Compare commits
1
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 kijai
|
2e7c00a03b |
+1
-3
@@ -9,6 +9,4 @@ logs/
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.idea
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tools/
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.vscode/
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convert_*
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*.pt
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*.pth
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convert_*
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@@ -1,42 +0,0 @@
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# Copyright (c) 2024-2025 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Dict, List, Optional, Tuple, Union
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import numpy as np
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import torch
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def process_tracks(tracks_np: np.ndarray, frame_size: Tuple[int, int], quant_multi: int = 8, **kwargs):
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# tracks: shape [t, h, w, 3] => samples align with 24 fps, model trained with 16 fps.
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# frame_size: tuple (W, H)
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tracks = torch.from_numpy(tracks_np).float()
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if tracks.shape[1] == 121:
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tracks = torch.permute(tracks, (1, 0, 2, 3))
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tracks, visibles = tracks[..., :2], tracks[..., 2:3]
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short_edge = min(*frame_size)
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tracks = tracks - torch.tensor([*frame_size]).type_as(tracks) / 2
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tracks = tracks / short_edge * 2
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visibles = visibles * 2 - 1
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trange = torch.linspace(-1, 1, tracks.shape[0]).view(-1, 1, 1, 1).expand(*visibles.shape)
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out_ = torch.cat([trange, tracks, visibles], dim=-1).view(121, -1, 4)
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out_0 = out_[:1]
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out_l = out_[1:] # 121 => 120 | 1
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out_l = torch.repeat_interleave(out_l, 2, dim=0)[1::3] # 120 => 240 => 80
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return torch.cat([out_0, out_l], dim=0)
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@@ -1,142 +0,0 @@
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# Copyright (c) 2024-2025 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Optional, Tuple, Union
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import torch
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# Refer to https://github.com/Angtian/VoGE/blob/main/VoGE/Utils.py
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def ind_sel(target: torch.Tensor, ind: torch.Tensor, dim: int = 1):
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"""
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:param target: [... (can be k or 1), n > M, ...]
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:param ind: [... (k), M]
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:param dim: dim to apply index on
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:return: sel_target [... (k), M, ...]
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"""
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assert (
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len(ind.shape) > dim
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), "Index must have the target dim, but get dim: %d, ind shape: %s" % (dim, str(ind.shape))
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target = target.expand(
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*tuple(
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[ind.shape[k] if target.shape[k] == 1 else -1 for k in range(dim)]
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+ [
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-1,
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]
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* (len(target.shape) - dim)
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)
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)
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ind_pad = ind
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if len(target.shape) > dim + 1:
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for _ in range(len(target.shape) - (dim + 1)):
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ind_pad = ind_pad.unsqueeze(-1)
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ind_pad = ind_pad.expand(*(-1,) * (dim + 1), *target.shape[(dim + 1) : :])
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return torch.gather(target, dim=dim, index=ind_pad)
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def merge_final(vert_attr: torch.Tensor, weight: torch.Tensor, vert_assign: torch.Tensor):
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"""
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:param vert_attr: [n, d] or [b, n, d] color or feature of each vertex
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:param weight: [b(optional), w, h, M] weight of selected vertices
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:param vert_assign: [b(optional), w, h, M] selective index
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:return:
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"""
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target_dim = len(vert_assign.shape) - 1
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if len(vert_attr.shape) == 2:
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assert vert_attr.shape[0] > vert_assign.max()
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# [n, d] ind: [b(optional), w, h, M]-> [b(optional), w, h, M, d]
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# sel_attr = ind_sel(
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# vert_attr[(None,) * target_dim], vert_assign.type(torch.long), dim=target_dim
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# )
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new_shape = [1] * target_dim + list(vert_attr.shape)
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tensor = vert_attr.reshape(new_shape)
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sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim)
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else:
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assert vert_attr.shape[1] > vert_assign.max()
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#sel_attr = ind_sel(
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# vert_attr[:, *(None,) * (target_dim - 1)], vert_assign.type(torch.long), dim=target_dim
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#)
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new_shape = [vert_attr.shape[0]] + [1] * (target_dim - 1) + list(vert_attr.shape[1:])
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tensor = vert_attr.reshape(new_shape)
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sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim)
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# [b(optional), w, h, M]
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final_attr = torch.sum(sel_attr * weight.unsqueeze(-1), dim=-2)
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return final_attr
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def patch_motion(
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tracks: torch.FloatTensor, # (B, T, N, 4)
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vid: torch.FloatTensor, # (C, T, H, W)
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temperature: float = 220.0,
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vae_divide: tuple = (4, 16),
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topk: int = 2,
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):
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with torch.no_grad():
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_, T, H, W = vid.shape
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N = tracks.shape[2]
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_, tracks, visible = torch.split(
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tracks, [1, 2, 1], dim=-1
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) # (B, T, N, 2) | (B, T, N, 1)
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tracks_n = tracks / torch.tensor([W / min(H, W), H / min(H, W)], device=tracks.device)
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tracks_n = tracks_n.clamp(-1, 1)
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visible = visible.clamp(0, 1)
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xx = torch.linspace(-W / min(H, W), W / min(H, W), W)
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yy = torch.linspace(-H / min(H, W), H / min(H, W), H)
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grid = torch.stack(torch.meshgrid(yy, xx, indexing="ij")[::-1], dim=-1).to(
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tracks.device
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)
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tracks_pad = tracks[:, 1:]
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visible_pad = visible[:, 1:]
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visible_align = visible_pad.view(T - 1, 4, *visible_pad.shape[2:]).sum(1)
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tracks_align = (tracks_pad * visible_pad).view(T - 1, 4, *tracks_pad.shape[2:]).sum(
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1
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) / (visible_align + 1e-5)
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dist_ = (
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(tracks_align[:, None, None] - grid[None, :, :, None]).pow(2).sum(-1)
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) # T, H, W, N
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weight = torch.exp(-dist_ * temperature) * visible_align.clamp(0, 1).view(
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T - 1, 1, 1, N
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)
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vert_weight, vert_index = torch.topk(
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weight, k=min(topk, weight.shape[-1]), dim=-1
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)
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grid_mode = "bilinear"
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point_feature = torch.nn.functional.grid_sample(
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vid[vae_divide[0]:].permute(1, 0, 2, 3)[:1],
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tracks_n[:, :1].type(vid.dtype),
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mode=grid_mode,
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padding_mode="zeros",
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align_corners=False,
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)
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point_feature = point_feature.squeeze(0).squeeze(1).permute(1, 0) # N, C=16
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out_feature = merge_final(point_feature, vert_weight, vert_index).permute(3, 0, 1, 2) # T - 1, H, W, C => C, T - 1, H, W
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out_weight = vert_weight.sum(-1) # T - 1, H, W
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# out feature -> already soft weighted
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mix_feature = out_feature + vid[vae_divide[0]:, 1:] * (1 - out_weight.clamp(0, 1))
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out_feature_full = torch.cat([vid[vae_divide[0]:, :1], mix_feature], dim=1) # C, T, H, W
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out_mask_full = torch.cat([torch.ones_like(out_weight[:1]), out_weight], dim=0) # T, H, W
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return torch.cat([out_mask_full[None].expand(vae_divide[0], -1, -1, -1), out_feature_full], dim=0)
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-329
@@ -1,329 +0,0 @@
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import json
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from .motion import process_tracks
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import numpy as np
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from typing import List, Tuple
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import torch
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FIXED_LENGTH = 121
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def pad_pts(tr):
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"""Convert list of {x,y} to (FIXED_LENGTH,1,3) array, padding/truncating."""
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pts = np.array([[p['x'], p['y'], 1] for p in tr], dtype=np.float32)
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n = pts.shape[0]
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if n < FIXED_LENGTH:
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pad = np.zeros((FIXED_LENGTH - n, 3), dtype=np.float32)
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pts = np.vstack((pts, pad))
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else:
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pts = pts[:FIXED_LENGTH]
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return pts.reshape(FIXED_LENGTH, 1, 3)
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def age_to_bgr(ratio: float) -> Tuple[int,int,int]:
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"""
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Map ratio∈[0,1] through: 0→blue, 1/3→green, 2/3→yellow, 1→red.
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Returns (B,G,R) for OpenCV.
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"""
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if ratio <= 1/3:
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# blue→green
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t = ratio / (1/3)
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b = int(255 * (1 - t))
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g = int(255 * t)
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r = 0
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elif ratio <= 2/3:
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# green→yellow
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t = (ratio - 1/3) / (1/3)
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b = 0
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g = 255
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r = int(255 * t)
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else:
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# yellow→red
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t = (ratio - 2/3) / (1/3)
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b = 0
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g = int(255 * (1 - t))
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r = 255
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return (r, g, b)
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def paint_point_track(
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frames: np.ndarray,
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point_tracks: np.ndarray,
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visibles: np.ndarray,
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min_radius: int = 1,
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max_radius: int = 6,
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max_retain: int = 50
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) -> np.ndarray:
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"""
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Draws every past point of each track on each frame, with radius and color
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interpolated by the point's age (old→small to new→large).
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Args:
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frames: [F, H, W, 3] uint8 RGB
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point_tracks:[N, F, 2] float32 – (x,y) in pixel coords
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visibles: [N, F] bool – visibility mask
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min_radius: radius for the very first point (oldest)
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max_radius: radius for the current point (newest)
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Returns:
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video: [F, H, W, 3] uint8 RGB
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"""
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import cv2
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num_points, num_frames = point_tracks.shape[:2]
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H, W = frames.shape[1:3]
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video = frames.copy()
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for t in range(num_frames):
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# start from the original frame
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frame = video[t].copy()
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for i in range(num_points):
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# draw every past step τ = 0..t
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for τ in range(t + 1):
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if not visibles[i, τ]:
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continue
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if t - τ > max_retain:
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continue
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# sub-pixel offset + clamp
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x, y = point_tracks[i, τ] + 0.5
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xi = int(np.clip(x, 0, W - 1))
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yi = int(np.clip(y, 0, H - 1))
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# age‐ratio in [0,1]
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if num_frames > 1:
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ratio = 1 - float(t - τ) / max_retain
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else:
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ratio = 1.0
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# interpolated radius
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radius = int(round(min_radius + (max_radius - min_radius) * ratio))
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# OpenCV draws in BGR order:
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color_rgb = age_to_bgr(ratio)
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# filled circle
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cv2.circle(frame, (xi, yi), radius, color_rgb, thickness=-1)
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video[t] = frame
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return video
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def parse_json_tracks(tracks):
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tracks_data = []
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try:
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# If tracks is a string, try to parse it as JSON
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if isinstance(tracks, str):
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parsed = json.loads(tracks.replace("'", '"'))
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tracks_data.extend(parsed)
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else:
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# If tracks is a list of strings, parse each one
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for track_str in tracks:
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parsed = json.loads(track_str.replace("'", '"'))
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tracks_data.append(parsed)
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# Check if we have a single track (dict with x,y) or a list of tracks
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if tracks_data and isinstance(tracks_data[0], dict) and 'x' in tracks_data[0]:
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# Single track detected, wrap it in a list
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tracks_data = [tracks_data]
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elif tracks_data and isinstance(tracks_data[0], list) and tracks_data[0] and isinstance(tracks_data[0][0], dict) and 'x' in tracks_data[0][0]:
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# Already a list of tracks, nothing to do
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pass
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else:
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# Unexpected format
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print(f"Warning: Unexpected track format: {type(tracks_data[0])}")
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except json.JSONDecodeError as e:
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print(f"Error parsing tracks JSON: {e}")
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tracks_data = []
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return tracks_data
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class WanVideoATITracks:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("WANVIDEOMODEL", ),
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"tracks": ("STRING",),
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"width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
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"height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}),
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"temperature": ("FLOAT", {"default": 220.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
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"topk": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}),
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply ATI"}),
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"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply ATI"}),
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},
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}
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RETURN_TYPES = ("WANVIDEOMODEL",)
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RETURN_NAMES = ("model",)
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FUNCTION = "patchmodel"
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CATEGORY = "WanVideoWrapper"
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def patchmodel(self, model, tracks, width, height, temperature, topk, start_percent, end_percent):
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tracks_data = parse_json_tracks(tracks)
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arrs = []
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for track in tracks_data:
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pts = pad_pts(track)
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arrs.append(pts)
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tracks_np = np.stack(arrs, axis=0)
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processed_tracks = process_tracks(tracks_np, (width, height))
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patcher = model.clone()
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patcher.model_options["transformer_options"]["ati_tracks"] = processed_tracks.unsqueeze(0)
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patcher.model_options["transformer_options"]["ati_temperature"] = temperature
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patcher.model_options["transformer_options"]["ati_topk"] = topk
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patcher.model_options["transformer_options"]["ati_start_percent"] = start_percent
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patcher.model_options["transformer_options"]["ati_end_percent"] = end_percent
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return (patcher,)
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class WanVideoATITracksVisualize:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
|
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"images": ("IMAGE",),
|
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"tracks": ("STRING",),
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"min_radius": ("INT", {"default": 1, "min": 0, "max": 100, "step": 1, "tooltip": "radius for the very first point (oldest)"}),
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"max_radius": ("INT", {"default": 6, "min": 0, "max": 100, "step": 1, "tooltip": "radius for the current point (newest)"}),
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"max_retain": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1, "tooltip": "Maximum number of points to retain"}),
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},
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||||
}
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|
||||
RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
|
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FUNCTION = "patchmodel"
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||||
CATEGORY = "WanVideoWrapper"
|
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|
||||
def patchmodel(self, images, tracks, min_radius, max_radius, max_retain):
|
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tracks_data = parse_json_tracks(tracks)
|
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arrs = []
|
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for track in tracks_data:
|
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pts = pad_pts(track)
|
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arrs.append(pts)
|
||||
|
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tracks_np = np.stack(arrs, axis=0)
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track = np.repeat(tracks_np, 2, axis=1)[:, ::3]
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points = track[:, :, 0, :2].astype(np.float32)
|
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visibles = track[:, :, 0, 2].astype(np.float32)
|
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|
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if images.shape[0] < points.shape[1]:
|
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repeat_count = (points.shape[1] + images.shape[0] - 1) // images.shape[0]
|
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images = images.repeat(repeat_count, 1, 1, 1)
|
||||
images = images[:points.shape[1]]
|
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elif images.shape[0] > points.shape[1]:
|
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images = images[:points.shape[1]]
|
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|
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video_viz = paint_point_track(images.cpu().numpy(), points, visibles, min_radius, max_radius, max_retain)
|
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video_viz = torch.from_numpy(video_viz).float()
|
||||
|
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return (video_viz,)
|
||||
|
||||
from comfy import utils
|
||||
import types
|
||||
from .motion_patch import patch_motion
|
||||
|
||||
class WanConcatCondPatch:
|
||||
def __init__(self, tracks, temperature, topk):
|
||||
self.tracks = tracks
|
||||
self.temperature = temperature
|
||||
self.topk = topk
|
||||
|
||||
def __get__(self, obj, objtype=None):
|
||||
# Create bound method with stored parameters
|
||||
def wrapped_concat_cond(self_module, *args, **kwargs):
|
||||
return modified_concat_cond(self_module, self.tracks, self.temperature, self.topk, *args, **kwargs)
|
||||
return types.MethodType(wrapped_concat_cond, obj)
|
||||
|
||||
def modified_concat_cond(self, tracks, temperature, topk, **kwargs):
|
||||
noise = kwargs.get("noise", None)
|
||||
extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1]
|
||||
if extra_channels == 0:
|
||||
return None
|
||||
|
||||
image = kwargs.get("concat_latent_image", None)
|
||||
device = kwargs["device"]
|
||||
|
||||
if image is None:
|
||||
shape_image = list(noise.shape)
|
||||
shape_image[1] = extra_channels
|
||||
image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)
|
||||
else:
|
||||
image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
|
||||
for i in range(0, image.shape[1], 16):
|
||||
image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 16])
|
||||
image = utils.resize_to_batch_size(image, noise.shape[0])
|
||||
|
||||
if not self.image_to_video or extra_channels == image.shape[1]:
|
||||
return image
|
||||
|
||||
if image.shape[1] > (extra_channels - 4):
|
||||
image = image[:, :(extra_channels - 4)]
|
||||
|
||||
mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
|
||||
if mask is None:
|
||||
mask = torch.zeros_like(noise)[:, :4]
|
||||
else:
|
||||
if mask.shape[1] != 4:
|
||||
mask = torch.mean(mask, dim=1, keepdim=True)
|
||||
mask = 1.0 - mask
|
||||
mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
|
||||
if mask.shape[-3] < noise.shape[-3]:
|
||||
mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
|
||||
if mask.shape[1] == 1:
|
||||
mask = mask.repeat(1, 4, 1, 1, 1)
|
||||
mask = utils.resize_to_batch_size(mask, noise.shape[0])
|
||||
|
||||
image_cond = torch.cat((mask, image), dim=1)
|
||||
image_cond_ati = patch_motion(tracks.to(image_cond.device, image_cond.dtype), image_cond[0],
|
||||
temperature=temperature, topk=topk)
|
||||
|
||||
return image_cond_ati.unsqueeze(0)
|
||||
|
||||
class WanVideoATI_comfy:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"model": ("MODEL", ),
|
||||
"width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}),
|
||||
"height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}),
|
||||
"tracks": ("STRING",),
|
||||
"temperature": ("FLOAT", {"default": 220.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
|
||||
"topk": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
RETURN_NAMES = ("model", )
|
||||
FUNCTION = "patchcond"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def patchcond(self, model, tracks, width, height, temperature, topk):
|
||||
|
||||
tracks_data = parse_json_tracks(tracks)
|
||||
arrs = []
|
||||
for track in tracks_data:
|
||||
pts = pad_pts(track)
|
||||
arrs.append(pts)
|
||||
|
||||
tracks_np = np.stack(arrs, axis=0)
|
||||
|
||||
processed_tracks = process_tracks(tracks_np, (width, height))
|
||||
|
||||
model_clone = model.clone()
|
||||
model_clone.add_object_patch(
|
||||
"concat_cond",
|
||||
WanConcatCondPatch(
|
||||
processed_tracks.unsqueeze(0), temperature, topk
|
||||
).__get__(model.model, model.model.__class__)
|
||||
)
|
||||
|
||||
return (model_clone,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoATITracks": WanVideoATITracks,
|
||||
"WanVideoATITracksVisualize": WanVideoATITracksVisualize,
|
||||
"WanVideoATI_comfy": WanVideoATI_comfy,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoATITracks": "WanVideo ATI Tracks",
|
||||
"WanVideoATITracksVisualize": "WanVideo ATI Tracks Visualize",
|
||||
"WanVideoATI_comfy": "WanVideo ATI Comfy",
|
||||
}
|
||||
@@ -1,157 +0,0 @@
|
||||
from einops import rearrange
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
CACHE_T = 2
|
||||
|
||||
class RMS_norm(nn.Module):
|
||||
|
||||
def __init__(self, dim, channel_first=True, images=True, bias=False):
|
||||
super().__init__()
|
||||
broadcastable_dims = (1, 1, 1) if not images else (1, 1)
|
||||
shape = (dim, *broadcastable_dims) if channel_first else (dim,)
|
||||
|
||||
self.channel_first = channel_first
|
||||
self.scale = dim**0.5
|
||||
self.gamma = nn.Parameter(torch.ones(shape))
|
||||
self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.
|
||||
|
||||
def forward(self, x):
|
||||
return F.normalize(
|
||||
x, dim=(1 if self.channel_first else
|
||||
-1)) * self.scale * self.gamma + self.bias
|
||||
|
||||
class CausalConv3d(nn.Conv3d):
|
||||
"""
|
||||
Causal 3d convolusion.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self._padding = (self.padding[2], self.padding[2], self.padding[1],
|
||||
self.padding[1], 2 * self.padding[0], 0)
|
||||
self.padding = (0, 0, 0)
|
||||
|
||||
def forward(self, x, cache_x=None):
|
||||
padding = list(self._padding)
|
||||
if cache_x is not None and self._padding[4] > 0:
|
||||
cache_x = cache_x.to(x.device)
|
||||
x = torch.cat([cache_x, x], dim=2)
|
||||
padding[4] -= cache_x.shape[2]
|
||||
x = F.pad(x, padding, mode='replicate')
|
||||
|
||||
return super().forward(x)
|
||||
|
||||
class PixelShuffle3d(nn.Module):
|
||||
def __init__(self, ff, hh, ww):
|
||||
super().__init__()
|
||||
self.ff = ff
|
||||
self.hh = hh
|
||||
self.ww = ww
|
||||
|
||||
def forward(self, x):
|
||||
# x: (B, C, F, H, W)
|
||||
return rearrange(x,
|
||||
'b c (f ff) (h hh) (w ww) -> b (c ff hh ww) f h w',
|
||||
ff=self.ff, hh=self.hh, ww=self.ww)
|
||||
|
||||
class Buffer_LQ4x_Proj(nn.Module):
|
||||
|
||||
def __init__(self, in_dim, out_dim, layer_num=30):
|
||||
super().__init__()
|
||||
self.ff = 1
|
||||
self.hh = 16
|
||||
self.ww = 16
|
||||
self.hidden_dim1 = 2048
|
||||
self.hidden_dim2 = 3072
|
||||
self.layer_num = layer_num
|
||||
|
||||
self.pixel_shuffle = PixelShuffle3d(self.ff, self.hh, self.ww)
|
||||
|
||||
self.conv1 = CausalConv3d(in_dim*self.ff*self.hh*self.ww, self.hidden_dim1, (4, 3, 3), stride=(2, 1, 1), padding=(1, 1, 1)) # f -> f/2 h -> h w -> w
|
||||
self.norm1 = RMS_norm(self.hidden_dim1, images=False)
|
||||
self.act1 = nn.SiLU()
|
||||
|
||||
self.conv2 = CausalConv3d(self.hidden_dim1, self.hidden_dim2, (4, 3, 3), stride=(2, 1, 1), padding=(1, 1, 1)) # f -> f/2 h -> h w -> w
|
||||
self.norm2 = RMS_norm(self.hidden_dim2, images=False)
|
||||
self.act2 = nn.SiLU()
|
||||
|
||||
self.linear_layers = nn.ModuleList([nn.Linear(self.hidden_dim2, out_dim) for _ in range(layer_num)])
|
||||
|
||||
self.clip_idx = 0
|
||||
|
||||
def forward(self, video):
|
||||
self.clear_cache()
|
||||
# x: (B, C, F, H, W)
|
||||
|
||||
t = video.shape[2]
|
||||
iter_ = 1 + (t - 1) // 4
|
||||
first_frame = video[:, :, :1, :, :].repeat(1, 1, 3, 1, 1)
|
||||
video = torch.cat([first_frame, video], dim=2)
|
||||
|
||||
out_x = []
|
||||
for i in range(iter_):
|
||||
x = self.pixel_shuffle(video[:,:,i*4:(i+1)*4,:,:])
|
||||
cache1_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
self.cache['conv1'] = cache1_x
|
||||
x = self.conv1(x, self.cache['conv1'])
|
||||
x = self.norm1(x)
|
||||
x = self.act1(x)
|
||||
cache2_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
self.cache['conv2'] = cache2_x
|
||||
if i == 0:
|
||||
continue
|
||||
x = self.conv2(x, self.cache['conv2'])
|
||||
x = self.norm2(x)
|
||||
x = self.act2(x)
|
||||
out_x.append(x)
|
||||
out_x = torch.cat(out_x, dim = 2)
|
||||
|
||||
out_x = rearrange(out_x, 'b c f h w -> b (f h w) c')
|
||||
outputs = []
|
||||
for i in range(self.layer_num):
|
||||
outputs.append(self.linear_layers[i](out_x))
|
||||
self.clear_cache()
|
||||
return outputs
|
||||
|
||||
def clear_cache(self):
|
||||
self.cache = {}
|
||||
self.cache['conv1'] = None
|
||||
self.cache['conv2'] = None
|
||||
self.clip_idx = 0
|
||||
|
||||
def stream_forward(self, video_clip):
|
||||
if self.clip_idx == 0:
|
||||
# self.clear_cache()
|
||||
first_frame = video_clip[:, :, :1, :, :].repeat(1, 1, 3, 1, 1)
|
||||
video_clip = torch.cat([first_frame, video_clip], dim=2)
|
||||
x = self.pixel_shuffle(video_clip)
|
||||
cache1_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
self.cache['conv1'] = cache1_x
|
||||
x = self.conv1(x, self.cache['conv1'])
|
||||
x = self.norm1(x)
|
||||
x = self.act1(x)
|
||||
cache2_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
self.cache['conv2'] = cache2_x
|
||||
self.clip_idx += 1
|
||||
return None
|
||||
else:
|
||||
x = self.pixel_shuffle(video_clip)
|
||||
cache1_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
self.cache['conv1'] = cache1_x
|
||||
x = self.conv1(x, self.cache['conv1'])
|
||||
x = self.norm1(x)
|
||||
x = self.act1(x)
|
||||
cache2_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
self.cache['conv2'] = cache2_x
|
||||
x = self.conv2(x, self.cache['conv2'])
|
||||
x = self.norm2(x)
|
||||
x = self.act2(x)
|
||||
out_x = rearrange(x, 'b c f h w -> b (f h w) c')
|
||||
outputs = []
|
||||
for i in range(self.layer_num):
|
||||
outputs.append(self.linear_layers[i](out_x))
|
||||
self.clip_idx += 1
|
||||
return outputs
|
||||
@@ -1,261 +0,0 @@
|
||||
"""
|
||||
Tiny AutoEncoder for Hunyuan Video (Decoder-only, pruned)
|
||||
- Encoder removed
|
||||
- Transplant/widening helpers removed
|
||||
- Deepening (IdentityConv2d+ReLU) is now built into the decoder structure itself
|
||||
"""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from tqdm.auto import tqdm
|
||||
from collections import namedtuple
|
||||
from einops import rearrange
|
||||
import torch.nn.init as init
|
||||
|
||||
DecoderResult = namedtuple("DecoderResult", ("frame", "memory"))
|
||||
TWorkItem = namedtuple("TWorkItem", ("input_tensor", "block_index"))
|
||||
|
||||
# ----------------------------
|
||||
# Utility / building blocks
|
||||
# ----------------------------
|
||||
|
||||
class IdentityConv2d(nn.Conv2d):
|
||||
"""Same-shape Conv2d initialized to identity (Dirac)."""
|
||||
def __init__(self, C, kernel_size=3, bias=False):
|
||||
pad = kernel_size // 2
|
||||
super().__init__(C, C, kernel_size, padding=pad, bias=bias)
|
||||
with torch.no_grad():
|
||||
init.dirac_(self.weight)
|
||||
if self.bias is not None:
|
||||
self.bias.zero_()
|
||||
|
||||
def conv(n_in, n_out, **kwargs):
|
||||
return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
|
||||
|
||||
class Clamp(nn.Module):
|
||||
def forward(self, x):
|
||||
return torch.tanh(x / 3) * 3
|
||||
|
||||
class MemBlock(nn.Module):
|
||||
def __init__(self, n_in, n_out):
|
||||
super().__init__()
|
||||
self.conv = nn.Sequential(
|
||||
conv(n_in * 2, n_out), nn.ReLU(inplace=True),
|
||||
conv(n_out, n_out), nn.ReLU(inplace=True),
|
||||
conv(n_out, n_out)
|
||||
)
|
||||
self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
|
||||
self.act = nn.ReLU(inplace=True)
|
||||
def forward(self, x, past):
|
||||
return self.act(self.conv(torch.cat([x, past], 1)) + self.skip(x))
|
||||
|
||||
class TPool(nn.Module):
|
||||
def __init__(self, n_f, stride):
|
||||
super().__init__()
|
||||
self.stride = stride
|
||||
self.conv = nn.Conv2d(n_f*stride, n_f, 1, bias=False)
|
||||
def forward(self, x):
|
||||
_NT, C, H, W = x.shape
|
||||
return self.conv(x.reshape(-1, self.stride * C, H, W))
|
||||
|
||||
class TGrow(nn.Module):
|
||||
def __init__(self, n_f, stride):
|
||||
super().__init__()
|
||||
self.stride = stride
|
||||
self.conv = nn.Conv2d(n_f, n_f*stride, 1, bias=False)
|
||||
def forward(self, x):
|
||||
_NT, C, H, W = x.shape
|
||||
x = self.conv(x)
|
||||
return x.reshape(-1, C, H, W)
|
||||
|
||||
class PixelShuffle3d(nn.Module):
|
||||
def __init__(self, ff, hh, ww):
|
||||
super().__init__()
|
||||
self.ff = ff
|
||||
self.hh = hh
|
||||
self.ww = ww
|
||||
def forward(self, x):
|
||||
# x: (B, C, F, H, W)
|
||||
B, C, F, H, W = x.shape
|
||||
if F % self.ff != 0:
|
||||
first_frame = x[:, :, 0:1, :, :].repeat(1, 1, self.ff - F % self.ff, 1, 1)
|
||||
x = torch.cat([first_frame, x], dim=2)
|
||||
return rearrange(
|
||||
x,
|
||||
'b c (f ff) (h hh) (w ww) -> b (c ff hh ww) f h w',
|
||||
ff=self.ff, hh=self.hh, ww=self.ww
|
||||
).transpose(1, 2)
|
||||
|
||||
# ----------------------------
|
||||
# Generic NTCHW graph executor (kept; used by decoder)
|
||||
# ----------------------------
|
||||
|
||||
def apply_model_with_memblocks(model, x, parallel, show_progress_bar, mem=None):
|
||||
"""
|
||||
Apply a sequential model with memblocks to the given input.
|
||||
Args:
|
||||
- model: nn.Sequential of blocks to apply
|
||||
- x: input data, of dimensions NTCHW
|
||||
- parallel: if True, parallelize over timesteps (fast but uses O(T) memory)
|
||||
if False, each timestep will be processed sequentially (slow but uses O(1) memory)
|
||||
- show_progress_bar: if True, enables tqdm progressbar display
|
||||
|
||||
Returns NTCHW tensor of output data.
|
||||
"""
|
||||
assert x.ndim == 5, f"TAEHV operates on NTCHW tensors, but got {x.ndim}-dim tensor"
|
||||
N, T, C, H, W = x.shape
|
||||
if parallel:
|
||||
x = x.reshape(N*T, C, H, W)
|
||||
for b in tqdm(model, disable=not show_progress_bar):
|
||||
if isinstance(b, MemBlock):
|
||||
NT, C, H, W = x.shape
|
||||
T = NT // N
|
||||
_x = x.reshape(N, T, C, H, W)
|
||||
mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape)
|
||||
x = b(x, mem)
|
||||
else:
|
||||
x = b(x)
|
||||
NT, C, H, W = x.shape
|
||||
T = NT // N
|
||||
x = x.view(N, T, C, H, W)
|
||||
else:
|
||||
out = []
|
||||
work_queue = [TWorkItem(xt, 0) for t, xt in enumerate(x.reshape(N, T * C, H, W).chunk(T, dim=1))]
|
||||
progress_bar = tqdm(range(T), disable=not show_progress_bar)
|
||||
while work_queue:
|
||||
xt, i = work_queue.pop(0)
|
||||
if i == 0:
|
||||
progress_bar.update(1)
|
||||
if i == len(model):
|
||||
out.append(xt)
|
||||
else:
|
||||
b = model[i]
|
||||
if isinstance(b, MemBlock):
|
||||
if mem[i] is None:
|
||||
xt_new = b(xt, xt * 0)
|
||||
mem[i] = xt
|
||||
else:
|
||||
xt_new = b(xt, mem[i])
|
||||
mem[i].copy_(xt)
|
||||
work_queue.insert(0, TWorkItem(xt_new, i+1))
|
||||
elif isinstance(b, TPool):
|
||||
if mem[i] is None:
|
||||
mem[i] = []
|
||||
mem[i].append(xt)
|
||||
if len(mem[i]) > b.stride:
|
||||
raise ValueError("TPool internal state invalid.")
|
||||
elif len(mem[i]) == b.stride:
|
||||
N_, C_, H_, W_ = xt.shape
|
||||
xt = b(torch.cat(mem[i], 1).view(N_*b.stride, C_, H_, W_))
|
||||
mem[i] = []
|
||||
work_queue.insert(0, TWorkItem(xt, i+1))
|
||||
elif isinstance(b, TGrow):
|
||||
xt = b(xt)
|
||||
NT, C_, H_, W_ = xt.shape
|
||||
for xt_next in reversed(xt.view(N, b.stride*C_, H_, W_).chunk(b.stride, 1)):
|
||||
work_queue.insert(0, TWorkItem(xt_next, i+1))
|
||||
else:
|
||||
xt = b(xt)
|
||||
work_queue.insert(0, TWorkItem(xt, i+1))
|
||||
progress_bar.close()
|
||||
x = torch.stack(out, 1)
|
||||
return x, mem
|
||||
|
||||
# ----------------------------
|
||||
# Decoder-only TAEHV
|
||||
# ----------------------------
|
||||
|
||||
class TAEHV(nn.Module):
|
||||
image_channels = 3
|
||||
def __init__(
|
||||
self,
|
||||
decoder_time_upscale=(True, True),
|
||||
decoder_space_upscale=(True, True, True),
|
||||
channels = [256, 128, 64, 64],
|
||||
latent_channels = 16,
|
||||
dtype=torch.float32
|
||||
):
|
||||
"""Initialize TAEHV (decoder-only) with built-in deepening after every ReLU.
|
||||
Deepening config: how_many_each=1, k=3 (fixed as requested).
|
||||
"""
|
||||
super().__init__()
|
||||
self.dtype = dtype
|
||||
self.latent_channels = latent_channels
|
||||
n_f = channels
|
||||
self.frames_to_trim = 2**sum(decoder_time_upscale) - 1
|
||||
|
||||
# Build the decoder "skeleton"
|
||||
base_decoder = nn.Sequential(
|
||||
Clamp(), conv(self.latent_channels, n_f[0]), nn.ReLU(inplace=True),
|
||||
|
||||
MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]),
|
||||
nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1),
|
||||
TGrow(n_f[0], 1),
|
||||
conv(n_f[0], n_f[1], bias=False),
|
||||
|
||||
MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]),
|
||||
nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1),
|
||||
TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1),
|
||||
conv(n_f[1], n_f[2], bias=False),
|
||||
|
||||
MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]),
|
||||
nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1),
|
||||
TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1),
|
||||
conv(n_f[2], n_f[3], bias=False),
|
||||
|
||||
nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels),
|
||||
)
|
||||
|
||||
# Inline deepening: insert (IdentityConv2d(k=3) + ReLU) after every ReLU
|
||||
self.decoder = self._apply_identity_deepen(base_decoder, how_many_each=1, k=3)
|
||||
|
||||
self.pixel_shuffle = PixelShuffle3d(4, 8, 8)
|
||||
|
||||
# Initialize decoder mem state
|
||||
self.clean_mem()
|
||||
|
||||
@staticmethod
|
||||
def _apply_identity_deepen(decoder: nn.Sequential, how_many_each=1, k=3) -> nn.Sequential:
|
||||
"""Return a new Sequential where every nn.ReLU is followed by how_many_each*(IdentityConv2d(k)+ReLU)."""
|
||||
new_layers = []
|
||||
for b in decoder:
|
||||
new_layers.append(b)
|
||||
if isinstance(b, nn.ReLU):
|
||||
# Deduce channel count from preceding layer
|
||||
C = None
|
||||
if len(new_layers) >= 2 and isinstance(new_layers[-2], nn.Conv2d):
|
||||
C = new_layers[-2].out_channels
|
||||
elif len(new_layers) >= 2 and isinstance(new_layers[-2], MemBlock):
|
||||
C = new_layers[-2].conv[-1].out_channels
|
||||
if C is not None:
|
||||
for _ in range(how_many_each):
|
||||
new_layers.append(IdentityConv2d(C, kernel_size=k, bias=False))
|
||||
new_layers.append(nn.ReLU(inplace=True))
|
||||
return nn.Sequential(*new_layers)
|
||||
|
||||
def decode_video(self, x, parallel=False, show_progress_bar=False, cond=None):
|
||||
"""Decode a sequence of frames from latents.
|
||||
x: NTCHW latent tensor; returns NTCHW RGB in ~[0, 1].
|
||||
"""
|
||||
trim_flag = self.mem[-8] is None # keeps original relative check
|
||||
|
||||
if cond is not None:
|
||||
shuffled = self.pixel_shuffle(cond.to(x))
|
||||
x = torch.cat([shuffled[:, :x.shape[1]], x], dim=2)
|
||||
|
||||
x, self.mem = apply_model_with_memblocks(self.decoder, x, parallel, show_progress_bar, mem=self.mem)
|
||||
self.clean_mem()
|
||||
|
||||
if trim_flag:
|
||||
return x[:, self.frames_to_trim:]
|
||||
|
||||
return x
|
||||
|
||||
def clean_mem(self):
|
||||
self.mem = [None] * len(self.decoder)
|
||||
|
||||
|
||||
def build_tcdecoder(new_channels = [512, 256, 128, 128], device="cuda", dtype=torch.bfloat16, new_latent_channels=None):
|
||||
big = TAEHV(channels=new_channels, latent_channels=new_latent_channels, dtype=dtype).to(device).to(dtype)
|
||||
return big
|
||||
@@ -1,71 +0,0 @@
|
||||
import folder_paths
|
||||
import torch
|
||||
|
||||
from comfy.utils import load_torch_file
|
||||
import comfy.model_management as mm
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
|
||||
class WanVideoAddFlashVSRInput:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"embeds": ("WANVIDIMAGE_EMBEDS",),
|
||||
"images": ("IMAGE", {"tooltip": "Low-res video frames to enhance"}),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01, "tooltip": "Strength to apply the FlashVSR latent"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
|
||||
RETURN_NAMES = ("image_embeds",)
|
||||
FUNCTION = "add"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def add(self, embeds, images, strength):
|
||||
updated = dict(embeds)
|
||||
updated["flashvsr_LQ_images"] = images
|
||||
updated["flashvsr_strength"] = strength
|
||||
return (updated,)
|
||||
|
||||
|
||||
class WanVideoFlashVSRDecoderLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
|
||||
},
|
||||
"optional": {
|
||||
"precision": (["fp16", "fp32", "bf16"],
|
||||
{"default": "bf16"}
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVAE",)
|
||||
RETURN_NAMES = ("vae", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
DESCRIPTION = "Loads Wan VAE model from 'ComfyUI/models/vae'"
|
||||
|
||||
def loadmodel(self, model_name, precision):
|
||||
from .TCDecoder import build_tcdecoder
|
||||
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
|
||||
model_path = folder_paths.get_full_path("vae", model_name)
|
||||
sd = load_torch_file(model_path, safe_load=True)
|
||||
|
||||
TCDecoder = build_tcdecoder(new_channels=[512, 256, 128, 128], new_latent_channels=16+768, dtype=dtype)
|
||||
TCDecoder.load_state_dict(sd, strict=True)
|
||||
TCDecoder.to(dtype)
|
||||
|
||||
return (TCDecoder,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoAddFlashVSRInput": WanVideoAddFlashVSRInput,
|
||||
"WanVideoFlashVSRDecoderLoader": WanVideoFlashVSRDecoderLoader,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoAddFlashVSRInput": "WanVideo Add FlashVSR Input",
|
||||
"WanVideoFlashVSRDecoderLoader": "WanVideo FlashVSR Decoder Loader",
|
||||
}
|
||||
@@ -1,87 +0,0 @@
|
||||
import torch
|
||||
from einops import rearrange
|
||||
from torch import nn
|
||||
from einops import rearrange
|
||||
|
||||
class WanRMSNorm(nn.Module):
|
||||
|
||||
def __init__(self, dim, eps=1e-5):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.eps = eps
|
||||
self.weight = nn.Parameter(torch.ones(dim))
|
||||
|
||||
def forward(self, x):
|
||||
r"""
|
||||
Args:
|
||||
x(Tensor): Shape [B, L, C]
|
||||
"""
|
||||
return self._norm(x.to(self.weight.dtype)) * self.weight
|
||||
|
||||
def _norm(self, x):
|
||||
return x * (torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)).to(x.dtype)
|
||||
|
||||
|
||||
class DummyAdapterLayer(nn.Module):
|
||||
def __init__(self, layer):
|
||||
super().__init__()
|
||||
self.layer = layer
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
return self.layer(*args, **kwargs)
|
||||
|
||||
|
||||
class AudioProjModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
seq_len=5,
|
||||
blocks=13, # add a new parameter blocks
|
||||
channels=768, # add a new parameter channels
|
||||
intermediate_dim=512,
|
||||
output_dim=1536,
|
||||
context_tokens=16,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.seq_len = seq_len
|
||||
self.blocks = blocks
|
||||
self.channels = channels
|
||||
self.input_dim = seq_len * blocks * channels # update input_dim to be the product of blocks and channels.
|
||||
self.intermediate_dim = intermediate_dim
|
||||
self.context_tokens = context_tokens
|
||||
self.output_dim = output_dim
|
||||
|
||||
# define multiple linear layers
|
||||
self.audio_proj_glob_1 = DummyAdapterLayer(nn.Linear(self.input_dim, intermediate_dim))
|
||||
self.audio_proj_glob_2 = DummyAdapterLayer(nn.Linear(intermediate_dim, intermediate_dim))
|
||||
self.audio_proj_glob_3 = DummyAdapterLayer(nn.Linear(intermediate_dim, context_tokens * output_dim))
|
||||
|
||||
self.audio_proj_glob_norm = DummyAdapterLayer(nn.LayerNorm(output_dim))
|
||||
|
||||
self.initialize_weights()
|
||||
|
||||
def initialize_weights(self):
|
||||
# Initialize transformer layers:
|
||||
def _basic_init(module):
|
||||
if isinstance(module, nn.Linear):
|
||||
torch.nn.init.xavier_uniform_(module.weight)
|
||||
if module.bias is not None:
|
||||
nn.init.constant_(module.bias, 0)
|
||||
|
||||
self.apply(_basic_init)
|
||||
|
||||
def forward(self, audio_embeds):
|
||||
video_length = audio_embeds.shape[1]
|
||||
audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c")
|
||||
batch_size, window_size, blocks, channels = audio_embeds.shape
|
||||
audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)
|
||||
|
||||
audio_embeds = torch.relu(self.audio_proj_glob_1(audio_embeds))
|
||||
audio_embeds = torch.relu(self.audio_proj_glob_2(audio_embeds))
|
||||
|
||||
context_tokens = self.audio_proj_glob_3(audio_embeds).reshape(batch_size, self.context_tokens, self.output_dim)
|
||||
|
||||
context_tokens = self.audio_proj_glob_norm(context_tokens.to(self.audio_proj_glob_norm.layer.weight.dtype)).to(audio_embeds.dtype)
|
||||
context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length)
|
||||
|
||||
return context_tokens
|
||||
-287
@@ -1,287 +0,0 @@
|
||||
import folder_paths
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import os
|
||||
import json
|
||||
import torchaudio
|
||||
|
||||
from comfy.utils import load_torch_file, common_upscale
|
||||
import comfy.model_management as mm
|
||||
|
||||
from accelerate import init_empty_weights
|
||||
from ..utils import set_module_tensor_to_device, log
|
||||
from ..nodes import WanVideoEncodeLatentBatch
|
||||
|
||||
script_directory = os.path.dirname(os.path.abspath(__file__))
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
|
||||
def linear_interpolation_fps(features, input_fps, output_fps, output_len=None):
|
||||
features = features.transpose(1, 2) # [1, C, T]
|
||||
seq_len = features.shape[2] / float(input_fps)
|
||||
if output_len is None:
|
||||
output_len = int(seq_len * output_fps)
|
||||
output_features = F.interpolate(features, size=output_len, align_corners=True, mode='linear')
|
||||
return output_features.transpose(1, 2)
|
||||
|
||||
def get_audio_emb_window(audio_emb, frame_num, frame0_idx, audio_shift=2):
|
||||
zero_audio_embed = torch.zeros((audio_emb.shape[1], audio_emb.shape[2]), dtype=audio_emb.dtype, device=audio_emb.device)
|
||||
zero_audio_embed_3 = torch.zeros((3, audio_emb.shape[1], audio_emb.shape[2]), dtype=audio_emb.dtype, device=audio_emb.device)
|
||||
iter_ = 1 + (frame_num - 1) // 4
|
||||
audio_emb_wind = []
|
||||
for lt_i in range(iter_):
|
||||
if lt_i == 0:
|
||||
st = frame0_idx + lt_i - 2
|
||||
ed = frame0_idx + lt_i + 3
|
||||
wind_feat = torch.stack([
|
||||
audio_emb[i] if (0 <= i < audio_emb.shape[0]) else zero_audio_embed
|
||||
for i in range(st, ed)
|
||||
], dim=0)
|
||||
wind_feat = torch.cat((zero_audio_embed_3, wind_feat), dim=0)
|
||||
else:
|
||||
st = frame0_idx + 1 + 4 * (lt_i - 1) - audio_shift
|
||||
ed = frame0_idx + 1 + 4 * lt_i + audio_shift
|
||||
wind_feat = torch.stack([
|
||||
audio_emb[i] if (0 <= i < audio_emb.shape[0]) else zero_audio_embed
|
||||
for i in range(st, ed)
|
||||
], dim=0)
|
||||
audio_emb_wind.append(wind_feat)
|
||||
audio_emb_wind = torch.stack(audio_emb_wind, dim=0)
|
||||
|
||||
return audio_emb_wind, ed - audio_shift
|
||||
|
||||
class WhisperModelLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": (folder_paths.get_filename_list("audio_encoders"), {"tooltip": "These models are loaded from the 'ComfyUI/models/audio_encoders' folder",}),
|
||||
"base_precision": (["fp32", "bf16", "fp16"], {"default": "fp16"}),
|
||||
"load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WHISPERMODEL",)
|
||||
RETURN_NAMES = ("whisper_model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def loadmodel(self, model, base_precision, load_device):
|
||||
from transformers import WhisperConfig, WhisperModel, WhisperFeatureExtractor
|
||||
|
||||
base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision]
|
||||
|
||||
if load_device == "offload_device":
|
||||
transformer_load_device = offload_device
|
||||
else:
|
||||
transformer_load_device = device
|
||||
|
||||
config_path = os.path.join(script_directory, "whisper_config.json")
|
||||
whisper_config = WhisperConfig(**json.load(open(config_path)))
|
||||
|
||||
with init_empty_weights():
|
||||
whisper = WhisperModel(whisper_config).eval()
|
||||
whisper.decoder = None # we only need the encoder
|
||||
|
||||
feature_extractor_config = {
|
||||
"chunk_length": 30,
|
||||
"feature_extractor_type": "WhisperFeatureExtractor",
|
||||
"feature_size": 128,
|
||||
"hop_length": 160,
|
||||
"n_fft": 400,
|
||||
"n_samples": 480000,
|
||||
"nb_max_frames": 3000,
|
||||
"padding_side": "right",
|
||||
"padding_value": 0.0,
|
||||
"processor_class": "WhisperProcessor",
|
||||
"return_attention_mask": False,
|
||||
"sampling_rate": 16000
|
||||
}
|
||||
|
||||
feature_extractor = WhisperFeatureExtractor(**feature_extractor_config)
|
||||
|
||||
model_path = folder_paths.get_full_path_or_raise("audio_encoders", model)
|
||||
sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True)
|
||||
|
||||
for name, param in whisper.named_parameters():
|
||||
key = "model." + name
|
||||
value=sd[key]
|
||||
set_module_tensor_to_device(whisper, name, device=offload_device, dtype=base_dtype, value=value)
|
||||
|
||||
whisper_model = {
|
||||
"feature_extractor": feature_extractor,
|
||||
"model": whisper,
|
||||
"dtype": base_dtype,
|
||||
}
|
||||
|
||||
return (whisper_model,)
|
||||
|
||||
class HuMoEmbeds:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"num_frames": ("INT", {"default": 81, "min": -1, "max": 10000, "step": 1, "tooltip": "The total frame count to generate."}),
|
||||
"width": ("INT", {"default": 832, "min": 64, "max": 4096, "step": 16}),
|
||||
"height": ("INT", {"default": 480, "min": 64, "max": 4096, "step": 16}),
|
||||
"audio_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "Strength of the audio conditioning"}),
|
||||
"audio_cfg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "When not 1.0, an extra model pass without audio conditioning is done: slower inference but more motion is allowed"}),
|
||||
"audio_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The percent of the video to start applying audio conditioning"}),
|
||||
"audio_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The percent of the video to stop applying audio conditioning"})
|
||||
},
|
||||
"optional" : {
|
||||
"whisper_model": ("WHISPERMODEL",),
|
||||
"vae": ("WANVAE", ),
|
||||
"reference_images": ("IMAGE", {"tooltip": "reference images for the humo model"}),
|
||||
"audio": ("AUDIO",),
|
||||
"tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", )
|
||||
RETURN_NAMES = ("image_embeds", )
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def process(self, num_frames, width, height, audio_scale, audio_cfg_scale, audio_start_percent, audio_end_percent, whisper_model=None, vae=None, reference_images=None, audio=None, tiled_vae=False):
|
||||
if reference_images is not None and vae is None:
|
||||
raise ValueError("VAE is required when reference images are provided")
|
||||
if whisper_model is None and audio is not None:
|
||||
raise ValueError("Whisper model is required when audio is provided")
|
||||
model = whisper_model["model"]
|
||||
feature_extractor = whisper_model["feature_extractor"]
|
||||
dtype = whisper_model["dtype"]
|
||||
|
||||
sampling_rate = 16000
|
||||
|
||||
if audio is not None:
|
||||
audio_input = audio["waveform"][0]
|
||||
sample_rate = audio["sample_rate"]
|
||||
|
||||
if sample_rate != sampling_rate:
|
||||
audio_input = torchaudio.functional.resample(audio_input, sample_rate, sampling_rate)
|
||||
if audio_input.shape[1] == 2:
|
||||
audio_input = audio_input.mean(dim=0, keepdim=False)
|
||||
else:
|
||||
audio_input = audio_input[0]
|
||||
|
||||
model.to(device)
|
||||
audio_len = len(audio_input) // 640
|
||||
|
||||
# feature extraction
|
||||
audio_features = []
|
||||
window = 750*640
|
||||
for i in range(0, len(audio_input), window):
|
||||
audio_feature = feature_extractor(audio_input[i:i+window], sampling_rate=sampling_rate, return_tensors="pt").input_features
|
||||
audio_features.append(audio_feature)
|
||||
audio_features = torch.cat(audio_features, dim=-1).to(device, dtype)
|
||||
|
||||
# preprocess
|
||||
window = 3000
|
||||
audio_prompts = []
|
||||
for i in range(0, audio_features.shape[-1], window):
|
||||
audio_prompt = model.encoder(audio_features[:,:,i:i+window], output_hidden_states=True).hidden_states
|
||||
audio_prompt = torch.stack(audio_prompt, dim=2)
|
||||
audio_prompts.append(audio_prompt)
|
||||
|
||||
model.to(offload_device)
|
||||
|
||||
audio_prompts = torch.cat(audio_prompts, dim=1)
|
||||
audio_prompts = audio_prompts[:,:audio_len*2]
|
||||
|
||||
feat0 = linear_interpolation_fps(audio_prompts[:, :, 0: 8].mean(dim=2), 50, 25)
|
||||
feat1 = linear_interpolation_fps(audio_prompts[:, :, 8: 16].mean(dim=2), 50, 25)
|
||||
feat2 = linear_interpolation_fps(audio_prompts[:, :, 16: 24].mean(dim=2), 50, 25)
|
||||
feat3 = linear_interpolation_fps(audio_prompts[:, :, 24: 32].mean(dim=2), 50, 25)
|
||||
feat4 = linear_interpolation_fps(audio_prompts[:, :, 32], 50, 25)
|
||||
audio_emb = torch.stack([feat0, feat1, feat2, feat3, feat4], dim=2)[0] # [T, 5, 1280]
|
||||
else:
|
||||
audio_emb = torch.zeros(num_frames, 5, 1280, device=device)
|
||||
audio_len = num_frames
|
||||
|
||||
pixel_frame_num = num_frames if num_frames != -1 else audio_len
|
||||
pixel_frame_num = 4 * ((pixel_frame_num - 1) // 4) + 1
|
||||
latent_frame_num = (pixel_frame_num - 1) // 4 + 1
|
||||
|
||||
log.info(f"HuMo set to generate {pixel_frame_num} frames")
|
||||
|
||||
#audio_emb, _ = get_audio_emb_window(audio_emb, pixel_frame_num, frame0_idx=0)
|
||||
|
||||
num_refs = 0
|
||||
if reference_images is not None:
|
||||
if reference_images.shape[1] != height or reference_images.shape[2] != width:
|
||||
reference_images_in = common_upscale(reference_images.movedim(-1, 1), width, height, "lanczos", "disabled").movedim(1, -1)
|
||||
else:
|
||||
reference_images_in = reference_images
|
||||
samples, = WanVideoEncodeLatentBatch.encode(self, vae, reference_images_in, tiled_vae, None, None, None, None)
|
||||
samples = samples["samples"].transpose(0, 2).squeeze(0)
|
||||
num_refs = samples.shape[1]
|
||||
|
||||
vae.to(device)
|
||||
zero_frames = torch.zeros(1, 3, pixel_frame_num + 4*num_refs, height, width, device=device, dtype=vae.dtype)
|
||||
zero_latents = vae.encode(zero_frames, device=device, tiled=tiled_vae)[0].to(offload_device)
|
||||
|
||||
vae.to(offload_device)
|
||||
mm.soft_empty_cache()
|
||||
|
||||
target_shape = (16, latent_frame_num + num_refs, height // 8, width // 8)
|
||||
|
||||
mask = torch.ones(4, target_shape[1], target_shape[2], target_shape[3], device=offload_device, dtype=vae.dtype)
|
||||
if reference_images is not None:
|
||||
mask[:,:-num_refs] = 0
|
||||
image_cond = torch.cat([zero_latents[:, :(target_shape[1]-num_refs)], samples], dim=1)
|
||||
#zero_audio_pad = torch.zeros(num_refs, *audio_emb.shape[1:]).to(audio_emb.device)
|
||||
#audio_emb = torch.cat([audio_emb, zero_audio_pad], dim=0)
|
||||
else:
|
||||
image_cond = zero_latents
|
||||
mask = torch.zeros_like(mask)
|
||||
image_cond = torch.cat([mask, image_cond], dim=0)
|
||||
image_cond_neg = torch.cat([mask, zero_latents], dim=0)
|
||||
|
||||
embeds = {
|
||||
"humo_audio_emb": audio_emb,
|
||||
"humo_audio_emb_neg": torch.zeros_like(audio_emb, dtype=audio_emb.dtype, device=audio_emb.device),
|
||||
"humo_image_cond": image_cond,
|
||||
"humo_image_cond_neg": image_cond_neg,
|
||||
"humo_reference_count": num_refs,
|
||||
"target_shape": target_shape,
|
||||
"num_frames": pixel_frame_num,
|
||||
"humo_audio_scale": audio_scale,
|
||||
"humo_audio_cfg_scale": audio_cfg_scale,
|
||||
"humo_start_percent": audio_start_percent,
|
||||
"humo_end_percent": audio_end_percent,
|
||||
}
|
||||
|
||||
return (embeds, )
|
||||
|
||||
class WanVideoCombineEmbeds:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"embeds_1": ("WANVIDIMAGE_EMBEDS",),
|
||||
"embeds_2": ("WANVIDIMAGE_EMBEDS",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
|
||||
RETURN_NAMES = ("image_embeds",)
|
||||
FUNCTION = "add"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
EXPERIMENTAL = True
|
||||
|
||||
def add(self, embeds_1, embeds_2):
|
||||
# Combine the two sets of embeds
|
||||
combined = {**embeds_1, **embeds_2}
|
||||
return (combined,)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WhisperModelLoader": WhisperModelLoader,
|
||||
"HuMoEmbeds": HuMoEmbeds,
|
||||
"WanVideoCombineEmbeds": WanVideoCombineEmbeds,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WhisperModelLoader": "Whisper Model Loader",
|
||||
"HuMoEmbeds": "HuMo Embeds",
|
||||
"WanVideoCombineEmbeds": "WanVideo Combine Embeds",
|
||||
}
|
||||
@@ -1,50 +0,0 @@
|
||||
{
|
||||
"_name_or_path": "openai/whisper-large-v3",
|
||||
"activation_dropout": 0.0,
|
||||
"activation_function": "gelu",
|
||||
"apply_spec_augment": false,
|
||||
"architectures": [
|
||||
"WhisperForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"begin_suppress_tokens": [
|
||||
220,
|
||||
50257
|
||||
],
|
||||
"bos_token_id": 50257,
|
||||
"classifier_proj_size": 256,
|
||||
"d_model": 1280,
|
||||
"decoder_attention_heads": 20,
|
||||
"decoder_ffn_dim": 5120,
|
||||
"decoder_layerdrop": 0.0,
|
||||
"decoder_layers": 32,
|
||||
"decoder_start_token_id": 50258,
|
||||
"dropout": 0.0,
|
||||
"encoder_attention_heads": 20,
|
||||
"encoder_ffn_dim": 5120,
|
||||
"encoder_layerdrop": 0.0,
|
||||
"encoder_layers": 32,
|
||||
"eos_token_id": 50257,
|
||||
"init_std": 0.02,
|
||||
"is_encoder_decoder": true,
|
||||
"mask_feature_length": 10,
|
||||
"mask_feature_min_masks": 0,
|
||||
"mask_feature_prob": 0.0,
|
||||
"mask_time_length": 10,
|
||||
"mask_time_min_masks": 2,
|
||||
"mask_time_prob": 0.05,
|
||||
"max_length": 448,
|
||||
"max_source_positions": 1500,
|
||||
"max_target_positions": 448,
|
||||
"median_filter_width": 7,
|
||||
"model_type": "whisper",
|
||||
"num_hidden_layers": 32,
|
||||
"num_mel_bins": 128,
|
||||
"pad_token_id": 50256,
|
||||
"scale_embedding": false,
|
||||
"torch_dtype": "float16",
|
||||
"transformers_version": "4.36.0.dev0",
|
||||
"use_cache": true,
|
||||
"use_weighted_layer_sum": false,
|
||||
"vocab_size": 51866
|
||||
}
|
||||
@@ -1,212 +0,0 @@
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch
|
||||
import math
|
||||
from einops import rearrange
|
||||
|
||||
from ..wanvideo.modules.model import WanRMSNorm, attention
|
||||
from ..multitalk.multitalk import RotaryPositionalEmbedding1D, normalize_and_scale
|
||||
|
||||
class FeedForwardSwiGLU(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
hidden_dim: int,
|
||||
multiple_of: int = 256,
|
||||
):
|
||||
super().__init__()
|
||||
hidden_dim = int(2 * hidden_dim / 3)
|
||||
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
||||
|
||||
self.dim = dim
|
||||
self.hidden_dim = hidden_dim
|
||||
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
|
||||
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
|
||||
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
return self.w2(F.silu(self.w1(x)) * self.w3(x))
|
||||
|
||||
class TimestepEmbedder(nn.Module):
|
||||
"""
|
||||
Embeds scalar timesteps into vector representations.
|
||||
"""
|
||||
|
||||
def __init__(self, t_embed_dim, frequency_embedding_size=256):
|
||||
super().__init__()
|
||||
self.t_embed_dim = t_embed_dim
|
||||
self.frequency_embedding_size = frequency_embedding_size
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(frequency_embedding_size, t_embed_dim, bias=True),
|
||||
nn.SiLU(),
|
||||
nn.Linear(t_embed_dim, t_embed_dim, bias=True),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def timestep_embedding(t, dim, max_period=10000):
|
||||
"""
|
||||
Create sinusoidal timestep embeddings.
|
||||
:param t: a 1-D Tensor of N indices, one per batch element.
|
||||
These may be fractional.
|
||||
:param dim: the dimension of the output.
|
||||
:param max_period: controls the minimum frequency of the embeddings.
|
||||
:return: an (N, D) Tensor of positional embeddings.
|
||||
"""
|
||||
half = dim // 2
|
||||
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half)
|
||||
freqs = freqs.to(device=t.device)
|
||||
args = t[:, None].float() * freqs[None]
|
||||
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
||||
if dim % 2:
|
||||
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
||||
return embedding
|
||||
|
||||
def forward(self, t, dtype):
|
||||
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
|
||||
if t_freq.dtype != dtype:
|
||||
t_freq = t_freq.to(dtype)
|
||||
t_emb = self.mlp(t_freq)
|
||||
return t_emb
|
||||
|
||||
|
||||
class SingleStreamAttention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
encoder_hidden_states_dim: int,
|
||||
num_heads: int,
|
||||
qkv_bias: bool,
|
||||
qk_norm: bool,
|
||||
attn_drop: float = 0.0,
|
||||
proj_drop: float = 0.0,
|
||||
eps: float = 1e-6,
|
||||
class_range: int = 24,
|
||||
class_interval: int = 4,
|
||||
attention_mode: str = "sdpa",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0, "dim should be divisible by num_heads"
|
||||
self.dim = dim
|
||||
self.encoder_hidden_states_dim = encoder_hidden_states_dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.scale = self.head_dim**-0.5
|
||||
|
||||
self.q_linear = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.q_norm = WanRMSNorm(self.head_dim, eps=eps) if qk_norm else nn.Identity()
|
||||
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
self.kv_linear = nn.Linear(encoder_hidden_states_dim, dim * 2, bias=qkv_bias)
|
||||
self.k_norm = WanRMSNorm(self.head_dim, eps=eps) if qk_norm else nn.Identity()
|
||||
|
||||
self.attention_mode = attention_mode
|
||||
|
||||
# multitalk related params
|
||||
self.class_interval = class_interval
|
||||
self.class_range = class_range
|
||||
self.rope_h1 = (0, self.class_interval)
|
||||
self.rope_h2 = (self.class_range - self.class_interval, self.class_range)
|
||||
self.rope_bak = int(self.class_range // 2)
|
||||
self.rope_1d = RotaryPositionalEmbedding1D(self.head_dim)
|
||||
|
||||
def _process_cross_attn(self, x, cond, frames_num=None, x_ref_attn_map=None):
|
||||
|
||||
N_t = frames_num
|
||||
out_dtype = x.dtype
|
||||
x = rearrange(x, "B (N_t S) C -> (B N_t) S C", N_t=N_t)
|
||||
|
||||
# get q for hidden_state
|
||||
B, N, C = x.shape
|
||||
q = self.q_linear(x)
|
||||
q_shape = (B, N, self.num_heads, self.head_dim)
|
||||
q = q.view(q_shape).permute((0, 2, 1, 3)) # [B, H, N, D]
|
||||
q = self.q_norm(q.to(self.q_norm.weight.dtype)).to(q.dtype)
|
||||
|
||||
# multitalk with rope1d pe
|
||||
if x_ref_attn_map is not None:
|
||||
max_values = x_ref_attn_map.max(1).values[:, None, None]
|
||||
min_values = x_ref_attn_map.min(1).values[:, None, None]
|
||||
max_min_values = torch.cat([max_values, min_values], dim=2)
|
||||
human1_max_value, human1_min_value = max_min_values[0, :, 0].max(), max_min_values[0, :, 1].min()
|
||||
human2_max_value, human2_min_value = max_min_values[1, :, 0].max(), max_min_values[1, :, 1].min()
|
||||
|
||||
human1 = normalize_and_scale(x_ref_attn_map[0], (human1_min_value, human1_max_value), (self.rope_h1[0], self.rope_h1[1]))
|
||||
human2 = normalize_and_scale(x_ref_attn_map[1], (human2_min_value, human2_max_value), (self.rope_h2[0], self.rope_h2[1]))
|
||||
back = torch.full((x_ref_attn_map.size(1),), self.rope_bak, dtype=human1.dtype).to(human1.device)
|
||||
max_indices = x_ref_attn_map.argmax(dim=0)
|
||||
normalized_map = torch.stack([human1, human2, back], dim=1)
|
||||
normalized_pos = normalized_map[range(x_ref_attn_map.size(1)), max_indices]
|
||||
|
||||
q = rearrange(q, "(B N_t) H S C -> B H (N_t S) C", N_t=N_t)
|
||||
q = self.rope_1d(q, normalized_pos)
|
||||
q = rearrange(q, "B H (N_t S) C -> (B N_t) H S C", N_t=N_t)
|
||||
|
||||
# get kv from encoder_hidden_states
|
||||
_, N_a, _ = cond.shape
|
||||
encoder_kv = self.kv_linear(cond)
|
||||
encoder_kv_shape = (B, N_a, 2, self.num_heads, self.head_dim)
|
||||
encoder_kv = encoder_kv.view(encoder_kv_shape).permute((2, 0, 3, 1, 4))
|
||||
|
||||
encoder_k, encoder_v = encoder_kv.unbind(0)
|
||||
encoder_k = self.k_norm(encoder_k.to(self.k_norm.weight.dtype)).to(encoder_k.dtype)
|
||||
|
||||
|
||||
# multitalk with rope1d pe
|
||||
if x_ref_attn_map is not None:
|
||||
per_frame = torch.zeros(N_a, dtype=encoder_k.dtype).to(encoder_k.device)
|
||||
per_frame[:per_frame.size(0)//2] = (self.rope_h1[0] + self.rope_h1[1]) / 2
|
||||
per_frame[per_frame.size(0)//2:] = (self.rope_h2[0] + self.rope_h2[1]) / 2
|
||||
encoder_pos = torch.concat([per_frame]*N_t, dim=0)
|
||||
encoder_k = rearrange(encoder_k, "(B N_t) H S C -> B H (N_t S) C", N_t=N_t)
|
||||
encoder_k = self.rope_1d(encoder_k, encoder_pos)
|
||||
encoder_k = rearrange(encoder_k, "B H (N_t S) C -> (B N_t) H S C", N_t=N_t)
|
||||
|
||||
# Input tensors must be in format ``[B, M, H, K]``, where B is the batch size, M \
|
||||
# the sequence length, H the number of heads, and K the embeding size per head
|
||||
|
||||
q = rearrange(q, "B H M K -> B M H K")
|
||||
encoder_k = rearrange(encoder_k, "B H M K -> B M H K")
|
||||
encoder_v = rearrange(encoder_v, "B H M K -> B M H K")
|
||||
x = attention(q, encoder_k, encoder_v, attention_mode=self.attention_mode)
|
||||
x = rearrange(x, "B M H K -> B H M K")
|
||||
|
||||
# linear transform
|
||||
x_output_shape = (B, N, C)
|
||||
x = x.transpose(1, 2)
|
||||
x = x.reshape(x_output_shape)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
|
||||
# reshape x to origin shape
|
||||
x = rearrange(x, "(B N_t) S C -> B (N_t S) C", N_t=N_t)
|
||||
|
||||
return x.type(out_dtype)
|
||||
|
||||
def forward(self, x, cond, num_latent_frames=None, num_cond_latents=None, x_ref_attn_map=None, human_num=None):
|
||||
|
||||
B, N, C = x.shape
|
||||
if (num_cond_latents is None or num_cond_latents == 0):
|
||||
# text to video
|
||||
output = self._process_cross_attn(x, cond, num_latent_frames, x_ref_attn_map)
|
||||
return None, output
|
||||
elif num_cond_latents is not None and num_cond_latents > 0:
|
||||
# image to video or video continuation
|
||||
num_cond_latents_thw = num_cond_latents * (N // num_latent_frames)
|
||||
x_noise = x[:, num_cond_latents_thw:]
|
||||
cond = rearrange(cond, "(B N_t) M C -> B N_t M C", B=B)
|
||||
cond = cond[:, num_cond_latents:]
|
||||
cond = rearrange(cond, "B N_t M C -> (B N_t) M C")
|
||||
frames_num = num_latent_frames - num_cond_latents
|
||||
if human_num is not None and human_num == 2:
|
||||
# multitalk mode
|
||||
output_noise = self._process_cross_attn(x_noise, cond, frames_num, x_ref_attn_map)
|
||||
else:
|
||||
# singletalk mode
|
||||
output_noise = self._process_cross_attn(x_noise, cond, frames_num)
|
||||
output_cond = torch.zeros((B, num_cond_latents_thw, C), dtype=output_noise.dtype, device=output_noise.device)
|
||||
return output_cond, output_noise
|
||||
else:
|
||||
raise NotImplementedError
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,120 +0,0 @@
|
||||
import torch
|
||||
from ..utils import log
|
||||
import comfy.model_management as mm
|
||||
from comfy_api.latest import io
|
||||
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
|
||||
|
||||
class WanVideoLongCatAvatarExtendEmbeds(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanVideoLongCatAvatarExtendEmbeds",
|
||||
category="WanVideoWrapper",
|
||||
inputs=[
|
||||
io.Latent.Input("prev_latents", tooltip="Full previous latents to be used to continue generation, continuation frames are selected based on 'overlap' parameter"),
|
||||
io.Custom("MULTITALK_EMBEDS").Input("audio_embeds", tooltip="Full length audio embeddings"),
|
||||
io.Int.Input("num_frames", default=93, min=1, max=256, step=1, tooltip="Number of new frames to generate"),
|
||||
io.Int.Input("overlap", default=13, min=0, max=16, step=1, tooltip="Number of overlapping frames from previous latents for video continuation, set to 0 for T2V"),
|
||||
io.Int.Input("frames_processed", default=0, min=0, max=10000, step=1, tooltip="Number of frames already processed in the video, used to select audio features"),
|
||||
io.Combo.Input("if_not_enough_audio", ["pad_with_start", "mirror_from_end"], default="pad_with_start", tooltip="What to do if there are not enough frames in pose_images for the window"),
|
||||
io.Int.Input("ref_frame_index", default=10, min=0, max=1000, step=1, tooltip="Values between 0 - 24 ensures better consistency, while selecting other ranges (e.g., -10 or 30) helps reduce repeated actions"),
|
||||
io.Int.Input("ref_mask_frame_range", default=3, min=0, max=20, step=1, tooltip="Larger range can further help mitigate repeated actions, but excessively large values may introduce artifacts"),
|
||||
io.Latent.Input("ref_latent", optional=True, tooltip="Reference latent used for consistency, generally should be either the init image, or first latent from first generation"),
|
||||
io.Latent.Input("samples", optional=True, tooltip="For the sampler 'samples' input, used for slicing samples per window for vid2vid"),
|
||||
],
|
||||
outputs=[
|
||||
io.Custom("WANVIDIMAGE_EMBEDS").Output(display_name="image_embeds", tooltip="Embeds for WanVideo LongCat Avatar generation"),
|
||||
io.Latent.Output(display_name="samples_slice", tooltip="Sliced latent samples for the new frames"),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, prev_latents, audio_embeds, num_frames, overlap, if_not_enough_audio, frames_processed, ref_frame_index, ref_mask_frame_range, ref_latent=None, samples=None) -> io.NodeOutput:
|
||||
|
||||
new_audio_embed = audio_embeds.copy()
|
||||
|
||||
audio_features = torch.stack(new_audio_embed["audio_features"])
|
||||
num_audio_features = audio_features.shape[1]
|
||||
if audio_features.shape[1] < frames_processed + num_frames:
|
||||
deficit = frames_processed + num_frames - audio_features.shape[1]
|
||||
if if_not_enough_audio == "pad_with_start":
|
||||
pad = audio_features[:, :1].repeat(1, deficit, 1, 1)
|
||||
audio_features = torch.cat([audio_features, pad], dim=1)
|
||||
elif if_not_enough_audio == "mirror_from_end":
|
||||
to_add = audio_features[:, -deficit:, :].flip(dims=[1])
|
||||
audio_features = torch.cat([audio_features, to_add], dim=1)
|
||||
log.warning(f"Not enough audio features, padded with strategy '{if_not_enough_audio}' from {num_audio_features} to {audio_features.shape[1]} frames")
|
||||
|
||||
ref_target_masks = new_audio_embed.get("ref_target_masks", None)
|
||||
if ref_target_masks is not None:
|
||||
new_audio_embed["ref_target_masks"] = ref_target_masks[:, frames_processed:frames_processed+num_frames, :]
|
||||
|
||||
prev_samples = prev_latents["samples"].clone()
|
||||
if overlap != 0:
|
||||
latent_overlap = (overlap - 1) // 4 + 1
|
||||
prev_samples = prev_samples[:, :, -latent_overlap:]
|
||||
|
||||
ref_sample = None
|
||||
if ref_latent is not None:
|
||||
ref_sample = ref_latent["samples"][0, :, :1].clone()
|
||||
log.info(f"Previous latents shape: {prev_samples.shape}, using last {latent_overlap} latent frames for overlap.")
|
||||
|
||||
new_latent_frames = (num_frames - 1) // 4 + 1
|
||||
target_shape = (16, new_latent_frames, prev_samples.shape[-2], prev_samples.shape[-1])
|
||||
|
||||
audio_stride = 2
|
||||
indices = torch.arange(2 * 2 + 1) - 2
|
||||
|
||||
if frames_processed == 0:
|
||||
audio_start_idx = 0
|
||||
else:
|
||||
audio_start_idx = (frames_processed - overlap) * audio_stride
|
||||
audio_end_idx = audio_start_idx + num_frames * audio_stride
|
||||
|
||||
log.info(f"Extracting audio embeddings from index {audio_start_idx} to {audio_end_idx}")
|
||||
|
||||
audio_embs = []
|
||||
for human_idx in range(len(audio_features)):
|
||||
center_indices = torch.arange(audio_start_idx, audio_end_idx, audio_stride).unsqueeze(1) + indices.unsqueeze(0)
|
||||
center_indices = torch.clamp(center_indices, min=0, max=audio_features[human_idx].shape[0] - 1)
|
||||
|
||||
audio_emb = audio_features[human_idx][center_indices].unsqueeze(0).to(device)
|
||||
audio_embs.append(audio_emb)
|
||||
audio_emb = torch.cat(audio_embs, dim=0)
|
||||
|
||||
new_audio_embed["audio_features"] = None
|
||||
new_audio_embed["audio_emb_slice"] = audio_emb
|
||||
|
||||
longcat_avatar_options = {
|
||||
"longcat_ref_latent": ref_sample,
|
||||
"ref_frame_index": ref_frame_index,
|
||||
"ref_mask_frame_range": ref_mask_frame_range,
|
||||
}
|
||||
|
||||
embeds = {
|
||||
"target_shape": target_shape,
|
||||
"num_frames": num_frames,
|
||||
"extra_latents": [{"samples": prev_samples, "index": 0}] if overlap != 0 else None,
|
||||
"multitalk_embeds": new_audio_embed,
|
||||
"longcat_avatar_options": longcat_avatar_options,
|
||||
}
|
||||
|
||||
samples_slice = None
|
||||
if samples is not None:
|
||||
latent_start_index = (frames_processed - 1) // 4 + 1 if frames_processed > 0 else 0
|
||||
latent_end_index = latent_start_index + new_latent_frames
|
||||
samples_slice = samples.copy()
|
||||
samples_slice["samples"] = samples["samples"][:, :, latent_start_index:latent_end_index].clone()
|
||||
|
||||
return io.NodeOutput(embeds, samples_slice)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoLongCatAvatarExtendEmbeds": WanVideoLongCatAvatarExtendEmbeds,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoLongCatAvatarExtendEmbeds": "WanVideo LongCat Avatar Extend Embeds",
|
||||
}
|
||||
@@ -1,199 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from einops import rearrange
|
||||
|
||||
from ..wanvideo.modules.attention import attention
|
||||
|
||||
def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
|
||||
return (x * (1 + scale) + shift)
|
||||
|
||||
|
||||
def sinusoidal_embedding_1d(dim, position):
|
||||
sinusoid = torch.outer(position.type(torch.float64), torch.pow(
|
||||
10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2)))
|
||||
x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
|
||||
return x.to(position.dtype)
|
||||
|
||||
|
||||
def precompute_freqs_cis_3d(dim: int, end: int = 1024, theta: float = 10000.0):
|
||||
# 3d rope precompute
|
||||
f_freqs_cis = precompute_freqs_cis(dim - 2 * (dim // 3), end, theta)
|
||||
h_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
|
||||
w_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
|
||||
return f_freqs_cis, h_freqs_cis, w_freqs_cis
|
||||
|
||||
|
||||
def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0):
|
||||
# 1d rope precompute
|
||||
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)
|
||||
[: (dim // 2)].double() / dim))
|
||||
freqs = torch.outer(torch.arange(end, device=freqs.device), freqs)
|
||||
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
|
||||
return freqs_cis
|
||||
|
||||
|
||||
def rope_apply(x, freqs, num_heads):
|
||||
x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
|
||||
x_out = torch.view_as_complex(x.to(torch.float64).reshape(
|
||||
x.shape[0], x.shape[1], x.shape[2], -1, 2))
|
||||
x_out = torch.view_as_real(x_out * freqs).flatten(2)
|
||||
return x_out.to(x.dtype)
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
def __init__(self, dim, eps=1e-5):
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
self.weight = nn.Parameter(torch.ones(dim))
|
||||
|
||||
def norm(self, x):
|
||||
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
|
||||
|
||||
def forward(self, x):
|
||||
dtype = x.dtype
|
||||
return self.norm(x.float()).to(dtype) * self.weight
|
||||
|
||||
|
||||
class AttentionModule(nn.Module):
|
||||
def __init__(self, num_heads, head_dim):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = head_dim
|
||||
|
||||
def forward(self, q, k, v):
|
||||
b, n, d = q.size(0), self.num_heads, self.head_dim
|
||||
x = attention(
|
||||
q.view(b, -1, n, d),
|
||||
k.view(b, -1, n, d),
|
||||
v.view(b, -1, n, d)
|
||||
)
|
||||
return x.flatten(2)
|
||||
|
||||
|
||||
class SelfAttention(nn.Module):
|
||||
def __init__(self, dim: int, num_heads: int, eps: float = 1e-6):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
|
||||
self.q = nn.Linear(dim, dim)
|
||||
self.k = nn.Linear(dim, dim)
|
||||
self.v = nn.Linear(dim, dim)
|
||||
self.o = nn.Linear(dim, dim)
|
||||
self.norm_q = RMSNorm(dim, eps=eps)
|
||||
self.norm_k = RMSNorm(dim, eps=eps)
|
||||
|
||||
self.attn = AttentionModule(self.num_heads, self.head_dim)
|
||||
|
||||
def forward(self, x, freqs):
|
||||
q = self.norm_q(self.q(x))
|
||||
k = self.norm_k(self.k(x))
|
||||
v = self.v(x)
|
||||
q = rope_apply(q, freqs, self.num_heads)
|
||||
k = rope_apply(k, freqs, self.num_heads)
|
||||
x = self.attn(q, k, v)
|
||||
return self.o(x)
|
||||
|
||||
|
||||
class CrossAttention(nn.Module):
|
||||
def __init__(self, dim: int, num_heads: int, eps: float = 1e-6, clip_fea: torch.Tensor = None):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
|
||||
self.q = nn.Linear(dim, dim)
|
||||
self.k = nn.Linear(dim, dim)
|
||||
self.v = nn.Linear(dim, dim)
|
||||
self.o = nn.Linear(dim, dim)
|
||||
self.norm_q = RMSNorm(dim, eps=eps)
|
||||
self.norm_k = RMSNorm(dim, eps=eps)
|
||||
|
||||
|
||||
self.k_img = nn.Linear(dim, dim)
|
||||
self.v_img = nn.Linear(dim, dim)
|
||||
self.norm_k_img = RMSNorm(dim, eps=eps)
|
||||
|
||||
self.attn = AttentionModule(self.num_heads, self.head_dim)
|
||||
|
||||
def forward(self, x: torch.Tensor, y: torch.Tensor, clip_fea: torch.Tensor = None):
|
||||
ctx = y
|
||||
q = self.norm_q(self.q(x))
|
||||
k = self.norm_k(self.k(ctx))
|
||||
v = self.v(ctx)
|
||||
x = self.attn(q, k, v)
|
||||
if clip_fea is not None:
|
||||
k_img = self.norm_k_img(self.k_img(clip_fea))
|
||||
v_img = self.v_img(clip_fea)
|
||||
y = self.attn(q, k_img, v_img)
|
||||
x = x + y
|
||||
return self.o(x)
|
||||
|
||||
|
||||
class GateModule(nn.Module):
|
||||
def __init__(self,):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x, gate, residual):
|
||||
return x + gate * residual
|
||||
|
||||
class DiTBlock(nn.Module):
|
||||
def __init__(self, dim: int, num_heads: int, ffn_dim: int, eps: float = 1e-6):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.ffn_dim = ffn_dim
|
||||
|
||||
self.self_attn = SelfAttention(dim, num_heads, eps)
|
||||
self.cross_attn = CrossAttention(dim, num_heads, eps)
|
||||
self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
|
||||
self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
|
||||
self.norm3 = nn.LayerNorm(dim, eps=eps)
|
||||
self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(
|
||||
approximate='tanh'), nn.Linear(ffn_dim, dim))
|
||||
self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
|
||||
self.gate = GateModule()
|
||||
|
||||
def forward(self, x, context, t_mod, freqs, clip_fea=None):
|
||||
has_seq = len(t_mod.shape) == 4
|
||||
chunk_dim = 2 if has_seq else 1
|
||||
# msa: multi-head self-attention mlp: multi-layer perceptron
|
||||
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
|
||||
self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=chunk_dim)
|
||||
if has_seq:
|
||||
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
|
||||
shift_msa.squeeze(2), scale_msa.squeeze(2), gate_msa.squeeze(2),
|
||||
shift_mlp.squeeze(2), scale_mlp.squeeze(2), gate_mlp.squeeze(2),
|
||||
)
|
||||
input_x = modulate(self.norm1(x), shift_msa, scale_msa)
|
||||
x = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
|
||||
x = x + self.cross_attn(self.norm3(x), context, clip_fea=clip_fea)
|
||||
input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
|
||||
x = self.gate(x, gate_mlp, self.ffn(input_x))
|
||||
return x
|
||||
|
||||
|
||||
class WanModelDualControl(torch.nn.Module):
|
||||
def __init__(self, dim: int, ffn_dim: int, eps: float, num_heads: int, control_layers = 12):
|
||||
super().__init__()
|
||||
self.control_layers = control_layers
|
||||
self.control_blocks_dense = nn.ModuleList([
|
||||
DiTBlock(dim//2, num_heads//2, ffn_dim//2, eps)
|
||||
for _ in range(self.control_layers)
|
||||
])
|
||||
|
||||
self.control_blocks_sparse = nn.ModuleList([
|
||||
DiTBlock(dim//2, num_heads//2, ffn_dim//2, eps)
|
||||
for _ in range(self.control_layers)
|
||||
])
|
||||
|
||||
self.control_initial_combine_linear_dense = torch.nn.Linear(dim, dim//2)
|
||||
self.control_initial_combine_linear_sparse = torch.nn.Linear(dim, dim//2)
|
||||
|
||||
self.control_text_linear = torch.nn.Linear(dim, dim//2)
|
||||
self.control_t_mod = torch.nn.Linear(dim, dim//2)
|
||||
|
||||
self.control_combine_linears = torch.nn.ModuleList([torch.nn.Linear(dim//2, dim) for _ in range(self.control_layers)])
|
||||
head_dim = dim // num_heads
|
||||
self.freqs = precompute_freqs_cis_3d(head_dim)
|
||||
@@ -1,88 +0,0 @@
|
||||
import torch
|
||||
from ..utils import log
|
||||
import comfy.model_management as mm
|
||||
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
|
||||
class WanVideoAddDualControlEmbeds:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"embeds": ("WANVIDIMAGE_EMBEDS",),
|
||||
"vae": ("WANVAE", {"tooltip": "VAE model"}),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Strength of the reference embedding"}),
|
||||
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the embedding application"}),
|
||||
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the embedding application"}),
|
||||
"first_frame_noise_level": ("FLOAT", {"default": 0.925926, "min": 0.0, "max": 1.0, "step": 0.000001, "tooltip": "Noise level for the first frame when using previous frames"}),
|
||||
},
|
||||
"optional": {
|
||||
"dense": ("IMAGE", {"tooltip": "Dense control signal (depth) video input"}),
|
||||
"sparse": ("IMAGE", {"tooltip": "Sparse control signal (tracks) video input"}),
|
||||
"prev_images": ("IMAGE", {"tooltip": "Previous frames for temporal consistency, default is 8 frames"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
|
||||
RETURN_NAMES = ("image_embeds",)
|
||||
FUNCTION = "add"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def add(self, embeds, vae, strength, start_percent, end_percent, first_frame_noise_level, dense=None, sparse=None, prev_images=None):
|
||||
updated = dict(embeds)
|
||||
updated.setdefault("dual_control", {})
|
||||
|
||||
if dense is None and sparse is None:
|
||||
raise ValueError("At least one of dense or sparse inputs must be provided.")
|
||||
|
||||
num_frames = dense.shape[0] if dense is not None else sparse.shape[0]
|
||||
height = dense.shape[1] if dense is not None else sparse.shape[1]
|
||||
width = dense.shape[2] if dense is not None else sparse.shape[2]
|
||||
msk = torch.ones(1, num_frames, height//8, width//8, device=device)
|
||||
msk[:, 1:] = 0
|
||||
msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
|
||||
msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
|
||||
msk = msk.transpose(1, 2)
|
||||
|
||||
dense_input_latent = sparse_input_latent = None
|
||||
|
||||
vae.to(device)
|
||||
if dense is not None:
|
||||
dense_images = 1 - dense[..., :3] # Invert colors for depth to match the usual range in comfy
|
||||
dense_images = dense_images.permute(3, 0, 1, 2) * 2 - 1
|
||||
dense_video_latent = vae.encode([dense_images.to(device, vae.dtype)], device, tiled=False)
|
||||
dense_first = (dense_images[:, :1]).to(device, vae.dtype)
|
||||
vae_input_dense = torch.cat([dense_first, torch.zeros(3, num_frames-1, height, width, device=device, dtype=vae.dtype)], dim=1)
|
||||
dense_concat_latent = vae.encode([vae_input_dense], device, tiled=False)
|
||||
dense_concat_latent = torch.cat([msk, dense_concat_latent], dim=1)
|
||||
dense_input_latent = torch.cat([dense_video_latent, dense_concat_latent],dim=1)
|
||||
if sparse is not None:
|
||||
sparse_images = sparse[..., :3].permute(3, 0, 1, 2) * 2 - 1
|
||||
sparse_video_latent = vae.encode([sparse_images.to(device, vae.dtype)], device, tiled=False)
|
||||
sparse_first = (sparse_images[:, :1]).to(device, vae.dtype)
|
||||
vae_input_sparse = torch.cat([sparse_first, torch.zeros(3, num_frames-1, height, width, device=device, dtype=vae.dtype)], dim=1)
|
||||
sparse_concat_latent = vae.encode([vae_input_sparse], device, tiled=False)
|
||||
sparse_concat_latent = torch.cat([msk, sparse_concat_latent], dim=1)
|
||||
sparse_input_latent = torch.cat([sparse_video_latent, sparse_concat_latent],dim=1)
|
||||
|
||||
if prev_images is not None:
|
||||
prev_images = prev_images[..., :3].permute(3, 0, 1, 2) * 2 - 1
|
||||
prev_video_latent = vae.encode([prev_images.to(device, vae.dtype)], device, tiled=False)
|
||||
updated["dual_control"]["prev_latent"] = prev_video_latent[0]
|
||||
|
||||
vae.to(offload_device)
|
||||
updated["dual_control"]["dense_input_latent"] = dense_input_latent
|
||||
updated["dual_control"]["sparse_input_latent"] = sparse_input_latent
|
||||
updated["dual_control"]["strength"] = strength
|
||||
updated["dual_control"]["start_percent"] = start_percent
|
||||
updated["dual_control"]["end_percent"] = end_percent
|
||||
updated["dual_control"]["first_frame_noise_level"] = first_frame_noise_level
|
||||
return (updated,)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoAddDualControlEmbeds": WanVideoAddDualControlEmbeds,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoAddDualControlEmbeds": "WanVideo Add Dual Control Embeds",
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
@@ -1,213 +0,0 @@
|
||||
import cv2
|
||||
import math
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from torchvision import transforms
|
||||
|
||||
|
||||
def intrinsic_matrix_from_field_of_view(imshape, fov_degrees:float =55 ): # nlf default fov_degrees 55
|
||||
imshape = np.array(imshape)
|
||||
fov_radians = fov_degrees * np.array(np.pi / 180)
|
||||
larger_side = np.max(imshape)
|
||||
focal_length = larger_side / (np.tan(fov_radians / 2) * 2)
|
||||
# intrinsic_matrix 3*3
|
||||
return np.array([
|
||||
[focal_length, 0, imshape[1] / 2],
|
||||
[0, focal_length, imshape[0] / 2],
|
||||
[0, 0, 1],
|
||||
])
|
||||
|
||||
|
||||
def p3d_to_p2d(point_3d, height, width): # point3d n*1024*3
|
||||
camera_matrix = intrinsic_matrix_from_field_of_view((height,width))
|
||||
camera_matrix = np.expand_dims(camera_matrix, axis=0)
|
||||
camera_matrix = np.expand_dims(camera_matrix, axis=0) # 1*1*3*3
|
||||
point_3d = np.expand_dims(point_3d,axis=-1) # n*1024*3*1
|
||||
point_2d = (camera_matrix@point_3d).squeeze(-1)
|
||||
point_2d[:,:,:2] = point_2d[:,:,:2]/point_2d[:,:,2:3]
|
||||
return point_2d[:,:,:] # n*1024*2
|
||||
|
||||
|
||||
def get_pose_images(smpl_data, offset):
|
||||
pose_images = []
|
||||
for data in smpl_data:
|
||||
if isinstance(data, np.ndarray):
|
||||
joints3d = data
|
||||
else:
|
||||
joints3d = data.numpy()
|
||||
canvas = np.zeros(shape=(offset[0], offset[1], 3), dtype=np.uint8)
|
||||
joints3d = p3d_to_p2d(joints3d, offset[0], offset[1])
|
||||
canvas = draw_3d_points(canvas, joints3d[0], stickwidth=int(offset[1]/350))
|
||||
pose_images.append(Image.fromarray(canvas))
|
||||
return pose_images
|
||||
|
||||
|
||||
def get_control_conditions(poses, h, w, stick_width=1.0, point_radius=2, style="original"):
|
||||
control_images = []
|
||||
for idx, pose in enumerate(poses):
|
||||
canvas = np.zeros(shape=(h, w, 3), dtype=np.uint8)
|
||||
try:
|
||||
joints3d = p3d_to_p2d(pose, h, w)
|
||||
if style == "original":
|
||||
canvas = draw_3d_points(
|
||||
canvas,
|
||||
joints3d[0],
|
||||
stickwidth=int(h / 350 * stick_width),
|
||||
r=point_radius,
|
||||
)
|
||||
elif style == "scail":
|
||||
canvas = draw_3d_points_scail(
|
||||
canvas,
|
||||
joints3d[0],
|
||||
stickwidth=int(h / 350 * stick_width),
|
||||
r=point_radius,
|
||||
)
|
||||
resized_canvas = cv2.resize(canvas, (w, h))
|
||||
# Image.fromarray(resized_canvas).save(f'tmp/{idx}_pose.jpg')
|
||||
control_images.append(resized_canvas)
|
||||
except Exception:
|
||||
control_images.append(Image.fromarray(canvas))
|
||||
control_pixel_values = np.array(control_images)
|
||||
control_pixel_values = torch.from_numpy(control_pixel_values).contiguous() / 255.
|
||||
return control_pixel_values
|
||||
|
||||
|
||||
def draw_3d_points(canvas, points, stickwidth=2, r=2, draw_line=True):
|
||||
colors = [
|
||||
[255, 0, 0], # 0
|
||||
[0, 255, 0], # 1
|
||||
[0, 0, 255], # 2
|
||||
[255, 0, 255], # 3
|
||||
[255, 255, 0], # 4
|
||||
[85, 255, 0], # 5
|
||||
[0, 75, 255], # 6
|
||||
[0, 255, 85], # 7
|
||||
[0, 255, 170], # 8
|
||||
[170, 0, 255], # 9
|
||||
[85, 0, 255], # 10
|
||||
[0, 85, 255], # 11
|
||||
[0, 255, 255], # 12
|
||||
[85, 0, 255], # 13
|
||||
[170, 0, 255], # 14
|
||||
[255, 0, 255], # 15
|
||||
[255, 0, 170], # 16
|
||||
[255, 0, 85], # 17
|
||||
]
|
||||
connetions = [
|
||||
[15,12],[12, 16],[16, 18],[18, 20],[20, 22],
|
||||
[12,17],[17,19],[19,21],
|
||||
[21,23],[12,9],[9,6],
|
||||
[6,3],[3,0],[0,1],
|
||||
[1,4],[4,7],[7,10],[0,2],[2,5],[5,8],[8,11]
|
||||
]
|
||||
connection_colors = [
|
||||
[255, 0, 0], # 0
|
||||
[0, 255, 0], # 1
|
||||
[0, 0, 255], # 2
|
||||
[255, 255, 0], # 3
|
||||
[255, 0, 255], # 4
|
||||
[0, 255, 0], # 5
|
||||
[0, 85, 255], # 6
|
||||
[255, 175, 0], # 7
|
||||
[0, 0, 255], # 8
|
||||
[255, 85, 0], # 9
|
||||
[0, 255, 85], # 10
|
||||
[255, 0, 255], # 11
|
||||
[255, 0, 0], # 12
|
||||
[0, 175, 255], # 13
|
||||
[255, 255, 0], # 14
|
||||
[0, 0, 255], # 15
|
||||
[0, 255, 0], # 16
|
||||
]
|
||||
|
||||
# draw point
|
||||
for i in range(len(points)):
|
||||
x,y = points[i][0:2]
|
||||
x,y = int(x),int(y)
|
||||
if i==13 or i == 14:
|
||||
continue
|
||||
cv2.circle(canvas, (x, y), r, colors[i%17], thickness=-1)
|
||||
|
||||
# draw line
|
||||
if draw_line:
|
||||
for i in range(len(connetions)):
|
||||
point1_idx,point2_idx = connetions[i][0:2]
|
||||
point1 = points[point1_idx]
|
||||
point2 = points[point2_idx]
|
||||
Y = [point2[0],point1[0]]
|
||||
X = [point2[1],point1[1]]
|
||||
mX = int(np.mean(X))
|
||||
mY = int(np.mean(Y))
|
||||
length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
|
||||
angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
|
||||
polygon = cv2.ellipse2Poly((mY, mX), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
|
||||
cv2.fillConvexPoly(canvas, polygon, connection_colors[i%17])
|
||||
|
||||
return canvas
|
||||
|
||||
def draw_3d_points_scail(canvas, points, stickwidth=2, r=2, draw_line=True):
|
||||
|
||||
connetions = [
|
||||
[15,12],[12, 16],[16, 18],[18, 20],[20, 22], # 0-4: Left arm chain
|
||||
[12,17],[17,19],[19,21], # 5-7: Right arm chain
|
||||
[21,23], # 8: Right hand
|
||||
[12,1],[1,4],[4,7], # 9-11: Neck to left leg (hip, thigh, shin)
|
||||
[12,2],[2,5],[5,8], # 12-14: Neck to right leg (hip, thigh, shin)
|
||||
]
|
||||
|
||||
# Warm colors for right side, cool colors for left side
|
||||
connection_colors = [
|
||||
[180, 180, 180], # 0: [15,12] - L. clavicle (Bright Cyan)
|
||||
[0, 200, 255], # 1: [12,16] - L. shoulder (Bright Cyan)
|
||||
[0, 120, 255], # 2: [16,18] - L. upper arm (Bright Blue)
|
||||
[0, 60, 255], # 3: [18,20] - L. forearm (Deep Blue)
|
||||
[60, 0, 255], # 4: [20,22] - L. hand (Blue-Purple)
|
||||
[255, 0, 0], # 5: [12,17] - R. clavicle (Bright Red)
|
||||
[255, 100, 0], # 6: [17,19] - R. upper arm (Bright Orange)
|
||||
[255, 180, 0], # 7: [19,21] - R. forearm (Golden Orange)
|
||||
[255, 255, 0], # 8: [21,23] - R. hand (Bright Yellow)
|
||||
[30, 27, 160], # 9: [12,1] - Neck to L. hip (purple-blue)
|
||||
[73, 27, 177], # 10: [1,4] - L. thigh (purple)
|
||||
[145, 27, 194], # 11: [4,7] - L. shin (magenta)
|
||||
[200, 255, 100], # 12: [12,2] - Neck to R. hip (yellow)
|
||||
[54, 201, 52], # 13: [2,5] - R. thigh (green)
|
||||
[30, 176, 85], # 14: [5,8] - R. shin (green)
|
||||
]
|
||||
|
||||
# draw line
|
||||
if draw_line:
|
||||
# Collect all joints that are part of connections
|
||||
joints_in_use = set()
|
||||
for connection in connetions:
|
||||
joints_in_use.add(connection[0])
|
||||
joints_in_use.add(connection[1])
|
||||
|
||||
for i in range(len(connetions)):
|
||||
point1_idx, point2_idx = connetions[i][0:2]
|
||||
point1 = points[point1_idx]
|
||||
point2 = points[point2_idx]
|
||||
x1, y1 = int(point1[0]), int(point1[1])
|
||||
x2, y2 = int(point2[0]), int(point2[1])
|
||||
cv2.line(canvas, (x1, y1), (x2, y2), connection_colors[i], stickwidth)
|
||||
|
||||
# draw points for joints that have connections
|
||||
joints_in_use = set()
|
||||
for connection in connetions:
|
||||
joints_in_use.add(connection[0])
|
||||
joints_in_use.add(connection[1])
|
||||
|
||||
for joint_idx in joints_in_use:
|
||||
if joint_idx >= len(points):
|
||||
continue
|
||||
x, y = points[joint_idx][0:2]
|
||||
x, y = int(x), int(y)
|
||||
# Use the color from the first connection involving this joint
|
||||
joint_color = [180, 180, 180] # default grey
|
||||
for i, connection in enumerate(connetions):
|
||||
if connection[0] == joint_idx or connection[1] == joint_idx:
|
||||
joint_color = connection_colors[i]
|
||||
break
|
||||
cv2.circle(canvas, (x, y), r, joint_color, thickness=-1)
|
||||
|
||||
return canvas
|
||||
@@ -1 +0,0 @@
|
||||
from .vqvae import SMPL_VQVAE, VectorQuantizer, Encoder, Decoder
|
||||
@@ -1,329 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import numpy as np
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels=3,
|
||||
mid_channels=[128, 512],
|
||||
out_channels=3072,
|
||||
downsample_time=[1, 1],
|
||||
downsample_joint=[1, 1],
|
||||
num_attention_heads=8,
|
||||
attention_head_dim=64,
|
||||
dim=3072,
|
||||
):
|
||||
super(Encoder, self).__init__()
|
||||
|
||||
self.conv_in = nn.Conv2d(in_channels, mid_channels[0], kernel_size=3, stride=1, padding=1)
|
||||
self.resnet1 = nn.ModuleList([ResBlock(mid_channels[0], mid_channels[0]) for _ in range(3)])
|
||||
self.downsample1 = Downsample(mid_channels[0], mid_channels[0], downsample_time[0], downsample_joint[0])
|
||||
self.resnet2 = ResBlock(mid_channels[0], mid_channels[1])
|
||||
self.resnet3 = nn.ModuleList([ResBlock(mid_channels[1], mid_channels[1]) for _ in range(3)])
|
||||
self.downsample2 = Downsample(mid_channels[1], mid_channels[1], downsample_time[1], downsample_joint[1])
|
||||
self.conv_out = nn.Conv2d(mid_channels[-1], out_channels, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv_in(x)
|
||||
for resnet in self.resnet1:
|
||||
x = resnet(x)
|
||||
x = self.downsample1(x)
|
||||
|
||||
x = self.resnet2(x)
|
||||
for resnet in self.resnet3:
|
||||
x = resnet(x)
|
||||
x = self.downsample2(x)
|
||||
|
||||
x = self.conv_out(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class VectorQuantizer(nn.Module):
|
||||
def __init__(self, nb_code, code_dim):
|
||||
super().__init__()
|
||||
self.nb_code = nb_code
|
||||
self.code_dim = code_dim
|
||||
self.mu = 0.99
|
||||
self.reset_codebook()
|
||||
self.reset_count = 0
|
||||
self.usage = torch.zeros((self.nb_code, 1))
|
||||
|
||||
def reset_codebook(self):
|
||||
self.init = False
|
||||
self.code_sum = None
|
||||
self.code_count = None
|
||||
self.register_buffer('codebook', torch.zeros(self.nb_code, self.code_dim).cuda())
|
||||
|
||||
def _tile(self, x):
|
||||
nb_code_x, code_dim = x.shape
|
||||
if nb_code_x < self.nb_code:
|
||||
n_repeats = (self.nb_code + nb_code_x - 1) // nb_code_x
|
||||
std = 0.01 / np.sqrt(code_dim)
|
||||
out = x.repeat(n_repeats, 1)
|
||||
out = out + torch.randn_like(out) * std
|
||||
else:
|
||||
out = x
|
||||
return out
|
||||
|
||||
def preprocess(self, x):
|
||||
# [bs, c, f, j] -> [bs * f * j, c]
|
||||
x = x.permute(0, 2, 3, 1).contiguous()
|
||||
x = x.view(-1, x.shape[-1])
|
||||
return x
|
||||
|
||||
def quantize(self, x):
|
||||
# [bs * f * j, dim=3072]
|
||||
# Calculate latent code x_l
|
||||
k_w = self.codebook.t()
|
||||
distance = torch.sum(x ** 2, dim=-1, keepdim=True) - 2 * torch.matmul(x, k_w) + torch.sum(k_w ** 2, dim=0, keepdim=True)
|
||||
_, code_idx = torch.min(distance, dim=-1)
|
||||
return code_idx
|
||||
|
||||
def dequantize(self, code_idx):
|
||||
x = F.embedding(code_idx, self.codebook) # indexing: [bs * f * j, 32]
|
||||
return x
|
||||
|
||||
def forward(self, x, return_vq=False):
|
||||
bs, c, f, j = x.shape # SMPL data frames: [bs, 3072, f, j]
|
||||
|
||||
# Preprocess
|
||||
x = self.preprocess(x)
|
||||
# return x.view(bs, f*j, c).contiguous(), None
|
||||
assert x.shape[-1] == self.code_dim
|
||||
|
||||
# quantize and dequantize through bottleneck
|
||||
code_idx = self.quantize(x)
|
||||
x_d = self.dequantize(code_idx)
|
||||
|
||||
# Loss
|
||||
commit_loss = F.mse_loss(x, x_d.detach())
|
||||
|
||||
# Passthrough
|
||||
x_d = x + (x_d - x).detach()
|
||||
|
||||
if return_vq:
|
||||
return x_d.view(bs, f*j, c).contiguous(), commit_loss
|
||||
# return (x_d, x_d.view(bs, f, j, c).permute(0, 3, 1, 2).contiguous()), commit_loss, perplexity
|
||||
|
||||
# Postprocess
|
||||
x_d = x_d.view(bs, f, j, c).permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
return x_d, commit_loss
|
||||
|
||||
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels=3072,
|
||||
mid_channels=[512, 128],
|
||||
out_channels=3,
|
||||
upsample_rate=None,
|
||||
frame_upsample_rate=[1.0, 1.0],
|
||||
joint_upsample_rate=[1.0, 1.0],
|
||||
dim=128,
|
||||
attention_head_dim=64,
|
||||
num_attention_heads=8,
|
||||
):
|
||||
super(Decoder, self).__init__()
|
||||
|
||||
self.conv_in = nn.Conv2d(in_channels, mid_channels[0], kernel_size=3, stride=1, padding=1)
|
||||
self.resnet1 = nn.ModuleList([ResBlock(mid_channels[0], mid_channels[0]) for _ in range(3)])
|
||||
self.upsample1 = Upsample(mid_channels[0], mid_channels[0], frame_upsample_rate=frame_upsample_rate[0], joint_upsample_rate=joint_upsample_rate[0])
|
||||
self.resnet2 = ResBlock(mid_channels[0], mid_channels[1])
|
||||
self.resnet3 = nn.ModuleList([ResBlock(mid_channels[1], mid_channels[1]) for _ in range(3)])
|
||||
self.upsample2 = Upsample(mid_channels[1], mid_channels[1], frame_upsample_rate=frame_upsample_rate[1], joint_upsample_rate=joint_upsample_rate[1])
|
||||
self.conv_out = nn.Conv2d(mid_channels[-1], out_channels, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv_in(x)
|
||||
for resnet in self.resnet1:
|
||||
x = resnet(x)
|
||||
x = self.upsample1(x)
|
||||
|
||||
x = self.resnet2(x)
|
||||
for resnet in self.resnet3:
|
||||
x = resnet(x)
|
||||
x = self.upsample2(x)
|
||||
|
||||
x = self.conv_out(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
upsample_rate=None,
|
||||
frame_upsample_rate=None,
|
||||
joint_upsample_rate=None,
|
||||
):
|
||||
super(Upsample, self).__init__()
|
||||
|
||||
self.upsampler = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
|
||||
self.upsample_rate = upsample_rate
|
||||
self.frame_upsample_rate = frame_upsample_rate
|
||||
self.joint_upsample_rate = joint_upsample_rate
|
||||
self.upsample_rate = upsample_rate
|
||||
|
||||
def forward(self, inputs):
|
||||
if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1:
|
||||
# split first frame
|
||||
x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:]
|
||||
|
||||
if self.upsample_rate is not None:
|
||||
# import pdb; pdb.set_trace()
|
||||
x_first = F.interpolate(x_first, scale_factor=self.upsample_rate)
|
||||
x_rest = F.interpolate(x_rest, scale_factor=self.upsample_rate)
|
||||
else:
|
||||
# import pdb; pdb.set_trace()
|
||||
# x_first = F.interpolate(x_first, scale_factor=(self.frame_upsample_rate, self.joint_upsample_rate), mode="bilinear", align_corners=True)
|
||||
x_rest = F.interpolate(x_rest, scale_factor=(self.frame_upsample_rate, self.joint_upsample_rate), mode="bilinear", align_corners=True)
|
||||
x_first = x_first[:, :, None, :]
|
||||
inputs = torch.cat([x_first, x_rest], dim=2)
|
||||
elif inputs.shape[2] > 1:
|
||||
if self.upsample_rate is not None:
|
||||
inputs = F.interpolate(inputs, scale_factor=self.upsample_rate)
|
||||
else:
|
||||
inputs = F.interpolate(inputs, scale_factor=(self.frame_upsample_rate, self.joint_upsample_rate), mode="bilinear", align_corners=True)
|
||||
else:
|
||||
inputs = inputs.squeeze(2)
|
||||
if self.upsample_rate is not None:
|
||||
inputs = F.interpolate(inputs, scale_factor=self.upsample_rate)
|
||||
else:
|
||||
inputs = F.interpolate(inputs, scale_factor=(self.frame_upsample_rate, self.joint_upsample_rate), mode="linear", align_corners=True)
|
||||
inputs = inputs[:, :, None, :, :]
|
||||
|
||||
b, c, t, j = inputs.shape
|
||||
inputs = inputs.permute(0, 2, 1, 3).reshape(b * t, c, j)
|
||||
inputs = self.upsampler(inputs)
|
||||
inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3)
|
||||
|
||||
return inputs
|
||||
|
||||
|
||||
class Downsample(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
frame_downsample_rate,
|
||||
joint_downsample_rate
|
||||
):
|
||||
super(Downsample, self).__init__()
|
||||
|
||||
self.frame_downsample_rate = frame_downsample_rate
|
||||
self.joint_downsample_rate = joint_downsample_rate
|
||||
self.joint_downsample = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=self.joint_downsample_rate, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
# (batch_size, channels, frames, joints) -> (batch_size * joints, channels, frames)
|
||||
if self.frame_downsample_rate > 1:
|
||||
batch_size, channels, frames, joints = x.shape
|
||||
x = x.permute(0, 3, 1, 2).reshape(batch_size * joints, channels, frames)
|
||||
if x.shape[-1] % 2 == 1:
|
||||
x_first, x_rest = x[..., 0], x[..., 1:]
|
||||
if x_rest.shape[-1] > 0:
|
||||
# (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2)
|
||||
x_rest = F.avg_pool1d(x_rest, kernel_size=self.frame_downsample_rate, stride=self.frame_downsample_rate)
|
||||
|
||||
x = torch.cat([x_first[..., None], x_rest], dim=-1)
|
||||
# (batch_size * joints, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, joints)
|
||||
x = x.reshape(batch_size, joints, channels, x.shape[-1]).permute(0, 2, 3, 1)
|
||||
else:
|
||||
# (batch_size * joints, channels, frames) -> (batch_size * joints, channels, frames // 2)
|
||||
x = F.avg_pool1d(x, kernel_size=2, stride=2)
|
||||
# (batch_size * joints, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width)
|
||||
x = x.reshape(batch_size, joints, channels, x.shape[-1]).permute(0, 2, 3, 1)
|
||||
|
||||
# Pad the tensor
|
||||
# pad = (0, 1)
|
||||
# x = F.pad(x, pad, mode="constant", value=0)
|
||||
batch_size, channels, frames, joints = x.shape
|
||||
# (batch_size, channels, frames, joints) -> (batch_size * frames, channels, joints)
|
||||
x = x.permute(0, 2, 1, 3).reshape(batch_size * frames, channels, joints)
|
||||
x = self.joint_downsample(x)
|
||||
# (batch_size * frames, channels, joints) -> (batch_size, channels, frames, joints)
|
||||
x = x.reshape(batch_size, frames, x.shape[1], x.shape[2]).permute(0, 2, 1, 3)
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class ResBlock(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
group_num=32,
|
||||
max_channels=512):
|
||||
super(ResBlock, self).__init__()
|
||||
skip = max(1, max_channels // out_channels - 1)
|
||||
self.block = nn.Sequential(
|
||||
nn.GroupNorm(group_num, in_channels, eps=1e-06, affine=True),
|
||||
nn.SiLU(),
|
||||
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=skip, dilation=skip),
|
||||
nn.GroupNorm(group_num, out_channels, eps=1e-06, affine=True),
|
||||
nn.SiLU(),
|
||||
nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0),
|
||||
)
|
||||
self.conv_short = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) if in_channels != out_channels else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
hidden_states = self.block(x)
|
||||
if hidden_states.shape != x.shape:
|
||||
x = self.conv_short(x)
|
||||
x = x + hidden_states
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class SMPL_VQVAE(nn.Module):
|
||||
def __init__(self, encoder, decoder, vq):
|
||||
super(SMPL_VQVAE, self).__init__()
|
||||
|
||||
self.encoder = encoder
|
||||
self.decoder = decoder
|
||||
self.vq = vq
|
||||
|
||||
def to(self, device):
|
||||
self.encoder = self.encoder.to(device)
|
||||
self.decoder = self.decoder.to(device)
|
||||
self.vq = self.vq.to(device)
|
||||
self.device = device
|
||||
return self
|
||||
|
||||
def encdec_slice_frames(self, x, frame_batch_size, encdec, return_vq):
|
||||
num_frames = x.shape[2]
|
||||
remaining_frames = num_frames % frame_batch_size
|
||||
x_output = []
|
||||
|
||||
for i in range(num_frames // frame_batch_size):
|
||||
remaining_frames = num_frames % frame_batch_size
|
||||
start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
|
||||
end_frame = frame_batch_size * (i + 1) + remaining_frames
|
||||
x_intermediate = x[:, :, start_frame:end_frame]
|
||||
x_intermediate = encdec(x_intermediate)
|
||||
x_output.append(x_intermediate)
|
||||
if encdec == self.encoder and self.vq is not None:
|
||||
x_output, loss = self.vq(torch.cat(x_output, dim=2), return_vq=return_vq)
|
||||
return x_output, loss
|
||||
else:
|
||||
return torch.cat(x_output, dim=2), None, None
|
||||
|
||||
def forward(self, x, return_vq=False):
|
||||
x = x.permute(0, 3, 1, 2)
|
||||
x, loss = self.encdec_slice_frames(x, frame_batch_size=8, encdec=self.encoder, return_vq=return_vq)
|
||||
|
||||
if return_vq:
|
||||
return x, loss
|
||||
x, _, _ = self.encdec_slice_frames(x, frame_batch_size=2, encdec=self.decoder, return_vq=return_vq)
|
||||
x = x.permute(0, 2, 3, 1)
|
||||
|
||||
return x, loss
|
||||
-193
@@ -1,193 +0,0 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
from typing import Union, Tuple
|
||||
|
||||
|
||||
def get_1d_rotary_pos_embed(
|
||||
dim: int,
|
||||
pos: Union[np.ndarray, int],
|
||||
theta: float = 10000.0,
|
||||
use_real=False,
|
||||
linear_factor=1.0,
|
||||
ntk_factor=1.0,
|
||||
repeat_interleave_real=True,
|
||||
freqs_dtype=torch.float32, # torch.float32, torch.float64 (flux)
|
||||
):
|
||||
"""
|
||||
Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
|
||||
|
||||
This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
|
||||
index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
|
||||
data type.
|
||||
|
||||
Args:
|
||||
dim (`int`): Dimension of the frequency tensor.
|
||||
pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
|
||||
theta (`float`, *optional*, defaults to 10000.0):
|
||||
Scaling factor for frequency computation. Defaults to 10000.0.
|
||||
use_real (`bool`, *optional*):
|
||||
If True, return real part and imaginary part separately. Otherwise, return complex numbers.
|
||||
linear_factor (`float`, *optional*, defaults to 1.0):
|
||||
Scaling factor for the context extrapolation. Defaults to 1.0.
|
||||
ntk_factor (`float`, *optional*, defaults to 1.0):
|
||||
Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
|
||||
repeat_interleave_real (`bool`, *optional*, defaults to `True`):
|
||||
If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`.
|
||||
Otherwise, they are concateanted with themselves.
|
||||
freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
|
||||
the dtype of the frequency tensor.
|
||||
Returns:
|
||||
`torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
|
||||
"""
|
||||
assert dim % 2 == 0
|
||||
|
||||
if isinstance(pos, int):
|
||||
pos = torch.arange(pos)
|
||||
if isinstance(pos, np.ndarray):
|
||||
pos = torch.from_numpy(pos) # type: ignore # [S]
|
||||
|
||||
theta = theta * ntk_factor
|
||||
freqs = (
|
||||
1.0
|
||||
/ (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim))
|
||||
/ linear_factor
|
||||
) # [D/2]
|
||||
freqs = torch.outer(pos, freqs) # type: ignore # [S, D/2]
|
||||
if use_real and repeat_interleave_real:
|
||||
freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float() # [S, D]
|
||||
freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float() # [S, D]
|
||||
return freqs_cos, freqs_sin
|
||||
elif use_real:
|
||||
freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float() # [S, D]
|
||||
freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float() # [S, D]
|
||||
return freqs_cos, freqs_sin
|
||||
else:
|
||||
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2]
|
||||
return freqs_cis
|
||||
|
||||
|
||||
def get_3d_rotary_pos_embed(
|
||||
embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
|
||||
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
|
||||
"""
|
||||
RoPE for video tokens with 3D structure.
|
||||
|
||||
Args:
|
||||
embed_dim: (`int`):
|
||||
The embedding dimension size, corresponding to hidden_size_head.
|
||||
crops_coords (`Tuple[int]`):
|
||||
The top-left and bottom-right coordinates of the crop.
|
||||
grid_size (`Tuple[int]`):
|
||||
The grid size of the spatial positional embedding (height, width).
|
||||
temporal_size (`int`):
|
||||
The size of the temporal dimension.
|
||||
theta (`float`):
|
||||
Scaling factor for frequency computation.
|
||||
|
||||
Returns:
|
||||
`torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
|
||||
"""
|
||||
if use_real is not True:
|
||||
raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")
|
||||
start, stop = crops_coords
|
||||
grid_size_h, grid_size_w = grid_size
|
||||
grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
|
||||
grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
|
||||
grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
|
||||
|
||||
# Compute dimensions for each axis
|
||||
dim_t = embed_dim // 4
|
||||
dim_h = embed_dim // 8 * 3
|
||||
dim_w = embed_dim // 8 * 3
|
||||
|
||||
# Temporal frequencies
|
||||
freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
|
||||
# Spatial frequencies for height and width
|
||||
freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
|
||||
freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)
|
||||
|
||||
# BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
|
||||
def combine_time_height_width(freqs_t, freqs_h, freqs_w):
|
||||
freqs_t = freqs_t[:, None, None, :].expand(
|
||||
-1, grid_size_h, grid_size_w, -1
|
||||
) # temporal_size, grid_size_h, grid_size_w, dim_t
|
||||
freqs_h = freqs_h[None, :, None, :].expand(
|
||||
temporal_size, -1, grid_size_w, -1
|
||||
) # temporal_size, grid_size_h, grid_size_2, dim_h
|
||||
freqs_w = freqs_w[None, None, :, :].expand(
|
||||
temporal_size, grid_size_h, -1, -1
|
||||
) # temporal_size, grid_size_h, grid_size_2, dim_w
|
||||
|
||||
freqs = torch.cat(
|
||||
[freqs_t, freqs_h, freqs_w], dim=-1
|
||||
) # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
|
||||
freqs = freqs.view(
|
||||
temporal_size * grid_size_h * grid_size_w, -1
|
||||
) # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
|
||||
return freqs
|
||||
|
||||
t_cos, t_sin = freqs_t # both t_cos and t_sin has shape: temporal_size, dim_t
|
||||
h_cos, h_sin = freqs_h # both h_cos and h_sin has shape: grid_size_h, dim_h
|
||||
w_cos, w_sin = freqs_w # both w_cos and w_sin has shape: grid_size_w, dim_w
|
||||
cos = combine_time_height_width(t_cos, h_cos, w_cos)
|
||||
sin = combine_time_height_width(t_sin, h_sin, w_sin)
|
||||
return cos, sin
|
||||
|
||||
|
||||
def get_3d_motion_spatial_embed(
|
||||
embed_dim: int, num_joints: int, joints_mean: np.ndarray, joints_std: np.ndarray, theta: float = 10000.0
|
||||
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
|
||||
assert embed_dim % 2 == 0 and embed_dim % 3 == 0
|
||||
|
||||
def create_rope_pe(dim, pos, freqs_dtype=torch.float32):
|
||||
if isinstance(pos, np.ndarray):
|
||||
pos = torch.from_numpy(pos)
|
||||
freqs = (
|
||||
1.0
|
||||
/ (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim))
|
||||
) # [D/2]
|
||||
freqs = torch.outer(pos, freqs) # type: ignore # [S, D/2]
|
||||
freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float() # [S, D]
|
||||
freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float() # [S, D]
|
||||
return freqs_cos, freqs_sin
|
||||
|
||||
pos_x = joints_mean[:, 0]
|
||||
pos_y = joints_mean[:, 1]
|
||||
pos_z = joints_mean[:, 2]
|
||||
|
||||
normalized_pos_x = (pos_x - pos_x.mean())
|
||||
normalized_pos_y = (pos_y - pos_y.mean())
|
||||
normalized_pos_z = (pos_z - pos_z.mean())
|
||||
|
||||
freqs_cos_x, freqs_sin_x = create_rope_pe(embed_dim // 3, normalized_pos_x)
|
||||
freqs_cos_y, freqs_sin_y = create_rope_pe(embed_dim // 3, normalized_pos_y)
|
||||
freqs_cos_z, freqs_sin_z = create_rope_pe(embed_dim // 3, normalized_pos_z)
|
||||
|
||||
freqs_cos = torch.cat([freqs_cos_x, freqs_cos_y, freqs_cos_z], dim=-1)
|
||||
freqs_sin = torch.cat([freqs_sin_x, freqs_sin_y, freqs_sin_z], dim=-1)
|
||||
|
||||
return freqs_cos, freqs_sin
|
||||
|
||||
def prepare_motion_embeddings(num_frames, num_joints, joints_mean, joints_std, theta=10000, device='cuda'):
|
||||
time_embed = get_1d_rotary_pos_embed(44, num_frames, theta, use_real=True)
|
||||
time_embed_cos = time_embed[0][:, None, :].expand(-1, num_joints, -1).reshape(num_frames*num_joints, -1)
|
||||
time_embed_sin = time_embed[1][:, None, :].expand(-1, num_joints, -1).reshape(num_frames*num_joints, -1)
|
||||
spatial_motion_embed = get_3d_motion_spatial_embed(84, num_joints, joints_mean, joints_std, theta)
|
||||
spatial_embed_cos = spatial_motion_embed[0][None, :, :].expand(num_frames, -1, -1).reshape(num_frames*num_joints, -1)
|
||||
spatial_embed_sin = spatial_motion_embed[1][None, :, :].expand(num_frames, -1, -1).reshape(num_frames*num_joints, -1)
|
||||
motion_embed_cos = torch.cat([time_embed_cos, spatial_embed_cos], dim=-1).to(device=device)
|
||||
motion_embed_sin = torch.cat([time_embed_sin, spatial_embed_sin], dim=-1).to(device=device)
|
||||
return motion_embed_cos, motion_embed_sin
|
||||
|
||||
def apply_rotary_emb(x, freqs_cis):
|
||||
cos, sin = freqs_cis # [S, D]
|
||||
cos = cos[None, None]
|
||||
sin = sin[None, None]
|
||||
cos, sin = cos.to(x.device), sin.to(x.device)
|
||||
|
||||
x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]
|
||||
x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
|
||||
|
||||
out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
|
||||
|
||||
return out
|
||||
-340
@@ -1,340 +0,0 @@
|
||||
import os
|
||||
import torch
|
||||
from ..utils import log
|
||||
import numpy as np
|
||||
|
||||
import comfy.model_management as mm
|
||||
from comfy.utils import load_torch_file
|
||||
import folder_paths
|
||||
|
||||
script_directory = os.path.dirname(os.path.abspath(__file__))
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
|
||||
local_model_path = os.path.join(folder_paths.models_dir, "nlf", "nlf_l_multi_0.3.2.torchscript")
|
||||
folder_paths.add_model_folder_path("nlf", os.path.join(folder_paths.models_dir, "nlf"))
|
||||
|
||||
from .motion4d import SMPL_VQVAE, VectorQuantizer, Encoder, Decoder
|
||||
|
||||
def check_jit_script_function():
|
||||
if torch.jit.script.__name__ != "script":
|
||||
# Get more details about what modified it
|
||||
module = torch.jit.script.__module__
|
||||
qualname = getattr(torch.jit.script, '__qualname__', 'unknown')
|
||||
code_file = None
|
||||
try:
|
||||
code_file = torch.jit.script.__code__.co_filename
|
||||
code_line = torch.jit.script.__code__.co_firstlineno
|
||||
log.warning(f"torch.jit.script has been modified by another custom node.\n"
|
||||
f" Function name: {torch.jit.script.__name__}\n"
|
||||
f" Module: {module}\n"
|
||||
f" Qualified name: {qualname}\n"
|
||||
f" Defined in: {code_file}:{code_line}\n"
|
||||
f"This may cause issues with the NLF model.")
|
||||
except:
|
||||
log.warning("--------------------------------")
|
||||
log.warning(f"torch.jit.script function is: {torch.jit.script.__name__} from module {module}, "
|
||||
f"this has been modified by another custom node. This may cause issues with the NLF model.")
|
||||
log.warning("--------------------------------")
|
||||
|
||||
model_list = [
|
||||
"https://github.com/isarandi/nlf/releases/download/v0.3.2/nlf_l_multi_0.3.2.torchscript",
|
||||
"https://github.com/isarandi/nlf/releases/download/v0.2.2/nlf_l_multi_0.2.2.torchscript",
|
||||
]
|
||||
|
||||
class DownloadAndLoadNLFModel:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"url": (model_list, {"default": "https://github.com/isarandi/nlf/releases/download/v0.3.2/nlf_l_multi_0.3.2.torchscript"}),
|
||||
},
|
||||
"optional": {
|
||||
"warmup": ("BOOLEAN", {"default": True, "tooltip": "Whether to warmup the model after loading"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("NLFMODEL",)
|
||||
RETURN_NAMES = ("nlf_model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def loadmodel(self, url, warmup=True):
|
||||
if url not in model_list:
|
||||
raise ValueError(f"URL {url} is not in the list of allowed models.")
|
||||
check_jit_script_function()
|
||||
|
||||
if not os.path.exists(local_model_path):
|
||||
log.info(f"Downloading NLF model to: {local_model_path}")
|
||||
import requests
|
||||
os.makedirs(os.path.dirname(local_model_path), exist_ok=True)
|
||||
response = requests.get(url)
|
||||
if response.status_code == 200:
|
||||
with open(local_model_path, "wb") as f:
|
||||
f.write(response.content)
|
||||
else:
|
||||
print("Failed to download file:", response.status_code)
|
||||
|
||||
model = torch.jit.load(local_model_path).eval()
|
||||
|
||||
if warmup:
|
||||
log.info("Warming up NLF model...")
|
||||
dummy_input = torch.zeros(1, 3, 256, 256, device=device)
|
||||
jit_profiling_prev_state = torch._C._jit_set_profiling_executor(True)
|
||||
try:
|
||||
for _ in range(2):
|
||||
_ = model.detect_smpl_batched(dummy_input)
|
||||
finally:
|
||||
torch._C._jit_set_profiling_executor(jit_profiling_prev_state)
|
||||
|
||||
log.info("NLF model warmed up")
|
||||
|
||||
model = model.to(offload_device)
|
||||
|
||||
return (model,)
|
||||
|
||||
class LoadNLFModel:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"nlf_model": (folder_paths.get_filename_list("nlf"), {"tooltip": "These models are loaded from the 'ComfyUI/models/nlf' -folder",}),
|
||||
|
||||
},
|
||||
"optional": {
|
||||
"warmup": ("BOOLEAN", {"default": True, "tooltip": "Whether to warmup the model after loading"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("NLFMODEL",)
|
||||
RETURN_NAMES = ("nlf_model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def loadmodel(self, nlf_model, warmup=True):
|
||||
check_jit_script_function()
|
||||
model = torch.jit.load(folder_paths.get_full_path_or_raise("nlf", nlf_model)).eval()
|
||||
|
||||
if warmup:
|
||||
log.info("Warming up NLF model...")
|
||||
dummy_input = torch.zeros(1, 3, 256, 256, device=device)
|
||||
jit_profiling_prev_state = torch._C._jit_set_profiling_executor(True)
|
||||
try:
|
||||
for _ in range(2):
|
||||
_ = model.detect_smpl_batched(dummy_input)
|
||||
finally:
|
||||
torch._C._jit_set_profiling_executor(jit_profiling_prev_state)
|
||||
log.info("NLF model warmed up")
|
||||
|
||||
model = model.to(offload_device)
|
||||
|
||||
return model,
|
||||
|
||||
class LoadVQVAE:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("VQVAE",)
|
||||
RETURN_NAMES = ("vqvae", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def loadmodel(self, model_name):
|
||||
model_path = folder_paths.get_full_path("vae", model_name)
|
||||
vae_sd = load_torch_file(model_path, safe_load=True)
|
||||
|
||||
# Get motion tokenizer
|
||||
motion_encoder = Encoder(
|
||||
in_channels=3,
|
||||
mid_channels=[128, 512],
|
||||
out_channels=3072,
|
||||
downsample_time=[2, 2],
|
||||
downsample_joint=[1, 1]
|
||||
)
|
||||
motion_quant = VectorQuantizer(nb_code=8192, code_dim=3072)
|
||||
motion_decoder = Decoder(
|
||||
in_channels=3072,
|
||||
mid_channels=[512, 128],
|
||||
out_channels=3,
|
||||
upsample_rate=2.0,
|
||||
frame_upsample_rate=[2.0, 2.0],
|
||||
joint_upsample_rate=[1.0, 1.0]
|
||||
)
|
||||
|
||||
vqvae = SMPL_VQVAE(motion_encoder, motion_decoder, motion_quant).to(device)
|
||||
vqvae.load_state_dict(vae_sd, strict=True)
|
||||
|
||||
return vqvae,
|
||||
|
||||
class MTVCrafterEncodePoses:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"vqvae": ("VQVAE", {"tooltip": "VQVAE model"}),
|
||||
"poses": ("NLFPRED", {"tooltip": "Input poses for the model"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MTVCRAFTERMOTION", "NLFPRED")
|
||||
RETURN_NAMES = ("mtvcrafter_motion", "pose_results")
|
||||
FUNCTION = "encode"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def encode(self, vqvae, poses):
|
||||
|
||||
global_mean = np.load(os.path.join(script_directory, "data", "mean.npy")) #global_mean.shape: (24, 3)
|
||||
global_std = np.load(os.path.join(script_directory, "data", "std.npy"))
|
||||
|
||||
smpl_poses = []
|
||||
for pose in poses['joints3d_nonparam'][0]:
|
||||
smpl_poses.append(pose[0].cpu().numpy())
|
||||
smpl_poses = np.array(smpl_poses)
|
||||
|
||||
norm_poses = torch.tensor((smpl_poses - global_mean) / global_std).unsqueeze(0)
|
||||
print(f"norm_poses shape: {norm_poses.shape}, dtype: {norm_poses.dtype}")
|
||||
|
||||
vqvae.to(device)
|
||||
motion_tokens, vq_loss = vqvae(norm_poses.to(device), return_vq=True)
|
||||
|
||||
recon_motion = vqvae(norm_poses.to(device))[0][0].to(dtype=torch.float32).cpu().detach() * global_std + global_mean
|
||||
vqvae.to(offload_device)
|
||||
|
||||
poses_dict = {
|
||||
'mtv_motion_tokens': motion_tokens,
|
||||
'global_mean': global_mean,
|
||||
'global_std': global_std
|
||||
}
|
||||
|
||||
return poses_dict, recon_motion
|
||||
|
||||
|
||||
class NLFPredict:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"model": ("NLFMODEL",),
|
||||
"images": ("IMAGE", {"tooltip": "Input images for the model"}),
|
||||
},
|
||||
"optional": {
|
||||
"per_batch": ("INT", {"default": -1, "min": -1, "max": 10000, "step": 1, "tooltip": "How many images to process at once. -1 means all at once."}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("NLFPRED", "BBOX",)
|
||||
RETURN_NAMES = ("pose_results", "bboxes")
|
||||
FUNCTION = "predict"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def predict(self, model, images, per_batch=-1):
|
||||
|
||||
check_jit_script_function()
|
||||
model = model.to(device)
|
||||
|
||||
num_images = images.shape[0]
|
||||
|
||||
# Determine batch size
|
||||
if per_batch == -1:
|
||||
batch_size = num_images
|
||||
else:
|
||||
batch_size = per_batch
|
||||
|
||||
# Initialize result containers
|
||||
all_boxes = []
|
||||
all_joints3d_nonparam = []
|
||||
|
||||
# Process in batches
|
||||
for i in range(0, num_images, batch_size):
|
||||
end_idx = min(i + batch_size, num_images)
|
||||
batch_images = images[i:end_idx]
|
||||
|
||||
jit_profiling_prev_state = torch._C._jit_set_profiling_executor(True)
|
||||
try:
|
||||
pred = model.detect_smpl_batched(batch_images.permute(0, 3, 1, 2).to(device))
|
||||
finally:
|
||||
torch._C._jit_set_profiling_executor(jit_profiling_prev_state)
|
||||
|
||||
# Collect boxes and joints from this batch
|
||||
if 'boxes' in pred:
|
||||
all_boxes.extend(pred['boxes'])
|
||||
if 'joints3d_nonparam' in pred:
|
||||
all_joints3d_nonparam.extend(pred['joints3d_nonparam'])
|
||||
|
||||
model = model.to(offload_device)
|
||||
|
||||
# Move collected results to offload device
|
||||
all_boxes = [box.to(offload_device) for box in all_boxes]
|
||||
all_joints3d_nonparam = [joints.to(offload_device) for joints in all_joints3d_nonparam]
|
||||
|
||||
# Maintain the original nested format: wrap in a list to match expected structure
|
||||
pose_results = {
|
||||
'joints3d_nonparam': [all_joints3d_nonparam],
|
||||
}
|
||||
|
||||
# Convert bboxes to list format: [x_min, y_min, x_max, y_max] for each detection
|
||||
# Each box tensor is shape (1, 5) with [x_min, y_min, x_max, y_max, confidence]
|
||||
formatted_boxes = []
|
||||
for box in all_boxes:
|
||||
# Handle empty detections (no person detected in frame)
|
||||
if box.numel() == 0 or box.shape[0] == 0:
|
||||
formatted_boxes.append([0.0, 0.0, 0.0, 0.0])
|
||||
else:
|
||||
# Extract first 4 values (x_min, y_min, x_max, y_max), drop confidence
|
||||
bbox_values = box[0, :4].cpu().tolist()
|
||||
formatted_boxes.append(bbox_values)
|
||||
|
||||
return (pose_results, formatted_boxes)
|
||||
|
||||
class DrawNLFPoses:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"poses": ("NLFPRED", {"tooltip": "Input poses for the model"}),
|
||||
"width": ("INT", {"default": 512}),
|
||||
"height": ("INT", {"default": 512}),
|
||||
},
|
||||
"optional": {
|
||||
"stick_width": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 1000.0, "step": 0.01, "tooltip": "Stick width multiplier"}),
|
||||
"point_radius": ("INT", {"default": 5, "min": 1, "max": 10, "step": 1, "tooltip": "Point radius for drawing the pose"}),
|
||||
"style": (["original", "scail"], {"default": "original", "tooltip": "style of the pose drawing"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", )
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "predict"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def predict(self, poses, width, height, stick_width=1.0, point_radius=2, style="original"):
|
||||
from .draw_pose import get_control_conditions
|
||||
|
||||
if isinstance(poses, dict):
|
||||
pose_input = poses['joints3d_nonparam'][0] if 'joints3d_nonparam' in poses else poses
|
||||
else:
|
||||
pose_input = poses
|
||||
|
||||
control_conditions = get_control_conditions(pose_input, height, width, stick_width=stick_width, point_radius=point_radius, style=style)
|
||||
|
||||
return (control_conditions,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"LoadNLFModel": LoadNLFModel,
|
||||
"DownloadAndLoadNLFModel": DownloadAndLoadNLFModel,
|
||||
"NLFPredict": NLFPredict,
|
||||
"DrawNLFPoses": DrawNLFPoses,
|
||||
"LoadVQVAE": LoadVQVAE,
|
||||
"MTVCrafterEncodePoses": MTVCrafterEncodePoses
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LoadNLFModel": "Load NLF Model",
|
||||
"DownloadAndLoadNLFModel": "(Download)Load NLF Model",
|
||||
"NLFPredict": "NLF Predict",
|
||||
"DrawNLFPoses": "Draw NLF Poses",
|
||||
"LoadVQVAE": "Load VQVAE",
|
||||
"MTVCrafterEncodePoses": "MTV Crafter Encode Poses"
|
||||
}
|
||||
@@ -1,48 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
class ChannelLastConv1d(nn.Conv1d):
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = x.permute(0, 2, 1)
|
||||
x = super().forward(x)
|
||||
x = x.permute(0, 2, 1)
|
||||
return x
|
||||
|
||||
|
||||
class ConvMLP(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
hidden_dim: int,
|
||||
multiple_of: int = 256,
|
||||
kernel_size: int = 3,
|
||||
padding: int = 1,
|
||||
):
|
||||
"""
|
||||
Initialize the FeedForward module.
|
||||
|
||||
Args:
|
||||
dim (int): Input dimension.
|
||||
hidden_dim (int): Hidden dimension of the feedforward layer.
|
||||
multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
|
||||
|
||||
Attributes:
|
||||
w1 (ColumnParallelLinear): Linear transformation for the first layer.
|
||||
w2 (RowParallelLinear): Linear transformation for the second layer.
|
||||
w3 (ColumnParallelLinear): Linear transformation for the third layer.
|
||||
|
||||
"""
|
||||
super().__init__()
|
||||
hidden_dim = int(2 * hidden_dim / 3)
|
||||
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
||||
|
||||
self.w1 = ChannelLastConv1d(dim, hidden_dim, bias=False, kernel_size=kernel_size, padding=padding)
|
||||
self.w2 = ChannelLastConv1d(hidden_dim, dim, bias=False, kernel_size=kernel_size, padding=padding)
|
||||
self.w3 = ChannelLastConv1d(dim, hidden_dim, bias=False, kernel_size=kernel_size, padding=padding)
|
||||
|
||||
def forward(self, x):
|
||||
return self.w2(F.silu(self.w1(x)) * self.w3(x))
|
||||
|
||||
@@ -1,21 +0,0 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2022 NVIDIA CORPORATION.
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -1 +0,0 @@
|
||||
from .bigvgan import BigVGAN
|
||||
@@ -1,120 +0,0 @@
|
||||
# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
|
||||
# LICENSE is in incl_licenses directory.
|
||||
|
||||
import torch
|
||||
from torch import nn, sin, pow
|
||||
from torch.nn import Parameter
|
||||
|
||||
|
||||
class Snake(nn.Module):
|
||||
'''
|
||||
Implementation of a sine-based periodic activation function
|
||||
Shape:
|
||||
- Input: (B, C, T)
|
||||
- Output: (B, C, T), same shape as the input
|
||||
Parameters:
|
||||
- alpha - trainable parameter
|
||||
References:
|
||||
- This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
|
||||
https://arxiv.org/abs/2006.08195
|
||||
Examples:
|
||||
>>> a1 = snake(256)
|
||||
>>> x = torch.randn(256)
|
||||
>>> x = a1(x)
|
||||
'''
|
||||
def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
|
||||
'''
|
||||
Initialization.
|
||||
INPUT:
|
||||
- in_features: shape of the input
|
||||
- alpha: trainable parameter
|
||||
alpha is initialized to 1 by default, higher values = higher-frequency.
|
||||
alpha will be trained along with the rest of your model.
|
||||
'''
|
||||
super(Snake, self).__init__()
|
||||
self.in_features = in_features
|
||||
|
||||
# initialize alpha
|
||||
self.alpha_logscale = alpha_logscale
|
||||
if self.alpha_logscale: # log scale alphas initialized to zeros
|
||||
self.alpha = Parameter(torch.zeros(in_features) * alpha)
|
||||
else: # linear scale alphas initialized to ones
|
||||
self.alpha = Parameter(torch.ones(in_features) * alpha)
|
||||
|
||||
self.alpha.requires_grad = alpha_trainable
|
||||
|
||||
self.no_div_by_zero = 0.000000001
|
||||
|
||||
def forward(self, x):
|
||||
'''
|
||||
Forward pass of the function.
|
||||
Applies the function to the input elementwise.
|
||||
Snake ∶= x + 1/a * sin^2 (xa)
|
||||
'''
|
||||
alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
|
||||
if self.alpha_logscale:
|
||||
alpha = torch.exp(alpha)
|
||||
x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SnakeBeta(nn.Module):
|
||||
'''
|
||||
A modified Snake function which uses separate parameters for the magnitude of the periodic components
|
||||
Shape:
|
||||
- Input: (B, C, T)
|
||||
- Output: (B, C, T), same shape as the input
|
||||
Parameters:
|
||||
- alpha - trainable parameter that controls frequency
|
||||
- beta - trainable parameter that controls magnitude
|
||||
References:
|
||||
- This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
|
||||
https://arxiv.org/abs/2006.08195
|
||||
Examples:
|
||||
>>> a1 = snakebeta(256)
|
||||
>>> x = torch.randn(256)
|
||||
>>> x = a1(x)
|
||||
'''
|
||||
def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
|
||||
'''
|
||||
Initialization.
|
||||
INPUT:
|
||||
- in_features: shape of the input
|
||||
- alpha - trainable parameter that controls frequency
|
||||
- beta - trainable parameter that controls magnitude
|
||||
alpha is initialized to 1 by default, higher values = higher-frequency.
|
||||
beta is initialized to 1 by default, higher values = higher-magnitude.
|
||||
alpha will be trained along with the rest of your model.
|
||||
'''
|
||||
super(SnakeBeta, self).__init__()
|
||||
self.in_features = in_features
|
||||
|
||||
# initialize alpha
|
||||
self.alpha_logscale = alpha_logscale
|
||||
if self.alpha_logscale: # log scale alphas initialized to zeros
|
||||
self.alpha = Parameter(torch.zeros(in_features) * alpha)
|
||||
self.beta = Parameter(torch.zeros(in_features) * alpha)
|
||||
else: # linear scale alphas initialized to ones
|
||||
self.alpha = Parameter(torch.ones(in_features) * alpha)
|
||||
self.beta = Parameter(torch.ones(in_features) * alpha)
|
||||
|
||||
self.alpha.requires_grad = alpha_trainable
|
||||
self.beta.requires_grad = alpha_trainable
|
||||
|
||||
self.no_div_by_zero = 0.000000001
|
||||
|
||||
def forward(self, x):
|
||||
'''
|
||||
Forward pass of the function.
|
||||
Applies the function to the input elementwise.
|
||||
SnakeBeta ∶= x + 1/b * sin^2 (xa)
|
||||
'''
|
||||
alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
|
||||
beta = self.beta.unsqueeze(0).unsqueeze(-1)
|
||||
if self.alpha_logscale:
|
||||
alpha = torch.exp(alpha)
|
||||
beta = torch.exp(beta)
|
||||
x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
|
||||
|
||||
return x
|
||||
@@ -1,6 +0,0 @@
|
||||
# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
|
||||
# LICENSE is in incl_licenses directory.
|
||||
|
||||
from .filter import *
|
||||
from .resample import *
|
||||
from .act import *
|
||||
@@ -1,28 +0,0 @@
|
||||
# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
|
||||
# LICENSE is in incl_licenses directory.
|
||||
|
||||
import torch.nn as nn
|
||||
from .resample import UpSample1d, DownSample1d
|
||||
|
||||
|
||||
class Activation1d(nn.Module):
|
||||
def __init__(self,
|
||||
activation,
|
||||
up_ratio: int = 2,
|
||||
down_ratio: int = 2,
|
||||
up_kernel_size: int = 12,
|
||||
down_kernel_size: int = 12):
|
||||
super().__init__()
|
||||
self.up_ratio = up_ratio
|
||||
self.down_ratio = down_ratio
|
||||
self.act = activation
|
||||
self.upsample = UpSample1d(up_ratio, up_kernel_size)
|
||||
self.downsample = DownSample1d(down_ratio, down_kernel_size)
|
||||
|
||||
# x: [B,C,T]
|
||||
def forward(self, x):
|
||||
x = self.upsample(x)
|
||||
x = self.act(x)
|
||||
x = self.downsample(x)
|
||||
|
||||
return x
|
||||
@@ -1,95 +0,0 @@
|
||||
# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
|
||||
# LICENSE is in incl_licenses directory.
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import math
|
||||
|
||||
if 'sinc' in dir(torch):
|
||||
sinc = torch.sinc
|
||||
else:
|
||||
# This code is adopted from adefossez's julius.core.sinc under the MIT License
|
||||
# https://adefossez.github.io/julius/julius/core.html
|
||||
# LICENSE is in incl_licenses directory.
|
||||
def sinc(x: torch.Tensor):
|
||||
"""
|
||||
Implementation of sinc, i.e. sin(pi * x) / (pi * x)
|
||||
__Warning__: Different to julius.sinc, the input is multiplied by `pi`!
|
||||
"""
|
||||
return torch.where(x == 0,
|
||||
torch.tensor(1., device=x.device, dtype=x.dtype),
|
||||
torch.sin(math.pi * x) / math.pi / x)
|
||||
|
||||
|
||||
# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
|
||||
# https://adefossez.github.io/julius/julius/lowpass.html
|
||||
# LICENSE is in incl_licenses directory.
|
||||
def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
|
||||
even = (kernel_size % 2 == 0)
|
||||
half_size = kernel_size // 2
|
||||
|
||||
#For kaiser window
|
||||
delta_f = 4 * half_width
|
||||
A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
|
||||
if A > 50.:
|
||||
beta = 0.1102 * (A - 8.7)
|
||||
elif A >= 21.:
|
||||
beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
|
||||
else:
|
||||
beta = 0.
|
||||
window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
|
||||
|
||||
# ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
|
||||
if even:
|
||||
time = (torch.arange(-half_size, half_size) + 0.5)
|
||||
else:
|
||||
time = torch.arange(kernel_size) - half_size
|
||||
if cutoff == 0:
|
||||
filter_ = torch.zeros_like(time)
|
||||
else:
|
||||
filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
|
||||
# Normalize filter to have sum = 1, otherwise we will have a small leakage
|
||||
# of the constant component in the input signal.
|
||||
filter_ /= filter_.sum()
|
||||
filter = filter_.view(1, 1, kernel_size)
|
||||
|
||||
return filter
|
||||
|
||||
|
||||
class LowPassFilter1d(nn.Module):
|
||||
def __init__(self,
|
||||
cutoff=0.5,
|
||||
half_width=0.6,
|
||||
stride: int = 1,
|
||||
padding: bool = True,
|
||||
padding_mode: str = 'replicate',
|
||||
kernel_size: int = 12):
|
||||
# kernel_size should be even number for stylegan3 setup,
|
||||
# in this implementation, odd number is also possible.
|
||||
super().__init__()
|
||||
if cutoff < -0.:
|
||||
raise ValueError("Minimum cutoff must be larger than zero.")
|
||||
if cutoff > 0.5:
|
||||
raise ValueError("A cutoff above 0.5 does not make sense.")
|
||||
self.kernel_size = kernel_size
|
||||
self.even = (kernel_size % 2 == 0)
|
||||
self.pad_left = kernel_size // 2 - int(self.even)
|
||||
self.pad_right = kernel_size // 2
|
||||
self.stride = stride
|
||||
self.padding = padding
|
||||
self.padding_mode = padding_mode
|
||||
filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
|
||||
self.register_buffer("filter", filter)
|
||||
|
||||
#input [B, C, T]
|
||||
def forward(self, x):
|
||||
_, C, _ = x.shape
|
||||
|
||||
if self.padding:
|
||||
x = F.pad(x, (self.pad_left, self.pad_right),
|
||||
mode=self.padding_mode)
|
||||
out = F.conv1d(x, self.filter.expand(C, -1, -1),
|
||||
stride=self.stride, groups=C)
|
||||
|
||||
return out
|
||||
@@ -1,49 +0,0 @@
|
||||
# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
|
||||
# LICENSE is in incl_licenses directory.
|
||||
|
||||
import torch.nn as nn
|
||||
from torch.nn import functional as F
|
||||
from .filter import LowPassFilter1d
|
||||
from .filter import kaiser_sinc_filter1d
|
||||
|
||||
|
||||
class UpSample1d(nn.Module):
|
||||
def __init__(self, ratio=2, kernel_size=None):
|
||||
super().__init__()
|
||||
self.ratio = ratio
|
||||
self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
|
||||
self.stride = ratio
|
||||
self.pad = self.kernel_size // ratio - 1
|
||||
self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
|
||||
self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
|
||||
filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
|
||||
half_width=0.6 / ratio,
|
||||
kernel_size=self.kernel_size)
|
||||
self.register_buffer("filter", filter)
|
||||
|
||||
# x: [B, C, T]
|
||||
def forward(self, x):
|
||||
_, C, _ = x.shape
|
||||
|
||||
x = F.pad(x, (self.pad, self.pad), mode='replicate')
|
||||
x = self.ratio * F.conv_transpose1d(
|
||||
x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
|
||||
x = x[..., self.pad_left:-self.pad_right]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class DownSample1d(nn.Module):
|
||||
def __init__(self, ratio=2, kernel_size=None):
|
||||
super().__init__()
|
||||
self.ratio = ratio
|
||||
self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
|
||||
self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
|
||||
half_width=0.6 / ratio,
|
||||
stride=ratio,
|
||||
kernel_size=self.kernel_size)
|
||||
|
||||
def forward(self, x):
|
||||
xx = self.lowpass(x)
|
||||
|
||||
return xx
|
||||
@@ -1,62 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from types import SimpleNamespace
|
||||
|
||||
from .models import BigVGANVocoder
|
||||
|
||||
from comfy.utils import load_torch_file
|
||||
|
||||
# BigVGAN vocoder configuration
|
||||
_bigvgan_vocoder_config = {
|
||||
'resblock': '1',
|
||||
'num_gpus': 0,
|
||||
'batch_size': 64,
|
||||
'num_mels': 80,
|
||||
'learning_rate': 0.0001,
|
||||
'adam_b1': 0.8,
|
||||
'adam_b2': 0.99,
|
||||
'lr_decay': 0.999,
|
||||
'seed': 1234,
|
||||
'upsample_rates': [4, 4, 2, 2, 2, 2],
|
||||
'upsample_kernel_sizes': [8, 8, 4, 4, 4, 4],
|
||||
'upsample_initial_channel': 1536,
|
||||
'resblock_kernel_sizes': [3, 7, 11],
|
||||
'resblock_dilation_sizes': [
|
||||
[1, 3, 5],
|
||||
[1, 3, 5],
|
||||
[1, 3, 5]
|
||||
],
|
||||
'activation': 'snakebeta',
|
||||
'snake_logscale': True,
|
||||
'resolutions': [
|
||||
[1024, 120, 600],
|
||||
[2048, 240, 1200],
|
||||
[512, 50, 240]
|
||||
],
|
||||
'mpd_reshapes': [2, 3, 5, 7, 11],
|
||||
'use_spectral_norm': False,
|
||||
'discriminator_channel_mult': 1,
|
||||
}
|
||||
|
||||
class BigVGAN(nn.Module):
|
||||
|
||||
def __init__(self, ckpt_path):
|
||||
super().__init__()
|
||||
# Convert dictionary to namespace object for attribute access
|
||||
vocoder_cfg = SimpleNamespace(**_bigvgan_vocoder_config)
|
||||
self.vocoder = BigVGANVocoder(vocoder_cfg).eval()
|
||||
vocoder_ckpt = load_torch_file(ckpt_path)
|
||||
self.vocoder.load_state_dict(vocoder_ckpt)
|
||||
|
||||
self.weight_norm_removed = False
|
||||
self.remove_weight_norm()
|
||||
|
||||
@torch.inference_mode()
|
||||
def forward(self, x):
|
||||
assert self.weight_norm_removed, 'call remove_weight_norm() before inference'
|
||||
return self.vocoder(x)
|
||||
|
||||
def remove_weight_norm(self):
|
||||
self.vocoder.remove_weight_norm()
|
||||
self.weight_norm_removed = True
|
||||
return self
|
||||
@@ -1,21 +0,0 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2020 Jungil Kong
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -1,21 +0,0 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2020 Edward Dixon
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -1,201 +0,0 @@
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
1. Definitions.
|
||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
|
||||
and distribution as defined by Sections 1 through 9 of this document.
|
||||
|
||||
"Licensor" shall mean the copyright owner or entity authorized by
|
||||
the copyright owner that is granting the License.
|
||||
|
||||
"Legal Entity" shall mean the union of the acting entity and all
|
||||
other entities that control, are controlled by, or are under common
|
||||
control with that entity. For the purposes of this definition,
|
||||
"control" means (i) the power, direct or indirect, to cause the
|
||||
direction or management of such entity, whether by contract or
|
||||
otherwise, or (ii) ownership of fifty percent (50%) or more of the
|
||||
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@@ -1,29 +0,0 @@
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BSD 3-Clause License
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Copyright (c) 2019, Seungwon Park 박승원
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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@@ -1,16 +0,0 @@
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Copyright 2020 Alexandre Défossez
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
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associated documentation files (the "Software"), to deal in the Software without restriction,
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The above copyright notice and this permission notice shall be included in all copies or
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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@@ -1,255 +0,0 @@
|
||||
# Copyright (c) 2022 NVIDIA CORPORATION.
|
||||
# Licensed under the MIT license.
|
||||
|
||||
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
|
||||
# LICENSE is in incl_licenses directory.
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn import Conv1d, ConvTranspose1d
|
||||
from torch.nn.utils.parametrizations import weight_norm
|
||||
from torch.nn.utils.parametrize import remove_parametrizations
|
||||
|
||||
from . import activations
|
||||
from .alias_free_torch import *
|
||||
from .utils import get_padding, init_weights
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class AMPBlock1(torch.nn.Module):
|
||||
|
||||
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
|
||||
super(AMPBlock1, self).__init__()
|
||||
self.h = h
|
||||
|
||||
self.convs1 = nn.ModuleList([
|
||||
weight_norm(
|
||||
Conv1d(channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(
|
||||
Conv1d(channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]))),
|
||||
weight_norm(
|
||||
Conv1d(channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2])))
|
||||
])
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList([
|
||||
weight_norm(
|
||||
Conv1d(channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(
|
||||
Conv1d(channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(
|
||||
Conv1d(channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1)))
|
||||
])
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
|
||||
|
||||
if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
|
||||
self.activations = nn.ModuleList([
|
||||
Activation1d(
|
||||
activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
|
||||
for _ in range(self.num_layers)
|
||||
])
|
||||
elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
|
||||
self.activations = nn.ModuleList([
|
||||
Activation1d(
|
||||
activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
|
||||
for _ in range(self.num_layers)
|
||||
])
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
"activation incorrectly specified. check the config file and look for 'activation'."
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
acts1, acts2 = self.activations[::2], self.activations[1::2]
|
||||
for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
|
||||
xt = a1(x)
|
||||
xt = c1(xt)
|
||||
xt = a2(xt)
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_parametrizations(l, 'weight')
|
||||
for l in self.convs2:
|
||||
remove_parametrizations(l, 'weight')
|
||||
|
||||
|
||||
class AMPBlock2(torch.nn.Module):
|
||||
|
||||
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
|
||||
super(AMPBlock2, self).__init__()
|
||||
self.h = h
|
||||
|
||||
self.convs = nn.ModuleList([
|
||||
weight_norm(
|
||||
Conv1d(channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(
|
||||
Conv1d(channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1])))
|
||||
])
|
||||
self.convs.apply(init_weights)
|
||||
|
||||
self.num_layers = len(self.convs) # total number of conv layers
|
||||
|
||||
if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
|
||||
self.activations = nn.ModuleList([
|
||||
Activation1d(
|
||||
activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
|
||||
for _ in range(self.num_layers)
|
||||
])
|
||||
elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
|
||||
self.activations = nn.ModuleList([
|
||||
Activation1d(
|
||||
activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
|
||||
for _ in range(self.num_layers)
|
||||
])
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
"activation incorrectly specified. check the config file and look for 'activation'."
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
for c, a in zip(self.convs, self.activations):
|
||||
xt = a(x)
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_parametrizations(l, 'weight')
|
||||
|
||||
|
||||
class BigVGANVocoder(torch.nn.Module):
|
||||
# this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
|
||||
def __init__(self, h):
|
||||
super().__init__()
|
||||
self.h = h
|
||||
|
||||
self.num_kernels = len(h.resblock_kernel_sizes)
|
||||
self.num_upsamples = len(h.upsample_rates)
|
||||
|
||||
# pre conv
|
||||
self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
|
||||
|
||||
# define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
|
||||
resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
|
||||
|
||||
# transposed conv-based upsamplers. does not apply anti-aliasing
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
|
||||
self.ups.append(
|
||||
nn.ModuleList([
|
||||
weight_norm(
|
||||
ConvTranspose1d(h.upsample_initial_channel // (2**i),
|
||||
h.upsample_initial_channel // (2**(i + 1)),
|
||||
k,
|
||||
u,
|
||||
padding=(k - u) // 2))
|
||||
]))
|
||||
|
||||
# residual blocks using anti-aliased multi-periodicity composition modules (AMP)
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = h.upsample_initial_channel // (2**(i + 1))
|
||||
for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
|
||||
self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
|
||||
|
||||
# post conv
|
||||
if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
|
||||
activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
|
||||
self.activation_post = Activation1d(activation=activation_post)
|
||||
elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
|
||||
activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
|
||||
self.activation_post = Activation1d(activation=activation_post)
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
"activation incorrectly specified. check the config file and look for 'activation'."
|
||||
)
|
||||
|
||||
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
|
||||
|
||||
# weight initialization
|
||||
for i in range(len(self.ups)):
|
||||
self.ups[i].apply(init_weights)
|
||||
self.conv_post.apply(init_weights)
|
||||
|
||||
def forward(self, x):
|
||||
# pre conv
|
||||
x = self.conv_pre(x)
|
||||
|
||||
for i in range(self.num_upsamples):
|
||||
# upsampling
|
||||
for i_up in range(len(self.ups[i])):
|
||||
x = self.ups[i][i_up](x)
|
||||
# AMP blocks
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||||
x = xs / self.num_kernels
|
||||
|
||||
# post conv
|
||||
x = self.activation_post(x)
|
||||
x = self.conv_post(x)
|
||||
x = torch.tanh(x)
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print('Removing weight norm...')
|
||||
for l in self.ups:
|
||||
for l_i in l:
|
||||
remove_parametrizations(l_i, 'weight')
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
remove_parametrizations(self.conv_pre, 'weight')
|
||||
remove_parametrizations(self.conv_post, 'weight')
|
||||
@@ -1,20 +0,0 @@
|
||||
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
|
||||
# LICENSE is in incl_licenses directory.
|
||||
|
||||
from torch.nn.utils.parametrizations import weight_norm
|
||||
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def apply_weight_norm(m):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
weight_norm(m)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size * dilation - dilation) / 2)
|
||||
@@ -1,212 +0,0 @@
|
||||
# Reference: # https://github.com/bytedance/Make-An-Audio-2
|
||||
from typing import Literal
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
|
||||
# following is from librosa
|
||||
|
||||
def hz_to_mel(frequencies, *, htk = False):
|
||||
frequencies = np.asanyarray(frequencies)
|
||||
|
||||
if htk:
|
||||
mels: np.ndarray = 2595.0 * np.log10(1.0 + frequencies / 700.0)
|
||||
return mels
|
||||
|
||||
# Fill in the linear part
|
||||
f_min = 0.0
|
||||
f_sp = 200.0 / 3
|
||||
|
||||
mels = (frequencies - f_min) / f_sp
|
||||
|
||||
# Fill in the log-scale part
|
||||
|
||||
min_log_hz = 1000.0 # beginning of log region (Hz)
|
||||
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
|
||||
logstep = np.log(6.4) / 27.0 # step size for log region
|
||||
|
||||
if frequencies.ndim:
|
||||
# If we have array data, vectorize
|
||||
log_t = frequencies >= min_log_hz
|
||||
mels[log_t] = min_log_mel + np.log(frequencies[log_t] / min_log_hz) / logstep
|
||||
elif frequencies >= min_log_hz:
|
||||
# If we have scalar data, heck directly
|
||||
mels = min_log_mel + np.log(frequencies / min_log_hz) / logstep
|
||||
|
||||
return mels
|
||||
|
||||
def mel_to_hz(mels, *, htk = False):
|
||||
mels = np.asanyarray(mels)
|
||||
|
||||
if htk:
|
||||
return 700.0 * (10.0 ** (mels / 2595.0) - 1.0)
|
||||
|
||||
# Fill in the linear scale
|
||||
f_min = 0.0
|
||||
f_sp = 200.0 / 3
|
||||
freqs = f_min + f_sp * mels
|
||||
|
||||
# And now the nonlinear scale
|
||||
min_log_hz = 1000.0 # beginning of log region (Hz)
|
||||
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
|
||||
logstep = np.log(6.4) / 27.0 # step size for log region
|
||||
|
||||
if mels.ndim:
|
||||
# If we have vector data, vectorize
|
||||
log_t = mels >= min_log_mel
|
||||
freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel))
|
||||
elif mels >= min_log_mel:
|
||||
# If we have scalar data, check directly
|
||||
freqs = min_log_hz * np.exp(logstep * (mels - min_log_mel))
|
||||
|
||||
return freqs
|
||||
|
||||
def mel_frequencies(n_mels = 128, *, fmin = 0.0, fmax = 11025.0, htk = False):
|
||||
min_mel = hz_to_mel(fmin, htk=htk)
|
||||
max_mel = hz_to_mel(fmax, htk=htk)
|
||||
mels = np.linspace(min_mel, max_mel, n_mels)
|
||||
hz: np.ndarray = mel_to_hz(mels, htk=htk)
|
||||
return hz
|
||||
|
||||
def librosa_mel_fn(
|
||||
*,
|
||||
sr: float,
|
||||
n_fft: int,
|
||||
n_mels: int = 128,
|
||||
fmin: float = 0.0,
|
||||
fmax = None,
|
||||
htk = False,
|
||||
norm = "slaney",
|
||||
dtype = np.float32,
|
||||
) -> np.ndarray:
|
||||
|
||||
if fmax is None:
|
||||
fmax = float(sr) / 2
|
||||
|
||||
# Initialize the weights
|
||||
n_mels = int(n_mels)
|
||||
weights = np.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)
|
||||
|
||||
# Center freqs of each FFT bin
|
||||
fftfreqs = np.fft.rfftfreq(n=n_fft, d=1.0 / sr)
|
||||
|
||||
# 'Center freqs' of mel bands - uniformly spaced between limits
|
||||
mel_f = mel_frequencies(n_mels + 2, fmin=fmin, fmax=fmax, htk=htk)
|
||||
|
||||
fdiff = np.diff(mel_f)
|
||||
ramps = np.subtract.outer(mel_f, fftfreqs)
|
||||
|
||||
for i in range(n_mels):
|
||||
# lower and upper slopes for all bins
|
||||
lower = -ramps[i] / fdiff[i]
|
||||
upper = ramps[i + 2] / fdiff[i + 1]
|
||||
|
||||
# .. then intersect them with each other and zero
|
||||
weights[i] = np.maximum(0, np.minimum(lower, upper))
|
||||
|
||||
# Slaney-style mel is scaled to be approx constant energy per channel
|
||||
enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
|
||||
weights *= enorm[:, np.newaxis]
|
||||
|
||||
return weights
|
||||
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
|
||||
return norm_fn(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
|
||||
def spectral_normalize_torch(magnitudes, norm_fn):
|
||||
output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)
|
||||
return output
|
||||
|
||||
|
||||
class MelConverter(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
sampling_rate: float,
|
||||
n_fft: int,
|
||||
num_mels: int,
|
||||
hop_size: int,
|
||||
win_size: int,
|
||||
fmin: float,
|
||||
fmax: float,
|
||||
norm_fn,
|
||||
):
|
||||
super().__init__()
|
||||
self.sampling_rate = sampling_rate
|
||||
self.n_fft = n_fft
|
||||
self.num_mels = num_mels
|
||||
self.hop_size = hop_size
|
||||
self.win_size = win_size
|
||||
self.fmin = fmin
|
||||
self.fmax = fmax
|
||||
self.norm_fn = norm_fn
|
||||
|
||||
mel = librosa_mel_fn(sr=self.sampling_rate,
|
||||
n_fft=self.n_fft,
|
||||
n_mels=self.num_mels,
|
||||
fmin=self.fmin,
|
||||
fmax=self.fmax)
|
||||
mel_basis = torch.from_numpy(mel).float()
|
||||
hann_window = torch.hann_window(self.win_size)
|
||||
|
||||
self.register_buffer('mel_basis', mel_basis)
|
||||
self.register_buffer('hann_window', hann_window)
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return self.mel_basis.device
|
||||
|
||||
def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:
|
||||
waveform = waveform.clamp(min=-1., max=1.).to(self.device)
|
||||
|
||||
waveform = torch.nn.functional.pad(
|
||||
waveform.unsqueeze(1),
|
||||
[int((self.n_fft - self.hop_size) / 2),
|
||||
int((self.n_fft - self.hop_size) / 2)],
|
||||
mode='reflect')
|
||||
waveform = waveform.squeeze(1)
|
||||
|
||||
spec = torch.stft(waveform,
|
||||
self.n_fft,
|
||||
hop_length=self.hop_size,
|
||||
win_length=self.win_size,
|
||||
window=self.hann_window,
|
||||
center=center,
|
||||
pad_mode='reflect',
|
||||
normalized=False,
|
||||
onesided=True,
|
||||
return_complex=True)
|
||||
|
||||
spec = torch.view_as_real(spec)
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)).float()
|
||||
spec = torch.matmul(self.mel_basis, spec)
|
||||
spec = spectral_normalize_torch(spec, self.norm_fn)
|
||||
|
||||
return spec
|
||||
|
||||
|
||||
def get_mel_converter(mode: Literal['16k', '44k']) -> MelConverter:
|
||||
if mode == '16k':
|
||||
return MelConverter(sampling_rate=16_000,
|
||||
n_fft=1024,
|
||||
num_mels=80,
|
||||
hop_size=256,
|
||||
win_size=1024,
|
||||
fmin=0,
|
||||
fmax=8_000,
|
||||
norm_fn=torch.log10)
|
||||
elif mode == '44k':
|
||||
return MelConverter(sampling_rate=44_100,
|
||||
n_fft=2048,
|
||||
num_mels=128,
|
||||
hop_size=512,
|
||||
win_size=2048,
|
||||
fmin=0,
|
||||
fmax=44100 / 2,
|
||||
norm_fn=torch.log)
|
||||
else:
|
||||
raise ValueError(f'Unknown mode: {mode}')
|
||||
@@ -1,267 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import folder_paths
|
||||
import os
|
||||
|
||||
from .mel_converter import get_mel_converter
|
||||
from .vae.autoencoder import AutoEncoderModule
|
||||
from .vae.distributions import DiagonalGaussianDistribution
|
||||
import torchaudio
|
||||
|
||||
from ..utils import log
|
||||
|
||||
from comfy import model_management as mm
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
|
||||
class FeaturesUtils(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
tod_vae_ckpt: str,
|
||||
bigvgan_vocoder_ckpt = None,
|
||||
mode=['16k', '44k'],
|
||||
need_vae_encoder: bool = True,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.mel_converter = get_mel_converter(mode)
|
||||
self.tod = AutoEncoderModule(vae_ckpt_path=tod_vae_ckpt,
|
||||
vocoder_ckpt_path=bigvgan_vocoder_ckpt,
|
||||
mode=mode,
|
||||
need_vae_encoder=need_vae_encoder)
|
||||
|
||||
def encode_audio(self, x) -> DiagonalGaussianDistribution:
|
||||
assert self.tod is not None, 'VAE is not loaded'
|
||||
# x: (B * L)
|
||||
mel = self.mel_converter(x)
|
||||
dist = self.tod.encode(mel)
|
||||
|
||||
return dist
|
||||
|
||||
def vocode(self, mel: torch.Tensor) -> torch.Tensor:
|
||||
assert self.tod is not None, 'VAE is not loaded'
|
||||
return self.tod.vocode(mel)
|
||||
|
||||
def decode(self, z: torch.Tensor) -> torch.Tensor:
|
||||
assert self.tod is not None, 'VAE is not loaded'
|
||||
return self.tod.decode(z)
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return next(self.parameters()).device
|
||||
|
||||
@property
|
||||
def dtype(self):
|
||||
return next(self.parameters()).dtype
|
||||
|
||||
def wrapped_decode(self, z):
|
||||
with torch.amp.autocast('cuda', dtype=self.dtype):
|
||||
mel_decoded = self.decode(z)
|
||||
audio = self.vocode(mel_decoded)
|
||||
|
||||
return audio
|
||||
|
||||
def wrapped_encode(self, audio):
|
||||
with torch.amp.autocast('cuda', dtype=self.dtype):
|
||||
dist = self.encode_audio(audio)
|
||||
|
||||
return dist.mean
|
||||
|
||||
if not "mmaudio" in folder_paths.folder_names_and_paths:
|
||||
folder_paths.add_model_folder_path("mmaudio", os.path.join(folder_paths.models_dir, "mmaudio"))
|
||||
|
||||
class OviMMAudioVAELoader:
|
||||
"""Loads MMAudio VAE for audio encoding/decoding in Ovi"""
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
s.vae_files = folder_paths.get_filename_list("vae")
|
||||
s.mmaudio_files = folder_paths.get_filename_list("mmaudio")
|
||||
s.all_files = s.vae_files + s.mmaudio_files
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"vae": (s.all_files, {"tooltip": "MMAudio VAE 16k (v1-16.pth) model from models/vae or models/mmaudio"}),
|
||||
"vocoder": (s.all_files, {"tooltip": "BigVGAN vocoder (best_netG.pt) from models/vae or models/mmaudio"}),
|
||||
"precision": (["bf16", "fp16", "fp32"], {"default": "bf16"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MMAUDIOVAE",)
|
||||
RETURN_NAMES = ("mmaudio_vae",)
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "WanVideoWrapper/Ovi"
|
||||
DESCRIPTION = "Loads MMAudio VAE for Ovi audio generation"
|
||||
|
||||
def loadmodel(self, vae, vocoder, precision):
|
||||
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
|
||||
|
||||
vae_path = folder_paths.get_full_path("vae", vae) if vae in self.vae_files else folder_paths.get_full_path("mmaudio", vae)
|
||||
vocoder_path = folder_paths.get_full_path("vae", vocoder) if vocoder in self.vae_files else folder_paths.get_full_path("mmaudio", vocoder)
|
||||
|
||||
vae = FeaturesUtils(
|
||||
tod_vae_ckpt=vae_path,
|
||||
bigvgan_vocoder_ckpt=vocoder_path,
|
||||
mode='16k',
|
||||
need_vae_encoder=True
|
||||
)
|
||||
|
||||
vae.to(device=offload_device, dtype=dtype)
|
||||
vae.eval()
|
||||
|
||||
return (vae,)
|
||||
|
||||
class WanVideoDecodeOviAudio:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"mmaudio_vae": ("MMAUDIOVAE",),
|
||||
"samples": ("LATENT",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("AUDIO",)
|
||||
RETURN_NAMES = ("audio",)
|
||||
FUNCTION = "decode"
|
||||
CATEGORY = "WanVideoWrapper/Ovi"
|
||||
|
||||
def decode(self, mmaudio_vae, samples):
|
||||
mm.soft_empty_cache()
|
||||
audio_latents = samples.get("latent_ovi_audio", None)
|
||||
if audio_latents is None:
|
||||
raise ValueError("No Ovi audio latents found in input samples")
|
||||
|
||||
mmaudio_vae.to(device)
|
||||
|
||||
waveform = mmaudio_vae.wrapped_decode(audio_latents.to(device=device, dtype=mmaudio_vae.dtype))
|
||||
audio = {"waveform": waveform.cpu().float(), "sample_rate": 16000}
|
||||
|
||||
mmaudio_vae.to(offload_device)
|
||||
mm.soft_empty_cache()
|
||||
|
||||
return (audio,)
|
||||
|
||||
class WanVideoEncodeOviAudio:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"mmaudio_vae": ("MMAUDIOVAE",),
|
||||
"audio": ("AUDIO",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
RETURN_NAMES = ("samples",)
|
||||
FUNCTION = "decode"
|
||||
CATEGORY = "WanVideoWrapper/Ovi"
|
||||
|
||||
def decode(self, mmaudio_vae, audio):
|
||||
|
||||
mmaudio_vae.to(device)
|
||||
|
||||
waveform = audio.get("waveform", None)
|
||||
sample_rate = audio.get("sample_rate", None)
|
||||
if sample_rate != 16000:
|
||||
waveform = torchaudio.functional.resample(waveform, sample_rate, 16000)
|
||||
waveform = waveform.to(device=device, dtype=mmaudio_vae.dtype)[0][0].unsqueeze(0)
|
||||
|
||||
samples = mmaudio_vae.wrapped_encode(waveform)
|
||||
|
||||
mmaudio_vae.to(offload_device)
|
||||
mm.soft_empty_cache()
|
||||
|
||||
return ({"latent_ovi_audio": samples},)
|
||||
|
||||
|
||||
class WanVideoAddOviAudioToLatents:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"original_samples": ("LATENT",),
|
||||
"audio_samples": ("LATENT",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
RETURN_NAMES = ("samples",)
|
||||
FUNCTION = "decode"
|
||||
CATEGORY = "WanVideoWrapper/Ovi"
|
||||
|
||||
def decode(self, original_samples, audio_samples):
|
||||
samples = original_samples.copy()
|
||||
samples.update(audio_samples)
|
||||
|
||||
return (samples,)
|
||||
|
||||
class WanVideoEmptyMMAudioLatents:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"length": ("INT", {"default": 157, "min": 1, "max": 10000, "step": 1, "tooltip": "Length of the audio latent sequence"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
RETURN_NAMES = ("samples",)
|
||||
FUNCTION = "decode"
|
||||
CATEGORY = "WanVideoWrapper/Ovi"
|
||||
|
||||
def decode(self, length):
|
||||
audio_latents = torch.zeros((length, 20), device=torch.device("cpu"), dtype=torch.float32) # 1, l c -> l, c
|
||||
|
||||
return ({"latent_ovi_audio": audio_latents},)
|
||||
|
||||
|
||||
class WanVideoOviCFG:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"original_text_embeds": ("WANVIDEOTEXTEMBEDS",),
|
||||
"ovi_audio_cfg": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01}),
|
||||
},
|
||||
"optional": {
|
||||
"ovi_negative_text_embeds": ("WANVIDEOTEXTEMBEDS",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
|
||||
RETURN_NAMES = ("text_embeds",)
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "WanVideoWrapper/Ovi"
|
||||
DESCRIPTION = "Adds Ovi negative text embeddings and audio CFG scale to the text embeddings dictionary"
|
||||
|
||||
def process(self, original_text_embeds, ovi_audio_cfg, ovi_negative_text_embeds=None):
|
||||
negative_text_embeds = None
|
||||
if ovi_negative_text_embeds is not None:
|
||||
negative_text_embeds = ovi_negative_text_embeds.get("prompt_embeds", None)
|
||||
if negative_text_embeds is None:
|
||||
negative_text_embeds = original_text_embeds["prompt_embeds"]
|
||||
log.info("WanVideoOviCFG: Ovi negative text embeddings not provided, using original prompt embeddings as negative embeddings")
|
||||
else:
|
||||
log.info("WanVideoOviCFG: Using provided Ovi audio negative text embeddings")
|
||||
log.info("WanVideoOviCFG: negative text embedding shape: {}".format(negative_text_embeds[0].shape))
|
||||
|
||||
prompt_embeds_dict_copy = original_text_embeds.copy()
|
||||
prompt_embeds_dict_copy.update({
|
||||
"ovi_negative_prompt_embeds": negative_text_embeds,
|
||||
"ovi_audio_cfg": ovi_audio_cfg,
|
||||
})
|
||||
return (prompt_embeds_dict_copy,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"OviMMAudioVAELoader": OviMMAudioVAELoader,
|
||||
"WanVideoDecodeOviAudio": WanVideoDecodeOviAudio,
|
||||
"WanVideoEncodeOviAudio": WanVideoEncodeOviAudio,
|
||||
"WanVideoOviCFG": WanVideoOviCFG,
|
||||
"WanVideoAddOviAudioToLatents": WanVideoAddOviAudioToLatents,
|
||||
"WanVideoEmptyMMAudioLatents": WanVideoEmptyMMAudioLatents,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"OviMMAudioVAELoader": "Ovi MMAudio VAE Loader",
|
||||
"WanVideoDecodeOviAudio": "WanVideo Decode Ovi Audio",
|
||||
"WanVideoEncodeOviAudio": "WanVideo Encode Ovi Audio",
|
||||
"WanVideoOviCFG": "WanVideo Ovi CFG",
|
||||
"WanVideoAddOviAudioToLatents": "WanVideo Add MMAudio To Latents",
|
||||
"WanVideoEmptyMMAudioLatents": "WanVideo Empty MMAudio Latents",
|
||||
}
|
||||
@@ -1,54 +0,0 @@
|
||||
from typing import Literal, Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from .vae import VAE, get_my_vae
|
||||
from .distributions import DiagonalGaussianDistribution
|
||||
from ..bigvgan import BigVGAN
|
||||
|
||||
from comfy.utils import load_torch_file
|
||||
|
||||
class AutoEncoderModule(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
vae_ckpt_path,
|
||||
vocoder_ckpt_path: Optional[str] = None,
|
||||
mode: Literal['16k', '44k'],
|
||||
need_vae_encoder: bool = True):
|
||||
super().__init__()
|
||||
self.vae: VAE = get_my_vae(mode).eval()
|
||||
#vae_state_dict = torch.load(vae_ckpt_path, weights_only=True, map_location='cpu')'
|
||||
vae_state_dict = load_torch_file(vae_ckpt_path)
|
||||
self.vae.load_state_dict(vae_state_dict)
|
||||
self.vae.remove_weight_norm()
|
||||
|
||||
if mode == '16k':
|
||||
assert vocoder_ckpt_path is not None
|
||||
self.vocoder = BigVGAN(vocoder_ckpt_path).eval()
|
||||
elif mode == '44k':
|
||||
raise NotImplementedError("44k mode requires BigVGANv2 which is not currently supported in this environment.")
|
||||
self.vocoder = BigVGANv2.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x',
|
||||
use_cuda_kernel=False)
|
||||
self.vocoder.remove_weight_norm()
|
||||
else:
|
||||
raise ValueError(f'Unknown mode: {mode}')
|
||||
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if not need_vae_encoder:
|
||||
del self.vae.encoder
|
||||
|
||||
@torch.inference_mode()
|
||||
def encode(self, x: torch.Tensor) -> DiagonalGaussianDistribution:
|
||||
return self.vae.encode(x)
|
||||
|
||||
@torch.inference_mode()
|
||||
def decode(self, z: torch.Tensor) -> torch.Tensor:
|
||||
return self.vae.decode(z)
|
||||
|
||||
@torch.inference_mode()
|
||||
def vocode(self, spec: torch.Tensor) -> torch.Tensor:
|
||||
return self.vocoder(spec)
|
||||
@@ -1,45 +0,0 @@
|
||||
from typing import Optional
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
|
||||
class DiagonalGaussianDistribution:
|
||||
|
||||
def __init__(self, parameters, deterministic=False):
|
||||
self.parameters = parameters
|
||||
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
|
||||
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
|
||||
self.deterministic = deterministic
|
||||
self.std = torch.exp(0.5 * self.logvar)
|
||||
self.var = torch.exp(self.logvar)
|
||||
if self.deterministic:
|
||||
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
|
||||
|
||||
def sample(self, rng: Optional[torch.Generator] = None):
|
||||
# x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
|
||||
|
||||
r = torch.empty_like(self.mean).normal_(generator=rng)
|
||||
x = self.mean + self.std * r
|
||||
|
||||
return x
|
||||
|
||||
def kl(self, other=None):
|
||||
if self.deterministic:
|
||||
return torch.Tensor([0.])
|
||||
else:
|
||||
if other is None:
|
||||
|
||||
return 0.5 * torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar
|
||||
else:
|
||||
return 0.5 * (torch.pow(self.mean - other.mean, 2) / other.var +
|
||||
self.var / other.var - 1.0 - self.logvar + other.logvar)
|
||||
|
||||
def nll(self, sample, dims=[1, 2, 3]):
|
||||
if self.deterministic:
|
||||
return torch.Tensor([0.])
|
||||
logtwopi = np.log(2.0 * np.pi)
|
||||
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
|
||||
dim=dims)
|
||||
|
||||
def mode(self):
|
||||
return self.mean
|
||||
@@ -1,168 +0,0 @@
|
||||
# Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
||||
#
|
||||
# This work is licensed under a Creative Commons
|
||||
# Attribution-NonCommercial-ShareAlike 4.0 International License.
|
||||
# You should have received a copy of the license along with this
|
||||
# work. If not, see http://creativecommons.org/licenses/by-nc-sa/4.0/
|
||||
"""Improved diffusion model architecture proposed in the paper
|
||||
"Analyzing and Improving the Training Dynamics of Diffusion Models"."""
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
#----------------------------------------------------------------------------
|
||||
# Variant of constant() that inherits dtype and device from the given
|
||||
# reference tensor by default.
|
||||
|
||||
_constant_cache = dict()
|
||||
|
||||
|
||||
def constant(value, shape=None, dtype=None, device=None, memory_format=None):
|
||||
value = np.asarray(value)
|
||||
if shape is not None:
|
||||
shape = tuple(shape)
|
||||
if dtype is None:
|
||||
dtype = torch.get_default_dtype()
|
||||
if device is None:
|
||||
device = torch.device('cpu')
|
||||
if memory_format is None:
|
||||
memory_format = torch.contiguous_format
|
||||
|
||||
key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
|
||||
tensor = _constant_cache.get(key, None)
|
||||
if tensor is None:
|
||||
tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
|
||||
if shape is not None:
|
||||
tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
|
||||
tensor = tensor.contiguous(memory_format=memory_format)
|
||||
_constant_cache[key] = tensor
|
||||
return tensor
|
||||
|
||||
|
||||
def const_like(ref, value, shape=None, dtype=None, device=None, memory_format=None):
|
||||
if dtype is None:
|
||||
dtype = ref.dtype
|
||||
if device is None:
|
||||
device = ref.device
|
||||
return constant(value, shape=shape, dtype=dtype, device=device, memory_format=memory_format)
|
||||
|
||||
|
||||
#----------------------------------------------------------------------------
|
||||
# Normalize given tensor to unit magnitude with respect to the given
|
||||
# dimensions. Default = all dimensions except the first.
|
||||
|
||||
|
||||
def normalize(x, dim=None, eps=1e-4):
|
||||
if dim is None:
|
||||
dim = list(range(1, x.ndim))
|
||||
norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)
|
||||
norm = torch.add(eps, norm, alpha=np.sqrt(norm.numel() / x.numel()))
|
||||
return x / norm.to(x.dtype)
|
||||
|
||||
|
||||
class Normalize(torch.nn.Module):
|
||||
|
||||
def __init__(self, dim=None, eps=1e-4):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.eps = eps
|
||||
|
||||
def forward(self, x):
|
||||
return normalize(x, dim=self.dim, eps=self.eps)
|
||||
|
||||
|
||||
#----------------------------------------------------------------------------
|
||||
# Upsample or downsample the given tensor with the given filter,
|
||||
# or keep it as is.
|
||||
|
||||
|
||||
def resample(x, f=[1, 1], mode='keep'):
|
||||
if mode == 'keep':
|
||||
return x
|
||||
f = np.float32(f)
|
||||
assert f.ndim == 1 and len(f) % 2 == 0
|
||||
pad = (len(f) - 1) // 2
|
||||
f = f / f.sum()
|
||||
f = np.outer(f, f)[np.newaxis, np.newaxis, :, :]
|
||||
f = const_like(x, f)
|
||||
c = x.shape[1]
|
||||
if mode == 'down':
|
||||
return torch.nn.functional.conv2d(x,
|
||||
f.tile([c, 1, 1, 1]),
|
||||
groups=c,
|
||||
stride=2,
|
||||
padding=(pad, ))
|
||||
assert mode == 'up'
|
||||
return torch.nn.functional.conv_transpose2d(x, (f * 4).tile([c, 1, 1, 1]),
|
||||
groups=c,
|
||||
stride=2,
|
||||
padding=(pad, ))
|
||||
|
||||
|
||||
#----------------------------------------------------------------------------
|
||||
# Magnitude-preserving SiLU (Equation 81).
|
||||
|
||||
|
||||
def mp_silu(x):
|
||||
return torch.nn.functional.silu(x) / 0.596
|
||||
|
||||
|
||||
class MPSiLU(torch.nn.Module):
|
||||
|
||||
def forward(self, x):
|
||||
return mp_silu(x)
|
||||
|
||||
|
||||
#----------------------------------------------------------------------------
|
||||
# Magnitude-preserving sum (Equation 88).
|
||||
|
||||
|
||||
def mp_sum(a, b, t=0.5):
|
||||
return a.lerp(b, t) / np.sqrt((1 - t)**2 + t**2)
|
||||
|
||||
|
||||
#----------------------------------------------------------------------------
|
||||
# Magnitude-preserving concatenation (Equation 103).
|
||||
|
||||
|
||||
def mp_cat(a, b, dim=1, t=0.5):
|
||||
Na = a.shape[dim]
|
||||
Nb = b.shape[dim]
|
||||
C = np.sqrt((Na + Nb) / ((1 - t)**2 + t**2))
|
||||
wa = C / np.sqrt(Na) * (1 - t)
|
||||
wb = C / np.sqrt(Nb) * t
|
||||
return torch.cat([wa * a, wb * b], dim=dim)
|
||||
|
||||
|
||||
#----------------------------------------------------------------------------
|
||||
# Magnitude-preserving convolution or fully-connected layer (Equation 47)
|
||||
# with force weight normalization (Equation 66).
|
||||
|
||||
|
||||
class MPConv1D(torch.nn.Module):
|
||||
|
||||
def __init__(self, in_channels, out_channels, kernel_size):
|
||||
super().__init__()
|
||||
self.out_channels = out_channels
|
||||
self.weight = torch.nn.Parameter(torch.randn(out_channels, in_channels, kernel_size))
|
||||
|
||||
self.weight_norm_removed = False
|
||||
|
||||
def forward(self, x, gain=1):
|
||||
assert self.weight_norm_removed, 'call remove_weight_norm() before inference'
|
||||
|
||||
w = self.weight * gain
|
||||
if w.ndim == 2:
|
||||
return x @ w.t()
|
||||
assert w.ndim == 3
|
||||
return torch.nn.functional.conv1d(x, w, padding=(w.shape[-1] // 2, ))
|
||||
|
||||
def remove_weight_norm(self):
|
||||
w = self.weight.to(torch.float32)
|
||||
w = normalize(w) # traditional weight normalization
|
||||
w = w / np.sqrt(w[0].numel())
|
||||
w = w.to(self.weight.dtype)
|
||||
self.weight.data.copy_(w)
|
||||
|
||||
self.weight_norm_removed = True
|
||||
return self
|
||||
-376
@@ -1,376 +0,0 @@
|
||||
import logging
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from .edm2_utils import MPConv1D
|
||||
from .vae_modules import (AttnBlock1D, Downsample1D, ResnetBlock1D,
|
||||
Upsample1D, nonlinearity)
|
||||
from .distributions import DiagonalGaussianDistribution
|
||||
|
||||
log = logging.getLogger()
|
||||
|
||||
DATA_MEAN_80D = [
|
||||
-1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927,
|
||||
-1.3170, -1.3543, -1.3401, -1.3836, -1.3907, -1.3912, -1.4313, -1.4152, -1.4527, -1.4728,
|
||||
-1.4568, -1.5101, -1.5051, -1.5172, -1.5623, -1.5373, -1.5746, -1.5687, -1.6032, -1.6131,
|
||||
-1.6081, -1.6331, -1.6489, -1.6489, -1.6700, -1.6738, -1.6953, -1.6969, -1.7048, -1.7280,
|
||||
-1.7361, -1.7495, -1.7658, -1.7814, -1.7889, -1.8064, -1.8221, -1.8377, -1.8417, -1.8643,
|
||||
-1.8857, -1.8929, -1.9173, -1.9379, -1.9531, -1.9673, -1.9824, -2.0042, -2.0215, -2.0436,
|
||||
-2.0766, -2.1064, -2.1418, -2.1855, -2.2319, -2.2767, -2.3161, -2.3572, -2.3954, -2.4282,
|
||||
-2.4659, -2.5072, -2.5552, -2.6074, -2.6584, -2.7107, -2.7634, -2.8266, -2.8981, -2.9673
|
||||
]
|
||||
|
||||
DATA_STD_80D = [
|
||||
1.0291, 1.0411, 1.0043, 0.9820, 0.9677, 0.9543, 0.9450, 0.9392, 0.9343, 0.9297, 0.9276, 0.9263,
|
||||
0.9242, 0.9254, 0.9232, 0.9281, 0.9263, 0.9315, 0.9274, 0.9247, 0.9277, 0.9199, 0.9188, 0.9194,
|
||||
0.9160, 0.9161, 0.9146, 0.9161, 0.9100, 0.9095, 0.9145, 0.9076, 0.9066, 0.9095, 0.9032, 0.9043,
|
||||
0.9038, 0.9011, 0.9019, 0.9010, 0.8984, 0.8983, 0.8986, 0.8961, 0.8962, 0.8978, 0.8962, 0.8973,
|
||||
0.8993, 0.8976, 0.8995, 0.9016, 0.8982, 0.8972, 0.8974, 0.8949, 0.8940, 0.8947, 0.8936, 0.8939,
|
||||
0.8951, 0.8956, 0.9017, 0.9167, 0.9436, 0.9690, 1.0003, 1.0225, 1.0381, 1.0491, 1.0545, 1.0604,
|
||||
1.0761, 1.0929, 1.1089, 1.1196, 1.1176, 1.1156, 1.1117, 1.1070
|
||||
]
|
||||
|
||||
DATA_MEAN_128D = [
|
||||
-3.3462, -2.6723, -2.4893, -2.3143, -2.2664, -2.3317, -2.1802, -2.4006, -2.2357, -2.4597,
|
||||
-2.3717, -2.4690, -2.5142, -2.4919, -2.6610, -2.5047, -2.7483, -2.5926, -2.7462, -2.7033,
|
||||
-2.7386, -2.8112, -2.7502, -2.9594, -2.7473, -3.0035, -2.8891, -2.9922, -2.9856, -3.0157,
|
||||
-3.1191, -2.9893, -3.1718, -3.0745, -3.1879, -3.2310, -3.1424, -3.2296, -3.2791, -3.2782,
|
||||
-3.2756, -3.3134, -3.3509, -3.3750, -3.3951, -3.3698, -3.4505, -3.4509, -3.5089, -3.4647,
|
||||
-3.5536, -3.5788, -3.5867, -3.6036, -3.6400, -3.6747, -3.7072, -3.7279, -3.7283, -3.7795,
|
||||
-3.8259, -3.8447, -3.8663, -3.9182, -3.9605, -3.9861, -4.0105, -4.0373, -4.0762, -4.1121,
|
||||
-4.1488, -4.1874, -4.2461, -4.3170, -4.3639, -4.4452, -4.5282, -4.6297, -4.7019, -4.7960,
|
||||
-4.8700, -4.9507, -5.0303, -5.0866, -5.1634, -5.2342, -5.3242, -5.4053, -5.4927, -5.5712,
|
||||
-5.6464, -5.7052, -5.7619, -5.8410, -5.9188, -6.0103, -6.0955, -6.1673, -6.2362, -6.3120,
|
||||
-6.3926, -6.4797, -6.5565, -6.6511, -6.8130, -6.9961, -7.1275, -7.2457, -7.3576, -7.4663,
|
||||
-7.6136, -7.7469, -7.8815, -8.0132, -8.1515, -8.3071, -8.4722, -8.7418, -9.3975, -9.6628,
|
||||
-9.7671, -9.8863, -9.9992, -10.0860, -10.1709, -10.5418, -11.2795, -11.3861
|
||||
]
|
||||
|
||||
DATA_STD_128D = [
|
||||
2.3804, 2.4368, 2.3772, 2.3145, 2.2803, 2.2510, 2.2316, 2.2083, 2.1996, 2.1835, 2.1769, 2.1659,
|
||||
2.1631, 2.1618, 2.1540, 2.1606, 2.1571, 2.1567, 2.1612, 2.1579, 2.1679, 2.1683, 2.1634, 2.1557,
|
||||
2.1668, 2.1518, 2.1415, 2.1449, 2.1406, 2.1350, 2.1313, 2.1415, 2.1281, 2.1352, 2.1219, 2.1182,
|
||||
2.1327, 2.1195, 2.1137, 2.1080, 2.1179, 2.1036, 2.1087, 2.1036, 2.1015, 2.1068, 2.0975, 2.0991,
|
||||
2.0902, 2.1015, 2.0857, 2.0920, 2.0893, 2.0897, 2.0910, 2.0881, 2.0925, 2.0873, 2.0960, 2.0900,
|
||||
2.0957, 2.0958, 2.0978, 2.0936, 2.0886, 2.0905, 2.0845, 2.0855, 2.0796, 2.0840, 2.0813, 2.0817,
|
||||
2.0838, 2.0840, 2.0917, 2.1061, 2.1431, 2.1976, 2.2482, 2.3055, 2.3700, 2.4088, 2.4372, 2.4609,
|
||||
2.4731, 2.4847, 2.5072, 2.5451, 2.5772, 2.6147, 2.6529, 2.6596, 2.6645, 2.6726, 2.6803, 2.6812,
|
||||
2.6899, 2.6916, 2.6931, 2.6998, 2.7062, 2.7262, 2.7222, 2.7158, 2.7041, 2.7485, 2.7491, 2.7451,
|
||||
2.7485, 2.7233, 2.7297, 2.7233, 2.7145, 2.6958, 2.6788, 2.6439, 2.6007, 2.4786, 2.2469, 2.1877,
|
||||
2.1392, 2.0717, 2.0107, 1.9676, 1.9140, 1.7102, 0.9101, 0.7164
|
||||
]
|
||||
|
||||
|
||||
class VAE(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
data_dim: int,
|
||||
embed_dim: int,
|
||||
hidden_dim: int,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if data_dim == 80:
|
||||
data_mean = torch.tensor(DATA_MEAN_80D, dtype=torch.float32)
|
||||
data_std = torch.tensor(DATA_STD_80D, dtype=torch.float32)
|
||||
elif data_dim == 128:
|
||||
data_mean = torch.tensor(DATA_MEAN_128D, dtype=torch.float32)
|
||||
data_std = torch.tensor(DATA_STD_128D, dtype=torch.float32)
|
||||
else:
|
||||
raise ValueError(f"Unsupported data_dim={data_dim}, expected 80 or 128")
|
||||
|
||||
# match old shape: (1, channels, 1)
|
||||
data_mean = data_mean.view(1, -1, 1)
|
||||
data_std = data_std.view(1, -1, 1)
|
||||
|
||||
# register as buffers so they move with .to(device) / .cuda()
|
||||
self.register_buffer("data_mean", data_mean)
|
||||
self.register_buffer("data_std", data_std)
|
||||
|
||||
self.encoder = Encoder1D(
|
||||
dim=hidden_dim,
|
||||
ch_mult=(1, 2, 4),
|
||||
num_res_blocks=2,
|
||||
attn_layers=[3],
|
||||
down_layers=[0],
|
||||
in_dim=data_dim,
|
||||
embed_dim=embed_dim,
|
||||
)
|
||||
self.decoder = Decoder1D(
|
||||
dim=hidden_dim,
|
||||
ch_mult=(1, 2, 4),
|
||||
num_res_blocks=2,
|
||||
attn_layers=[3],
|
||||
down_layers=[0],
|
||||
in_dim=data_dim,
|
||||
out_dim=data_dim,
|
||||
embed_dim=embed_dim,
|
||||
)
|
||||
|
||||
self.embed_dim = embed_dim
|
||||
# self.quant_conv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, 1)
|
||||
# self.post_quant_conv = nn.Conv1d(embed_dim, embed_dim, 1)
|
||||
|
||||
self.initialize_weights()
|
||||
|
||||
def initialize_weights(self):
|
||||
pass
|
||||
|
||||
def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalGaussianDistribution:
|
||||
if normalize:
|
||||
x = self.normalize(x)
|
||||
moments = self.encoder(x)
|
||||
posterior = DiagonalGaussianDistribution(moments)
|
||||
return posterior
|
||||
|
||||
def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.Tensor:
|
||||
dec = self.decoder(z)
|
||||
if unnormalize:
|
||||
dec = self.unnormalize(dec)
|
||||
return dec
|
||||
|
||||
def normalize(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return (x - self.data_mean) / self.data_std
|
||||
|
||||
def unnormalize(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return x * self.data_std + self.data_mean
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
sample_posterior: bool = True,
|
||||
rng: Optional[torch.Generator] = None,
|
||||
normalize: bool = True,
|
||||
unnormalize: bool = True,
|
||||
) -> tuple[torch.Tensor, DiagonalGaussianDistribution]:
|
||||
|
||||
posterior = self.encode(x, normalize=normalize)
|
||||
if sample_posterior:
|
||||
z = posterior.sample(rng)
|
||||
else:
|
||||
z = posterior.mode()
|
||||
dec = self.decode(z, unnormalize=unnormalize)
|
||||
return dec, posterior
|
||||
|
||||
def load_weights(self, src_dict) -> None:
|
||||
self.load_state_dict(src_dict, strict=True)
|
||||
|
||||
@property
|
||||
def device(self) -> torch.device:
|
||||
return next(self.parameters()).device
|
||||
|
||||
def get_last_layer(self):
|
||||
return self.decoder.conv_out.weight
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for name, m in self.named_modules():
|
||||
if isinstance(m, MPConv1D):
|
||||
m.remove_weight_norm()
|
||||
log.debug(f"Removed weight norm from {name}")
|
||||
return self
|
||||
|
||||
|
||||
class Encoder1D(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
dim: int,
|
||||
ch_mult: tuple[int] = (1, 2, 4, 8),
|
||||
num_res_blocks: int,
|
||||
attn_layers: list[int] = [],
|
||||
down_layers: list[int] = [],
|
||||
resamp_with_conv: bool = True,
|
||||
in_dim: int,
|
||||
embed_dim: int,
|
||||
double_z: bool = True,
|
||||
kernel_size: int = 3,
|
||||
clip_act: float = 256.0):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_layers = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.in_channels = in_dim
|
||||
self.clip_act = clip_act
|
||||
self.down_layers = down_layers
|
||||
self.attn_layers = attn_layers
|
||||
self.conv_in = MPConv1D(in_dim, self.dim, kernel_size=kernel_size)
|
||||
|
||||
in_ch_mult = (1, ) + tuple(ch_mult)
|
||||
self.in_ch_mult = in_ch_mult
|
||||
# downsampling
|
||||
self.down = nn.ModuleList()
|
||||
for i_level in range(self.num_layers):
|
||||
block = nn.ModuleList()
|
||||
attn = nn.ModuleList()
|
||||
block_in = dim * in_ch_mult[i_level]
|
||||
block_out = dim * ch_mult[i_level]
|
||||
for i_block in range(self.num_res_blocks):
|
||||
block.append(
|
||||
ResnetBlock1D(in_dim=block_in,
|
||||
out_dim=block_out,
|
||||
kernel_size=kernel_size,
|
||||
use_norm=True))
|
||||
block_in = block_out
|
||||
if i_level in attn_layers:
|
||||
attn.append(AttnBlock1D(block_in))
|
||||
down = nn.Module()
|
||||
down.block = block
|
||||
down.attn = attn
|
||||
if i_level in down_layers:
|
||||
down.downsample = Downsample1D(block_in, resamp_with_conv)
|
||||
self.down.append(down)
|
||||
|
||||
# middle
|
||||
self.mid = nn.Module()
|
||||
self.mid.block_1 = ResnetBlock1D(in_dim=block_in,
|
||||
out_dim=block_in,
|
||||
kernel_size=kernel_size,
|
||||
use_norm=True)
|
||||
self.mid.attn_1 = AttnBlock1D(block_in)
|
||||
self.mid.block_2 = ResnetBlock1D(in_dim=block_in,
|
||||
out_dim=block_in,
|
||||
kernel_size=kernel_size,
|
||||
use_norm=True)
|
||||
|
||||
# end
|
||||
self.conv_out = MPConv1D(block_in,
|
||||
2 * embed_dim if double_z else embed_dim,
|
||||
kernel_size=kernel_size)
|
||||
|
||||
self.learnable_gain = nn.Parameter(torch.zeros([]))
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
# downsampling
|
||||
hs = [self.conv_in(x)]
|
||||
for i_level in range(self.num_layers):
|
||||
for i_block in range(self.num_res_blocks):
|
||||
h = self.down[i_level].block[i_block](hs[-1])
|
||||
if len(self.down[i_level].attn) > 0:
|
||||
h = self.down[i_level].attn[i_block](h)
|
||||
h = h.clamp(-self.clip_act, self.clip_act)
|
||||
hs.append(h)
|
||||
if i_level in self.down_layers:
|
||||
hs.append(self.down[i_level].downsample(hs[-1]))
|
||||
|
||||
# middle
|
||||
h = hs[-1]
|
||||
h = self.mid.block_1(h)
|
||||
h = self.mid.attn_1(h)
|
||||
h = self.mid.block_2(h)
|
||||
h = h.clamp(-self.clip_act, self.clip_act)
|
||||
|
||||
# end
|
||||
h = nonlinearity(h)
|
||||
h = self.conv_out(h, gain=(self.learnable_gain + 1))
|
||||
return h
|
||||
|
||||
|
||||
class Decoder1D(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
dim: int,
|
||||
out_dim: int,
|
||||
ch_mult: tuple[int] = (1, 2, 4, 8),
|
||||
num_res_blocks: int,
|
||||
attn_layers: list[int] = [],
|
||||
down_layers: list[int] = [],
|
||||
kernel_size: int = 3,
|
||||
resamp_with_conv: bool = True,
|
||||
in_dim: int,
|
||||
embed_dim: int,
|
||||
clip_act: float = 256.0):
|
||||
super().__init__()
|
||||
self.ch = dim
|
||||
self.num_layers = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.in_channels = in_dim
|
||||
self.clip_act = clip_act
|
||||
self.down_layers = [i + 1 for i in down_layers] # each downlayer add one
|
||||
|
||||
# compute in_ch_mult, block_in and curr_res at lowest res
|
||||
block_in = dim * ch_mult[self.num_layers - 1]
|
||||
|
||||
# z to block_in
|
||||
self.conv_in = MPConv1D(embed_dim, block_in, kernel_size=kernel_size)
|
||||
|
||||
# middle
|
||||
self.mid = nn.Module()
|
||||
self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)
|
||||
self.mid.attn_1 = AttnBlock1D(block_in)
|
||||
self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)
|
||||
|
||||
# upsampling
|
||||
self.up = nn.ModuleList()
|
||||
for i_level in reversed(range(self.num_layers)):
|
||||
block = nn.ModuleList()
|
||||
attn = nn.ModuleList()
|
||||
block_out = dim * ch_mult[i_level]
|
||||
for i_block in range(self.num_res_blocks + 1):
|
||||
block.append(ResnetBlock1D(in_dim=block_in, out_dim=block_out, use_norm=True))
|
||||
block_in = block_out
|
||||
if i_level in attn_layers:
|
||||
attn.append(AttnBlock1D(block_in))
|
||||
up = nn.Module()
|
||||
up.block = block
|
||||
up.attn = attn
|
||||
if i_level in self.down_layers:
|
||||
up.upsample = Upsample1D(block_in, resamp_with_conv)
|
||||
self.up.insert(0, up) # prepend to get consistent order
|
||||
|
||||
# end
|
||||
self.conv_out = MPConv1D(block_in, out_dim, kernel_size=kernel_size)
|
||||
self.learnable_gain = nn.Parameter(torch.zeros([]))
|
||||
|
||||
def forward(self, z):
|
||||
# z to block_in
|
||||
h = self.conv_in(z)
|
||||
|
||||
# middle
|
||||
h = self.mid.block_1(h)
|
||||
h = self.mid.attn_1(h)
|
||||
h = self.mid.block_2(h)
|
||||
h = h.clamp(-self.clip_act, self.clip_act)
|
||||
|
||||
# upsampling
|
||||
for i_level in reversed(range(self.num_layers)):
|
||||
for i_block in range(self.num_res_blocks + 1):
|
||||
h = self.up[i_level].block[i_block](h)
|
||||
if len(self.up[i_level].attn) > 0:
|
||||
h = self.up[i_level].attn[i_block](h)
|
||||
h = h.clamp(-self.clip_act, self.clip_act)
|
||||
if i_level in self.down_layers:
|
||||
h = self.up[i_level].upsample(h)
|
||||
|
||||
h = nonlinearity(h)
|
||||
h = self.conv_out(h, gain=(self.learnable_gain + 1))
|
||||
return h
|
||||
|
||||
|
||||
def VAE_16k(**kwargs) -> VAE:
|
||||
return VAE(data_dim=80, embed_dim=20, hidden_dim=384, **kwargs)
|
||||
|
||||
|
||||
def VAE_44k(**kwargs) -> VAE:
|
||||
return VAE(data_dim=128, embed_dim=40, hidden_dim=512, **kwargs)
|
||||
|
||||
|
||||
def get_my_vae(name: str, **kwargs) -> VAE:
|
||||
if name == '16k':
|
||||
return VAE_16k(**kwargs)
|
||||
if name == '44k':
|
||||
return VAE_44k(**kwargs)
|
||||
raise ValueError(f'Unknown model: {name}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
network = get_my_vae('standard')
|
||||
|
||||
# print the number of parameters in terms of millions
|
||||
num_params = sum(p.numel() for p in network.parameters()) / 1e6
|
||||
print(f'Number of parameters: {num_params:.2f}M')
|
||||
@@ -1,117 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
|
||||
from .edm2_utils import (MPConv1D, mp_silu, mp_sum, normalize)
|
||||
|
||||
|
||||
def nonlinearity(x):
|
||||
# swish
|
||||
return mp_silu(x)
|
||||
|
||||
|
||||
class ResnetBlock1D(nn.Module):
|
||||
|
||||
def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True):
|
||||
super().__init__()
|
||||
self.in_dim = in_dim
|
||||
out_dim = in_dim if out_dim is None else out_dim
|
||||
self.out_dim = out_dim
|
||||
self.use_conv_shortcut = conv_shortcut
|
||||
self.use_norm = use_norm
|
||||
|
||||
self.conv1 = MPConv1D(in_dim, out_dim, kernel_size=kernel_size)
|
||||
self.conv2 = MPConv1D(out_dim, out_dim, kernel_size=kernel_size)
|
||||
if self.in_dim != self.out_dim:
|
||||
if self.use_conv_shortcut:
|
||||
self.conv_shortcut = MPConv1D(in_dim, out_dim, kernel_size=kernel_size)
|
||||
else:
|
||||
self.nin_shortcut = MPConv1D(in_dim, out_dim, kernel_size=1)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
|
||||
# pixel norm
|
||||
if self.use_norm:
|
||||
x = normalize(x, dim=1)
|
||||
|
||||
h = x
|
||||
h = nonlinearity(h)
|
||||
h = self.conv1(h)
|
||||
|
||||
h = nonlinearity(h)
|
||||
h = self.conv2(h)
|
||||
|
||||
if self.in_dim != self.out_dim:
|
||||
if self.use_conv_shortcut:
|
||||
x = self.conv_shortcut(x)
|
||||
else:
|
||||
x = self.nin_shortcut(x)
|
||||
|
||||
return mp_sum(x, h, t=0.3)
|
||||
|
||||
|
||||
class AttnBlock1D(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, num_heads=1):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.num_heads = num_heads
|
||||
self.qkv = MPConv1D(in_channels, in_channels * 3, kernel_size=1)
|
||||
self.proj_out = MPConv1D(in_channels, in_channels, kernel_size=1)
|
||||
|
||||
def forward(self, x):
|
||||
h = x
|
||||
y = self.qkv(h)
|
||||
y = y.reshape(y.shape[0], self.num_heads, -1, 3, y.shape[-1])
|
||||
q, k, v = normalize(y, dim=2).unbind(3)
|
||||
|
||||
q = rearrange(q, 'b h c l -> b h l c')
|
||||
k = rearrange(k, 'b h c l -> b h l c')
|
||||
v = rearrange(v, 'b h c l -> b h l c')
|
||||
|
||||
h = F.scaled_dot_product_attention(q, k, v)
|
||||
h = rearrange(h, 'b h l c -> b (h c) l')
|
||||
|
||||
h = self.proj_out(h)
|
||||
|
||||
return mp_sum(x, h, t=0.3)
|
||||
|
||||
|
||||
class Upsample1D(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, with_conv):
|
||||
super().__init__()
|
||||
self.with_conv = with_conv
|
||||
if self.with_conv:
|
||||
self.conv = MPConv1D(in_channels, in_channels, kernel_size=3)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact') # support 3D tensor(B,C,T)
|
||||
if self.with_conv:
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
class Downsample1D(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, with_conv):
|
||||
super().__init__()
|
||||
self.with_conv = with_conv
|
||||
if self.with_conv:
|
||||
# no asymmetric padding in torch conv, must do it ourselves
|
||||
self.conv1 = MPConv1D(in_channels, in_channels, kernel_size=1)
|
||||
self.conv2 = MPConv1D(in_channels, in_channels, kernel_size=1)
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
if self.with_conv:
|
||||
x = self.conv1(x)
|
||||
|
||||
x = F.avg_pool1d(x, kernel_size=2, stride=2)
|
||||
|
||||
if self.with_conv:
|
||||
x = self.conv2(x)
|
||||
|
||||
return x
|
||||
@@ -1,96 +0,0 @@
|
||||
import torch
|
||||
from ..utils import log
|
||||
import comfy.model_management as mm
|
||||
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
|
||||
class WanVideoAddSCAILReferenceEmbeds:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"embeds": ("WANVIDIMAGE_EMBEDS",),
|
||||
"vae": ("WANVAE", {"tooltip": "VAE model"}),
|
||||
"ref_image": ("IMAGE",),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Strength of the reference embedding"}),
|
||||
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the embedding application"}),
|
||||
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the embedding application"}),
|
||||
},
|
||||
"optional": {
|
||||
"clip_embeds": ("WANVIDIMAGE_CLIPEMBEDS", {"tooltip": "Clip vision encoded image"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
|
||||
RETURN_NAMES = ("image_embeds",)
|
||||
FUNCTION = "add"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def add(self, embeds, vae, ref_image, strength, start_percent, end_percent, clip_embeds=None):
|
||||
updated = dict(embeds)
|
||||
|
||||
vae.to(device)
|
||||
ref_image_in = (ref_image[..., :3].permute(3, 0, 1, 2) * 2 - 1).to(device, vae.dtype)
|
||||
ref_latent = vae.encode([ref_image_in], device, tiled=False)[0]
|
||||
log.info(f"SCAIL ref_latent shape: {ref_latent.shape}")
|
||||
|
||||
ref_mask = torch.ones_like(ref_latent[:4])
|
||||
ref_latent = torch.cat([ref_latent, ref_mask], dim=0)
|
||||
vae.to(offload_device)
|
||||
|
||||
updated.setdefault("scail_embeds", {})
|
||||
updated["scail_embeds"]["ref_latent_pos"] = ref_latent * strength
|
||||
updated["scail_embeds"]["ref_latent_neg"] = torch.zeros_like(ref_latent)
|
||||
updated["scail_embeds"]["ref_start_percent"] = start_percent
|
||||
updated["scail_embeds"]["ref_end_percent"] = end_percent
|
||||
updated["clip_context"] = clip_embeds.get("clip_embeds", None) if clip_embeds is not None else None
|
||||
|
||||
return (updated,)
|
||||
|
||||
class WanVideoAddSCAILPoseEmbeds:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"embeds": ("WANVIDIMAGE_EMBEDS",),
|
||||
"vae": ("WANVAE", {"tooltip": "VAE model"}),
|
||||
"pose_images": ("IMAGE", {"tooltip": "Pose images for the entire video"}),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Strength of the pose control"}),
|
||||
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the pose control application"}),
|
||||
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the pose control application"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
|
||||
RETURN_NAMES = ("image_embeds",)
|
||||
FUNCTION = "add"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def add(self, embeds, vae, pose_images, strength, start_percent=0.0, end_percent=1.0):
|
||||
updated = dict(embeds)
|
||||
|
||||
vae.to(device)
|
||||
pose_images_in = (pose_images[..., :3].permute(3, 0, 1, 2) * 2 - 1).to(device, vae.dtype)
|
||||
pose_latent = vae.encode([pose_images_in], device, tiled=False)[0]
|
||||
pose_mask = torch.ones_like(pose_latent[:4])
|
||||
pose_latent = torch.cat([pose_latent, pose_mask], dim=0)
|
||||
log.info(f"SCAIL pose_latent shape: {pose_latent.shape}")
|
||||
|
||||
vae.to(offload_device)
|
||||
|
||||
updated.setdefault("scail_embeds", {})
|
||||
updated["scail_embeds"]["pose_latent"] = pose_latent
|
||||
updated["scail_embeds"]["pose_strength"] = strength
|
||||
updated["scail_embeds"]["pose_start_percent"] = start_percent
|
||||
updated["scail_embeds"]["pose_end_percent"] = end_percent
|
||||
|
||||
return (updated,)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoAddSCAILPoseEmbeds": WanVideoAddSCAILPoseEmbeds,
|
||||
"WanVideoAddSCAILReferenceEmbeds": WanVideoAddSCAILReferenceEmbeds,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoAddSCAILReferenceEmbeds": "WanVideo Add SCAIL Reference Embeds",
|
||||
"WanVideoAddSCAILPoseEmbeds": "WanVideo Add SCAIL Pose Embeds",
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
@@ -1,207 +0,0 @@
|
||||
import json
|
||||
import torch
|
||||
import torchvision.transforms.functional as TF
|
||||
from ..utils import log
|
||||
from .trajectory import create_pos_feature_map, draw_tracks_on_video, replace_feature
|
||||
import os
|
||||
from comfy import model_management as mm
|
||||
device = mm.get_torch_device()
|
||||
script_directory = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
VAE_STRIDE = (4, 8, 8) # t, h, w
|
||||
|
||||
class WanVideoWanDrawWanMoveTracks:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"images": ("IMAGE",),
|
||||
"tracks": ("TRACKS",),
|
||||
},
|
||||
"optional": {
|
||||
"line_resolution": ("INT", {"default": 24, "min": 4, "max": 64, "step": 1, "tooltip": "Number of points to use for each line segment"}),
|
||||
"circle_size": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1, "tooltip": "Size of the circle to draw for each track point"}),
|
||||
"opacity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Opacity of the circle to draw for each track point"}),
|
||||
"line_width": ("INT", {"default": 14, "min": 1, "max": 50, "step": 1, "tooltip": "Width of the line to draw for each track"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def execute(self, images, tracks, line_resolution=24, circle_size=10, opacity=0.5, line_width=14):
|
||||
if tracks is None or "track_path" not in tracks:
|
||||
log.warning("WanVideoWanDrawWanMoveTracks: No tracks provided.")
|
||||
return (images.float().cpu(), )
|
||||
track = tracks["track_path"].unsqueeze(0)
|
||||
track_visibility = tracks["track_visibility"].unsqueeze(0)
|
||||
images_in = images * 255.0
|
||||
if images_in.shape[0] != track.shape[1]:
|
||||
repeat_count = track.shape[1] // images.shape[0]
|
||||
images_in = images_in.repeat(repeat_count, 1, 1, 1)
|
||||
track_video = draw_tracks_on_video(images_in, track, track_visibility, track_frame=line_resolution, circle_size=circle_size, opacity=opacity, line_width=line_width)
|
||||
track_video = torch.stack([TF.to_tensor(frame) for frame in track_video], dim=0).movedim(1, -1)
|
||||
|
||||
return (track_video.float().cpu(), )
|
||||
|
||||
|
||||
class WanVideoAddWanMoveTracks:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"image_embeds": ("WANVIDIMAGE_EMBEDS",),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Strength of the reference embedding"}),
|
||||
},
|
||||
"optional": {
|
||||
"track_mask": ("MASK",),
|
||||
"track_coords": ("STRING", {"forceInput": True, "tooltip": "JSON string or list of JSON strings representing the tracks"}),
|
||||
"tracks": ("TRACKS", {"tooltip": "Alternatively use Comfy Tracks dictionary"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", "TRACKS")
|
||||
RETURN_NAMES = ("image_embeds", "tracks")
|
||||
FUNCTION = "add"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def add(self, image_embeds, track_coords=None, tracks=None, strength=1.0, track_mask=None):
|
||||
updated = dict(image_embeds)
|
||||
|
||||
track_visibility = None
|
||||
|
||||
target_shape = image_embeds.get("target_shape")
|
||||
if target_shape is not None:
|
||||
height = target_shape[2] * VAE_STRIDE[1]
|
||||
width = target_shape[3] * VAE_STRIDE[2]
|
||||
else:
|
||||
height = image_embeds["lat_h"] * VAE_STRIDE[1]
|
||||
width = image_embeds["lat_w"] * VAE_STRIDE[2]
|
||||
num_frames = image_embeds["num_frames"]
|
||||
|
||||
if track_coords is not None:
|
||||
tracks_data = parse_json_tracks(track_coords)
|
||||
track_list = [
|
||||
[[track[frame]['x'], track[frame]['y']] for track in tracks_data]
|
||||
for frame in range(len(tracks_data[0]))
|
||||
]
|
||||
track = torch.tensor(track_list, dtype=torch.float32, device=device) # shape: (frames, num_tracks, 2)
|
||||
elif tracks is not None and "track_path" in tracks:
|
||||
track = tracks["track_path"]
|
||||
if track_mask is None:
|
||||
track_visibility = tracks.get("track_visibility", None)
|
||||
track = track[:num_frames]
|
||||
|
||||
num_tracks = track.shape[-2]
|
||||
if track_visibility is None:
|
||||
if track_mask is None:
|
||||
track_visibility = torch.ones((num_frames, num_tracks), dtype=torch.bool, device=device)
|
||||
else:
|
||||
track_visibility = (track_mask > 0).any(dim=(1, 2)).unsqueeze(-1)
|
||||
feature_map, track_pos = create_pos_feature_map(track, track_visibility, VAE_STRIDE, height, width, 16, track_num=num_tracks, device=device)
|
||||
|
||||
updated.setdefault("wanmove_embeds", {})
|
||||
updated["wanmove_embeds"]["track_pos"] = track_pos
|
||||
updated["wanmove_embeds"]["strength"] = strength
|
||||
|
||||
tracks_dict = {
|
||||
"track_path": track,
|
||||
"track_visibility": track_visibility,
|
||||
}
|
||||
|
||||
return (updated, tracks_dict,)
|
||||
|
||||
|
||||
def parse_json_tracks(tracks):
|
||||
tracks_data = []
|
||||
try:
|
||||
# If tracks is a string, try to parse it as JSON
|
||||
if isinstance(tracks, str):
|
||||
parsed = json.loads(tracks.replace("'", '"'))
|
||||
tracks_data.extend(parsed)
|
||||
else:
|
||||
# If tracks is a list of strings, parse each one
|
||||
for track_str in tracks:
|
||||
parsed = json.loads(track_str.replace("'", '"'))
|
||||
tracks_data.append(parsed)
|
||||
|
||||
# Check if we have a single track (dict with x,y) or a list of tracks
|
||||
if tracks_data and isinstance(tracks_data[0], dict) and 'x' in tracks_data[0]:
|
||||
# Single track detected, wrap it in a list
|
||||
tracks_data = [tracks_data]
|
||||
elif tracks_data and isinstance(tracks_data[0], list) and tracks_data[0] and isinstance(tracks_data[0][0], dict) and 'x' in tracks_data[0][0]:
|
||||
# Already a list of tracks, nothing to do
|
||||
pass
|
||||
else:
|
||||
# Unexpected format
|
||||
log.warning(f"Warning: Unexpected track format: {type(tracks_data[0])}")
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
log.warning(f"Error parsing tracks JSON: {e}")
|
||||
tracks_data = []
|
||||
|
||||
return tracks_data
|
||||
|
||||
import node_helpers
|
||||
|
||||
class WanMove_native:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"positive": ("CONDITIONING",),
|
||||
"track_coords": ("STRING", {"forceInput": True, "tooltip": "JSON string or list of JSON strings representing the tracks"}),
|
||||
},
|
||||
"optional": {
|
||||
"track_mask": ("MASK",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING", "TRACKS")
|
||||
RETURN_NAMES = ("positive", "tracks")
|
||||
FUNCTION = "patchcond"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
DEPRECATED = True
|
||||
|
||||
def patchcond(self, positive, track_coords, track_mask=None):
|
||||
|
||||
concat_latent_image = positive[0][1]["concat_latent_image"]
|
||||
B, C, T, H, W = concat_latent_image.shape
|
||||
num_frames = (T-1) * 4 + 1
|
||||
width = W * 8
|
||||
height = H * 8
|
||||
|
||||
tracks_data = parse_json_tracks(track_coords)
|
||||
track_list = [
|
||||
[[track[frame]['x'], track[frame]['y']] for track in tracks_data]
|
||||
for frame in range(len(tracks_data[0]))
|
||||
]
|
||||
track = torch.tensor(track_list, dtype=torch.float32, device=device) # shape: (frames, num_tracks, 2)
|
||||
track = track[:num_frames]
|
||||
|
||||
num_tracks = track.shape[-2]
|
||||
if track_mask is None:
|
||||
track_visibility = torch.ones((num_frames, num_tracks), dtype=torch.bool, device=device)
|
||||
else:
|
||||
track_visibility = (track_mask > 0).any(dim=(1, 2)).unsqueeze(-1)
|
||||
|
||||
feature_map, track_pos = create_pos_feature_map(track, track_visibility, VAE_STRIDE, height, width, 16, track_num=num_tracks, device=device)
|
||||
wanmove_cond = replace_feature(concat_latent_image, track_pos.unsqueeze(0))
|
||||
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": wanmove_cond})
|
||||
|
||||
tracks_dict = {
|
||||
"track_path": track,
|
||||
"track_visibility": track_visibility,
|
||||
}
|
||||
return (positive, tracks_dict)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoAddWanMoveTracks": WanVideoAddWanMoveTracks,
|
||||
"WanVideoWanDrawWanMoveTracks": WanVideoWanDrawWanMoveTracks,
|
||||
"WanMove_native": WanMove_native,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoAddWanMoveTracks": "WanVideo Add WanMove Tracks",
|
||||
"WanVideoWanDrawWanMoveTracks": "WanVideo Draw WanMove Tracks",
|
||||
"WanMove_native": "WanMove Native",
|
||||
}
|
||||
@@ -1,340 +0,0 @@
|
||||
# https://github.com/ali-vilab/Wan-Move/blob/main/wan/modules/trajectory.py
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image, ImageDraw
|
||||
|
||||
SKIP_ZERO = False
|
||||
|
||||
def get_pos_emb(
|
||||
pos_k: torch.Tensor,
|
||||
pos_emb_dim: int,
|
||||
theta_func: callable = lambda i, d: torch.pow(10000, torch.mul(2, torch.div(i.to(torch.float32), d))),
|
||||
device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
|
||||
dtype: torch.dtype = torch.float32,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Generate batch position embeddings.
|
||||
|
||||
Args:
|
||||
pos_k (torch.Tensor): A 1D tensor containing positions for which to generate embeddings.
|
||||
pos_emb_dim (int): The dimension of position embeddings.
|
||||
theta_func (callable): Function to compute thetas based on position and embedding dimensions.
|
||||
device (torch.device): Device to store the position embeddings.
|
||||
dtype (torch.dtype): Desired data type for computations.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The position embeddings with shape (batch_size, pos_emb_dim).
|
||||
"""
|
||||
assert pos_emb_dim % 2 == 0, "The dimension of position embeddings must be even."
|
||||
pos_k = pos_k.to(device, dtype)
|
||||
if SKIP_ZERO:
|
||||
pos_k = pos_k + 1
|
||||
batch_size = pos_k.size(0)
|
||||
|
||||
denominator = torch.arange(0, pos_emb_dim // 2, device=device, dtype=dtype)
|
||||
# Expand denominator to match the shape needed for broadcasting
|
||||
denominator_expanded = denominator.view(1, -1).expand(batch_size, -1)
|
||||
|
||||
thetas = theta_func(denominator_expanded, pos_emb_dim)
|
||||
|
||||
# Ensure pos_k is in the correct shape for broadcasting
|
||||
pos_k_expanded = pos_k.view(-1, 1).to(dtype)
|
||||
sin_thetas = torch.sin(torch.div(pos_k_expanded, thetas))
|
||||
cos_thetas = torch.cos(torch.div(pos_k_expanded, thetas))
|
||||
|
||||
# Concatenate sine and cosine embeddings along the last dimension
|
||||
pos_emb = torch.cat([sin_thetas, cos_thetas], dim=-1)
|
||||
|
||||
return pos_emb
|
||||
|
||||
def create_pos_feature_map(
|
||||
pred_tracks: torch.Tensor, # [T, N, 2]
|
||||
pred_visibility: torch.Tensor, # [T, N]
|
||||
downsample_ratios: list[int],
|
||||
height: int,
|
||||
width: int,
|
||||
pos_emb_dim: int,
|
||||
track_num: int = -1,
|
||||
t_down_strategy: str = "sample",
|
||||
device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
|
||||
dtype: torch.dtype = torch.float32,
|
||||
):
|
||||
"""
|
||||
Create a feature map from the predicted tracks.
|
||||
|
||||
Args:
|
||||
- pred_tracks: torch.Tensor, the predicted tracks, [T, N, 2]
|
||||
- pred_visibility: torch.Tensor, the predicted visibility, [T, N]
|
||||
- downsample_ratios: list[int], the ratios for downsampling time, height, and width
|
||||
- height: int, the height of the feature map
|
||||
- width: int, the width of the feature map
|
||||
- pos_emb_dim: int, the dimension of the position embeddings
|
||||
- track_num: int, the number of tracks to use
|
||||
- t_down_strategy: str, the strategy for downsampling time dimension
|
||||
- device: torch.device, the device
|
||||
- dtype: torch.dtype, the data type
|
||||
|
||||
Returns:
|
||||
- feature_map: torch.Tensor, the feature map, [T', H', W', pos_emb_dim]
|
||||
- track_pos: torch.Tensor, the position embeddings, [N, T', 2], 2 = height, width
|
||||
"""
|
||||
|
||||
assert t_down_strategy in ["sample", "average"], "Invalid strategy for downsampling time dimension."
|
||||
|
||||
t, n, _ = pred_tracks.shape
|
||||
t_down, h_down, w_down = downsample_ratios
|
||||
feature_map = torch.zeros((t-1) // t_down + 1, height // h_down, width // w_down, pos_emb_dim, device=device, dtype=dtype)
|
||||
track_pos = - torch.ones(n, (t-1) // t_down + 1, 2, dtype=torch.long)
|
||||
|
||||
if track_num == -1:
|
||||
track_num = n
|
||||
|
||||
tracks_idx = torch.randperm(n)[:track_num]
|
||||
tracks = pred_tracks[:, tracks_idx]
|
||||
visibility = pred_visibility[:, tracks_idx]
|
||||
#tracks_embs = get_pos_emb(torch.randperm(n)[:track_num], pos_emb_dim, device=device, dtype=dtype)
|
||||
|
||||
for t_idx in range(0, t, t_down):
|
||||
if t_down_strategy == "sample" or t_idx == 0:
|
||||
cur_tracks = tracks[t_idx] # [N, 2]
|
||||
cur_visibility = visibility[t_idx] # [N]
|
||||
else:
|
||||
cur_tracks = tracks[t_idx:t_idx+t_down].mean(dim=0)
|
||||
cur_visibility = torch.any(visibility[t_idx:t_idx+t_down], dim=0)
|
||||
|
||||
for i in range(track_num):
|
||||
if not cur_visibility[i] or cur_tracks[i][0] < 0 or cur_tracks[i][1] < 0 or cur_tracks[i][0] >= width or cur_tracks[i][1] >= height:
|
||||
continue
|
||||
x, y = cur_tracks[i]
|
||||
x, y = int(x // w_down), int(y // h_down)
|
||||
#feature_map[t_idx // t_down, y, x] += tracks_embs[i]
|
||||
track_pos[i, t_idx // t_down, 0], track_pos[i, t_idx // t_down, 1] = y, x
|
||||
|
||||
return feature_map, track_pos
|
||||
|
||||
|
||||
def replace_feature(
|
||||
vae_feature: torch.Tensor, # [B, C', T', H', W']
|
||||
track_pos: torch.Tensor, # [B, N, T', 2]
|
||||
strength: float = 1.0,
|
||||
) -> torch.Tensor:
|
||||
b, _, t, h, w = vae_feature.shape
|
||||
assert b == track_pos.shape[0], "Batch size mismatch."
|
||||
n = track_pos.shape[1]
|
||||
|
||||
# Shuffle the trajectory order
|
||||
track_pos = track_pos[:, torch.randperm(n), :, :]
|
||||
|
||||
# Extract coordinates at time steps ≥ 1 and generate a valid mask
|
||||
current_pos = track_pos[:, :, 1:, :] # [B, N, T-1, 2]
|
||||
mask = (current_pos[..., 0] >= 0) & (current_pos[..., 1] >= 0) # [B, N, T-1]
|
||||
|
||||
# Get all valid indices
|
||||
valid_indices = mask.nonzero(as_tuple=False) # [num_valid, 3]
|
||||
num_valid = valid_indices.shape[0]
|
||||
|
||||
if num_valid == 0:
|
||||
return vae_feature
|
||||
|
||||
# Decompose valid indices into each dimension
|
||||
batch_idx = valid_indices[:, 0]
|
||||
track_idx = valid_indices[:, 1]
|
||||
t_rel = valid_indices[:, 2]
|
||||
t_target = t_rel + 1 # Convert to original time step indices
|
||||
|
||||
# Extract target position coordinates
|
||||
h_target = current_pos[batch_idx, track_idx, t_rel, 0].long() # Ensure integer indices
|
||||
w_target = current_pos[batch_idx, track_idx, t_rel, 1].long()
|
||||
|
||||
# Extract source position coordinates (t=0)
|
||||
h_source = track_pos[batch_idx, track_idx, 0, 0].long()
|
||||
w_source = track_pos[batch_idx, track_idx, 0, 1].long()
|
||||
|
||||
# Get source features and assign to target positions
|
||||
src_features = vae_feature[batch_idx, :, 0, h_source, w_source]
|
||||
dst_features = vae_feature[batch_idx, :, t_target, h_target, w_target]
|
||||
|
||||
vae_feature[batch_idx, :, t_target, h_target, w_target] = dst_features + (src_features - dst_features) * strength
|
||||
|
||||
return vae_feature
|
||||
|
||||
def get_video_track_video(
|
||||
model,
|
||||
video_tensor: torch.Tensor, # [T, C, H, W]
|
||||
downsample_ratios: list[int],
|
||||
pos_emb_dim: int,
|
||||
grid_size: int = 32,
|
||||
track_num: int = -1,
|
||||
t_down_strategy: str = "sample",
|
||||
device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
|
||||
dtype: torch.dtype = torch.float32,
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Get the track video from the video tensor.
|
||||
|
||||
Args:
|
||||
- model: torch.nn.Module, the model for tracking, CoTracker
|
||||
- video_tensor: torch.Tensor, the video tensor, [T, C, H, W]
|
||||
- downsample_ratios: list[int], the ratios for downsampling time, height, and width
|
||||
- height: int, the height of the feature map
|
||||
- width: int, the width of the feature map
|
||||
- pos_emb_dim: int, the dimension of the position embeddings
|
||||
- grid_size: int, the size of the grid
|
||||
- track_num: int, the number of tracks to use
|
||||
- t_down_strategy: str, the strategy for downsampling time dimension
|
||||
- device: torch.device, the device
|
||||
- dtype: torch.dtype, the data type
|
||||
|
||||
Returns:
|
||||
- track_video: torch.Tensor, the track video, [pos_emb_dim, T', H', W']
|
||||
- track_pos: torch.Tensor, the position embeddings, [N, T', 2], 2 = height, width
|
||||
- pred_tracks: the predicted point trajectories
|
||||
- pred_visibility: visibility of the predicted point trajectories
|
||||
"""
|
||||
|
||||
t, c, height, width = video_tensor.shape
|
||||
with (
|
||||
torch.autocast(device_type=device.type, dtype=dtype),
|
||||
torch.no_grad(),
|
||||
):
|
||||
pred_tracks, pred_visibility = model(
|
||||
video_tensor.unsqueeze(0),
|
||||
grid_size=grid_size,
|
||||
backward_tracking=False,
|
||||
)
|
||||
|
||||
track_video, track_pos = create_pos_feature_map(
|
||||
pred_tracks[0], pred_visibility[0], downsample_ratios, height, width, pos_emb_dim, track_num, t_down_strategy, device, dtype
|
||||
)
|
||||
|
||||
return track_video.permute(3, 0, 1, 2), track_pos, pred_tracks, pred_visibility
|
||||
|
||||
# ---------------------------
|
||||
# Visualize functions
|
||||
# --------------------------
|
||||
|
||||
def add_weighted(rgb, track):
|
||||
rgb = np.array(rgb) # [H, W, C] "RGB"
|
||||
track = np.array(track) # [H, W, C] "RGBA"
|
||||
|
||||
# Compute weights from the alpha channel
|
||||
alpha = track[:, :, 3] / 255.0
|
||||
|
||||
# Expand alpha to 3 channels to match RGB
|
||||
alpha = np.stack([alpha] * 3, axis=-1)
|
||||
|
||||
# Blend the two images
|
||||
blend_img = track[:, :, :3] * alpha + rgb * (1 - alpha)
|
||||
|
||||
return Image.fromarray(blend_img.astype(np.uint8))
|
||||
|
||||
def draw_tracks_on_video(video, tracks, visibility=None, track_frame=24, circle_size=12, opacity=0.5, line_width=16):
|
||||
color_map = [(102, 153, 255), (0, 255, 255), (255, 255, 0), (255, 102, 204), (0, 255, 0)]
|
||||
|
||||
video = video.byte().cpu().numpy() # (81, 480, 832, 3)
|
||||
tracks = tracks[0].long().detach().cpu().numpy()
|
||||
if visibility is not None:
|
||||
visibility = visibility[0].detach().cpu().numpy()
|
||||
|
||||
num_frames, height, width = video.shape[:3]
|
||||
num_tracks = tracks.shape[1]
|
||||
alpha_opacity = int(255 * opacity)
|
||||
|
||||
output_frames = []
|
||||
for t in range(num_frames):
|
||||
frame_rgb = video[t].astype(np.float32)
|
||||
|
||||
# Create a single RGBA overlay for all tracks in this frame
|
||||
overlay = Image.new("RGBA", (width, height), (0, 0, 0, 0))
|
||||
draw_overlay = ImageDraw.Draw(overlay)
|
||||
|
||||
polyline_data = []
|
||||
|
||||
# Draw all circles on a single overlay
|
||||
for n in range(num_tracks):
|
||||
if visibility is not None and visibility[t, n] == 0:
|
||||
continue
|
||||
|
||||
track_coord = tracks[t, n]
|
||||
color = color_map[n % len(color_map)]
|
||||
circle_color = color + (alpha_opacity,)
|
||||
|
||||
draw_overlay.ellipse(
|
||||
(
|
||||
track_coord[0] - circle_size,
|
||||
track_coord[1] - circle_size,
|
||||
track_coord[0] + circle_size,
|
||||
track_coord[1] + circle_size
|
||||
),
|
||||
fill=circle_color
|
||||
)
|
||||
|
||||
# Store polyline data for batch processing
|
||||
tracks_coord = tracks[max(t - track_frame, 0):t + 1, n]
|
||||
if len(tracks_coord) > 1:
|
||||
polyline_data.append((tracks_coord, color))
|
||||
|
||||
# Blend circles overlay once
|
||||
overlay_np = np.array(overlay)
|
||||
alpha = overlay_np[:, :, 3:4] / 255.0
|
||||
frame_rgb = overlay_np[:, :, :3] * alpha + frame_rgb * (1 - alpha)
|
||||
|
||||
# Draw all polylines on a single overlay
|
||||
if polyline_data:
|
||||
polyline_overlay = Image.new("RGBA", (width, height), (0, 0, 0, 0))
|
||||
for tracks_coord, color in polyline_data:
|
||||
_draw_gradient_polyline_on_overlay(polyline_overlay, line_width, tracks_coord, color, opacity)
|
||||
|
||||
# Blend polylines overlay once
|
||||
polyline_np = np.array(polyline_overlay)
|
||||
alpha = polyline_np[:, :, 3:4] / 255.0
|
||||
frame_rgb = polyline_np[:, :, :3] * alpha + frame_rgb * (1 - alpha)
|
||||
|
||||
output_frames.append(Image.fromarray(frame_rgb.astype(np.uint8)))
|
||||
|
||||
return output_frames
|
||||
|
||||
|
||||
def _draw_gradient_polyline_on_overlay(overlay, line_width, points, start_color, opacity=1.0):
|
||||
"""
|
||||
Draw a gradient polyline directly onto an existing RGBA overlay image.
|
||||
This is an optimized version that doesn't create new images.
|
||||
"""
|
||||
draw = ImageDraw.Draw(overlay, 'RGBA')
|
||||
points = points[::-1]
|
||||
|
||||
# Compute total length
|
||||
total_length = 0
|
||||
segment_lengths = []
|
||||
for i in range(len(points) - 1):
|
||||
dx = points[i + 1][0] - points[i][0]
|
||||
dy = points[i + 1][1] - points[i][1]
|
||||
length = (dx * dx + dy * dy) ** 0.5
|
||||
segment_lengths.append(length)
|
||||
total_length += length
|
||||
|
||||
if total_length == 0:
|
||||
return
|
||||
|
||||
accumulated_length = 0
|
||||
|
||||
# Draw the gradient polyline
|
||||
for idx, (start_point, end_point) in enumerate(zip(points[:-1], points[1:])):
|
||||
segment_length = segment_lengths[idx]
|
||||
steps = max(int(segment_length), 1)
|
||||
|
||||
for i in range(steps):
|
||||
current_length = accumulated_length + (i / steps) * segment_length
|
||||
ratio = current_length / total_length
|
||||
|
||||
alpha = int(255 * (1 - ratio) * opacity)
|
||||
color = (*start_color, alpha)
|
||||
|
||||
x = int(start_point[0] + (end_point[0] - start_point[0]) * i / steps)
|
||||
y = int(start_point[1] + (end_point[1] - start_point[1]) * i / steps)
|
||||
|
||||
dynamic_line_width = max(int(line_width * (1 - ratio)), 1)
|
||||
draw.line([(x, y), (x + 1, y)], fill=color, width=dynamic_line_width)
|
||||
|
||||
accumulated_length += segment_length
|
||||
+5
-73
@@ -1,75 +1,7 @@
|
||||
try:
|
||||
from .utils import check_duplicate_nodes, log, color_text
|
||||
duplicate_dirs = check_duplicate_nodes()
|
||||
if duplicate_dirs:
|
||||
warning_msg = f"WARNING: Found {len(duplicate_dirs)} other WanVideoWrapper directories:\n"
|
||||
for dir_path in duplicate_dirs:
|
||||
warning_msg += f" - {color_text(dir_path, 'yellow')}\n"
|
||||
log.warning(color_text(warning_msg + "Please remove duplicates to avoid possible conflicts.", "red"))
|
||||
except:
|
||||
pass
|
||||
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
||||
from .recammaster.nodes import NODE_CLASS_MAPPINGS as RECAM_MASTER_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as RECAM_MASTER_NODE_DISPLAY_NAME_MAPPINGS
|
||||
|
||||
from .utils import log
|
||||
|
||||
NODE_CLASS_MAPPINGS = {}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {}
|
||||
|
||||
# Required modules (will raise on import failure)
|
||||
REQUIRED_MODULES = [
|
||||
(".nodes", "Main"),
|
||||
(".nodes_sampler", "Sampler"),
|
||||
(".nodes_model_loading", "ModelLoading"),
|
||||
(".nodes_utility", "Utility"),
|
||||
(".cache_methods.nodes_cache", "Cache"),
|
||||
]
|
||||
|
||||
# Optional modules (will warn on import failure)
|
||||
OPTIONAL_MODULES = [
|
||||
(".nodes_deprecated", "Deprecated"),
|
||||
(".s2v.nodes", "S2V"),
|
||||
(".FlashVSR.flashvsr_nodes", "FlashVSR"),
|
||||
(".mocha.nodes", "Mocha"),
|
||||
(".fun_camera.nodes", "FunCamera"),
|
||||
(".uni3c.nodes", "Uni3C"),
|
||||
(".controlnet.nodes", "ControlNet"),
|
||||
(".ATI.nodes", "ATI"),
|
||||
(".multitalk.nodes", "MultiTalk"),
|
||||
(".recammaster.nodes", "RecamMaster"),
|
||||
(".skyreels.nodes", "SkyReels"),
|
||||
(".fantasytalking.nodes", "FantasyTalking"),
|
||||
(".qwen.qwen", "Qwen"),
|
||||
(".fantasyportrait.nodes", "FantasyPortrait"),
|
||||
(".unianimate.nodes", "UniAnimate"),
|
||||
(".MTV.nodes", "MTV"),
|
||||
(".HuMo.nodes", "HuMo"),
|
||||
(".lynx.nodes", "Lynx"),
|
||||
(".Ovi.nodes_ovi", "Ovi"),
|
||||
(".steadydancer.nodes", "SteadyDancer"),
|
||||
(".onetoall.nodes", "OneToAll"),
|
||||
(".WanMove.nodes", "WanMove"),
|
||||
(".SCAIL.nodes", "SCAIL"),
|
||||
(".LongCat.nodes", "LongCat"),
|
||||
(".LongVie2.nodes", "LongVie2"),
|
||||
]
|
||||
|
||||
def register_nodes(module_path: str, name: str, optional: bool) -> None:
|
||||
"""Import and register nodes from a module."""
|
||||
try:
|
||||
import importlib
|
||||
module = importlib.import_module(module_path, package=__package__)
|
||||
NODE_CLASS_MAPPINGS.update(getattr(module, "NODE_CLASS_MAPPINGS", {}))
|
||||
NODE_DISPLAY_NAME_MAPPINGS.update(getattr(module, "NODE_DISPLAY_NAME_MAPPINGS", {}))
|
||||
except Exception as e:
|
||||
if optional:
|
||||
log.warning(f"WanVideoWrapper WARNING: {name} nodes not available: {e}")
|
||||
else:
|
||||
raise
|
||||
|
||||
# Register all node modules
|
||||
for module_path, name in REQUIRED_MODULES:
|
||||
register_nodes(module_path, name, optional=False)
|
||||
|
||||
for module_path, name in OPTIONAL_MODULES:
|
||||
register_nodes(module_path, name, optional=True)
|
||||
|
||||
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
|
||||
NODE_CLASS_MAPPINGS.update(RECAM_MASTER_NODE_CLASS_MAPPINGS)
|
||||
NODE_DISPLAY_NAME_MAPPINGS.update(RECAM_MASTER_NODE_DISPLAY_NAME_MAPPINGS)
|
||||
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
|
||||
@@ -1,159 +0,0 @@
|
||||
from ..utils import log
|
||||
import torch
|
||||
|
||||
def set_transformer_cache_method(transformer, timesteps, cache_args=None):
|
||||
transformer.cache_device = cache_args["cache_device"]
|
||||
if cache_args["cache_type"] == "TeaCache":
|
||||
log.info(f"TeaCache: Using cache device: {transformer.cache_device}")
|
||||
transformer.teacache_state.clear_all()
|
||||
transformer.enable_teacache = True
|
||||
transformer.rel_l1_thresh = cache_args["rel_l1_thresh"]
|
||||
transformer.teacache_start_step = cache_args["start_step"]
|
||||
transformer.teacache_end_step = len(timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"]
|
||||
transformer.teacache_use_coefficients = cache_args["use_coefficients"]
|
||||
transformer.teacache_mode = cache_args["mode"]
|
||||
elif cache_args["cache_type"] == "MagCache":
|
||||
log.info(f"MagCache: Using cache device: {transformer.cache_device}")
|
||||
transformer.magcache_state.clear_all()
|
||||
transformer.enable_magcache = True
|
||||
transformer.magcache_start_step = cache_args["start_step"]
|
||||
transformer.magcache_end_step = len(timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"]
|
||||
transformer.magcache_thresh = cache_args["magcache_thresh"]
|
||||
transformer.magcache_K = cache_args["magcache_K"]
|
||||
elif cache_args["cache_type"] == "EasyCache":
|
||||
log.info(f"EasyCache: Using cache device: {transformer.cache_device}")
|
||||
transformer.easycache_state.clear_all()
|
||||
transformer.enable_easycache = True
|
||||
transformer.easycache_start_step = cache_args["start_step"]
|
||||
transformer.easycache_end_step = len(timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"]
|
||||
transformer.easycache_thresh = cache_args["easycache_thresh"]
|
||||
return transformer
|
||||
|
||||
class TeaCacheState:
|
||||
def __init__(self, cache_device='cpu'):
|
||||
self.cache_device = cache_device
|
||||
self.states = {}
|
||||
self._next_pred_id = 0
|
||||
|
||||
def new_prediction(self, cache_device='cpu'):
|
||||
"""Create new prediction state and return its ID"""
|
||||
self.cache_device = cache_device
|
||||
pred_id = self._next_pred_id
|
||||
self._next_pred_id += 1
|
||||
self.states[pred_id] = {
|
||||
'previous_residual': None,
|
||||
'accumulated_rel_l1_distance': 0,
|
||||
'previous_modulated_input': None,
|
||||
'skipped_steps': [],
|
||||
}
|
||||
return pred_id
|
||||
|
||||
def update(self, pred_id, **kwargs):
|
||||
"""Update state for specific prediction"""
|
||||
if pred_id not in self.states:
|
||||
return None
|
||||
for key, value in kwargs.items():
|
||||
self.states[pred_id][key] = value
|
||||
|
||||
def get(self, pred_id):
|
||||
return self.states.get(pred_id, {})
|
||||
|
||||
def clear_all(self):
|
||||
self.states = {}
|
||||
self._next_pred_id = 0
|
||||
|
||||
class MagCacheState:
|
||||
def __init__(self, cache_device='cpu'):
|
||||
self.cache_device = cache_device
|
||||
self.states = {}
|
||||
self._next_pred_id = 0
|
||||
|
||||
def new_prediction(self, cache_device='cpu'):
|
||||
"""Create new prediction state and return its ID"""
|
||||
self.cache_device = cache_device
|
||||
pred_id = self._next_pred_id
|
||||
self._next_pred_id += 1
|
||||
self.states[pred_id] = {
|
||||
'residual_cache': None,
|
||||
'accumulated_ratio': 1.0,
|
||||
'accumulated_steps': 0,
|
||||
'accumulated_err': 0,
|
||||
'skipped_steps': [],
|
||||
}
|
||||
return pred_id
|
||||
|
||||
def update(self, pred_id, **kwargs):
|
||||
"""Update state for specific prediction"""
|
||||
if pred_id not in self.states:
|
||||
return None
|
||||
for key, value in kwargs.items():
|
||||
self.states[pred_id][key] = value
|
||||
|
||||
def get(self, pred_id):
|
||||
return self.states.get(pred_id, {})
|
||||
|
||||
def clear_all(self):
|
||||
self.states = {}
|
||||
self._next_pred_id = 0
|
||||
|
||||
class EasyCacheState:
|
||||
def __init__(self, cache_device='cpu'):
|
||||
self.cache_device = cache_device
|
||||
self.states = {}
|
||||
self._next_pred_id = 0
|
||||
|
||||
def new_prediction(self, cache_device='cpu'):
|
||||
"""Create a new prediction state and return its ID."""
|
||||
self.cache_device = cache_device
|
||||
pred_id = self._next_pred_id
|
||||
self._next_pred_id += 1
|
||||
self.states[pred_id] = {
|
||||
'previous_raw_input': None,
|
||||
'previous_raw_output': None,
|
||||
'cache': None,
|
||||
'accumulated_error': 0.0,
|
||||
'skipped_steps': [],
|
||||
'cache_ovi': None,
|
||||
}
|
||||
return pred_id
|
||||
|
||||
def update(self, pred_id, **kwargs):
|
||||
"""Update state for a specific prediction."""
|
||||
if pred_id not in self.states:
|
||||
return None
|
||||
for key, value in kwargs.items():
|
||||
self.states[pred_id][key] = value
|
||||
|
||||
def get(self, pred_id):
|
||||
return self.states.get(pred_id, {})
|
||||
|
||||
def clear_all(self):
|
||||
self.states = {}
|
||||
self._next_pred_id = 0
|
||||
|
||||
def relative_l1_distance(last_tensor, current_tensor):
|
||||
l1_distance = torch.abs(last_tensor.to(current_tensor.device) - current_tensor).mean()
|
||||
norm = torch.abs(last_tensor).mean()
|
||||
relative_l1_distance = l1_distance / norm
|
||||
return relative_l1_distance.to(torch.float32).to(current_tensor.device)
|
||||
|
||||
def cache_report(transformer, cache_args):
|
||||
cache_type = cache_args["cache_type"]
|
||||
states = (
|
||||
transformer.teacache_state.states if cache_type == "TeaCache" else
|
||||
transformer.magcache_state.states if cache_type == "MagCache" else
|
||||
transformer.easycache_state.states if cache_type == "EasyCache" else
|
||||
None
|
||||
)
|
||||
state_names = {
|
||||
0: "conditional",
|
||||
1: "unconditional"
|
||||
}
|
||||
for pred_id, state in states.items():
|
||||
name = state_names.get(pred_id, f"prediction_{pred_id}")
|
||||
if 'skipped_steps' in state:
|
||||
log.info(f"{cache_type} skipped: {len(state['skipped_steps'])} {name} steps: {state['skipped_steps']}")
|
||||
transformer.teacache_state.clear_all()
|
||||
transformer.magcache_state.clear_all()
|
||||
transformer.easycache_state.clear_all()
|
||||
del states
|
||||
@@ -1,128 +0,0 @@
|
||||
from comfy import model_management as mm
|
||||
|
||||
class WanVideoTeaCache:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"rel_l1_thresh": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.001,
|
||||
"tooltip": "Higher values will make TeaCache more aggressive, faster, but may cause artifacts. Good value range for 1.3B: 0.05 - 0.08, for other models 0.15-0.30"}),
|
||||
"start_step": ("INT", {"default": 1, "min": 0, "max": 9999, "step": 1, "tooltip": "Start percentage of the steps to apply TeaCache"}),
|
||||
"end_step": ("INT", {"default": -1, "min": -1, "max": 9999, "step": 1, "tooltip": "End steps to apply TeaCache"}),
|
||||
"cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
|
||||
"use_coefficients": ("BOOLEAN", {"default": True, "tooltip": "Use calculated coefficients for more accuracy. When enabled therel_l1_thresh should be about 10 times higher than without"}),
|
||||
},
|
||||
"optional": {
|
||||
"mode": (["e", "e0"], {"default": "e", "tooltip": "Choice between using e (time embeds, default) or e0 (modulated time embeds)"}),
|
||||
},
|
||||
}
|
||||
RETURN_TYPES = ("CACHEARGS",)
|
||||
RETURN_NAMES = ("cache_args",)
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
DESCRIPTION = """
|
||||
Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and
|
||||
applying it instead of doing the step. Best results are achieved by choosing the
|
||||
appropriate coefficients for the model. Early steps should never be skipped, with too
|
||||
aggressive values this can happen and the motion suffers. Starting later can help with that too.
|
||||
When NOT using coefficients, the threshold value should be
|
||||
about 10 times smaller than the value used with coefficients.
|
||||
|
||||
Official recommended values https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1
|
||||
"""
|
||||
|
||||
def process(self, rel_l1_thresh, start_step, end_step, cache_device, use_coefficients, mode="e"):
|
||||
if cache_device == "main_device":
|
||||
cache_device = mm.get_torch_device()
|
||||
else:
|
||||
cache_device = mm.unet_offload_device()
|
||||
cache_args = {
|
||||
"cache_type": "TeaCache",
|
||||
"rel_l1_thresh": rel_l1_thresh,
|
||||
"start_step": start_step,
|
||||
"end_step": end_step,
|
||||
"cache_device": cache_device,
|
||||
"use_coefficients": use_coefficients,
|
||||
"mode": mode,
|
||||
}
|
||||
return (cache_args,)
|
||||
|
||||
class WanVideoMagCache:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"magcache_thresh": ("FLOAT", {"default": 0.02, "min": 0.0, "max": 0.3, "step": 0.001, "tooltip": "How strongly to cache the output of diffusion model. This value must be non-negative."}),
|
||||
"magcache_K": ("INT", {"default": 4, "min": 0, "max": 6, "step": 1, "tooltip": "The maxium skip steps of MagCache."}),
|
||||
"start_step": ("INT", {"default": 1, "min": 0, "max": 9999, "step": 1, "tooltip": "Step to start applying MagCache"}),
|
||||
"end_step": ("INT", {"default": -1, "min": -1, "max": 9999, "step": 1, "tooltip": "Step to end applying MagCache"}),
|
||||
"cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
|
||||
},
|
||||
}
|
||||
RETURN_TYPES = ("CACHEARGS",)
|
||||
RETURN_NAMES = ("cache_args",)
|
||||
FUNCTION = "setargs"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
EXPERIMENTAL = True
|
||||
DESCRIPTION = "MagCache for WanVideoWrapper, source https://github.com/Zehong-Ma/MagCache"
|
||||
|
||||
def setargs(self, magcache_thresh, magcache_K, start_step, end_step, cache_device):
|
||||
if cache_device == "main_device":
|
||||
cache_device = mm.get_torch_device()
|
||||
else:
|
||||
cache_device = mm.unet_offload_device()
|
||||
|
||||
cache_args = {
|
||||
"cache_type": "MagCache",
|
||||
"magcache_thresh": magcache_thresh,
|
||||
"magcache_K": magcache_K,
|
||||
"start_step": start_step,
|
||||
"end_step": end_step,
|
||||
"cache_device": cache_device,
|
||||
}
|
||||
return (cache_args,)
|
||||
|
||||
class WanVideoEasyCache:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"easycache_thresh": ("FLOAT", {"default": 0.015, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "How strongly to cache the output of diffusion model. This value must be non-negative."}),
|
||||
"start_step": ("INT", {"default": 10, "min": 0, "max": 9999, "step": 1, "tooltip": "Step to start applying EasyCache"}),
|
||||
"end_step": ("INT", {"default": -1, "min": -1, "max": 9999, "step": 1, "tooltip": "Step to end applying EasyCache"}),
|
||||
"cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
|
||||
},
|
||||
}
|
||||
RETURN_TYPES = ("CACHEARGS",)
|
||||
RETURN_NAMES = ("cache_args",)
|
||||
FUNCTION = "setargs"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
EXPERIMENTAL = True
|
||||
DESCRIPTION = "EasyCache for WanVideoWrapper, source https://github.com/H-EmbodVis/EasyCache"
|
||||
|
||||
def setargs(self, easycache_thresh, start_step, end_step, cache_device):
|
||||
if cache_device == "main_device":
|
||||
cache_device = mm.get_torch_device()
|
||||
else:
|
||||
cache_device = mm.unet_offload_device()
|
||||
|
||||
cache_args = {
|
||||
"cache_type": "EasyCache",
|
||||
"easycache_thresh": easycache_thresh,
|
||||
"start_step": start_step,
|
||||
"end_step": end_step,
|
||||
"cache_device": cache_device,
|
||||
}
|
||||
return (cache_args,)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoTeaCache": WanVideoTeaCache,
|
||||
"WanVideoMagCache": WanVideoMagCache,
|
||||
"WanVideoEasyCache": WanVideoEasyCache,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoTeaCache": "WanVideo TeaCache",
|
||||
"WanVideoMagCache": "WanVideo MagCache",
|
||||
"WanVideoEasyCache": "WanVideo EasyCache"
|
||||
}
|
||||
@@ -1,7 +1,6 @@
|
||||
import numpy as np
|
||||
from typing import Callable, Optional, List
|
||||
import torch
|
||||
from ..utils import log
|
||||
|
||||
|
||||
def ordered_halving(val):
|
||||
bin_str = f"{val:064b}"
|
||||
@@ -183,76 +182,3 @@ def get_total_steps(
|
||||
)
|
||||
for i in range(len(timesteps))
|
||||
)
|
||||
|
||||
def create_window_mask(noise_pred_context, c, latent_video_length, context_overlap, looped=False, window_type="linear"):
|
||||
window_mask = torch.ones_like(noise_pred_context)
|
||||
|
||||
if window_type == "pyramid":
|
||||
# Create pyramid weights that peak in the middle
|
||||
length = noise_pred_context.shape[1]
|
||||
if length % 2 == 0:
|
||||
max_weight = length // 2
|
||||
weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1))
|
||||
else:
|
||||
max_weight = (length + 1) // 2
|
||||
weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1))
|
||||
|
||||
# Normalize weights to range from 0 to 1
|
||||
max_val = max(weight_sequence)
|
||||
weight_sequence = [w / max_val for w in weight_sequence]
|
||||
|
||||
# Apply the weights to create the mask
|
||||
weights_tensor = torch.tensor(weight_sequence, device=noise_pred_context.device)
|
||||
weights_tensor = weights_tensor.view(1, -1, 1, 1)
|
||||
window_mask = weights_tensor.expand_as(window_mask).clone()
|
||||
|
||||
# Adjust for position in sequence if needed
|
||||
if not looped:
|
||||
if min(c) == 0: # First chunk
|
||||
left_ramp = torch.linspace(0, 1, context_overlap, device=noise_pred_context.device).view(1, -1, 1, 1)
|
||||
# Clone to avoid in-place memory conflict
|
||||
left_section = window_mask[:, :context_overlap].clone()
|
||||
window_mask[:, :context_overlap] = torch.maximum(left_section, left_ramp)
|
||||
|
||||
if max(c) == latent_video_length - 1: # Last chunk
|
||||
right_ramp = torch.linspace(1, 0, context_overlap, device=noise_pred_context.device).view(1, -1, 1, 1)
|
||||
# Clone to avoid in-place memory conflict
|
||||
right_section = window_mask[:, -context_overlap:].clone()
|
||||
window_mask[:, -context_overlap:] = torch.maximum(right_section, right_ramp)
|
||||
else: # Original "linear" window masking
|
||||
# Apply left-side blending for all except first chunk (or always in loop mode)
|
||||
if min(c) > 0 or (looped and max(c) == latent_video_length - 1):
|
||||
ramp_up = torch.linspace(0, 1, context_overlap, device=noise_pred_context.device)
|
||||
ramp_up = ramp_up.view(1, -1, 1, 1)
|
||||
window_mask[:, :context_overlap] = ramp_up
|
||||
|
||||
# Apply right-side blending for all except last chunk (or always in loop mode)
|
||||
if max(c) < latent_video_length - 1 or (looped and min(c) == 0):
|
||||
ramp_down = torch.linspace(1, 0, context_overlap, device=noise_pred_context.device)
|
||||
ramp_down = ramp_down.view(1, -1, 1, 1)
|
||||
window_mask[:, -context_overlap:] = ramp_down
|
||||
|
||||
return window_mask
|
||||
|
||||
class WindowTracker:
|
||||
def __init__(self, verbose=False):
|
||||
self.window_map = {} # Maps frame sequence to persistent ID
|
||||
self.next_id = 0
|
||||
self.cache_states = {} # Maps persistent ID to teacache state
|
||||
self.verbose = verbose
|
||||
|
||||
def get_window_id(self, frames):
|
||||
key = tuple(sorted(frames)) # Order-independent frame sequence
|
||||
if key not in self.window_map:
|
||||
self.window_map[key] = self.next_id
|
||||
if self.verbose:
|
||||
log.info(f"New window pattern {key} -> ID {self.next_id}")
|
||||
self.next_id += 1
|
||||
return self.window_map[key]
|
||||
|
||||
def get_teacache(self, window_id, base_state):
|
||||
if window_id not in self.cache_states:
|
||||
if self.verbose:
|
||||
log.info(f"Initializing persistent teacache for window {window_id}")
|
||||
self.cache_states[window_id] = base_state.copy()
|
||||
return self.cache_states[window_id]
|
||||
@@ -1,173 +0,0 @@
|
||||
|
||||
import torch
|
||||
from ..utils import log
|
||||
import comfy.model_management as mm
|
||||
from comfy.utils import load_torch_file
|
||||
from tqdm import tqdm
|
||||
import gc
|
||||
|
||||
from accelerate import init_empty_weights
|
||||
from accelerate.utils import set_module_tensor_to_device
|
||||
import folder_paths
|
||||
|
||||
class WanVideoControlnetLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": (folder_paths.get_filename_list("controlnet"), {"tooltip": "These models are loaded from the 'ComfyUI/models/controlnet' -folder",}),
|
||||
|
||||
"base_precision": (["fp32", "bf16", "fp16"], {"default": "bf16"}),
|
||||
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_e5m2', 'fp8_e4m3fn_fast_no_ffn'], {"default": 'disabled', "tooltip": "optional quantization method"}),
|
||||
"load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDEOCONTROLNET",)
|
||||
RETURN_NAMES = ("controlnet", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
DESCRIPTION = "Loads ControlNet model from 'https://huggingface.co/collections/TheDenk/wan21-controlnets-68302b430411dafc0d74d2fc'"
|
||||
|
||||
def loadmodel(self, model, base_precision, load_device, quantization):
|
||||
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
|
||||
transformer_load_device = device if load_device == "main_device" else offload_device
|
||||
|
||||
base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision]
|
||||
|
||||
model_path = folder_paths.get_full_path_or_raise("controlnet", model)
|
||||
|
||||
sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True)
|
||||
|
||||
num_layers = 8 if "blocks.7.scale_shift_table" in sd else 6
|
||||
out_proj_dim = sd["controlnet_blocks.0.bias"].shape[0]
|
||||
downscale_coef = 16 if out_proj_dim == 3072 else 8
|
||||
vae_channels = 48 if out_proj_dim == 3072 else 16
|
||||
|
||||
if not "control_encoder.0.0.weight" in sd:
|
||||
raise ValueError("Invalid ControlNet model")
|
||||
|
||||
controlnet_cfg = {
|
||||
"added_kv_proj_dim": None,
|
||||
"attention_head_dim": 128,
|
||||
"cross_attn_norm": None,
|
||||
"downscale_coef": downscale_coef,
|
||||
"eps": 1e-06,
|
||||
"ffn_dim": 8960,
|
||||
"freq_dim": 256,
|
||||
"image_dim": None,
|
||||
"in_channels": 3,
|
||||
"num_attention_heads": 12,
|
||||
"num_layers": num_layers,
|
||||
"out_proj_dim": out_proj_dim,
|
||||
"patch_size": [
|
||||
1,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"qk_norm": "rms_norm_across_heads",
|
||||
"rope_max_seq_len": 1024,
|
||||
"text_dim": 4096,
|
||||
"vae_channels": vae_channels
|
||||
}
|
||||
print(f"Loading WanControlnet with config: {controlnet_cfg}")
|
||||
|
||||
from .wan_controlnet import WanControlnet
|
||||
|
||||
with init_empty_weights():
|
||||
controlnet = WanControlnet(**controlnet_cfg)
|
||||
controlnet.eval()
|
||||
|
||||
if quantization == "disabled":
|
||||
for k, v in sd.items():
|
||||
if isinstance(v, torch.Tensor):
|
||||
if v.dtype == torch.float8_e4m3fn:
|
||||
quantization = "fp8_e4m3fn"
|
||||
break
|
||||
elif v.dtype == torch.float8_e5m2:
|
||||
quantization = "fp8_e5m2"
|
||||
break
|
||||
|
||||
if "fp8_e4m3fn" in quantization:
|
||||
dtype = torch.float8_e4m3fn
|
||||
elif quantization == "fp8_e5m2":
|
||||
dtype = torch.float8_e5m2
|
||||
else:
|
||||
dtype = base_dtype
|
||||
params_to_keep = {"norm", "head", "time_in", "vector_in", "controlnet_patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter"}
|
||||
|
||||
log.info("Using accelerate to load and assign controlnet model weights to device...")
|
||||
param_count = sum(1 for _ in controlnet.named_parameters())
|
||||
for name, param in tqdm(controlnet.named_parameters(),
|
||||
desc=f"Loading transformer parameters to {transformer_load_device}",
|
||||
total=param_count,
|
||||
leave=True):
|
||||
dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype
|
||||
if "controlnet_patch_embedding" in name:
|
||||
dtype_to_use = torch.float32
|
||||
set_module_tensor_to_device(controlnet, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name])
|
||||
|
||||
del sd
|
||||
|
||||
if load_device == "offload_device" and controlnet.device != offload_device:
|
||||
log.info(f"Moving controlnet model from {controlnet.device} to {offload_device}")
|
||||
controlnet.to(offload_device)
|
||||
gc.collect()
|
||||
mm.soft_empty_cache()
|
||||
|
||||
return (controlnet,)
|
||||
|
||||
class WanVideoControlnetApply:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("WANVIDEOMODEL", ),
|
||||
"controlnet": ("WANVIDEOCONTROLNET", ),
|
||||
"control_images": ("IMAGE", ),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001, "tooltip": "controlnet strength"}),
|
||||
"control_stride": ("INT", {"default": 3, "min": 1, "max": 8, "step": 1, "tooltip": "controlnet stride"}),
|
||||
"control_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply controlnet"}),
|
||||
"control_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply controlnet"}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("WANVIDEOMODEL",)
|
||||
RETURN_NAMES = ("model", )
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "WanVideoWrapper"
|
||||
|
||||
def loadmodel(self, model, controlnet, control_images, strength, control_stride, control_start_percent, control_end_percent):
|
||||
|
||||
patcher = model.clone()
|
||||
if 'transformer_options' not in patcher.model_options:
|
||||
patcher.model_options['transformer_options'] = {}
|
||||
|
||||
control_input = control_images.permute(3, 0, 1, 2).unsqueeze(0).contiguous()
|
||||
control_input = control_input * 2.0 - 1.0
|
||||
|
||||
controlnet = {
|
||||
"controlnet": controlnet,
|
||||
"control_latents": control_input,
|
||||
"controlnet_strength": strength,
|
||||
"control_stride": control_stride,
|
||||
"controlnet_start": control_start_percent,
|
||||
"controlnet_end": control_end_percent
|
||||
}
|
||||
patcher.model_options["transformer_options"]["controlnet"] = controlnet
|
||||
|
||||
return (patcher,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanVideoControlnetLoader": WanVideoControlnetLoader,
|
||||
"WanVideoControlnet": WanVideoControlnetApply,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"WanVideoControlnetLoader": "WanVideo Controlnet Loader",
|
||||
"WanVideoControlnet": "WanVideo Controlnet Apply",
|
||||
}
|
||||
|
||||
|
||||
@@ -1,236 +0,0 @@
|
||||
# source https://github.com/TheDenk/wan2.1-dilated-controlnet/blob/main/wan_controlnet.py
|
||||
from typing import Any, Dict, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
||||
from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
|
||||
from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
|
||||
from diffusers.models.modeling_outputs import Transformer2DModelOutput
|
||||
from diffusers.models.modeling_utils import ModelMixin
|
||||
from diffusers.models.transformers.transformer_wan import (
|
||||
WanTimeTextImageEmbedding,
|
||||
WanRotaryPosEmbed,
|
||||
WanTransformerBlock
|
||||
)
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
class WanControlnet(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
|
||||
r"""
|
||||
A Controlnet Transformer model for video-like data used in the Wan model.
|
||||
|
||||
Args:
|
||||
patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):
|
||||
3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
|
||||
num_attention_heads (`int`, defaults to `40`):
|
||||
Fixed length for text embeddings.
|
||||
attention_head_dim (`int`, defaults to `128`):
|
||||
The number of channels in each head.
|
||||
vae_channels (`int`, defaults to `16`):
|
||||
The number of channels in the vae input.
|
||||
in_channels (`int`, defaults to `16`):
|
||||
The number of channels in the controlnet input.
|
||||
text_dim (`int`, defaults to `512`):
|
||||
Input dimension for text embeddings.
|
||||
freq_dim (`int`, defaults to `256`):
|
||||
Dimension for sinusoidal time embeddings.
|
||||
ffn_dim (`int`, defaults to `13824`):
|
||||
Intermediate dimension in feed-forward network.
|
||||
num_layers (`int`, defaults to `40`):
|
||||
The number of layers of transformer blocks to use.
|
||||
window_size (`Tuple[int]`, defaults to `(-1, -1)`):
|
||||
Window size for local attention (-1 indicates global attention).
|
||||
cross_attn_norm (`bool`, defaults to `True`):
|
||||
Enable cross-attention normalization.
|
||||
qk_norm (`bool`, defaults to `True`):
|
||||
Enable query/key normalization.
|
||||
eps (`float`, defaults to `1e-6`):
|
||||
Epsilon value for normalization layers.
|
||||
add_img_emb (`bool`, defaults to `False`):
|
||||
Whether to use img_emb.
|
||||
added_kv_proj_dim (`int`, *optional*, defaults to `None`):
|
||||
The number of channels to use for the added key and value projections. If `None`, no projection is used.
|
||||
downscale_coef (`int`, *optional*, defaults to `8`):
|
||||
Coeficient for downscale controlnet input video.
|
||||
out_proj_dim (`int`, *optional*, defaults to `128 * 12`):
|
||||
Output projection dimention for last linear layers.
|
||||
"""
|
||||
|
||||
_supports_gradient_checkpointing = True
|
||||
_skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]
|
||||
_no_split_modules = ["WanTransformerBlock"]
|
||||
_keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
|
||||
_keys_to_ignore_on_load_unexpected = ["norm_added_q"]
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
patch_size: Tuple[int] = (1, 2, 2),
|
||||
num_attention_heads: int = 40,
|
||||
attention_head_dim: int = 128,
|
||||
in_channels: int = 3,
|
||||
vae_channels: int = 16,
|
||||
text_dim: int = 4096,
|
||||
freq_dim: int = 256,
|
||||
ffn_dim: int = 13824,
|
||||
num_layers: int = 20,
|
||||
cross_attn_norm: bool = True,
|
||||
qk_norm: Optional[str] = "rms_norm_across_heads",
|
||||
eps: float = 1e-6,
|
||||
image_dim: Optional[int] = None,
|
||||
added_kv_proj_dim: Optional[int] = None,
|
||||
rope_max_seq_len: int = 1024,
|
||||
downscale_coef: int = 8,
|
||||
out_proj_dim: int = 128 * 12,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
start_channels = in_channels * (downscale_coef ** 2)
|
||||
input_channels = [start_channels, start_channels // 2, start_channels // 4]
|
||||
|
||||
self.control_encoder = nn.ModuleList([
|
||||
## Spatial compression with time awareness
|
||||
nn.Sequential(
|
||||
nn.Conv3d(
|
||||
in_channels,
|
||||
input_channels[0],
|
||||
kernel_size=(3, downscale_coef + 1, downscale_coef + 1),
|
||||
stride=(1, downscale_coef, downscale_coef),
|
||||
padding=(1, downscale_coef // 2, downscale_coef // 2)
|
||||
),
|
||||
nn.GELU(approximate="tanh"),
|
||||
nn.GroupNorm(2, input_channels[0]),
|
||||
),
|
||||
## Spatio-Temporal compression with spatial awareness
|
||||
nn.Sequential(
|
||||
nn.Conv3d(input_channels[0], input_channels[1], kernel_size=3, stride=(2, 1, 1), padding=1),
|
||||
nn.GELU(approximate="tanh"),
|
||||
nn.GroupNorm(2, input_channels[1]),
|
||||
),
|
||||
## Temporal compression with spatial awareness
|
||||
nn.Sequential(
|
||||
nn.Conv3d(input_channels[1], input_channels[2], kernel_size=3, stride=(2, 1, 1), padding=1),
|
||||
nn.GELU(approximate="tanh"),
|
||||
nn.GroupNorm(2, input_channels[2]),
|
||||
)
|
||||
])
|
||||
|
||||
inner_dim = num_attention_heads * attention_head_dim
|
||||
|
||||
# 1. Patch & position embedding
|
||||
self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)
|
||||
self.patch_embedding = nn.Conv3d(vae_channels + input_channels[2], inner_dim, kernel_size=patch_size, stride=patch_size)
|
||||
|
||||
# 2. Condition embeddings
|
||||
# image_embedding_dim=1280 for I2V model
|
||||
self.condition_embedder = WanTimeTextImageEmbedding(
|
||||
dim=inner_dim,
|
||||
time_freq_dim=freq_dim,
|
||||
time_proj_dim=inner_dim * 6,
|
||||
text_embed_dim=text_dim,
|
||||
image_embed_dim=image_dim,
|
||||
)
|
||||
# 3. Transformer blocks
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
WanTransformerBlock(
|
||||
inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim
|
||||
)
|
||||
for _ in range(num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# 4 Controlnet modules
|
||||
self.controlnet_blocks = nn.ModuleList([])
|
||||
|
||||
for _ in range(len(self.blocks)):
|
||||
controlnet_block = nn.Linear(inner_dim, out_proj_dim)
|
||||
self.controlnet_blocks.append(controlnet_block)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
timestep: torch.LongTensor,
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
controlnet_states: torch.Tensor,
|
||||
encoder_hidden_states_image: Optional[torch.Tensor] = None,
|
||||
return_dict: bool = True,
|
||||
attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
|
||||
if attention_kwargs is not None:
|
||||
attention_kwargs = attention_kwargs.copy()
|
||||
lora_scale = attention_kwargs.pop("scale", 1.0)
|
||||
else:
|
||||
lora_scale = 1.0
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# weight the lora layers by setting `lora_scale` for each PEFT layer
|
||||
scale_lora_layers(self, lora_scale)
|
||||
else:
|
||||
if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
|
||||
logger.warning(
|
||||
"Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
|
||||
)
|
||||
rotary_emb = self.rope(hidden_states)
|
||||
|
||||
# 0. Controlnet encoder
|
||||
for control_encoder_block in self.control_encoder:
|
||||
controlnet_states = control_encoder_block(controlnet_states)
|
||||
|
||||
hidden_states = torch.cat([hidden_states, controlnet_states], dim=1)
|
||||
|
||||
## 1. Patch embedding and stack
|
||||
hidden_states = self.patch_embedding(hidden_states)
|
||||
hidden_states = hidden_states.flatten(2).transpose(1, 2)
|
||||
|
||||
# timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
|
||||
if timestep.ndim == 2:
|
||||
## for ComfyUI workflow
|
||||
if hidden_states.shape[1] != timestep.shape[1]:
|
||||
timestep = timestep.repeat_interleave(hidden_states.shape[1] // timestep.shape[1], dim=1)
|
||||
ts_seq_len = timestep.shape[1]
|
||||
timestep = timestep.flatten() # batch_size * seq_len
|
||||
else:
|
||||
ts_seq_len = None
|
||||
|
||||
temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
|
||||
timestep, encoder_hidden_states, encoder_hidden_states_image, timestep_seq_len=ts_seq_len
|
||||
)
|
||||
if ts_seq_len is not None:
|
||||
# batch_size, seq_len, 6, inner_dim
|
||||
timestep_proj = timestep_proj.unflatten(2, (6, -1))
|
||||
else:
|
||||
# batch_size, 6, inner_dim
|
||||
timestep_proj = timestep_proj.unflatten(1, (6, -1))
|
||||
|
||||
if encoder_hidden_states_image is not None:
|
||||
encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)
|
||||
|
||||
# 4. Transformer blocks
|
||||
controlnet_hidden_states = ()
|
||||
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
||||
for block, controlnet_block in zip(self.blocks, self.controlnet_blocks):
|
||||
hidden_states = self._gradient_checkpointing_func(
|
||||
block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb
|
||||
)
|
||||
controlnet_hidden_states += (controlnet_block(hidden_states),)
|
||||
else:
|
||||
for block, controlnet_block in zip(self.blocks, self.controlnet_blocks):
|
||||
hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)
|
||||
controlnet_hidden_states += (controlnet_block(hidden_states),)
|
||||
|
||||
|
||||
if USE_PEFT_BACKEND:
|
||||
# remove `lora_scale` from each PEFT layer
|
||||
unscale_lora_layers(self, lora_scale)
|
||||
|
||||
if not return_dict:
|
||||
return (controlnet_hidden_states,)
|
||||
|
||||
return Transformer2DModelOutput(sample=controlnet_hidden_states)
|
||||
|
||||
@@ -1,281 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from accelerate import init_empty_weights
|
||||
from .gguf.gguf_utils import GGUFParameter, dequantize_gguf_tensor
|
||||
|
||||
@torch.library.custom_op("wanvideo::apply_lora", mutates_args=())
|
||||
def apply_lora(weight: torch.Tensor, lora_diff_0: torch.Tensor, lora_diff_1: torch.Tensor, lora_diff_2: float, lora_strength: torch.Tensor) -> torch.Tensor:
|
||||
patch_diff = torch.mm(
|
||||
lora_diff_0.flatten(start_dim=1),
|
||||
lora_diff_1.flatten(start_dim=1)
|
||||
).reshape(weight.shape)
|
||||
|
||||
alpha = lora_diff_2 / lora_diff_1.shape[0] if lora_diff_2 != 0.0 else 1.0
|
||||
scale = lora_strength * alpha
|
||||
|
||||
return weight + patch_diff * scale
|
||||
|
||||
@apply_lora.register_fake
|
||||
def _(weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
|
||||
# Return weight with same metadata
|
||||
return weight.clone()
|
||||
|
||||
@torch.library.custom_op("wanvideo::apply_single_lora", mutates_args=())
|
||||
def apply_single_lora(weight: torch.Tensor, lora_diff: torch.Tensor, lora_strength: torch.Tensor) -> torch.Tensor:
|
||||
return weight + lora_diff * lora_strength
|
||||
|
||||
@apply_single_lora.register_fake
|
||||
def _(weight, lora_diff, lora_strength):
|
||||
# Return weight with same metadata
|
||||
return weight.clone()
|
||||
|
||||
@torch.library.custom_op("wanvideo::linear_forward", mutates_args=())
|
||||
def linear_forward(input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor | None) -> torch.Tensor:
|
||||
return torch.nn.functional.linear(input, weight, bias)
|
||||
|
||||
@linear_forward.register_fake
|
||||
def _(input, weight, bias):
|
||||
# Calculate output shape: (..., out_features)
|
||||
out_features = weight.shape[0]
|
||||
output_shape = list(input.shape[:-1]) + [out_features]
|
||||
return input.new_empty(output_shape)
|
||||
|
||||
#based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/quantizers/gguf/utils.py
|
||||
def _replace_linear(model, compute_dtype, state_dict, prefix="", patches=None, scale_weights=None, compile_args=None, modules_to_not_convert=[]):
|
||||
|
||||
has_children = list(model.children())
|
||||
if not has_children:
|
||||
return
|
||||
|
||||
allow_compile = False
|
||||
|
||||
for name, module in model.named_children():
|
||||
if compile_args is not None:
|
||||
allow_compile = compile_args.get("allow_unmerged_lora_compile", False)
|
||||
module_prefix = prefix + name + "."
|
||||
module_prefix = module_prefix.replace("_orig_mod.", "")
|
||||
_replace_linear(module, compute_dtype, state_dict, module_prefix, patches, scale_weights, compile_args, modules_to_not_convert)
|
||||
|
||||
if isinstance(module, nn.Linear) and "loras" not in module_prefix and "dual_controller" not in module_prefix and name not in modules_to_not_convert:
|
||||
weight_key = module_prefix + "weight"
|
||||
if weight_key not in state_dict:
|
||||
continue
|
||||
|
||||
in_features = state_dict[weight_key].shape[1]
|
||||
out_features = state_dict[weight_key].shape[0]
|
||||
|
||||
is_gguf = isinstance(state_dict[weight_key], GGUFParameter)
|
||||
|
||||
scale_weight = None
|
||||
if not is_gguf and scale_weights is not None:
|
||||
scale_key = f"{module_prefix}scale_weight"
|
||||
scale_weight = scale_weights.get(scale_key)
|
||||
|
||||
with init_empty_weights():
|
||||
model._modules[name] = CustomLinear(
|
||||
in_features,
|
||||
out_features,
|
||||
module.bias is not None,
|
||||
compute_dtype=compute_dtype,
|
||||
scale_weight=scale_weight,
|
||||
allow_compile=allow_compile,
|
||||
is_gguf=is_gguf
|
||||
)
|
||||
model._modules[name].source_cls = type(module)
|
||||
model._modules[name].requires_grad_(False)
|
||||
|
||||
return model
|
||||
|
||||
def set_lora_params(module, patches, module_prefix="", device=torch.device("cpu")):
|
||||
remove_lora_from_module(module)
|
||||
# Recursively set lora_diffs and lora_strengths for all CustomLinear layers
|
||||
for name, child in module.named_children():
|
||||
params = list(child.parameters())
|
||||
if params:
|
||||
device = params[0].device
|
||||
else:
|
||||
device = torch.device("cpu")
|
||||
child_prefix = (f"{module_prefix}{name}.")
|
||||
set_lora_params(child, patches, child_prefix, device)
|
||||
if isinstance(module, CustomLinear):
|
||||
key = f"diffusion_model.{module_prefix}weight"
|
||||
patch = patches.get(key, [])
|
||||
#print(f"Processing LoRA patches for {key}: {len(patch)} patches found")
|
||||
if len(patch) == 0:
|
||||
key = key.replace("_orig_mod.", "")
|
||||
patch = patches.get(key, [])
|
||||
#print(f"Processing LoRA patches for {key}: {len(patch)} patches found")
|
||||
if len(patch) != 0:
|
||||
lora_diffs = []
|
||||
for p in patch:
|
||||
lora_obj = p[1]
|
||||
if "head" in key:
|
||||
continue # For now skip LoRA for head layers
|
||||
elif hasattr(lora_obj, "weights"):
|
||||
lora_diffs.append(lora_obj.weights)
|
||||
elif isinstance(lora_obj, tuple) and lora_obj[0] == "diff":
|
||||
lora_diffs.append(lora_obj[1])
|
||||
else:
|
||||
continue
|
||||
lora_strengths = [p[0] for p in patch]
|
||||
module.set_lora_diffs(lora_diffs, device=device)
|
||||
module.set_lora_strengths(lora_strengths, device=device)
|
||||
module._step.fill_(0) # Initialize step for LoRA scheduling
|
||||
|
||||
|
||||
class CustomLinear(nn.Linear):
|
||||
def __init__(
|
||||
self,
|
||||
in_features,
|
||||
out_features,
|
||||
bias=False,
|
||||
compute_dtype=None,
|
||||
device=None,
|
||||
scale_weight=None,
|
||||
allow_compile=False,
|
||||
is_gguf=False
|
||||
) -> None:
|
||||
super().__init__(in_features, out_features, bias, device)
|
||||
self.compute_dtype = compute_dtype
|
||||
self.lora_diffs = []
|
||||
self.register_buffer("_step", torch.zeros((), dtype=torch.long))
|
||||
self.scale_weight = scale_weight
|
||||
self.lora_strengths = []
|
||||
self.allow_compile = allow_compile
|
||||
self.is_gguf = is_gguf
|
||||
|
||||
if not allow_compile:
|
||||
self._apply_lora_impl = self._apply_lora_custom_op
|
||||
self._apply_single_lora_impl = self._apply_single_lora_custom_op
|
||||
self._linear_forward_impl = self._linear_forward_custom_op
|
||||
else:
|
||||
self._apply_lora_impl = self._apply_lora_direct
|
||||
self._apply_single_lora_impl = self._apply_single_lora_direct
|
||||
self._linear_forward_impl = self._linear_forward_direct
|
||||
|
||||
|
||||
# Direct implementations (no custom ops)
|
||||
def _apply_lora_direct(self, weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
|
||||
patch_diff = torch.mm(
|
||||
lora_diff_0.flatten(start_dim=1),
|
||||
lora_diff_1.flatten(start_dim=1)
|
||||
).reshape(weight.shape) + 0
|
||||
alpha = lora_diff_2 / lora_diff_1.shape[0] if lora_diff_2 != 0.0 else 1.0
|
||||
scale = lora_strength * alpha
|
||||
return weight + patch_diff * scale
|
||||
|
||||
def _apply_single_lora_direct(self, weight, lora_diff, lora_strength):
|
||||
return weight + lora_diff * lora_strength
|
||||
|
||||
def _linear_forward_direct(self, input, weight, bias):
|
||||
return torch.nn.functional.linear(input, weight, bias)
|
||||
|
||||
# Custom op implementations
|
||||
def _apply_lora_custom_op(self, weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
|
||||
return torch.ops.wanvideo.apply_lora(weight, lora_diff_0, lora_diff_1,
|
||||
float(lora_diff_2) if lora_diff_2 is not None else 0.0, lora_strength
|
||||
)
|
||||
|
||||
def _apply_single_lora_custom_op(self, weight, lora_diff, lora_strength):
|
||||
return torch.ops.wanvideo.apply_single_lora(weight, lora_diff, lora_strength)
|
||||
|
||||
def _linear_forward_custom_op(self, input, weight, bias):
|
||||
return torch.ops.wanvideo.linear_forward(input, weight, bias)
|
||||
|
||||
def set_lora_diffs(self, lora_diffs, device=torch.device("cpu")):
|
||||
self.lora_diffs = []
|
||||
for i, diff in enumerate(lora_diffs):
|
||||
if len(diff) > 1:
|
||||
self.register_buffer(f"lora_diff_{i}_0", diff[0].to(device, self.compute_dtype))
|
||||
self.register_buffer(f"lora_diff_{i}_1", diff[1].to(device, self.compute_dtype))
|
||||
setattr(self, f"lora_diff_{i}_2", diff[2])
|
||||
self.lora_diffs.append((f"lora_diff_{i}_0", f"lora_diff_{i}_1", f"lora_diff_{i}_2"))
|
||||
else:
|
||||
self.register_buffer(f"lora_diff_{i}_0", diff[0].to(device, self.compute_dtype))
|
||||
self.lora_diffs.append(f"lora_diff_{i}_0")
|
||||
|
||||
def set_lora_strengths(self, lora_strengths, device=torch.device("cpu")):
|
||||
self._lora_strength_tensors = []
|
||||
self._lora_strength_is_scheduled = []
|
||||
self._step = self._step.to(device)
|
||||
for i, strength in enumerate(lora_strengths):
|
||||
if isinstance(strength, list):
|
||||
tensor = torch.tensor(strength, dtype=self.compute_dtype, device=device)
|
||||
self.register_buffer(f"_lora_strength_{i}", tensor)
|
||||
self._lora_strength_is_scheduled.append(True)
|
||||
else:
|
||||
tensor = torch.tensor([strength], dtype=self.compute_dtype, device=device)
|
||||
self.register_buffer(f"_lora_strength_{i}", tensor)
|
||||
self._lora_strength_is_scheduled.append(False)
|
||||
|
||||
def _get_lora_strength(self, idx):
|
||||
strength_tensor = getattr(self, f"_lora_strength_{idx}")
|
||||
if self._lora_strength_is_scheduled[idx]:
|
||||
return strength_tensor.index_select(0, self._step).squeeze(0)
|
||||
return strength_tensor[0]
|
||||
|
||||
def _get_weight_with_lora(self, weight):
|
||||
"""Apply LoRA using custom ops to avoid graph breaks"""
|
||||
if not hasattr(self, "lora_diff_0_0"):
|
||||
return weight
|
||||
|
||||
for idx, lora_diff_names in enumerate(self.lora_diffs):
|
||||
lora_strength = self._get_lora_strength(idx)
|
||||
|
||||
if isinstance(lora_diff_names, tuple):
|
||||
lora_diff_0 = getattr(self, lora_diff_names[0])
|
||||
lora_diff_1 = getattr(self, lora_diff_names[1])
|
||||
lora_diff_2 = getattr(self, lora_diff_names[2])
|
||||
|
||||
weight = self._apply_lora_impl(
|
||||
weight, lora_diff_0, lora_diff_1,
|
||||
float(lora_diff_2) if lora_diff_2 is not None else 0.0, lora_strength
|
||||
)
|
||||
else:
|
||||
lora_diff = getattr(self, lora_diff_names)
|
||||
weight = self._apply_single_lora_impl(weight, lora_diff, lora_strength)
|
||||
return weight
|
||||
|
||||
def _prepare_weight(self, input):
|
||||
"""Prepare weight tensor - handles both regular and GGUF weights"""
|
||||
if self.is_gguf:
|
||||
weight = dequantize_gguf_tensor(self.weight).to(self.compute_dtype)
|
||||
else:
|
||||
weight = self.weight.to(input)
|
||||
return weight
|
||||
|
||||
def forward(self, input):
|
||||
weight = self._prepare_weight(input)
|
||||
|
||||
if self.bias is not None:
|
||||
bias = self.bias.to(input if not self.is_gguf else self.compute_dtype)
|
||||
else:
|
||||
bias = None
|
||||
|
||||
# Only apply scale_weight for non-GGUF models
|
||||
if not self.is_gguf and self.scale_weight is not None:
|
||||
if weight.numel() < input.numel():
|
||||
weight = weight * self.scale_weight
|
||||
else:
|
||||
input = input * self.scale_weight
|
||||
|
||||
weight = self._get_weight_with_lora(weight)
|
||||
out = self._linear_forward_impl(input, weight, bias)
|
||||
del weight, input, bias
|
||||
return out
|
||||
|
||||
def update_lora_step(module, step):
|
||||
for name, submodule in module.named_modules():
|
||||
if isinstance(submodule, CustomLinear) and hasattr(submodule, "_step"):
|
||||
submodule._step.fill_(step)
|
||||
|
||||
def remove_lora_from_module(module):
|
||||
for name, submodule in module.named_modules():
|
||||
if hasattr(submodule, "lora_diffs"):
|
||||
for i in range(len(submodule.lora_diffs)):
|
||||
if hasattr(submodule, f"lora_diff_{i}_0"):
|
||||
delattr(submodule, f"lora_diff_{i}_0")
|
||||
if hasattr(submodule, f"lora_diff_{i}_1"):
|
||||
delattr(submodule, f"lora_diff_{i}_1")
|
||||
if hasattr(submodule, f"lora_diff_{i}_2"):
|
||||
delattr(submodule, f"lora_diff_{i}_2")
|
||||
@@ -75,7 +75,7 @@ def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict,
|
||||
for name, module in model.named_children():
|
||||
for source_module, target_module in module_map.items():
|
||||
if isinstance(module, source_module):
|
||||
if "rope_embedder" in name or "patch_embedding" in name or "emb_pos" in name:
|
||||
if "rope_embedder" in name or "patch_embedding" in name:
|
||||
continue
|
||||
|
||||
num_param = sum(p.numel() for p in module.parameters())
|
||||
|
||||
@@ -1,104 +0,0 @@
|
||||
import torch
|
||||
from comfy.model_management import get_autocast_device, get_torch_device
|
||||
|
||||
@torch.autocast(device_type=get_autocast_device(get_torch_device()), enabled=False)
|
||||
@torch.compiler.disable()
|
||||
def rope_apply_z(x, grid_sizes, freqs, inner_t, shift=6):
|
||||
n, c = x.size(2), x.size(3) // 2
|
||||
|
||||
# loop over samples
|
||||
output = []
|
||||
for i, (f, h, w) in enumerate(grid_sizes.tolist()):
|
||||
seq_len = f * h * w
|
||||
|
||||
# precompute multipliers
|
||||
x_i = torch.view_as_complex(
|
||||
x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2)
|
||||
)
|
||||
start_ind = [sum(inner_t[i][:_]) for _ in range(len(inner_t[i]))]
|
||||
end_ind = [sum(inner_t[i][:_+1]) for _ in range(len(inner_t[i]))]
|
||||
|
||||
freq_select = []
|
||||
for shot_ind, (s, e) in enumerate(zip(start_ind, end_ind)):
|
||||
freq_select += [shot_ind * shift] * (e - s)
|
||||
shot_freqs = freqs[freq_select]
|
||||
|
||||
freqs_i = shot_freqs.view(f, 1, 1, -1).expand(f, h, w, -1).reshape(seq_len, 1, -1)
|
||||
|
||||
# apply rotary embedding
|
||||
x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
|
||||
x_i = torch.cat([x_i, x[i, seq_len:]])
|
||||
|
||||
# append to collection
|
||||
output.append(x_i)
|
||||
return torch.stack(output).float()
|
||||
|
||||
|
||||
@torch.autocast(device_type=get_autocast_device(get_torch_device()), enabled=False)
|
||||
@torch.compiler.disable()
|
||||
def rope_apply_c(x, freqs, inner_c, shift=6):
|
||||
|
||||
b, s, n, c = x.size(0), x.size(1), x.size(2), x.size(3) // 2
|
||||
|
||||
# loop over samples
|
||||
output = []
|
||||
for i in range(b):
|
||||
|
||||
# precompute multipliers
|
||||
x_i = torch.view_as_complex(
|
||||
x[i].to(torch.float64).reshape(s, n, -1, 2)
|
||||
)
|
||||
|
||||
freq_select = []
|
||||
for shot_ind, c_len in enumerate(inner_c[i]):
|
||||
freq_select += [shot_ind * shift] * c_len
|
||||
freq_select += [shot_ind+10] * (s-len(freq_select)) # extra suppression for the empty token
|
||||
shot_freqs = freqs[freq_select]
|
||||
|
||||
freqs_i = shot_freqs.view(s, 1, -1)
|
||||
|
||||
# apply rotary embedding
|
||||
x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
|
||||
|
||||
# append to collection
|
||||
output.append(x_i)
|
||||
return torch.stack(output).float()
|
||||
|
||||
@torch.autocast(device_type=get_autocast_device(get_torch_device()), enabled=False)
|
||||
@torch.compiler.disable()
|
||||
def rope_apply_echoshot(x, grid_sizes, freqs, inner_t, shift=4):
|
||||
n, c = x.size(2), x.size(3) // 2
|
||||
|
||||
# split freqs
|
||||
freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
|
||||
|
||||
# loop over samples
|
||||
output = []
|
||||
for i, (f, h, w) in enumerate(grid_sizes.tolist()):
|
||||
seq_len = f * h * w
|
||||
|
||||
# precompute multipliers
|
||||
x_i = torch.view_as_complex(
|
||||
x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2)
|
||||
)
|
||||
start_ind = [sum(inner_t[i][:_]) for _ in range(len(inner_t[i]))]
|
||||
end_ind = [sum(inner_t[i][:_+1]) for _ in range(len(inner_t[i]))]
|
||||
freq_select = []
|
||||
for shot_ind, (s, e) in enumerate(zip(start_ind, end_ind)):
|
||||
freq_select += list(range(shot_ind * shift + s, shot_ind * shift + e))
|
||||
t_freqs = freqs[0][freq_select]
|
||||
|
||||
freqs_i = torch.cat([
|
||||
# freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
|
||||
t_freqs.view(f, 1, 1, -1).expand(f, h, w, -1), ###
|
||||
freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
|
||||
freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
|
||||
], dim=-1).reshape(seq_len, 1, -1)
|
||||
|
||||
# apply rotary embedding
|
||||
x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
|
||||
x_i = torch.cat([x_i, x[i, seq_len:]])
|
||||
|
||||
# append to collection
|
||||
output.append(x_i)
|
||||
return torch.stack(output).float()
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 192 KiB |
Binary file not shown.
File diff suppressed because it is too large
Load Diff
@@ -1,8 +1,8 @@
|
||||
{
|
||||
"id": "c6e410bc-5e2c-460b-ae81-c91b6094fbb1",
|
||||
"revision": 0,
|
||||
"last_node_id": 206,
|
||||
"last_link_id": 341,
|
||||
"last_node_id": 204,
|
||||
"last_link_id": 336,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 42,
|
||||
@@ -101,7 +101,7 @@
|
||||
200
|
||||
],
|
||||
"flags": {},
|
||||
"order": 22,
|
||||
"order": 21,
|
||||
"mode": 2,
|
||||
"inputs": [
|
||||
{
|
||||
@@ -258,7 +258,7 @@
|
||||
174
|
||||
],
|
||||
"flags": {},
|
||||
"order": 39,
|
||||
"order": 34,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@@ -371,7 +371,7 @@
|
||||
200
|
||||
],
|
||||
"flags": {},
|
||||
"order": 23,
|
||||
"order": 22,
|
||||
"mode": 2,
|
||||
"inputs": [
|
||||
{
|
||||
@@ -413,7 +413,7 @@
|
||||
86
|
||||
],
|
||||
"flags": {},
|
||||
"order": 25,
|
||||
"order": 24,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@@ -647,7 +647,9 @@
|
||||
"ver": "0.3.27",
|
||||
"Node name for S&R": "PreviewImage"
|
||||
},
|
||||
"widgets_values": []
|
||||
"widgets_values": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 125,
|
||||
@@ -661,12 +663,15 @@
|
||||
190.28567504882812
|
||||
],
|
||||
"flags": {},
|
||||
"order": 41,
|
||||
"order": 40,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "text",
|
||||
"type": "STRING",
|
||||
"widget": {
|
||||
"name": "text"
|
||||
},
|
||||
"link": 215
|
||||
}
|
||||
],
|
||||
@@ -684,7 +689,7 @@
|
||||
"Node name for S&R": "ShowText|pysssss"
|
||||
},
|
||||
"widgets_values": [
|
||||
"A man in a suit and tie walking down a hallway. He has a friendly expression and is looking directly at the camera. The hallway has beige walls adorned with framed black and white photographs. There is a door on the left side of the hallway and a poster on the wall. The lighting is soft and natural. The image is high quality and has a watermark in the bottom right corner.",
|
||||
"",
|
||||
"A man in a suit and tie walking down a hallway. He has a friendly expression and is looking directly at the camera. The hallway has beige walls adorned with framed black and white photographs. There is a door on the left side of the hallway and a poster on the wall. The lighting is soft and natural. The image is high quality and has a watermark in the bottom right corner."
|
||||
]
|
||||
},
|
||||
@@ -740,7 +745,7 @@
|
||||
261.5306701660156
|
||||
],
|
||||
"flags": {},
|
||||
"order": 42,
|
||||
"order": 41,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@@ -837,7 +842,7 @@
|
||||
"flags": {
|
||||
"collapsed": true
|
||||
},
|
||||
"order": 24,
|
||||
"order": 23,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@@ -909,7 +914,7 @@
|
||||
266
|
||||
],
|
||||
"flags": {},
|
||||
"order": 29,
|
||||
"order": 28,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@@ -917,22 +922,28 @@
|
||||
"type": "IMAGE",
|
||||
"link": 244
|
||||
},
|
||||
{
|
||||
"name": "get_image_size",
|
||||
"shape": 7,
|
||||
"type": "IMAGE",
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "width_input",
|
||||
"shape": 7,
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "width_input"
|
||||
},
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "height_input",
|
||||
"shape": 7,
|
||||
"type": "INT",
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "get_image_size",
|
||||
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||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "STRING",
|
||||
"type": "STRING",
|
||||
"links": [
|
||||
212
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfyui-kjnodes",
|
||||
"ver": "d57154c3a808b8a3f232ed293eaa2d000867c884",
|
||||
"Node name for S&R": "WidgetToString"
|
||||
},
|
||||
"widgets_values": [
|
||||
0,
|
||||
"camera_type",
|
||||
false,
|
||||
"",
|
||||
2
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 205,
|
||||
"type": "WanVideoReCamMasterDefaultCamera",
|
||||
"pos": [
|
||||
1317.4481201171875,
|
||||
-241.10047912597656
|
||||
],
|
||||
"size": [
|
||||
388.8835754394531,
|
||||
58
|
||||
],
|
||||
"flags": {},
|
||||
"order": 27,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "latents",
|
||||
"type": "LATENT",
|
||||
"link": 338
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "camera_poses",
|
||||
"type": "CAMERAPOSES",
|
||||
"links": [
|
||||
340,
|
||||
341
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "ComfyUI-WanVideoWrapper",
|
||||
"ver": "8257cd1f8abaa6504248b946f31c5173c0228b3d",
|
||||
"Node name for S&R": "WanVideoReCamMasterDefaultCamera"
|
||||
},
|
||||
"widgets_values": [
|
||||
"pan_right"
|
||||
]
|
||||
}
|
||||
],
|
||||
"links": [
|
||||
@@ -2121,6 +2060,14 @@
|
||||
1,
|
||||
"CONDITIONING"
|
||||
],
|
||||
[
|
||||
102,
|
||||
56,
|
||||
0,
|
||||
74,
|
||||
0,
|
||||
"*"
|
||||
],
|
||||
[
|
||||
210,
|
||||
28,
|
||||
@@ -2310,7 +2257,7 @@
|
||||
157,
|
||||
0,
|
||||
56,
|
||||
1,
|
||||
0,
|
||||
"LATENT"
|
||||
],
|
||||
[
|
||||
@@ -2344,30 +2291,6 @@
|
||||
155,
|
||||
6,
|
||||
"TEACACHEARGS"
|
||||
],
|
||||
[
|
||||
338,
|
||||
157,
|
||||
0,
|
||||
205,
|
||||
0,
|
||||
"LATENT"
|
||||
],
|
||||
[
|
||||
340,
|
||||
205,
|
||||
0,
|
||||
56,
|
||||
0,
|
||||
"CAMERAPOSES"
|
||||
],
|
||||
[
|
||||
341,
|
||||
205,
|
||||
0,
|
||||
74,
|
||||
0,
|
||||
"*"
|
||||
]
|
||||
],
|
||||
"groups": [
|
||||
@@ -2414,12 +2337,13 @@
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 0.611590904484162,
|
||||
"scale": 0.7400249944258357,
|
||||
"offset": [
|
||||
1176.5764579377562,
|
||||
1095.9393193240473
|
||||
1311.6629502036258,
|
||||
1366.2150672288358
|
||||
]
|
||||
},
|
||||
"linkExtensions": [],
|
||||
"node_versions": {
|
||||
"ComfyUI-WanVideoWrapper": "5a2383621a05825d0d0437781afcb8552d9590fd",
|
||||
"comfy-core": "0.3.26",
|
||||
@@ -2428,8 +2352,7 @@
|
||||
"VHS_latentpreview": true,
|
||||
"VHS_latentpreviewrate": 0,
|
||||
"VHS_MetadataImage": true,
|
||||
"VHS_KeepIntermediate": true,
|
||||
"frontendVersion": "1.16.7"
|
||||
"VHS_KeepIntermediate": true
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
+3195
-3520
File diff suppressed because it is too large
Load Diff
@@ -176,8 +176,8 @@
|
||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "cache_args",
|
||||
"type": "CACHEARGS",
|
||||
"name": "teacache_args",
|
||||
"type": "TEACACHEARGS",
|
||||
"links": [
|
||||
103
|
||||
]
|
||||
@@ -826,9 +826,9 @@
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "cache_args",
|
||||
"name": "teacache_args",
|
||||
"shape": 7,
|
||||
"type": "CACHEARGS",
|
||||
"type": "TEACACHEARGS",
|
||||
"link": 103
|
||||
},
|
||||
{
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
-5562
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
-1507
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
+1022
-1043
File diff suppressed because it is too large
Load Diff
+805
-833
File diff suppressed because it is too large
Load Diff
+4
-4
@@ -794,8 +794,8 @@
|
||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "cache_args",
|
||||
"type": "CACHEARGS",
|
||||
"name": "teacache_args",
|
||||
"type": "TEACACHEARGS",
|
||||
"links": [
|
||||
56
|
||||
]
|
||||
@@ -1719,9 +1719,9 @@
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"name": "cache_args",
|
||||
"name": "teacache_args",
|
||||
"shape": 7,
|
||||
"type": "CACHEARGS",
|
||||
"type": "TEACACHEARGS",
|
||||
"link": 56
|
||||
},
|
||||
{
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user