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5c93746 | 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 | from models.model_interface import (
DiffusionModelInterface,
TextEncoderInterface,
VAEInterface
)
from models.wan.wan_base.modules.tokenizers import HuggingfaceTokenizer
from models.wan.wan_base.modules.model import WanModel
from models.wan.wan_base.modules.vae import _video_vae
from models.wan.wan_base.modules.t5 import umt5_xxl
from models.wan.flow_match import FlowMatchScheduler
from models.wan.causal_model import CausalWanModel
from typing import List, Tuple, Dict, Optional
import torch
import os
import torch.distributed as dist
import time
from pathlib import Path
def _resolve_project_root() -> Path:
env_root = os.environ.get("STREAMDIFFUSIONV2_ROOT")
if env_root:
return Path(env_root).expanduser().resolve()
repo_root = Path(__file__).resolve().parents[2]
if (repo_root / "wan_models").exists():
return repo_root
cwd = Path.cwd().resolve()
if (cwd / "wan_models").exists():
return cwd
return repo_root
PROJECT_ROOT = _resolve_project_root()
class WanTextEncoder(TextEncoderInterface):
def __init__(self, model_type="T2V-1.3B") -> None:
super().__init__()
self.text_encoder = umt5_xxl(
encoder_only=True,
return_tokenizer=False,
dtype=torch.float32,
device=torch.device('cpu')
).eval().requires_grad_(False)
self.text_encoder.load_state_dict(
torch.load(
PROJECT_ROOT / f"wan_models/Wan2.1-{model_type}/models_t5_umt5-xxl-enc-bf16.pth",
map_location='cpu', weights_only=False
)
)
self.tokenizer = HuggingfaceTokenizer(
name=str(PROJECT_ROOT / f"wan_models/Wan2.1-{model_type}/google/umt5-xxl/"), seq_len=512, clean='whitespace')
@property
def device(self):
return next(self.parameters()).device
def forward(self, text_prompts: List[str]) -> dict:
ids, mask = self.tokenizer(
text_prompts, return_mask=True, add_special_tokens=True)
ids = ids.to(self.device)
mask = mask.to(self.device)
seq_lens = mask.gt(0).sum(dim=1).long()
context = self.text_encoder(ids, mask)
for u, v in zip(context, seq_lens):
u[v:] = 0.0 # set padding to 0.0
return {
"prompt_embeds": context
}
class WanVAEWrapper(VAEInterface):
def __init__(self, model_type="T2V-1.3B"):
super().__init__()
mean = [
-0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921
]
std = [
2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160
]
self.mean = torch.tensor(mean, dtype=torch.float32)
self.std = torch.tensor(std, dtype=torch.float32)
# init model
self.model = _video_vae(
pretrained_path=str(PROJECT_ROOT / f"wan_models/Wan2.1-{model_type}/Wan2.1_VAE.pth"),
z_dim=16,
).eval().requires_grad_(False)
def decode_to_pixel(self, latent: torch.Tensor) -> torch.Tensor:
# from [batch_size, num_frames, num_channels, height, width]
# to [batch_size, num_channels, num_frames, height, width]
zs = latent.permute(0, 2, 1, 3, 4)
device, dtype = latent.device, latent.dtype
scale = [self.mean.to(device=device, dtype=dtype),
1.0 / self.std.to(device=device, dtype=dtype)]
output = [
self.model.decode(u.unsqueeze(0),
scale).float().clamp_(-1, 1).squeeze(0)
for u in zs
]
output = torch.stack(output, dim=0)
# from [batch_size, num_channels, num_frames, height, width]
# to [batch_size, num_frames, num_channels, height, width]
output = output.permute(0, 2, 1, 3, 4)
return output
def decode(self, latent: torch.Tensor) -> torch.Tensor:
# from [batch_size, num_frames, num_channels, height, width]
# to [batch_size, num_channels, num_frames, height, width]
zs = latent.permute(0, 2, 1, 3, 4)
device, dtype = latent.device, latent.dtype
scale = [self.mean.to(device=device, dtype=dtype),
1.0 / self.std.to(device=device, dtype=dtype)]
output = self.model.decode(zs, scale).clamp_(-1, 1)
# from [batch_size, num_channels, num_frames, height, width]
# to [batch_size, num_frames, num_channels, height, width]
# output = output.permute(0, 2, 1, 3, 4)
return output
def stream_encode(self, video: torch.Tensor, is_scale=False) -> torch.Tensor:
if is_scale:
device, dtype = video.device, video.dtype
scale = [self.mean.to(device=device, dtype=dtype),
1.0 / self.std.to(device=device, dtype=dtype)]
else:
scale = None
return self.model.stream_encode(video, scale)
def stream_decode_to_pixel(self, latent: torch.Tensor) -> torch.Tensor:
zs = latent.permute(0, 2, 1, 3, 4)
zs = zs.to(device=latent.device, dtype=torch.bfloat16)
device, dtype = latent.device, latent.dtype
scale = [self.mean.to(device=device, dtype=dtype),
1.0 / self.std.to(device=device, dtype=dtype)]
output = self.model.stream_decode(zs, scale).float().clamp_(-1, 1)
output = output.permute(0, 2, 1, 3, 4)
return output
class WanDiffusionWrapper(DiffusionModelInterface):
def __init__(self, model_type="T2V-1.3B"):
super().__init__()
self.model = WanModel.from_pretrained(str(PROJECT_ROOT / f"wan_models/Wan2.1-{model_type}/"))
self.model.eval()
self.uniform_timestep = True
self.scheduler = FlowMatchScheduler(
shift=8.0, sigma_min=0.0, extra_one_step=True
)
self.scheduler.set_timesteps(1000, training=True)
self.seq_len = 32760 # [1, 21, 16, 60, 104]
super().post_init()
def enable_gradient_checkpointing(self) -> None:
self.model.enable_gradient_checkpointing()
def _convert_flow_pred_to_x0(self, flow_pred: torch.Tensor, xt: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:
"""
Convert flow matching's prediction to x0 prediction.
flow_pred: the prediction with shape [B, C, H, W]
xt: the input noisy data with shape [B, C, H, W]
timestep: the timestep with shape [B]
pred = noise - x0
x_t = (1-sigma_t) * x0 + sigma_t * noise
we have x0 = x_t - sigma_t * pred
see derivations https://chatgpt.com/share/67bf8589-3d04-8008-bc6e-4cf1a24e2d0e
"""
# use higher precision for calculations
original_dtype = flow_pred.dtype
flow_pred, xt, sigmas, timesteps = map(
lambda x: x.double().to(flow_pred.device), [flow_pred, xt,
self.scheduler.sigmas,
self.scheduler.timesteps]
)
timestep_id = torch.argmin(
(timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1)
x0_pred = xt - sigma_t * flow_pred
return x0_pred.to(original_dtype)
@staticmethod
def _convert_x0_to_flow_pred(scheduler, x0_pred: torch.Tensor, xt: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:
"""
Convert x0 prediction to flow matching's prediction.
x0_pred: the x0 prediction with shape [B, C, H, W]
xt: the input noisy data with shape [B, C, H, W]
timestep: the timestep with shape [B]
pred = (x_t - x_0) / sigma_t
"""
# use higher precision for calculations
original_dtype = x0_pred.dtype
x0_pred, xt, sigmas, timesteps = map(
lambda x: x.double().to(x0_pred.device), [x0_pred, xt,
scheduler.sigmas,
scheduler.timesteps]
)
timestep_id = torch.argmin(
(timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1)
flow_pred = (xt - x0_pred) / sigma_t
return flow_pred.to(original_dtype)
def forward(
self, noisy_image_or_video: torch.Tensor, conditional_dict: dict,
timestep: torch.Tensor, kv_cache: Optional[List[dict]] = None,
crossattn_cache: Optional[List[dict]] = None,
current_start: Optional[int] = None,
current_end: Optional[int] = None
) -> torch.Tensor:
prompt_embeds = conditional_dict["prompt_embeds"]
# [B, F] -> [B]
if self.uniform_timestep:
input_timestep = timestep[:, 0]
else:
input_timestep = timestep
if kv_cache is not None:
flow_pred = self.model(
noisy_image_or_video.permute(0, 2, 1, 3, 4),
t=input_timestep, context=prompt_embeds,
seq_len=self.seq_len,
kv_cache=kv_cache,
crossattn_cache=crossattn_cache,
current_start=current_start,
current_end=current_end
).permute(0, 2, 1, 3, 4)
else:
flow_pred = self.model(
noisy_image_or_video.permute(0, 2, 1, 3, 4),
t=input_timestep, context=prompt_embeds,
seq_len=self.seq_len
).permute(0, 2, 1, 3, 4)
pred_x0 = self._convert_flow_pred_to_x0(
flow_pred=flow_pred.flatten(0, 1),
xt=noisy_image_or_video.flatten(0, 1),
timestep=timestep.flatten(0, 1)
).unflatten(0, flow_pred.shape[:2])
return pred_x0
def forward_input(
self, noisy_image_or_video: torch.Tensor, conditional_dict: dict,
timestep: torch.Tensor,block_mode: str='input', block_num = None, kv_cache: Optional[List[dict]] = None,
crossattn_cache: Optional[List[dict]] = None,
current_start: Optional[int] = None,
current_end: Optional[int] = None,
patched_x_shape: torch.Tensor = None,
block_x: torch.Tensor = None,
) -> torch.Tensor:
assert kv_cache is not None, "kv_cache must be provided"
prompt_embeds = conditional_dict["prompt_embeds"]
# [B, F] -> [B]
if self.uniform_timestep:
input_timestep = timestep[:, 0]
else:
input_timestep = timestep
if block_x is not None and block_mode == 'middle':
noisy_image_or_video = block_x
else:
noisy_image_or_video = noisy_image_or_video.permute(0, 2, 1, 3, 4)
output, patched_x_shape = self.model(
noisy_image_or_video,
t=input_timestep, context=prompt_embeds,
seq_len=self.seq_len,
kv_cache=kv_cache,
crossattn_cache=crossattn_cache,
current_start=current_start,
current_end=current_end,
block_mode=block_mode,
block_num=block_num,
patched_x_shape=patched_x_shape,
)
return output, patched_x_shape
def forward_output(
self, noisy_image_or_video: torch.Tensor, conditional_dict: dict,
timestep: torch.Tensor, block_mode: str='output', block_num = None, kv_cache: Optional[List[dict]] = None,
crossattn_cache: Optional[List[dict]] = None,
current_start: Optional[int] = None,
current_end: Optional[int] = None,
patched_x_shape: torch.Tensor = None,
block_x: torch.Tensor = None,
) -> torch.Tensor:
assert kv_cache is not None, "kv_cache must be provided"
prompt_embeds = conditional_dict["prompt_embeds"]
# [B, F] -> [B]
if self.uniform_timestep:
input_timestep = timestep[:, 0]
else:
input_timestep = timestep
flow_pred = self.model(
block_x,
t=input_timestep, context=prompt_embeds,
seq_len=self.seq_len,
kv_cache=kv_cache,
crossattn_cache=crossattn_cache,
current_start=current_start,
current_end=current_end,
block_mode=block_mode,
block_num=block_num,
patched_x_shape=patched_x_shape,
).permute(0, 2, 1, 3, 4)
pred_x0 = self._convert_flow_pred_to_x0(
flow_pred=flow_pred.flatten(0, 1),
xt=noisy_image_or_video.flatten(0, 1),
timestep=timestep.flatten(0, 1)
).unflatten(0, flow_pred.shape[:2])
return pred_x0
class CausalWanDiffusionWrapper(WanDiffusionWrapper):
def __init__(self, model_type="T2V-1.3B"):
super().__init__()
self.model = CausalWanModel.from_pretrained(
str(PROJECT_ROOT / f"wan_models/Wan2.1-{model_type}/"))
self.model.eval()
self.uniform_timestep = False
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