    attn_weight = torch.dropout(attn_weight, dropout_p, train=training)
+    return attn_weight @ value
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=True, qk_norm=True, attn_drop=0.0, proj_drop=0.0):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.q_norm = RMSNorm(head_dim) if qk_norm else nn.Identity()
+        self.k_norm = RMSNorm(head_dim) if qk_norm else nn.Identity()
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, rope):
+        b, n, c = x.shape
+        qkv = self.qkv(x).reshape(b, n, 3, self.num_heads, c // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        q = rope(q)
+        k = rope(k)
+
+        x = scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            dropout_p=self.attn_drop.p if self.training else 0.0,
+            training=self.training,
+        )
+
+        x = x.transpose(1, 2).reshape(b, n, c)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(self, dim: int, hidden_dim: int, drop=0.0, bias=True) -> None:
+        super().__init__()
+        hidden_dim = int(hidden_dim * 2 / 3)
+        self.w12 = nn.Linear(dim, 2 * hidden_dim, bias=bias)
+        self.w3 = nn.Linear(hidden_dim, dim, bias=bias)
+        self.ffn_dropout = nn.Dropout(drop)
+
+    def forward(self, x):
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(self.ffn_dropout(hidden))
+
+
+class FinalLayer(nn.Module):
+    """The final layer of JiT."""
+
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = RMSNorm(hidden_size)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        return self.linear(x)
+
+
+class JiTBlock(nn.Module):
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, attn_drop=0.0, proj_drop=0.0):
+        super().__init__()
+        self.norm1 = RMSNorm(hidden_size, eps=1e-6)
+        self.attn = Attention(
+            hidden_size,
+            num_heads=num_heads,
+            qkv_bias=True,
+            qk_norm=True,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+        )
+        self.norm2 = RMSNorm(hidden_size, eps=1e-6)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.mlp = SwiGLUFFN(hidden_size, mlp_hidden_dim, drop=proj_drop)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+
+    def forward(self, x, c, feat_rope=None):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1)
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            modulate(self.norm1(x), shift_msa, scale_msa), rope=feat_rope
+        )
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
+        return x
+
+
+class JiT(nn.Module):
+    """
+    Just image Transformer — unconditional (time embedding only).
+    """
+
+    def __init__(
+        self,
+        input_size=256,
+        patch_size=16,
+        in_channels=3,
+        hidden_size=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4.0,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        bottleneck_dim=128,
+        in_context_len=32,
+        in_context_start=8,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.input_size = input_size
+        self.in_context_len = in_context_len
+        self.in_context_start = in_context_start
+
+        self.t_embedder = TimestepEmbedder(hidden_size)
+
+        self.x_embedder = BottleneckPatchEmbed(
+            input_size, patch_size, in_channels, bottleneck_dim, hidden_size, bias=True
+        )
+
+        num_patches = self.x_embedder.num_patches
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
+
+        if self.in_context_len > 0:
+            self.in_context_posemb = nn.Parameter(torch.zeros(1, self.in_context_len, hidden_size))
+            torch.nn.init.normal_(self.in_context_posemb, std=0.02)
+
+        half_head_dim = hidden_size // num_heads // 2
+        hw_seq_len = input_size // patch_size
+        self.feat_rope = VisionRotaryEmbeddingFast(
+            dim=half_head_dim, pt_seq_len=hw_seq_len, num_cls_token=0
+        )
+        self.feat_rope_incontext = VisionRotaryEmbeddingFast(
+            dim=half_head_dim, pt_seq_len=hw_seq_len, num_cls_token=self.in_context_len
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                JiTBlock(
+                    hidden_size,
+                    num_heads,
+                    mlp_ratio=mlp_ratio,
+                    attn_drop=attn_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0,
+                    proj_drop=proj_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches**0.5))
+        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        w1 = self.x_embedder.proj1.weight.data
+        nn.init.xavier_uniform_(w1.view([w1.shape[0], -1]))
+        w2 = self.x_embedder.proj2.weight.data
+        nn.init.xavier_uniform_(w2.view([w2.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj2.bias, 0)
+
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        for block in self.blocks:
+            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
+
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def unpatchify(self, x, p):
+        c = self.out_channels
+        h = w = int(x.shape[1] ** 0.5)
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum("nhwpqc->nchpwq", x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
+        return imgs
+
+    def forward(self, x: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: (N, C, H, W)
+            t: (N,) timesteps in [0, 1] (or arbitrary floats, as in upstream)
+        Returns:
+            (N, C, H, W) predicted velocity / noise depending on training objective
+        """
+        c_emb = self.t_embedder(t)
+
+        x = self.x_embedder(x)
+        x = x + self.pos_embed
+
+        for i, block in enumerate(self.blocks):
+            if self.in_context_len > 0 and i == self.in_context_start:
+                b = x.shape[0]
+                in_context_tokens = self.in_context_posemb.expand(b, self.in_context_len, -1)
+                x = torch.cat([in_context_tokens, x], dim=1)
+            x = block(x, c_emb, self.feat_rope if i < self.in_context_start else self.feat_rope_incontext)
+
+        x = x[:, self.in_context_len :]
+
+        x = self.final_layer(x, c_emb)
+        return self.unpatchify(x, self.patch_size)
+
+
+def JiT_B_16(**kwargs):
+    return JiT(
+        depth=12,
+        hidden_size=768,
+        num_heads=12,
+        bottleneck_dim=128,
+        in_context_len=32,
+        in_context_start=4,
+        patch_size=16,
+        **kwargs,
+    )
+
+
+def JiT_B_32(**kwargs):
+    return JiT(
+        depth=12,
+        hidden_size=768,
+        num_heads=12,
+        bottleneck_dim=128,
+        in_context_len=32,
+        in_context_start=4,
+        patch_size=32,
+        **kwargs,
+    )
+
+
+def JiT_L_16(**kwargs):
+    return JiT(
+        depth=24,
+        hidden_size=1024,
+        num_heads=16,
+        bottleneck_dim=128,
+        in_context_len=32,
+        in_context_start=8,
+        patch_size=16,
+        **kwargs,
+    )
+
+
+def JiT_L_32(**kwargs):
+    return JiT(
+        depth=24,
+        hidden_size=1024,
+        num_heads=16,
+        bottleneck_dim=128,
+        in_context_len=32,
+        in_context_start=8,
+        patch_size=32,
+        **kwargs,
+    )
+
+
+def JiT_H_16(**kwargs):
+    return JiT(
+        depth=32,
+        hidden_size=1280,
+        num_heads=16,
+        bottleneck_dim=256,
+        in_context_len=32,
+        in_context_start=10,
+        patch_size=16,
+        **kwargs,
+    )
+
+
+def JiT_H_32(**kwargs):
+    return JiT(
+        depth=32,
+        hidden_size=1280,
+        num_heads=16,
+        bottleneck_dim=256,
+        in_context_len=32,
+        in_context_start=10,
+        patch_size=32,
+        **kwargs,
+    )
+
+
+JiT_models = {
+    "JiT-B/16": JiT_B_16,
+    "JiT-B/32": JiT_B_32,
+    "JiT-L/16": JiT_L_16,
+    "JiT-L/32": JiT_L_32,
+    "JiT-H/16": JiT_H_16,
+    "JiT-H/32": JiT_H_32,
+}