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a61978a | 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 | import torch
import torch.nn.functional as F
from diffsynth.pipelines.wan_video import WanVideoPipeline
from diffsynth.diffusion.base_pipeline import PipelineUnit
from PIL import Image
def _frame_to_vae_video_tensor(pipe: WanVideoPipeline, frame):
if isinstance(frame, Image.Image):
return pipe.preprocess_video([frame])
if not torch.is_tensor(frame):
raise TypeError(f"Unsupported frame type for VAE encoding: {type(frame)}")
tensor = frame
if tensor.dim() == 3:
# (C, H, W) -> (1, C, 1, H, W)
if tensor.shape[0] not in (1, 3):
tensor = tensor.permute(2, 0, 1).contiguous()
tensor = tensor.unsqueeze(0).unsqueeze(2)
elif tensor.dim() == 4:
# Accept (1, C, H, W) or (C, T, H, W)
if tensor.shape[0] == 1 and tensor.shape[1] in (1, 3):
tensor = tensor.unsqueeze(2)
elif tensor.shape[0] in (1, 3):
tensor = tensor.unsqueeze(0)
else:
raise ValueError(f"Unexpected 4D frame tensor shape: {tuple(tensor.shape)}")
elif tensor.dim() == 5:
pass
else:
raise ValueError(f"Unsupported frame tensor rank: {tensor.dim()}")
return tensor.to(dtype=pipe.torch_dtype, device=pipe.device)
# custom_units.py
class WanVideoUnit_GeoNeXtFused(PipelineUnit):
"""
1. Receives noise from NoiseInitializer.
2. Receives input_video (multi-frame).
3. Outputs input_latents (clean GT latents) for loss computation.
4. Outputs latents (noisy latents) for model_fn input.
5. Outputs clean_reference_latents used by model_fn for frame replacement.
"""
def __init__(self, num_condition_frames=1, rgb_condition_mode="first_frame", rgb_condition_scale=1.0, loss_mask_mode="depth_only"):
super().__init__(
input_params=("input_video", "noise", "tiled", "tile_size", "tile_stride", "valid_mask", "sky_mask", "target_frame_loss_mask"),
output_params=("latents", "input_latents", "clean_reference_latents", "rgb_condition_latents", "rgb_condition_mode", "rgb_condition_scale"),
onload_model_names=("vae",)
)
self.num_condition_frames = num_condition_frames
self.rgb_condition_mode = rgb_condition_mode
self.rgb_condition_scale = float(rgb_condition_scale)
self.loss_mask_mode = loss_mask_mode
def _downsample_mask(self, mask, latent_h, latent_w):
if mask is None:
return None
if mask.dim() == 2:
mask = mask.unsqueeze(0).unsqueeze(0)
elif mask.dim() == 3:
mask = mask.unsqueeze(0)
mask = mask.float()
invalid_mask = ~mask.bool()
src_h, src_w = invalid_mask.shape[-2:]
scale_h = max(src_h // latent_h, 1)
scale_w = max(src_w // latent_w, 1)
if src_h % latent_h == 0 and src_w % latent_w == 0:
invalid_down = F.max_pool2d(invalid_mask.float(), kernel_size=(scale_h, scale_w), stride=(scale_h, scale_w))
else:
invalid_down = F.interpolate(invalid_mask.float(), size=(latent_h, latent_w), mode="nearest")
return (~invalid_down.bool()).to(mask.device)
def _build_loss_mask(self, input_latents, valid_mask=None, sky_mask=None, target_frame_loss_mask=None):
bsz, channels, frames, latent_h, latent_w = input_latents.shape
loss_mask = torch.zeros((bsz, channels, frames, latent_h, latent_w), device=input_latents.device, dtype=input_latents.dtype)
default_mask = torch.ones((1, 1, latent_h, latent_w), device=input_latents.device, dtype=torch.bool)
valid_down = self._downsample_mask(valid_mask, latent_h, latent_w) if valid_mask is not None else default_mask
sky_down = self._downsample_mask(sky_mask, latent_h, latent_w) if sky_mask is not None else None
for frame_idx in range(frames):
frame_mask = default_mask
if frame_idx >= self.num_condition_frames:
if self.loss_mask_mode == "all_targets":
frame_mask = valid_down
if sky_down is not None:
frame_mask = frame_mask | sky_down
elif self.loss_mask_mode == "depth_only":
# Backward-compatible behavior: only apply geometric masks on
# the first predicted frame (typically depth).
if frame_idx == self.num_condition_frames:
frame_mask = valid_down
if sky_down is not None:
frame_mask = frame_mask | sky_down
else:
raise ValueError(f"Unsupported loss_mask_mode: {self.loss_mask_mode}")
loss_mask[:, :, frame_idx, :, :] = frame_mask.to(dtype=input_latents.dtype)
# Optional source-specific supervision gating for predicted target frames.
# target_frame_loss_mask is expected to align with frames after condition frames.
if target_frame_loss_mask is not None:
tfm = target_frame_loss_mask
if isinstance(tfm, torch.Tensor):
tfm = tfm.to(device=input_latents.device, dtype=input_latents.dtype).flatten()
else:
tfm = torch.tensor(tfm, device=input_latents.device, dtype=input_latents.dtype).flatten()
n_pred = max(frames - self.num_condition_frames, 0)
if tfm.numel() > 0 and n_pred > 0:
n = min(n_pred, int(tfm.numel()))
gate = tfm[:n].view(1, 1, n, 1, 1)
loss_mask[:, :, self.num_condition_frames:self.num_condition_frames + n, :, :] *= gate
return loss_mask
def process(self, pipe: WanVideoPipeline, input_video, noise, tiled, tile_size, tile_stride, valid_mask=None, sky_mask=None, target_frame_loss_mask=None):
if input_video is None or len(input_video) == 0:
return {}
if pipe.scheduler.training and len(input_video) < 2:
raise ValueError(f"Training expects at least 2-frame input_video, got {len(input_video)}")
if not pipe.scheduler.training and len(input_video) < self.num_condition_frames:
raise ValueError(f"Inference expects at least {self.num_condition_frames} input frames, got {len(input_video)}")
if pipe.scheduler.training and not (1 <= self.num_condition_frames < len(input_video)):
raise ValueError(f"num_condition_frames must be in [1, {len(input_video) - 1}], got {self.num_condition_frames}")
pipe.load_models_to_device(self.onload_model_names)
if not pipe.scheduler.training:
cond_latents = []
for img in input_video[:self.num_condition_frames]:
video_tensor = _frame_to_vae_video_tensor(pipe, img)
z = pipe.vae.encode(
video_tensor,
device=pipe.device,
tiled=tiled,
tile_size=tile_size,
tile_stride=tile_stride
).to(dtype=pipe.torch_dtype, device=pipe.device)
cond_latents.append(z)
clean_ref = torch.cat(cond_latents, dim=2)
return {
"latents": noise,
"clean_reference_latents": clean_ref,
"rgb_condition_latents": clean_ref[:, :, :1],
"rgb_condition_mode": self.rgb_condition_mode,
"rgb_condition_scale": self.rgb_condition_scale,
}
encoded_latents_list = []
for img in input_video:
video_tensor = _frame_to_vae_video_tensor(pipe, img)
z = pipe.vae.encode(
video_tensor,
device=pipe.device,
tiled=tiled,
tile_size=tile_size,
tile_stride=tile_stride
).to(dtype=pipe.torch_dtype, device=pipe.device)
encoded_latents_list.append(z)
full_clean_latents = torch.cat(encoded_latents_list, dim=2)
clean_ref = torch.cat(encoded_latents_list[:self.num_condition_frames], dim=2)
rgb_condition = encoded_latents_list[0]
mask = self._build_loss_mask(
full_clean_latents,
valid_mask=valid_mask,
sky_mask=sky_mask,
target_frame_loss_mask=target_frame_loss_mask,
)
if pipe.scheduler.training:
return {
"latents": noise,
"input_latents": full_clean_latents,
"clean_reference_latents": clean_ref,
"rgb_condition_latents": rgb_condition,
"rgb_condition_mode": self.rgb_condition_mode,
"rgb_condition_scale": self.rgb_condition_scale,
"loss_mask": mask
}
else:
return {
"latents": noise,
"clean_reference_latents": clean_ref,
"rgb_condition_latents": clean_ref[:, :, :1],
"rgb_condition_mode": self.rgb_condition_mode,
"rgb_condition_scale": self.rgb_condition_scale,
}
# Backward-compatible alias.
WanVideoUnit_ThreeFrameFused = WanVideoUnit_GeoNeXtFused
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