Spaces:
Runtime error
Runtime error
File size: 6,597 Bytes
bd366ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import random
from .nn import timestep_embedding
from .unet import UNetModel
from .xf import LayerNorm, Transformer, convert_module_to_f16
from timm.models.vision_transformer import PatchEmbed
class Text2ImModel(nn.Module):
def __init__(
self,
text_ctx,
xf_width,
xf_layers,
xf_heads,
xf_final_ln,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout,
channel_mult,
use_fp16,
num_heads,
num_heads_upsample,
num_head_channels,
use_scale_shift_norm,
resblock_updown,
in_channels = 3,
n_class = 3,
image_size = 64,
):
super().__init__()
self.encoder = Encoder(img_size=image_size, patch_size=image_size//16, in_chans=n_class,
xf_width=xf_width, xf_layers=8, xf_heads=xf_heads, model_channels=model_channels)
self.in_channels = in_channels
self.decoder = Text2ImUNet(
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=dropout,
channel_mult=channel_mult,
use_fp16=use_fp16,
num_heads=num_heads,
num_heads_upsample=num_heads_upsample,
num_head_channels=num_head_channels,
use_scale_shift_norm=use_scale_shift_norm,
resblock_updown=resblock_updown,
encoder_channels=xf_width
)
def forward(self, xt, timesteps, ref=None, uncond_p=0.0):
latent_outputs =self.encoder(ref, uncond_p)
pred = self.decoder(xt, timesteps, latent_outputs)
return pred
class Text2ImUNet(UNetModel):
def __init__(
self,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
self.transformer_proj = nn.Linear(512, self.model_channels * 4) ###
def forward(self, x, timesteps, latent_outputs):
hs = []
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
xf_proj, xf_out = latent_outputs["xf_proj"], latent_outputs["xf_out"]
xf_proj = self.transformer_proj(xf_proj) ###
emb = emb + xf_proj.to(emb)
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb, xf_out)
hs.append(h)
h = self.middle_block(h, emb, xf_out)
for module in self.output_blocks:
h = th.cat([h, hs.pop()], dim=1)
h = module(h, emb, xf_out)
h = h.type(x.dtype)
h = self.out(h)
return h
class Encoder(nn.Module):
def __init__(
self,
img_size,
patch_size,
in_chans,
xf_width,
xf_layers,
xf_heads,
model_channels,
):
super().__init__( )
self.transformer = Transformer(
xf_width,
xf_layers,
xf_heads,
)
self.cnn = CNN(in_chans)
self.final_ln = LayerNorm(xf_width)
self.cls_token = nn.Parameter(th.empty(1, 1, xf_width, dtype=th.float32))
self.positional_embedding = nn.Parameter(th.empty(1, 256 + 1, xf_width, dtype=th.float32))
def forward(self, ref, uncond_p=0.0):
x = self.cnn(ref)
x = x.flatten(2).transpose(1, 2)
x = x + self.positional_embedding[:, 1:, :]
cls_token = self.cls_token + self.positional_embedding[:, :1, :]
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
x = th.cat((x, cls_tokens), dim=1)
xf_out = self.transformer(x)
if self.final_ln is not None:
xf_out = self.final_ln(xf_out)
xf_proj = xf_out[:, -1]
xf_out = xf_out[:, :-1].permute(0, 2, 1) # NLC -> NCL
outputs = dict(xf_proj=xf_proj, xf_out=xf_out)
return outputs
class SuperResText2ImModel(Text2ImModel):
"""
A text2im model that performs super-resolution.
Expects an extra kwarg `low_res` to condition on a low-resolution image.
"""
def __init__(self, *args, **kwargs):
if "in_channels" in kwargs:
kwargs = dict(kwargs)
kwargs["in_channels"] = kwargs["in_channels"] * 2
else:
# Curse you, Python. Or really, just curse positional arguments :|.
args = list(args)
args[1] = args[1] * 2
super().__init__(*args, **kwargs)
def forward(self, x, timesteps, low_res=None, **kwargs):
_, _, new_height, new_width = x.shape
upsampled = F.interpolate(
low_res, (new_height, new_width), mode="bilinear", align_corners=False
)
# ##########
# upsampled = upsampled + th.randn_like(upsampled)*0.0005*th.log(1 + 0.1* timesteps.reshape(timesteps.shape[0], 1,1,1))
# ##########
x = th.cat([x, upsampled], dim=1)
return super().forward(x, timesteps, **kwargs)
def conv3x3(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=True)
def conv7x7(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=7,
stride=stride, padding=3, bias=True)
class CNN(nn.Module):
def __init__(self, in_channels=3):
super(CNN, self).__init__()
self.conv1 = conv7x7(in_channels, 32) #256
self.norm1 = nn.InstanceNorm2d(32, affine=True)
self.LReLU1 = nn.LeakyReLU(0.2)
self.conv2 = conv3x3(32, 64, 2) #128
self.norm2 = nn.InstanceNorm2d(64, affine=True)
self.LReLU2 = nn.LeakyReLU(0.2)
self.conv3 = conv3x3(64, 128, 2) #64
self.norm3 = nn.InstanceNorm2d(128, affine=True)
self.LReLU3 = nn.LeakyReLU(0.2)
self.conv4 = conv3x3(128, 256, 2) #32
self.norm4 = nn.InstanceNorm2d(256, affine=True)
self.LReLU4 = nn.LeakyReLU(0.2)
self.conv5 = conv3x3(256, 512, 2) #16
self.norm5 = nn.InstanceNorm2d(512, affine=True)
self.LReLU5 = nn.LeakyReLU(0.2)
self.conv6 = conv3x3(512, 512, 1)
def forward(self, x):
x = self.LReLU1(self.norm1(self.conv1(x)))
x = self.LReLU2(self.norm2(self.conv2(x)))
x = self.LReLU3(self.norm3(self.conv3(x)))
x = self.LReLU4(self.norm4(self.conv4(x)))
x = self.LReLU5(self.norm5(self.conv5(x)))
x = self.conv6(x)
return x |