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3e936b2 | 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 | import types
from typing import List, Optional
import torch
from torch import nn
from utils.scheduler import SchedulerInterface, FlowMatchScheduler
from wan.modules.tokenizers import HuggingfaceTokenizer
from wan.modules.model import WanModel, RegisterTokens, GanAttentionBlock
from wan.modules.vae import _video_vae
from wan.modules.t5 import umt5_xxl
from wan.modules.causal_model import CausalWanModel
from wan.modules.causal_model_infinity import CausalWanModel as CausalWanModelInfinity
from wan.modules.causal_model_infinity_memory import CausalWanModelInfinityMemory
class WanTextEncoder(torch.nn.Module):
def __init__(self) -> 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('wan_models/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth', map_location='cpu', weights_only=False))
if torch.cuda.is_available():
self.text_encoder = self.text_encoder.cuda()
self.tokenizer = HuggingfaceTokenizer(name='wan_models/Wan2.1-T2V-1.3B/google/umt5-xxl/', seq_len=512, clean='whitespace')
@property
def device(self):
return torch.cuda.current_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
return {'prompt_embeds': context}
class WanVAEWrapper(torch.nn.Module):
def __init__(self):
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, 1.916]
self.mean = torch.tensor(mean, dtype=torch.float32)
self.std = torch.tensor(std, dtype=torch.float32)
self.model = _video_vae(pretrained_path='wan_models/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth', z_dim=16).eval().requires_grad_(False)
def encode_to_latent(self, pixel: torch.Tensor) -> torch.Tensor:
device, dtype = (pixel.device, pixel.dtype)
scale = [self.mean.to(device=device, dtype=dtype), 1.0 / self.std.to(device=device, dtype=dtype)]
output = [self.model.encode(u.unsqueeze(0), scale).float().squeeze(0) for u in pixel]
output = torch.stack(output, dim=0)
output = output.permute(0, 2, 1, 3, 4)
return output
def decode_to_pixel(self, latent: torch.Tensor, use_cache: bool=False) -> torch.Tensor:
zs = latent.permute(0, 2, 1, 3, 4)
if use_cache:
assert latent.shape[0] == 1, 'Batch size must be 1 when using cache'
device, dtype = (latent.device, latent.dtype)
scale = [self.mean.to(device=device, dtype=dtype), 1.0 / self.std.to(device=device, dtype=dtype)]
if use_cache:
decode_function = self.model.cached_decode
else:
decode_function = self.model.decode
output = []
for u in zs:
output.append(decode_function(u.unsqueeze(0), scale).float().clamp_(-1, 1).squeeze(0))
output = torch.stack(output, dim=0)
output = output.permute(0, 2, 1, 3, 4)
return output
def decode_to_pixel_chunk(self, latent: torch.Tensor, use_cache: bool=False, chunk_size: int=120) -> torch.Tensor:
zs = latent.permute(0, 2, 1, 3, 4)
if use_cache:
assert latent.shape[0] == 1, 'Batch size must be 1 when using cache'
device, dtype = (latent.device, latent.dtype)
scale = [self.mean.to(device=device, dtype=dtype), 1.0 / self.std.to(device=device, dtype=dtype)]
if use_cache:
decode_function = self.model.cached_decode
else:
decode_function = self.model.decode
output = []
for u in zs:
num_frames = u.shape[1]
if num_frames <= chunk_size:
decoded = decode_function(u.unsqueeze(0), scale).float().clamp_(-1, 1).squeeze(0)
decoded = decoded.cpu()
else:
decoded_chunks = []
for start_idx in range(0, num_frames, chunk_size):
end_idx = min(start_idx + chunk_size, num_frames)
chunk = u[:, start_idx:end_idx, :, :]
self.model.clear_cache()
decoded_chunk = decode_function(chunk.unsqueeze(0), scale).float().clamp_(-1, 1).squeeze(0)
decoded_chunks.append(decoded_chunk.cpu())
del decoded_chunk
torch.cuda.empty_cache()
decoded = torch.cat(decoded_chunks, dim=1)
self.model.clear_cache()
output.append(decoded)
output = torch.stack(output, dim=0)
output = output.permute(0, 2, 1, 3, 4)
return output
class WanDiffusionWrapper(torch.nn.Module):
def __init__(self, model_name='Wan2.1-T2V-1.3B', timestep_shift=8.0, is_causal=False, local_attn_size=-1, sink_size=0, use_infinite_attention=False, dr_rope=False, tri_rope_cont=False, tri_rope_pmax=21, relative_rope=False, relative_rope_pmax=21):
super().__init__()
if is_causal:
if use_infinite_attention:
if relative_rope:
self.model = CausalWanModelInfinityMemory.from_pretrained(f'wan_models/{model_name}/', local_attn_size=local_attn_size, sink_size=sink_size)
self.model.enable_infmem(relative_rope=True, relative_rope_pmax=relative_rope_pmax, num_frame_per_block_attr=3)
else:
self.model = CausalWanModelInfinity.from_pretrained(f'wan_models/{model_name}/', local_attn_size=local_attn_size, sink_size=sink_size)
else:
self.model = CausalWanModel.from_pretrained(f'wan_models/{model_name}/', local_attn_size=local_attn_size, sink_size=sink_size, dr_rope=dr_rope, tri_rope_cont=tri_rope_cont, tri_rope_pmax=tri_rope_pmax, relative_rope=relative_rope, relative_rope_pmax=relative_rope_pmax)
else:
self.model = WanModel.from_pretrained(f'wan_models/{model_name}/')
self.model.eval()
self.uniform_timestep = not is_causal
self.scheduler = FlowMatchScheduler(shift=timestep_shift, sigma_min=0.0, extra_one_step=True)
self.scheduler.set_timesteps(1000, training=True)
self.seq_len = 1560 * local_attn_size if local_attn_size > 21 else 32760
self.post_init()
def enable_gradient_checkpointing(self) -> None:
self.model.enable_gradient_checkpointing()
def adding_cls_branch(self, atten_dim=1536, num_class=4, time_embed_dim=0) -> None:
self._cls_pred_branch = nn.Sequential(nn.LayerNorm(atten_dim * 3 + time_embed_dim), nn.Linear(atten_dim * 3 + time_embed_dim, 1536), nn.SiLU(), nn.Linear(atten_dim, num_class))
self._cls_pred_branch.requires_grad_(True)
num_registers = 3
self._register_tokens = RegisterTokens(num_registers=num_registers, dim=atten_dim)
self._register_tokens.requires_grad_(True)
gan_ca_blocks = []
for _ in range(num_registers):
block = GanAttentionBlock()
gan_ca_blocks.append(block)
self._gan_ca_blocks = nn.ModuleList(gan_ca_blocks)
self._gan_ca_blocks.requires_grad_(True)
def _convert_flow_pred_to_x0(self, flow_pred: torch.Tensor, xt: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:
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:
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, classify_mode: Optional[bool]=False, concat_time_embeddings: Optional[bool]=False, clean_x: Optional[torch.Tensor]=None, aug_t: Optional[torch.Tensor]=None, cache_start: Optional[int]=None, sink_recache_after_switch=False) -> torch.Tensor:
prompt_embeds = conditional_dict['prompt_embeds']
if self.uniform_timestep:
input_timestep = timestep[:, 0]
else:
input_timestep = timestep
logits = None
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, cache_start=cache_start, sink_recache_after_switch=sink_recache_after_switch).permute(0, 2, 1, 3, 4)
elif clean_x 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, clean_x=clean_x.permute(0, 2, 1, 3, 4), aug_t=aug_t, sink_recache_after_switch=sink_recache_after_switch).permute(0, 2, 1, 3, 4)
elif classify_mode:
flow_pred, logits = self.model(noisy_image_or_video.permute(0, 2, 1, 3, 4), t=input_timestep, context=prompt_embeds, seq_len=self.seq_len, classify_mode=True, register_tokens=self._register_tokens, cls_pred_branch=self._cls_pred_branch, gan_ca_blocks=self._gan_ca_blocks, concat_time_embeddings=concat_time_embeddings, sink_recache_after_switch=sink_recache_after_switch)
flow_pred = flow_pred.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, sink_recache_after_switch=sink_recache_after_switch).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])
if logits is not None:
return (flow_pred, pred_x0, logits)
return (flow_pred, pred_x0)
def get_scheduler(self) -> SchedulerInterface:
scheduler = self.scheduler
scheduler.convert_x0_to_noise = types.MethodType(SchedulerInterface.convert_x0_to_noise, scheduler)
scheduler.convert_noise_to_x0 = types.MethodType(SchedulerInterface.convert_noise_to_x0, scheduler)
scheduler.convert_velocity_to_x0 = types.MethodType(SchedulerInterface.convert_velocity_to_x0, scheduler)
self.scheduler = scheduler
return scheduler
def post_init(self):
self.get_scheduler()
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