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import torch
import torch.nn as nn
from jaxtyping import Float
from einops import rearrange, repeat
from jutils.nn.transformer import TimestepEmbedder
from jutils.nn.rope import make_axial_pos_2d, AxialRoPEBase
from jutils.nn.transformer import TransformerLayer, TokenMerge2D, TokenSplitLast2D
def make_axial_pos_2d_with_meta(meta, size, device="cpu", latent_ds_factor=8):
"""
Args:
meta: dict with keys 'top', 'left', 'orig_h', 'orig_w'
size: int, size of the square patch
device: device to create the tensor on
"""
top, left = meta["top"], meta["left"]
orig_h, orig_w = meta["orig_h"], meta["orig_w"]
# convert to latent space size
top = math.floor(top / latent_ds_factor)
left = math.floor(left / latent_ds_factor)
orig_h = math.floor(orig_h / latent_ds_factor)
orig_w = math.floor(orig_w / latent_ds_factor)
pos = make_axial_pos_2d(orig_h, orig_w, device=device, align_corners=False, relative_pos=True)
pos = rearrange(pos, "(h w) d -> h w d", h=orig_h, w=orig_w)
pos = pos[top : top + size, left : left + size, :]
return pos
class AxialRoPETime(AxialRoPEBase):
"""
Simple 1D RoPE for text/time-like token positions.
Uses fixed frequencies (non-learnable), matching standard text RoPE behavior.
"""
def __init__(
self,
dim: int,
n_heads: int,
learnable_freqs: bool = False,
relative_canvas: bool = True, # kept for API compatibility
in_place: bool = False,
half_embedding: bool = True,
):
if half_embedding:
assert dim % 2 == 0, "Half embedding is only supported for even dimensions"
dim //= 2
super().__init__(dim, n_heads, in_place=in_place)
# Best default for text: fixed frequencies, no learned RoPE params.
min_freq, max_freq = 1 / 10_000, 1.0
log_min = math.log(min_freq)
log_max = math.log(max_freq)
freqs = torch.linspace(log_min, log_max, n_heads * dim // 2 + 1)[:-1].exp()
self.freqs = nn.Parameter(
freqs.view(dim // 2, n_heads).T.contiguous(),
requires_grad=False,
)
def forward(self, pos):
if pos.shape[-1:] == (1,):
pos = pos[..., 0]
return pos[..., None, None] * self.freqs.to(pos.dtype)
class DiTT2I(nn.Module):
def __init__(
self,
in_dim: int = 4,
depth: int = 28,
hidden_dim: int = 1152,
head_dim: int = 72,
mapping_dim: int = 384,
mapping_depth: int = 2,
patch_size: int = 2,
txt_in_dim: int = 2048,
txt_refiner_dim: int = 1536,
txt_refiner_head_dim: int = 128,
txt_refiner_depth: int = 2,
compile: bool = False,
):
super().__init__()
self.in_dim = in_dim
self.depth = depth
self.head_dim = head_dim
self.hidden_dim = hidden_dim
self.mapping_dim = mapping_dim
self.mapping_depth = mapping_depth
self.patch_size = patch_size
# timestep embedding
self.t_embedder = TimestepEmbedder(mapping_dim, mapping_depth, dim_mlp=3 * mapping_dim)
# model
self.merge = TokenMerge2D(in_dim, hidden_dim, patch_size)
self.blocks = nn.ModuleList(
[
TransformerLayer(
d_model=hidden_dim,
d_head=head_dim,
d_cond_norm=mapping_dim,
d_cross=txt_refiner_dim, # cross attend to refined txt embs
ff_expand=3,
rope_cls="jutils.nn.rope.AxialRoPE2D",
compile=compile,
)
for _ in range(depth)
]
)
# predict uncertainty per patch, so we have an additional out dim
self.split = TokenSplitLast2D(hidden_dim, in_dim, patch_size)
# text embedding refiner
self.txt_proj = nn.Linear(txt_in_dim, txt_refiner_dim)
self.txt_refiner = nn.ModuleList(
[
TransformerLayer(
d_model=txt_refiner_dim,
d_head=txt_refiner_head_dim,
ff_expand=3,
rope_cls="patch_flow.models.pf_transformer_t2i.AxialRoPETime",
compile=compile,
)
for _ in range(txt_refiner_depth)
]
)
def forward(
self,
x: Float[torch.Tensor, "b c h w"],
t: Float[torch.Tensor, "b n"],
txt_emb: Float[torch.Tensor, "b n d"],
img_meta: dict = None,
):
b, c, h, w = x.shape
# preprocess text with small refiner stack
txt_emb = self.txt_proj(txt_emb)
pos_txt = torch.arange(txt_emb.shape[1], device=txt_emb.device)
pos_txt = repeat(pos_txt, "n -> b n 1", b=txt_emb.shape[0]) # (b, n, 1)
for block in self.txt_refiner:
txt_emb = block(txt_emb, pos=pos_txt)
# timestep conditioning
t = t[..., None] # (b,) -> (b, n, 1)
t_emb = self.t_embedder(t) # (b, n, c)
# positional embeddings
if img_meta is None:
pos = make_axial_pos_2d(h, w, device=x.device)
pos = repeat(pos, "(h w) d -> b h w d", b=b, h=h, w=w)
else:
pos = torch.stack([make_axial_pos_2d_with_meta(m, size=h, device=x.device) for m in img_meta], dim=0)
x = rearrange(x, "b c h w -> b h w c")
x, pos = self.merge(x, pos)
nh, nw, _ = x.shape[1:]
x = rearrange(x, "b h w c -> b (h w) c")
pos = rearrange(pos, "b h w d -> b (h w) d")
assert x.shape[1] == pos.shape[1], f"x: {x.shape}, pos: {pos.shape}"
# model
for block in self.blocks:
x = block(x, pos=pos, cond_norm=t_emb, x_cross=txt_emb)
x = rearrange(x, "b (h w) c -> b h w c", h=nh, w=nw)
# final layer
x = self.split(x)
# switch back to channel first
x = rearrange(x, "b h w c -> b c h w")
return x
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