Spaces:
Sleeping
Sleeping
File size: 14,636 Bytes
826d651 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 |
from typing import Any, Dict, List, Optional, Tuple, Union
from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.configuration_utils import register_to_config
from diffusers.utils import BaseOutput
from models.controlnet_sdv import ControlNetSDVModel, zero_module
from models.softsplat import softsplat
import models.cmp.models as cmp_models
import models.cmp.utils as cmp_utils
import yaml
import os
import torchvision.transforms as transforms
class ArgObj(object):
def __init__(self):
pass
class CMP_demo(nn.Module):
def __init__(self, configfn, load_iter):
super().__init__()
args = ArgObj()
with open(configfn) as f:
config = yaml.full_load(f)
for k, v in config.items():
setattr(args, k, v)
setattr(args, 'load_iter', load_iter)
setattr(args, 'exp_path', os.path.dirname(configfn))
self.model = cmp_models.__dict__[args.model['arch']](args.model, dist_model=False)
self.model.load_state("{}/checkpoints".format(args.exp_path), args.load_iter, False)
self.model.switch_to('eval')
self.data_mean = args.data['data_mean']
self.data_div = args.data['data_div']
self.img_transform = transforms.Compose([
transforms.Normalize(self.data_mean, self.data_div)])
self.args = args
self.fuser = cmp_utils.Fuser(args.model['module']['nbins'], args.model['module']['fmax'])
torch.cuda.synchronize()
def run(self, image, sparse, mask):
dtype = image.dtype
image = image * 2 - 1
self.model.set_input(image.float(), torch.cat([sparse, mask], dim=1).float(), None)
cmp_output = self.model.model(self.model.image_input, self.model.sparse_input)
flow = self.fuser.convert_flow(cmp_output)
if flow.shape[2] != self.model.image_input.shape[2]:
flow = nn.functional.interpolate(
flow, size=self.model.image_input.shape[2:4],
mode="bilinear", align_corners=True)
return flow.to(dtype) # [b, 2, h, w]
class FlowControlNetConditioningEmbeddingSVD(nn.Module):
def __init__(
self,
conditioning_embedding_channels: int,
conditioning_channels: int = 3,
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
):
super().__init__()
self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
self.blocks = nn.ModuleList([])
for i in range(len(block_out_channels) - 1):
channel_in = block_out_channels[i]
channel_out = block_out_channels[i + 1]
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
self.conv_out = zero_module(
nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
)
def forward(self, conditioning):
embedding = self.conv_in(conditioning)
embedding = F.silu(embedding)
for block in self.blocks:
embedding = block(embedding)
embedding = F.silu(embedding)
embedding = self.conv_out(embedding)
return embedding
class FlowControlNetFirstFrameEncoderLayer(nn.Module):
def __init__(
self,
c_in,
c_out,
is_downsample=False
):
super().__init__()
self.conv_in = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1, stride=2 if is_downsample else 1)
def forward(self, feature):
'''
feature: [b, c, h, w]
'''
embedding = self.conv_in(feature)
embedding = F.silu(embedding)
return embedding
class FlowControlNetFirstFrameEncoder(nn.Module):
def __init__(
self,
c_in=320,
channels=[320, 640, 1280],
downsamples=[True, True, True],
use_zeroconv=True
):
super().__init__()
self.encoders = nn.ModuleList([])
self.zeroconvs = nn.ModuleList([])
for channel, downsample in zip(channels, downsamples):
self.encoders.append(FlowControlNetFirstFrameEncoderLayer(c_in, channel, is_downsample=downsample))
self.zeroconvs.append(zero_module(nn.Conv2d(channel, channel, kernel_size=1)) if use_zeroconv else nn.Identity())
c_in = channel
def forward(self, first_frame):
feature = first_frame
deep_features = []
for encoder, zeroconv in zip(self.encoders, self.zeroconvs):
feature = encoder(feature)
# print(feature.shape)
deep_features.append(zeroconv(feature))
return deep_features
@dataclass
class FlowControlNetOutput(BaseOutput):
"""
The output of [`FlowControlNetOutput`].
Args:
down_block_res_samples (`tuple[torch.Tensor]`):
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
used to condition the original UNet's downsampling activations.
mid_down_block_re_sample (`torch.Tensor`):
The activation of the midde block (the lowest sample resolution). Each tensor should be of shape
`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
Output can be used to condition the original UNet's middle block activation.
"""
down_block_res_samples: Tuple[torch.Tensor]
mid_block_res_sample: torch.Tensor
controlnet_flow: torch.Tensor
cmp_output: torch.Tensor
class FlowControlNet(ControlNetSDVModel):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 8,
out_channels: int = 4,
down_block_types: Tuple[str] = (
"CrossAttnDownBlockSpatioTemporal",
"CrossAttnDownBlockSpatioTemporal",
"CrossAttnDownBlockSpatioTemporal",
"DownBlockSpatioTemporal",
),
up_block_types: Tuple[str] = (
"UpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
),
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
addition_time_embed_dim: int = 256,
projection_class_embeddings_input_dim: int = 768,
layers_per_block: Union[int, Tuple[int]] = 2,
cross_attention_dim: Union[int, Tuple[int]] = 1024,
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20),
num_frames: int = 25,
conditioning_channels: int = 3,
conditioning_embedding_out_channels : Optional[Tuple[int, ...]] = (16, 32, 96, 256),
):
super().__init__()
self.flow_encoder = FlowControlNetFirstFrameEncoder()
self.controlnet_cond_embedding = FlowControlNetConditioningEmbeddingSVD(
conditioning_embedding_channels=block_out_channels[0],
block_out_channels=conditioning_embedding_out_channels,
conditioning_channels=conditioning_channels,
)
def get_warped_frames(self, first_frame, flows):
'''
video_frame: [b, c, w, h]
flows: [b, t-1, c, w, h]
'''
dtype = first_frame.dtype
warped_frames = []
for i in range(flows.shape[1]):
warped_frame = softsplat(tenIn=first_frame.float(), tenFlow=flows[:, i].float(), tenMetric=None, strMode='avg').to(dtype) # [b, c, w, h]
warped_frames.append(warped_frame.unsqueeze(1)) # [b, 1, c, w, h]
warped_frames = torch.cat(warped_frames, dim=1) # [b, t-1, c, w, h]
return warped_frames
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
added_time_ids: torch.Tensor,
controlnet_cond: torch.FloatTensor = None, # [b, 3, h, w]
controlnet_flow: torch.FloatTensor = None, # [b, 13, 2, h, w]
image_only_indicator: Optional[torch.Tensor] = None,
return_dict: bool = True,
guess_mode: bool = False,
conditioning_scale: float = 1.0,
) -> Union[FlowControlNetOutput, Tuple]:
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
batch_size, num_frames = sample.shape[:2]
timesteps = timesteps.expand(batch_size)
t_emb = self.time_proj(timesteps)
# `Timesteps` does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=sample.dtype)
emb = self.time_embedding(t_emb)
time_embeds = self.add_time_proj(added_time_ids.flatten())
time_embeds = time_embeds.reshape((batch_size, -1))
time_embeds = time_embeds.to(emb.dtype)
aug_emb = self.add_embedding(time_embeds)
emb = emb + aug_emb
# Flatten the batch and frames dimensions
# sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width]
sample = sample.flatten(0, 1)
# Repeat the embeddings num_video_frames times
# emb: [batch, channels] -> [batch * frames, channels]
emb = emb.repeat_interleave(num_frames, dim=0)
# encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels]
encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0)
# 2. pre-process
sample = self.conv_in(sample) # [b*l, 320, h//8, w//8]
# controlnet cond
if controlnet_cond != None:
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) # [b, 320, h//8, w//8]
controlnet_cond_features = [controlnet_cond] + self.flow_encoder(controlnet_cond) # [4]
scales = [8, 16, 32, 64]
scale_flows = {}
fb, fl, fc, fh, fw = controlnet_flow.shape
# print(controlnet_flow.shape)
for scale in scales:
scaled_flow = F.interpolate(controlnet_flow.reshape(-1, fc, fh, fw), scale_factor=1/scale)
scaled_flow = scaled_flow.reshape(fb, fl, fc, fh // scale, fw // scale) / scale
scale_flows[scale] = scaled_flow
warped_cond_features = []
for cond_feature in controlnet_cond_features:
cb, cc, ch, cw = cond_feature.shape
# print(cond_feature.shape)
warped_cond_feature = self.get_warped_frames(cond_feature, scale_flows[fh // ch])
warped_cond_feature = torch.cat([cond_feature.unsqueeze(1), warped_cond_feature], dim=1) # [b, c, h, w]
wb, wl, wc, wh, ww = warped_cond_feature.shape
# print(warped_cond_feature.shape)
warped_cond_features.append(warped_cond_feature.reshape(wb * wl, wc, wh, ww))
image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device)
count = 0
length = len(warped_cond_features)
# add the warped feature in the first scale
sample = sample + warped_cond_features[count]
count += 1
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
image_only_indicator=image_only_indicator,
)
else:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
image_only_indicator=image_only_indicator,
)
sample = sample + warped_cond_features[min(count, length - 1)]
count += 1
down_block_res_samples += res_samples
# add the warped feature in the last scale
sample = sample + warped_cond_features[-1]
# 4. mid
sample = self.mid_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
image_only_indicator=image_only_indicator,
)
controlnet_down_block_res_samples = ()
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
down_block_res_sample = controlnet_block(down_block_res_sample)
controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
down_block_res_samples = controlnet_down_block_res_samples
mid_block_res_sample = self.controlnet_mid_block(sample)
# 6. scaling
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample = mid_block_res_sample * conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample, controlnet_flow, None)
return FlowControlNetOutput(
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample, controlnet_flow=controlnet_flow, cmp_output=None
)
|