|
|
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import math |
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from typing import Any, Callable, Optional |
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|
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import numpy as np |
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import torch |
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import torch.nn as nn |
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from einops import rearrange |
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from packaging import version |
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|
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try: |
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import xformers |
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import xformers.ops |
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|
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XFORMERS_IS_AVAILABLE = True |
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except: |
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XFORMERS_IS_AVAILABLE = False |
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print("no module 'xformers'. Processing without...") |
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|
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from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention |
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|
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def get_timestep_embedding(timesteps, embedding_dim): |
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""" |
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This matches the implementation in Denoising Diffusion Probabilistic Models: |
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From Fairseq. |
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Build sinusoidal embeddings. |
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This matches the implementation in tensor2tensor, but differs slightly |
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from the description in Section 3.5 of "Attention Is All You Need". |
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""" |
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assert len(timesteps.shape) == 1 |
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|
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half_dim = embedding_dim // 2 |
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emb = math.log(10000) / (half_dim - 1) |
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emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) |
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emb = emb.to(device=timesteps.device) |
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emb = timesteps.float()[:, None] * emb[None, :] |
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) |
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if embedding_dim % 2 == 1: |
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emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) |
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return emb |
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|
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def nonlinearity(x): |
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|
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return x * torch.sigmoid(x) |
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|
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def Normalize(in_channels, num_groups=32): |
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return torch.nn.GroupNorm( |
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num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True |
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) |
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|
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class Upsample(nn.Module): |
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def __init__(self, in_channels, with_conv): |
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super().__init__() |
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self.with_conv = with_conv |
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if self.with_conv: |
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self.conv = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=3, stride=1, padding=1 |
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) |
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|
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def forward(self, x): |
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x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") |
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if self.with_conv: |
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x = self.conv(x) |
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return x |
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|
|
|
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class Downsample(nn.Module): |
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def __init__(self, in_channels, with_conv): |
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super().__init__() |
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self.with_conv = with_conv |
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if self.with_conv: |
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|
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self.conv = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=3, stride=2, padding=0 |
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) |
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|
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def forward(self, x): |
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if self.with_conv: |
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pad = (0, 1, 0, 1) |
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x = torch.nn.functional.pad(x, pad, mode="constant", value=0) |
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x = self.conv(x) |
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else: |
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x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) |
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return x |
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|
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class ResnetBlock(nn.Module): |
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def __init__( |
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self, |
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*, |
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in_channels, |
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out_channels=None, |
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conv_shortcut=False, |
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dropout, |
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temb_channels=512, |
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): |
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super().__init__() |
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self.in_channels = in_channels |
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out_channels = in_channels if out_channels is None else out_channels |
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self.out_channels = out_channels |
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self.use_conv_shortcut = conv_shortcut |
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|
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self.norm1 = Normalize(in_channels) |
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self.conv1 = torch.nn.Conv2d( |
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in_channels, out_channels, kernel_size=3, stride=1, padding=1 |
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) |
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if temb_channels > 0: |
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self.temb_proj = torch.nn.Linear(temb_channels, out_channels) |
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self.norm2 = Normalize(out_channels) |
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self.dropout = torch.nn.Dropout(dropout) |
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self.conv2 = torch.nn.Conv2d( |
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out_channels, out_channels, kernel_size=3, stride=1, padding=1 |
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) |
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if self.in_channels != self.out_channels: |
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if self.use_conv_shortcut: |
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self.conv_shortcut = torch.nn.Conv2d( |
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in_channels, out_channels, kernel_size=3, stride=1, padding=1 |
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) |
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else: |
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self.nin_shortcut = torch.nn.Conv2d( |
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in_channels, out_channels, kernel_size=1, stride=1, padding=0 |
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) |
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|
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def forward(self, x, temb): |
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h = x |
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h = self.norm1(h) |
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h = nonlinearity(h) |
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h = self.conv1(h) |
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|
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if temb is not None: |
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h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] |
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|
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h = self.norm2(h) |
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h = nonlinearity(h) |
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h = self.dropout(h) |
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h = self.conv2(h) |
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|
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if self.in_channels != self.out_channels: |
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if self.use_conv_shortcut: |
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x = self.conv_shortcut(x) |
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else: |
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x = self.nin_shortcut(x) |
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|
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return x + h |
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|
|
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class LinAttnBlock(LinearAttention): |
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"""to match AttnBlock usage""" |
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|
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def __init__(self, in_channels): |
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super().__init__(dim=in_channels, heads=1, dim_head=in_channels) |
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|
|
|
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class AttnBlock(nn.Module): |
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def __init__(self, in_channels): |
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super().__init__() |
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self.in_channels = in_channels |
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|
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self.norm = Normalize(in_channels) |
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self.q = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=1, stride=1, padding=0 |
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) |
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self.k = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=1, stride=1, padding=0 |
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) |
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self.v = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=1, stride=1, padding=0 |
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) |
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self.proj_out = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=1, stride=1, padding=0 |
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) |
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|
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def attention(self, h_: torch.Tensor) -> torch.Tensor: |
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h_ = self.norm(h_) |
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q = self.q(h_) |
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k = self.k(h_) |
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v = self.v(h_) |
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|
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b, c, h, w = q.shape |
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q, k, v = map( |
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lambda x: rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v) |
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) |
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h_ = torch.nn.functional.scaled_dot_product_attention( |
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q, k, v |
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) |
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|
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return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) |
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|
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def forward(self, x, **kwargs): |
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h_ = x |
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h_ = self.attention(h_) |
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h_ = self.proj_out(h_) |
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return x + h_ |
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|
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class MemoryEfficientAttnBlock(nn.Module): |
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""" |
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Uses xformers efficient implementation, |
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see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 |
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Note: this is a single-head self-attention operation |
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""" |
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|
|
|
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def __init__(self, in_channels): |
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super().__init__() |
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self.in_channels = in_channels |
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|
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self.norm = Normalize(in_channels) |
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self.q = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=1, stride=1, padding=0 |
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) |
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self.k = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=1, stride=1, padding=0 |
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) |
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self.v = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=1, stride=1, padding=0 |
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) |
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self.proj_out = torch.nn.Conv2d( |
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in_channels, in_channels, kernel_size=1, stride=1, padding=0 |
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) |
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self.attention_op: Optional[Any] = None |
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|
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def attention(self, h_: torch.Tensor) -> torch.Tensor: |
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h_ = self.norm(h_) |
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q = self.q(h_) |
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k = self.k(h_) |
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v = self.v(h_) |
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|
|
|
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B, C, H, W = q.shape |
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q, k, v = map(lambda x: rearrange(x, "b c h w -> b (h w) c"), (q, k, v)) |
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|
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q, k, v = map( |
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lambda t: t.unsqueeze(3) |
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.reshape(B, t.shape[1], 1, C) |
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.permute(0, 2, 1, 3) |
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.reshape(B * 1, t.shape[1], C) |
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.contiguous(), |
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(q, k, v), |
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) |
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out = xformers.ops.memory_efficient_attention( |
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q, k, v, attn_bias=None, op=self.attention_op |
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) |
|
|
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out = ( |
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out.unsqueeze(0) |
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.reshape(B, 1, out.shape[1], C) |
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.permute(0, 2, 1, 3) |
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.reshape(B, out.shape[1], C) |
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) |
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return rearrange(out, "b (h w) c -> b c h w", b=B, h=H, w=W, c=C) |
|
|
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def forward(self, x, **kwargs): |
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h_ = x |
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h_ = self.attention(h_) |
|
h_ = self.proj_out(h_) |
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return x + h_ |
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|
|
|
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class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): |
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def forward(self, x, context=None, mask=None, **unused_kwargs): |
|
b, c, h, w = x.shape |
|
x = rearrange(x, "b c h w -> b (h w) c") |
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out = super().forward(x, context=context, mask=mask) |
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out = rearrange(out, "b (h w) c -> b c h w", h=h, w=w, c=c) |
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return x + out |
|
|
|
|
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def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): |
|
assert attn_type in [ |
|
"vanilla", |
|
"vanilla-xformers", |
|
"memory-efficient-cross-attn", |
|
"linear", |
|
"none", |
|
], f"attn_type {attn_type} unknown" |
|
if ( |
|
version.parse(torch.__version__) < version.parse("2.0.0") |
|
and attn_type != "none" |
|
): |
|
assert XFORMERS_IS_AVAILABLE, ( |
|
f"We do not support vanilla attention in {torch.__version__} anymore, " |
|
f"as it is too expensive. Please install xformers via e.g. 'pip install xformers==0.0.16'" |
|
) |
|
attn_type = "vanilla-xformers" |
|
print(f"making attention of type '{attn_type}' with {in_channels} in_channels") |
|
if attn_type == "vanilla": |
|
assert attn_kwargs is None |
|
return AttnBlock(in_channels) |
|
elif attn_type == "vanilla-xformers": |
|
print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...") |
|
return MemoryEfficientAttnBlock(in_channels) |
|
elif type == "memory-efficient-cross-attn": |
|
attn_kwargs["query_dim"] = in_channels |
|
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs) |
|
elif attn_type == "none": |
|
return nn.Identity(in_channels) |
|
else: |
|
return LinAttnBlock(in_channels) |
|
|
|
|
|
class Model(nn.Module): |
|
def __init__( |
|
self, |
|
*, |
|
ch, |
|
out_ch, |
|
ch_mult=(1, 2, 4, 8), |
|
num_res_blocks, |
|
attn_resolutions, |
|
dropout=0.0, |
|
resamp_with_conv=True, |
|
in_channels, |
|
resolution, |
|
use_timestep=True, |
|
use_linear_attn=False, |
|
attn_type="vanilla", |
|
): |
|
super().__init__() |
|
if use_linear_attn: |
|
attn_type = "linear" |
|
self.ch = ch |
|
self.temb_ch = self.ch * 4 |
|
self.num_resolutions = len(ch_mult) |
|
self.num_res_blocks = num_res_blocks |
|
self.resolution = resolution |
|
self.in_channels = in_channels |
|
|
|
self.use_timestep = use_timestep |
|
if self.use_timestep: |
|
|
|
self.temb = nn.Module() |
|
self.temb.dense = nn.ModuleList( |
|
[ |
|
torch.nn.Linear(self.ch, self.temb_ch), |
|
torch.nn.Linear(self.temb_ch, self.temb_ch), |
|
] |
|
) |
|
|
|
|
|
self.conv_in = torch.nn.Conv2d( |
|
in_channels, self.ch, kernel_size=3, stride=1, padding=1 |
|
) |
|
|
|
curr_res = resolution |
|
in_ch_mult = (1,) + tuple(ch_mult) |
|
self.down = nn.ModuleList() |
|
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 i_block in range(self.num_res_blocks): |
|
block.append( |
|
ResnetBlock( |
|
in_channels=block_in, |
|
out_channels=block_out, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
) |
|
block_in = block_out |
|
if curr_res in attn_resolutions: |
|
attn.append(make_attn(block_in, attn_type=attn_type)) |
|
down = nn.Module() |
|
down.block = block |
|
down.attn = attn |
|
if i_level != self.num_resolutions - 1: |
|
down.downsample = Downsample(block_in, resamp_with_conv) |
|
curr_res = curr_res // 2 |
|
self.down.append(down) |
|
|
|
|
|
self.mid = nn.Module() |
|
self.mid.block_1 = ResnetBlock( |
|
in_channels=block_in, |
|
out_channels=block_in, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) |
|
self.mid.block_2 = ResnetBlock( |
|
in_channels=block_in, |
|
out_channels=block_in, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
|
|
|
|
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] |
|
skip_in = ch * ch_mult[i_level] |
|
for i_block in range(self.num_res_blocks + 1): |
|
if i_block == self.num_res_blocks: |
|
skip_in = ch * in_ch_mult[i_level] |
|
block.append( |
|
ResnetBlock( |
|
in_channels=block_in + skip_in, |
|
out_channels=block_out, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
) |
|
block_in = block_out |
|
if curr_res in attn_resolutions: |
|
attn.append(make_attn(block_in, attn_type=attn_type)) |
|
up = nn.Module() |
|
up.block = block |
|
up.attn = attn |
|
if i_level != 0: |
|
up.upsample = Upsample(block_in, resamp_with_conv) |
|
curr_res = curr_res * 2 |
|
self.up.insert(0, up) |
|
|
|
|
|
self.norm_out = Normalize(block_in) |
|
self.conv_out = torch.nn.Conv2d( |
|
block_in, out_ch, kernel_size=3, stride=1, padding=1 |
|
) |
|
|
|
def forward(self, x, t=None, context=None): |
|
|
|
if context is not None: |
|
|
|
x = torch.cat((x, context), dim=1) |
|
if self.use_timestep: |
|
|
|
assert t is not None |
|
temb = get_timestep_embedding(t, self.ch) |
|
temb = self.temb.dense[0](temb) |
|
temb = nonlinearity(temb) |
|
temb = self.temb.dense[1](temb) |
|
else: |
|
temb = None |
|
|
|
|
|
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], temb) |
|
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])) |
|
|
|
|
|
h = hs[-1] |
|
h = self.mid.block_1(h, temb) |
|
h = self.mid.attn_1(h) |
|
h = self.mid.block_2(h, temb) |
|
|
|
|
|
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]( |
|
torch.cat([h, hs.pop()], dim=1), temb |
|
) |
|
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) |
|
|
|
|
|
h = self.norm_out(h) |
|
h = nonlinearity(h) |
|
h = self.conv_out(h) |
|
return h |
|
|
|
def get_last_layer(self): |
|
return self.conv_out.weight |
|
|
|
|
|
class Encoder(nn.Module): |
|
def __init__( |
|
self, |
|
*, |
|
ch, |
|
out_ch, |
|
ch_mult=(1, 2, 4, 8), |
|
num_res_blocks, |
|
attn_resolutions, |
|
dropout=0.0, |
|
resamp_with_conv=True, |
|
in_channels, |
|
resolution, |
|
z_channels, |
|
double_z=True, |
|
use_linear_attn=False, |
|
attn_type="vanilla", |
|
**ignore_kwargs, |
|
): |
|
super().__init__() |
|
if use_linear_attn: |
|
attn_type = "linear" |
|
self.ch = ch |
|
self.temb_ch = 0 |
|
self.num_resolutions = len(ch_mult) |
|
self.num_res_blocks = num_res_blocks |
|
self.resolution = resolution |
|
self.in_channels = in_channels |
|
|
|
|
|
self.conv_in = torch.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() |
|
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 i_block in range(self.num_res_blocks): |
|
block.append( |
|
ResnetBlock( |
|
in_channels=block_in, |
|
out_channels=block_out, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
) |
|
block_in = block_out |
|
if curr_res in attn_resolutions: |
|
attn.append(make_attn(block_in, attn_type=attn_type)) |
|
down = nn.Module() |
|
down.block = block |
|
down.attn = attn |
|
if i_level != self.num_resolutions - 1: |
|
down.downsample = Downsample(block_in, resamp_with_conv) |
|
curr_res = curr_res // 2 |
|
self.down.append(down) |
|
|
|
|
|
self.mid = nn.Module() |
|
self.mid.block_1 = ResnetBlock( |
|
in_channels=block_in, |
|
out_channels=block_in, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) |
|
self.mid.block_2 = ResnetBlock( |
|
in_channels=block_in, |
|
out_channels=block_in, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
|
|
|
|
self.norm_out = Normalize(block_in) |
|
self.conv_out = torch.nn.Conv2d( |
|
block_in, |
|
2 * z_channels if double_z else z_channels, |
|
kernel_size=3, |
|
stride=1, |
|
padding=1, |
|
) |
|
|
|
def forward(self, x): |
|
|
|
temb = None |
|
|
|
|
|
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], temb) |
|
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])) |
|
|
|
|
|
h = hs[-1] |
|
h = self.mid.block_1(h, temb) |
|
h = self.mid.attn_1(h) |
|
h = self.mid.block_2(h, temb) |
|
|
|
|
|
h = self.norm_out(h) |
|
h = nonlinearity(h) |
|
h = self.conv_out(h) |
|
return h |
|
|
|
|
|
class Decoder(nn.Module): |
|
def __init__( |
|
self, |
|
*, |
|
ch, |
|
out_ch, |
|
ch_mult=(1, 2, 4, 8), |
|
num_res_blocks, |
|
attn_resolutions, |
|
dropout=0.0, |
|
resamp_with_conv=True, |
|
in_channels, |
|
resolution, |
|
z_channels, |
|
give_pre_end=False, |
|
tanh_out=False, |
|
use_linear_attn=False, |
|
attn_type="vanilla", |
|
**ignorekwargs, |
|
): |
|
super().__init__() |
|
if use_linear_attn: |
|
attn_type = "linear" |
|
self.ch = ch |
|
self.temb_ch = 0 |
|
self.num_resolutions = len(ch_mult) |
|
self.num_res_blocks = num_res_blocks |
|
self.resolution = resolution |
|
self.in_channels = in_channels |
|
self.give_pre_end = give_pre_end |
|
self.tanh_out = tanh_out |
|
|
|
|
|
in_ch_mult = (1,) + tuple(ch_mult) |
|
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) |
|
print( |
|
"Working with z of shape {} = {} dimensions.".format( |
|
self.z_shape, np.prod(self.z_shape) |
|
) |
|
) |
|
|
|
make_attn_cls = self._make_attn() |
|
make_resblock_cls = self._make_resblock() |
|
make_conv_cls = self._make_conv() |
|
|
|
self.conv_in = torch.nn.Conv2d( |
|
z_channels, block_in, kernel_size=3, stride=1, padding=1 |
|
) |
|
|
|
|
|
self.mid = nn.Module() |
|
self.mid.block_1 = make_resblock_cls( |
|
in_channels=block_in, |
|
out_channels=block_in, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
self.mid.attn_1 = make_attn_cls(block_in, attn_type=attn_type) |
|
self.mid.block_2 = make_resblock_cls( |
|
in_channels=block_in, |
|
out_channels=block_in, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
|
|
|
|
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 i_block in range(self.num_res_blocks + 1): |
|
block.append( |
|
make_resblock_cls( |
|
in_channels=block_in, |
|
out_channels=block_out, |
|
temb_channels=self.temb_ch, |
|
dropout=dropout, |
|
) |
|
) |
|
block_in = block_out |
|
if curr_res in attn_resolutions: |
|
attn.append(make_attn_cls(block_in, attn_type=attn_type)) |
|
up = nn.Module() |
|
up.block = block |
|
up.attn = attn |
|
if i_level != 0: |
|
up.upsample = Upsample(block_in, resamp_with_conv) |
|
curr_res = curr_res * 2 |
|
self.up.insert(0, up) |
|
|
|
|
|
self.norm_out = Normalize(block_in) |
|
self.conv_out = make_conv_cls( |
|
block_in, out_ch, kernel_size=3, stride=1, padding=1 |
|
) |
|
|
|
def _make_attn(self) -> Callable: |
|
return make_attn |
|
|
|
def _make_resblock(self) -> Callable: |
|
return ResnetBlock |
|
|
|
def _make_conv(self) -> Callable: |
|
return torch.nn.Conv2d |
|
|
|
def get_last_layer(self, **kwargs): |
|
return self.conv_out.weight |
|
|
|
def forward(self, z, **kwargs): |
|
|
|
self.last_z_shape = z.shape |
|
|
|
|
|
temb = None |
|
|
|
|
|
h = self.conv_in(z) |
|
|
|
|
|
h = self.mid.block_1(h, temb, **kwargs) |
|
h = self.mid.attn_1(h, **kwargs) |
|
h = self.mid.block_2(h, temb, **kwargs) |
|
|
|
|
|
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, temb, **kwargs) |
|
if len(self.up[i_level].attn) > 0: |
|
h = self.up[i_level].attn[i_block](h, **kwargs) |
|
if i_level != 0: |
|
h = self.up[i_level].upsample(h) |
|
|
|
|
|
if self.give_pre_end: |
|
return h |
|
|
|
h = self.norm_out(h) |
|
h = nonlinearity(h) |
|
h = self.conv_out(h, **kwargs) |
|
if self.tanh_out: |
|
h = torch.tanh(h) |
|
return h |
|
|