Merge remote-tracking branch 'origin/master'
This commit is contained in:
James Betker
commit
0ded106562
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|
@ -42,6 +42,9 @@ class ImageFolderDataset:
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self.corrupt_before_downsize = opt['corrupt_before_downsize'] if 'corrupt_before_downsize' in opt.keys() else False
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self.fetch_alt_image = opt['fetch_alt_image'] # If specified, this dataset will attempt to find a second image
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# from the same video source. Search for 'fetch_alt_image' for more info.
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self.fetch_alt_tiled_image = opt['fetch_alt_tiled_image'] # If specified, this dataset will attempt to find anoter tile from the same source image
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# Search for 'fetch_alt_tiled_image' for more info.
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assert not (self.fetch_alt_image and self.fetch_alt_tiled_image) # These are mutually exclusive.
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self.skip_lq = opt_get(opt, ['skip_lq'], False)
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self.disable_flip = opt_get(opt, ['disable_flip'], False)
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self.rgb_n1_to_1 = opt_get(opt, ['rgb_n1_to_1'], False)
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@ -201,6 +204,25 @@ class ImageFolderDataset:
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for_lq.append(hs[0])
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out_dict['alt_hq'] = alt_hq
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if self.fetch_alt_tiled_image:
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# This assumes the output format generated by the tiled image generation scripts included with DLAS. Specifically,
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# all image read by this dataset are assumed to be in subfolders with other tiles from the same source image. When
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# this option is set, another random image from the same folder is selected and returned as the alt image.
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sel_path = self.image_paths[item]
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other_images = random.shuffle(os.listdir(sel_path))
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# Assume that the directory contains at least <image>, <ref.jpg>, <centers.pt>
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if len(other_images) <= 3:
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alt_hq = hq # This is a fallback in case an alt image can't be found.
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else:
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for oi in other_images:
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if oi == sel_path or 'ref.' in oi or 'centers.pt' in oi:
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continue
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alt_hq = util.read_img(None, oi, rgb=True)
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alt_hs = self.resize_hq([alt_hq])
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alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hs[0], (2, 0, 1)))).float()
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out_dict['has_alt'] = True
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out_dict['alt_hq'] = alt_hq
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if not self.skip_lq:
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lqs, ent = self.synthesize_lq(for_lq)
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ls = lqs[0]
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|
|
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907
codes/models/diffusion/unet_latent_guide.py
Normal file
907
codes/models/diffusion/unet_latent_guide.py
Normal file
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@ -0,0 +1,907 @@
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from abc import abstractmethod
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import math
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import numpy as np
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import torch
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import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision # For debugging, not actually used.
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from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32
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from models.diffusion.nn import (
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conv_nd,
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linear,
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avg_pool_nd,
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zero_module,
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normalization,
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timestep_embedding,
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)
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from trainer.networks import register_model
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from utils.util import checkpoint
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class AttentionPool2d(nn.Module):
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"""
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Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
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"""
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def __init__(
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self,
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spacial_dim: int,
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embed_dim: int,
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num_heads_channels: int,
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output_dim: int = None,
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):
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super().__init__()
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self.positional_embedding = nn.Parameter(
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th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
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)
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self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
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self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
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self.num_heads = embed_dim // num_heads_channels
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self.attention = QKVAttention(self.num_heads)
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def forward(self, x):
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b, c, *_spatial = x.shape
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x = x.reshape(b, c, -1) # NC(HW)
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x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
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x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
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x = self.qkv_proj(x)
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x = self.attention(x)
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x = self.c_proj(x)
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return x[:, :, 0]
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class TimestepBlock(nn.Module):
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"""
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Any module where forward() takes timestep embeddings as a second argument.
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"""
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@abstractmethod
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def forward(self, x, emb):
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"""
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Apply the module to `x` given `emb` timestep embeddings.
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"""
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class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
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"""
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A sequential module that passes timestep embeddings to the children that
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support it as an extra input.
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"""
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def forward(self, x, emb):
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for layer in self:
|
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb)
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else:
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x = layer(x)
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return x
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class Upsample(nn.Module):
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"""
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An upsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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upsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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if use_conv:
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self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
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def forward(self, x):
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assert x.shape[1] == self.channels
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if self.dims == 3:
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x = F.interpolate(
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x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
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)
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else:
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x = F.interpolate(x, scale_factor=2, mode="nearest")
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if self.use_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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downsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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stride = 2 if dims != 3 else (1, 2, 2)
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if use_conv:
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self.op = conv_nd(
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dims, self.channels, self.out_channels, 3, stride=stride, padding=1
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)
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else:
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assert self.channels == self.out_channels
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self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
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def forward(self, x):
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assert x.shape[1] == self.channels
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return self.op(x)
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class ResBlock(TimestepBlock):
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"""
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A residual block that can optionally change the number of channels.
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:param channels: the number of input channels.
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:param emb_channels: the number of timestep embedding channels.
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:param dropout: the rate of dropout.
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:param out_channels: if specified, the number of out channels.
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:param use_conv: if True and out_channels is specified, use a spatial
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convolution instead of a smaller 1x1 convolution to change the
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channels in the skip connection.
|
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:param dims: determines if the signal is 1D, 2D, or 3D.
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:param up: if True, use this block for upsampling.
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:param down: if True, use this block for downsampling.
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"""
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|
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def __init__(
|
||||
self,
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channels,
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emb_channels,
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dropout,
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out_channels=None,
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use_conv=False,
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use_scale_shift_norm=False,
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dims=2,
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up=False,
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down=False,
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):
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super().__init__()
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self.channels = channels
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self.emb_channels = emb_channels
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self.dropout = dropout
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.use_scale_shift_norm = use_scale_shift_norm
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|
||||
self.in_layers = nn.Sequential(
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normalization(channels),
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nn.SiLU(),
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conv_nd(dims, channels, self.out_channels, 3, padding=1),
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)
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|
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self.updown = up or down
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|
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if up:
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self.h_upd = Upsample(channels, False, dims)
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self.x_upd = Upsample(channels, False, dims)
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elif down:
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self.h_upd = Downsample(channels, False, dims)
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self.x_upd = Downsample(channels, False, dims)
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else:
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self.h_upd = self.x_upd = nn.Identity()
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|
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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||||
),
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)
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self.out_layers = nn.Sequential(
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normalization(self.out_channels),
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nn.SiLU(),
|
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nn.Dropout(p=dropout),
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||||
zero_module(
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conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
|
||||
),
|
||||
)
|
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|
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if self.out_channels == channels:
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self.skip_connection = nn.Identity()
|
||||
elif use_conv:
|
||||
self.skip_connection = conv_nd(
|
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dims, channels, self.out_channels, 3, padding=1
|
||||
)
|
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else:
|
||||
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
|
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|
||||
def forward(self, x, emb):
|
||||
"""
|
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Apply the block to a Tensor, conditioned on a timestep embedding.
|
||||
|
||||
:param x: an [N x C x ...] Tensor of features.
|
||||
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
return checkpoint(
|
||||
self._forward, x, emb
|
||||
)
|
||||
|
||||
def _forward(self, x, emb):
|
||||
if self.updown:
|
||||
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
|
||||
h = in_rest(x)
|
||||
h = self.h_upd(h)
|
||||
x = self.x_upd(x)
|
||||
h = in_conv(h)
|
||||
else:
|
||||
h = self.in_layers(x)
|
||||
emb_out = self.emb_layers(emb).type(h.dtype)
|
||||
while len(emb_out.shape) < len(h.shape):
|
||||
emb_out = emb_out[..., None]
|
||||
if self.use_scale_shift_norm:
|
||||
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
|
||||
scale, shift = th.chunk(emb_out, 2, dim=1)
|
||||
h = out_norm(h) * (1 + scale) + shift
|
||||
h = out_rest(h)
|
||||
else:
|
||||
h = h + emb_out
|
||||
h = self.out_layers(h)
|
||||
return self.skip_connection(x) + h
|
||||
|
||||
|
||||
class AttentionBlock(nn.Module):
|
||||
"""
|
||||
An attention block that allows spatial positions to attend to each other.
|
||||
|
||||
Originally ported from here, but adapted to the N-d case.
|
||||
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
num_heads=1,
|
||||
num_head_channels=-1,
|
||||
use_new_attention_order=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
if num_head_channels == -1:
|
||||
self.num_heads = num_heads
|
||||
else:
|
||||
assert (
|
||||
channels % num_head_channels == 0
|
||||
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
|
||||
self.num_heads = channels // num_head_channels
|
||||
self.norm = normalization(channels)
|
||||
self.qkv = conv_nd(1, channels, channels * 3, 1)
|
||||
if use_new_attention_order:
|
||||
# split qkv before split heads
|
||||
self.attention = QKVAttention(self.num_heads)
|
||||
else:
|
||||
# split heads before split qkv
|
||||
self.attention = QKVAttentionLegacy(self.num_heads)
|
||||
|
||||
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
|
||||
|
||||
def forward(self, x):
|
||||
return checkpoint(self._forward, x)
|
||||
|
||||
def _forward(self, x):
|
||||
b, c, *spatial = x.shape
|
||||
x = x.reshape(b, c, -1)
|
||||
qkv = self.qkv(self.norm(x))
|
||||
h = self.attention(qkv)
|
||||
h = self.proj_out(h)
|
||||
return (x + h).reshape(b, c, *spatial)
|
||||
|
||||
|
||||
def count_flops_attn(model, _x, y):
|
||||
"""
|
||||
A counter for the `thop` package to count the operations in an
|
||||
attention operation.
|
||||
Meant to be used like:
|
||||
macs, params = thop.profile(
|
||||
model,
|
||||
inputs=(inputs, timestamps),
|
||||
custom_ops={QKVAttention: QKVAttention.count_flops},
|
||||
)
|
||||
"""
|
||||
b, c, *spatial = y[0].shape
|
||||
num_spatial = int(np.prod(spatial))
|
||||
# We perform two matmuls with the same number of ops.
|
||||
# The first computes the weight matrix, the second computes
|
||||
# the combination of the value vectors.
|
||||
matmul_ops = 2 * b * (num_spatial ** 2) * c
|
||||
model.total_ops += th.DoubleTensor([matmul_ops])
|
||||
|
||||
|
||||
class QKVAttentionLegacy(nn.Module):
|
||||
"""
|
||||
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
|
||||
"""
|
||||
|
||||
def __init__(self, n_heads):
|
||||
super().__init__()
|
||||
self.n_heads = n_heads
|
||||
|
||||
def forward(self, qkv):
|
||||
"""
|
||||
Apply QKV attention.
|
||||
|
||||
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
|
||||
:return: an [N x (H * C) x T] tensor after attention.
|
||||
"""
|
||||
bs, width, length = qkv.shape
|
||||
assert width % (3 * self.n_heads) == 0
|
||||
ch = width // (3 * self.n_heads)
|
||||
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
|
||||
scale = 1 / math.sqrt(math.sqrt(ch))
|
||||
weight = th.einsum(
|
||||
"bct,bcs->bts", q * scale, k * scale
|
||||
) # More stable with f16 than dividing afterwards
|
||||
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
|
||||
a = th.einsum("bts,bcs->bct", weight, v)
|
||||
return a.reshape(bs, -1, length)
|
||||
|
||||
@staticmethod
|
||||
def count_flops(model, _x, y):
|
||||
return count_flops_attn(model, _x, y)
|
||||
|
||||
|
||||
class QKVAttention(nn.Module):
|
||||
"""
|
||||
A module which performs QKV attention and splits in a different order.
|
||||
"""
|
||||
|
||||
def __init__(self, n_heads):
|
||||
super().__init__()
|
||||
self.n_heads = n_heads
|
||||
|
||||
def forward(self, qkv):
|
||||
"""
|
||||
Apply QKV attention.
|
||||
|
||||
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
|
||||
:return: an [N x (H * C) x T] tensor after attention.
|
||||
"""
|
||||
bs, width, length = qkv.shape
|
||||
assert width % (3 * self.n_heads) == 0
|
||||
ch = width // (3 * self.n_heads)
|
||||
q, k, v = qkv.chunk(3, dim=1)
|
||||
scale = 1 / math.sqrt(math.sqrt(ch))
|
||||
weight = th.einsum(
|
||||
"bct,bcs->bts",
|
||||
(q * scale).view(bs * self.n_heads, ch, length),
|
||||
(k * scale).view(bs * self.n_heads, ch, length),
|
||||
) # More stable with f16 than dividing afterwards
|
||||
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
|
||||
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
|
||||
return a.reshape(bs, -1, length)
|
||||
|
||||
@staticmethod
|
||||
def count_flops(model, _x, y):
|
||||
return count_flops_attn(model, _x, y)
|
||||
|
||||
|
||||
class UNetModel(nn.Module):
|
||||
"""
|
||||
The full UNet model with attention and timestep embedding.
|
||||
|
||||
:param in_channels: channels in the input Tensor.
|
||||
:param model_channels: base channel count for the model.
|
||||
:param out_channels: channels in the output Tensor.
|
||||
:param num_res_blocks: number of residual blocks per downsample.
|
||||
:param attention_resolutions: a collection of downsample rates at which
|
||||
attention will take place. May be a set, list, or tuple.
|
||||
For example, if this contains 4, then at 4x downsampling, attention
|
||||
will be used.
|
||||
:param dropout: the dropout probability.
|
||||
:param channel_mult: channel multiplier for each level of the UNet.
|
||||
:param conv_resample: if True, use learned convolutions for upsampling and
|
||||
downsampling.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param num_classes: if specified (as an int), then this model will be
|
||||
class-conditional with `num_classes` classes.
|
||||
:param num_heads: the number of attention heads in each attention layer.
|
||||
:param num_heads_channels: if specified, ignore num_heads and instead use
|
||||
a fixed channel width per attention head.
|
||||
:param num_heads_upsample: works with num_heads to set a different number
|
||||
of heads for upsampling. Deprecated.
|
||||
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
||||
:param resblock_updown: use residual blocks for up/downsampling.
|
||||
:param use_new_attention_order: use a different attention pattern for potentially
|
||||
increased efficiency.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
image_size,
|
||||
in_channels,
|
||||
model_channels,
|
||||
out_channels,
|
||||
num_res_blocks,
|
||||
attention_resolutions,
|
||||
dropout=0,
|
||||
channel_mult=(1, 2, 4, 8),
|
||||
conv_resample=True,
|
||||
dims=2,
|
||||
num_classes=None,
|
||||
use_fp16=False,
|
||||
num_heads=1,
|
||||
num_head_channels=-1,
|
||||
num_heads_upsample=-1,
|
||||
use_scale_shift_norm=False,
|
||||
resblock_updown=False,
|
||||
use_new_attention_order=False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if num_heads_upsample == -1:
|
||||
num_heads_upsample = num_heads
|
||||
|
||||
self.image_size = image_size
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.num_classes = num_classes
|
||||
self.dtype = th.float16 if use_fp16 else th.float32
|
||||
self.num_heads = num_heads
|
||||
self.num_head_channels = num_head_channels
|
||||
self.num_heads_upsample = num_heads_upsample
|
||||
|
||||
time_embed_dim = model_channels * 4
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
)
|
||||
|
||||
if self.num_classes is not None:
|
||||
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, 3, padding=1)
|
||||
)
|
||||
]
|
||||
)
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
for level, mult in enumerate(channel_mult):
|
||||
for _ in range(num_res_blocks):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=mult * model_channels,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
)
|
||||
]
|
||||
ch = mult * model_channels
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
use_new_attention_order=use_new_attention_order,
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=out_ch,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
down=True,
|
||||
)
|
||||
if resblock_updown
|
||||
else Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
self.latent_join_reduce = ResBlock(ch*2, time_embed_dim, dropout, out_channels=ch, dims=dims, use_scale_shift_norm=use_scale_shift_norm)
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
),
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
use_new_attention_order=use_new_attention_order,
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, mult in list(enumerate(channel_mult))[::-1]:
|
||||
for i in range(num_res_blocks + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch + ich,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=model_channels * mult,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
)
|
||||
]
|
||||
ch = model_channels * mult
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads_upsample,
|
||||
num_head_channels=num_head_channels,
|
||||
use_new_attention_order=use_new_attention_order,
|
||||
)
|
||||
)
|
||||
if level and i == num_res_blocks:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=out_ch,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
up=True,
|
||||
)
|
||||
if resblock_updown
|
||||
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
nn.SiLU(),
|
||||
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
|
||||
)
|
||||
|
||||
def convert_to_fp16(self):
|
||||
"""
|
||||
Convert the torso of the model to float16.
|
||||
"""
|
||||
self.input_blocks.apply(convert_module_to_f16)
|
||||
self.middle_block.apply(convert_module_to_f16)
|
||||
self.output_blocks.apply(convert_module_to_f16)
|
||||
|
||||
def convert_to_fp32(self):
|
||||
"""
|
||||
Convert the torso of the model to float32.
|
||||
"""
|
||||
self.input_blocks.apply(convert_module_to_f32)
|
||||
self.middle_block.apply(convert_module_to_f32)
|
||||
self.output_blocks.apply(convert_module_to_f32)
|
||||
|
||||
def forward(self, x, latent, timesteps, y=None):
|
||||
"""
|
||||
Apply the model to an input batch.
|
||||
|
||||
:param x: an [N x C x ...] Tensor of inputs.
|
||||
:param timesteps: a 1-D batch of timesteps.
|
||||
:param y: an [N] Tensor of labels, if class-conditional.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
assert (y is not None) == (
|
||||
self.num_classes is not None
|
||||
), "must specify y if and only if the model is class-conditional"
|
||||
|
||||
hs = []
|
||||
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
|
||||
if self.num_classes is not None:
|
||||
assert y.shape == (x.shape[0],)
|
||||
emb = emb + self.label_emb(y)
|
||||
|
||||
h = x.type(self.dtype)
|
||||
for module in self.input_blocks:
|
||||
h = module(h, emb)
|
||||
hs.append(h)
|
||||
b, c = latent.shape
|
||||
h = torch.cat([h, latent.view(b,c,1,1).repeat(1,1,h.shape[-2],h.shape[-1])], dim=1)
|
||||
h = self.latent_join_reduce(h, emb)
|
||||
h = self.middle_block(h, emb)
|
||||
for module in self.output_blocks:
|
||||
h = th.cat([h, hs.pop()], dim=1)
|
||||
h = module(h, emb)
|
||||
h = h.type(x.dtype)
|
||||
return self.out(h)
|
||||
|
||||
|
||||
class SuperResModel(UNetModel):
|
||||
"""
|
||||
A UNetModel that performs super-resolution.
|
||||
|
||||
Expects an extra kwarg `low_res` to condition on a low-resolution image.
|
||||
"""
|
||||
|
||||
def __init__(self, image_size, in_channels, num_corruptions=0, *args, **kwargs):
|
||||
self.num_corruptions = 0
|
||||
super().__init__(image_size, in_channels * 2 + num_corruptions, *args, **kwargs)
|
||||
|
||||
def forward(self, x, timesteps, latent, low_res=None, corruption_factor=None, **kwargs):
|
||||
b, _, new_height, new_width = x.shape
|
||||
upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
|
||||
if corruption_factor is not None:
|
||||
corruption_factor = corruption_factor.view(b, -1, 1, 1).repeat(1, 1, new_height, new_width)
|
||||
else:
|
||||
corruption_factor = torch.zeros((b, self.num_corruptions, new_height, new_width), dtype=torch.float, device=x.device)
|
||||
upsampled = torch.cat([upsampled, corruption_factor], dim=1)
|
||||
x = th.cat([x, upsampled], dim=1)
|
||||
res = super().forward(x, timesteps, latent, **kwargs)
|
||||
return res
|
||||
|
||||
|
||||
class EncoderUNetModel(nn.Module):
|
||||
"""
|
||||
The half UNet model with attention and timestep embedding.
|
||||
|
||||
For usage, see UNet.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
image_size,
|
||||
in_channels,
|
||||
model_channels,
|
||||
out_channels,
|
||||
num_res_blocks,
|
||||
attention_resolutions,
|
||||
dropout=0,
|
||||
channel_mult=(1, 2, 4, 8),
|
||||
conv_resample=True,
|
||||
dims=2,
|
||||
use_fp16=False,
|
||||
num_heads=1,
|
||||
num_head_channels=-1,
|
||||
num_heads_upsample=-1,
|
||||
use_scale_shift_norm=False,
|
||||
resblock_updown=False,
|
||||
use_new_attention_order=False,
|
||||
pool="adaptive",
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if num_heads_upsample == -1:
|
||||
num_heads_upsample = num_heads
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.dtype = th.float16 if use_fp16 else th.float32
|
||||
self.num_heads = num_heads
|
||||
self.num_head_channels = num_head_channels
|
||||
self.num_heads_upsample = num_heads_upsample
|
||||
|
||||
time_embed_dim = model_channels * 4
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
)
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, 3, padding=1)
|
||||
)
|
||||
]
|
||||
)
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
for level, mult in enumerate(channel_mult):
|
||||
for _ in range(num_res_blocks):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=mult * model_channels,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
)
|
||||
]
|
||||
ch = mult * model_channels
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
use_new_attention_order=use_new_attention_order,
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=out_ch,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
down=True,
|
||||
)
|
||||
if resblock_updown
|
||||
else Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
),
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
use_new_attention_order=use_new_attention_order,
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
self.pool = pool
|
||||
if pool == "adaptive":
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
nn.SiLU(),
|
||||
nn.AdaptiveAvgPool2d((1, 1)),
|
||||
zero_module(conv_nd(dims, ch, out_channels, 1)),
|
||||
nn.Flatten(),
|
||||
)
|
||||
elif pool == "attention":
|
||||
assert num_head_channels != -1
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
nn.SiLU(),
|
||||
AttentionPool2d(
|
||||
(image_size // ds), ch, num_head_channels, out_channels
|
||||
),
|
||||
)
|
||||
elif pool == "spatial":
|
||||
self.out = nn.Sequential(
|
||||
nn.Linear(self._feature_size, 2048),
|
||||
nn.ReLU(),
|
||||
nn.Linear(2048, self.out_channels),
|
||||
)
|
||||
elif pool == "spatial_v2":
|
||||
self.out = nn.Sequential(
|
||||
nn.Linear(self._feature_size, 2048),
|
||||
normalization(2048),
|
||||
nn.SiLU(),
|
||||
nn.Linear(2048, self.out_channels),
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(f"Unexpected {pool} pooling")
|
||||
|
||||
def convert_to_fp16(self):
|
||||
"""
|
||||
Convert the torso of the model to float16.
|
||||
"""
|
||||
self.input_blocks.apply(convert_module_to_f16)
|
||||
self.middle_block.apply(convert_module_to_f16)
|
||||
|
||||
def convert_to_fp32(self):
|
||||
"""
|
||||
Convert the torso of the model to float32.
|
||||
"""
|
||||
self.input_blocks.apply(convert_module_to_f32)
|
||||
self.middle_block.apply(convert_module_to_f32)
|
||||
|
||||
def forward(self, x, timesteps):
|
||||
"""
|
||||
Apply the model to an input batch.
|
||||
|
||||
:param x: an [N x C x ...] Tensor of inputs.
|
||||
:param timesteps: a 1-D batch of timesteps.
|
||||
:return: an [N x K] Tensor of outputs.
|
||||
"""
|
||||
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
|
||||
results = []
|
||||
h = x.type(self.dtype)
|
||||
for module in self.input_blocks:
|
||||
h = module(h, emb)
|
||||
if self.pool.startswith("spatial"):
|
||||
results.append(h.type(x.dtype).mean(dim=(2, 3)))
|
||||
h = self.middle_block(h, emb)
|
||||
if self.pool.startswith("spatial"):
|
||||
results.append(h.type(x.dtype).mean(dim=(2, 3)))
|
||||
h = th.cat(results, axis=-1)
|
||||
return self.out(h)
|
||||
else:
|
||||
h = h.type(x.dtype)
|
||||
return self.out(h)
|
||||
|
||||
@register_model
|
||||
def register_unet_diffusion(opt_net, opt):
|
||||
return SuperResModel(**opt_net['args'])
|
||||
|
||||
if __name__ == '__main__':
|
||||
attention_ds = []
|
||||
for res in "16,8".split(","):
|
||||
attention_ds.append(128 // int(res))
|
||||
srm = SuperResModel(image_size=128, in_channels=3, model_channels=64, out_channels=3, num_res_blocks=1, attention_resolutions=attention_ds, num_heads=4,
|
||||
num_heads_upsample=-1, use_scale_shift_norm=True)
|
||||
x = torch.randn(1,3,128,128)
|
||||
l = torch.randn(1,3,32,32)
|
||||
ts = torch.LongTensor([555])
|
||||
y = srm(x, ts, low_res=l)
|
||||
print(y.shape, y.mean(), y.std(), y.min(), y.max())
|
||||
Loading…
Reference in New Issue
Block a user