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"""k-diffusion transformer diffusion models, version 2.
Codes adopted from https://github.com/crowsonkb/k-diffusion
"""
import math
import torch
import torch._dynamo
from torch import nn
from . import flags
if flags.get_use_compile():
torch._dynamo.config.suppress_errors = True
def rotate_half(x):
x1, x2 = x[..., 0::2], x[..., 1::2]
x = torch.stack((-x2, x1), dim=-1)
*shape, d, r = x.shape
return x.view(*shape, d * r)
@flags.compile_wrap
def apply_rotary_emb(freqs, t, start_index=0, scale=1.0):
freqs = freqs.to(t)
rot_dim = freqs.shape[-1]
end_index = start_index + rot_dim
assert rot_dim <= t.shape[-1], f"feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}"
t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:]
t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
return torch.cat((t_left, t, t_right), dim=-1)
def centers(start, stop, num, dtype=None, device=None):
edges = torch.linspace(start, stop, num + 1, dtype=dtype, device=device)
return (edges[:-1] + edges[1:]) / 2
def make_grid(h_pos, w_pos):
grid = torch.stack(torch.meshgrid(h_pos, w_pos, indexing='ij'), dim=-1)
h, w, d = grid.shape
return grid.view(h * w, d)
def bounding_box(h, w, pixel_aspect_ratio=1.0):
# Adjusted dimensions
w_adj = w
h_adj = h * pixel_aspect_ratio
# Adjusted aspect ratio
ar_adj = w_adj / h_adj
# Determine bounding box based on the adjusted aspect ratio
y_min, y_max, x_min, x_max = -1.0, 1.0, -1.0, 1.0
if ar_adj > 1:
y_min, y_max = -1 / ar_adj, 1 / ar_adj
elif ar_adj < 1:
x_min, x_max = -ar_adj, ar_adj
return y_min, y_max, x_min, x_max
def make_axial_pos(h, w, pixel_aspect_ratio=1.0, align_corners=False, dtype=None, device=None):
y_min, y_max, x_min, x_max = bounding_box(h, w, pixel_aspect_ratio)
if align_corners:
h_pos = torch.linspace(y_min, y_max, h, dtype=dtype, device=device)
w_pos = torch.linspace(x_min, x_max, w, dtype=dtype, device=device)
else:
h_pos = centers(y_min, y_max, h, dtype=dtype, device=device)
w_pos = centers(x_min, x_max, w, dtype=dtype, device=device)
return make_grid(h_pos, w_pos)
def freqs_pixel(max_freq=10.0):
def init(shape):
freqs = torch.linspace(1.0, max_freq / 2, shape[-1]) * math.pi
return freqs.log().expand(shape)
return init
def freqs_pixel_log(max_freq=10.0):
def init(shape):
log_min = math.log(math.pi)
log_max = math.log(max_freq * math.pi / 2)
return torch.linspace(log_min, log_max, shape[-1]).expand(shape)
return init
class AxialRoPE(nn.Module):
def __init__(self, dim, n_heads, start_index=0, freqs_init=freqs_pixel_log(max_freq=10.0)):
super().__init__()
self.n_heads = n_heads
self.start_index = start_index
log_freqs = freqs_init((n_heads, dim // 4))
self.freqs_h = nn.Parameter(log_freqs.clone())
self.freqs_w = nn.Parameter(log_freqs.clone())
def extra_repr(self):
dim = (self.freqs_h.shape[-1] + self.freqs_w.shape[-1]) * 2
return f"dim={dim}, n_heads={self.n_heads}, start_index={self.start_index}"
def get_freqs(self, pos):
if pos.shape[-1] != 2:
raise ValueError("input shape must be (..., 2)")
freqs_h = pos[..., None, None, 0] * self.freqs_h.exp()
freqs_w = pos[..., None, None, 1] * self.freqs_w.exp()
freqs = torch.cat((freqs_h, freqs_w), dim=-1).repeat_interleave(2, dim=-1)
return freqs.transpose(-2, -3)
def forward(self, x, pos):
freqs = self.get_freqs(pos)
return apply_rotary_emb(freqs, x, self.start_index) |