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