From f446cca02da868513f2a917e97d962213a6661b9 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 5 Sep 2026 22:54:56 +0200 Subject: [PATCH] installer handler for numpy Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 1 + constraints.txt | 1 - installer.py | 16 + launch.py | 1 + modules/ben2/__init__.py | 24 - modules/ben2/ben2_model.py | 1298 ------------------------------- modules/masking.py | 2 +- modules/rembg/rembg_api.py | 4 +- requirements.txt | 1 - scripts/postprocessing_rembg.py | 4 +- 10 files changed, 23 insertions(+), 1329 deletions(-) delete mode 100644 modules/ben2/__init__.py delete mode 100644 modules/ben2/ben2_model.py diff --git a/CHANGELOG.md b/CHANGELOG.md index b53f17b52..9547c05f6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -94,6 +94,7 @@ All-about-optimizations: - log: ansi color handling - compile: keep model compiled state - prompt: cache checks when cfg changes + - lucida: handle requirements ## Update for 2026-08-26 diff --git a/constraints.txt b/constraints.txt index 2ba2bddf7..34078295e 100644 --- a/constraints.txt +++ b/constraints.txt @@ -1,3 +1,2 @@ fastapi==0.124.4 Pillow==12.2.0 -# numpy==2.5.2 diff --git a/installer.py b/installer.py index 73aa1fa01..f8db6404c 100644 --- a/installer.py +++ b/installer.py @@ -640,6 +640,22 @@ def check_onnx(): ts('onnx', t_start) +# check numpy version +def check_numpy(): + t_start = time.time() + if args.skip_all or args.skip_requirements: + return + torch_ver = package_version('torch') + torch_minor = int(torch_ver.split('.')[1]) if torch_ver else 99 + if torch_minor < 11: + if not installed('numpy==2.1.2', quiet=True): + install('numpy==2.1.2', 'numpy==2.1.2', ignore=True) + else: + if not installed('numpy==2.4.6', quiet=True): + install('numpy==2.4.6', 'numpy==2.4.6', ignore=True) + ts('numpy', t_start) + + def install_cuda(): t_start = time.time() log.info('CUDA: nVidia toolkit detected') diff --git a/launch.py b/launch.py index 8583f403b..34c007331 100755 --- a/launch.py +++ b/launch.py @@ -262,6 +262,7 @@ def main(): installer.install('uv', 'uv') installer.install_gradio() installer.check_torch() + installer.check_numpy() installer.check_onnx() installer.check_transformers() installer.check_diffusers() diff --git a/modules/ben2/__init__.py b/modules/ben2/__init__.py deleted file mode 100644 index f9375a990..000000000 --- a/modules/ben2/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -model = None - - -def remove(image, refine: bool = True): - global model # pylint: disable=global-statement - from modules import shared, devices - - if model is None: - from huggingface_hub import hf_hub_download - from .ben2_model import BEN_Base - model = BEN_Base() - model_file = hf_hub_download( - repo_id='PramaLLC/BEN2', - filename='BEN2_Base.pth', - cache_dir=shared.opts.hfcache_dir) - model.loadcheckpoints(model_file) - model = model.to(device=devices.device, dtype=devices.dtype).eval() - - model = model.to(device=devices.device) - foreground = model.inference(image, refine_foreground=refine) - model = model.to(device=devices.cpu) - if foreground is None: - return image - return foreground diff --git a/modules/ben2/ben2_model.py b/modules/ben2/ben2_model.py deleted file mode 100644 index c36f40b21..000000000 --- a/modules/ben2/ben2_model.py +++ /dev/null @@ -1,1298 +0,0 @@ -import os -import math -import subprocess -import tempfile -import cv2 -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint -import numpy as np -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ -from PIL import Image -from torchvision import transforms -from einops import rearrange - - -class Mlp(nn.Module): - """ Multilayer perceptron.""" - - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - """ Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ Forward function. - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = q @ k.transpose(-2, -1) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class SwinTransformerBlock(nn.Module): - """ Swin Transformer Block. - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - self.H = None - self.W = None - - def forward(self, x, mask_matrix): - """ Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - mask_matrix: Attention mask for cyclic shift. - """ - B, L, C = x.shape - H, W = self.H, self.W - assert L == H * W, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # pad feature maps to multiples of window size - pad_l = pad_t = 0 - pad_r = (self.window_size - W % self.window_size) % self.window_size - pad_b = (self.window_size - H % self.window_size) % self.window_size - x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) - _, Hp, Wp, _ = x.shape - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - attn_mask = mask_matrix - else: - shifted_x = x - attn_mask = None - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - - if pad_r > 0 or pad_b > 0: - x = x[:, :H, :W, :].contiguous() - - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - -class PatchMerging(nn.Module): - """ Patch Merging Layer - Args: - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - def __init__(self, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x, H, W): - """ Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - - x = x.view(B, H, W, C) - - # padding - pad_input = (H % 2 == 1) or (W % 2 == 1) - if pad_input: - x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - Args: - dim (int): Number of feature channels - depth (int): Depths of this stage. - num_heads (int): Number of attention head. - window_size (int): Local window size. Default: 7. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, - dim, - depth, - num_heads, - window_size=7, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False): - super().__init__() - self.window_size = window_size - self.shift_size = window_size // 2 - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock( - dim=dim, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) - for i in range(depth)]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x, H, W): - """ Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - - # calculate attention mask for SW-MSA - Hp = int(np.ceil(H / self.window_size)) * self.window_size - Wp = int(np.ceil(W / self.window_size)) * self.window_size - img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, (-100.0)).masked_fill(attn_mask == 0, 0.0) - - for blk in self.blocks: - blk.H, blk.W = H, W - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x, attn_mask) - else: - x = blk(x, attn_mask) - if self.downsample is not None: - x_down = self.downsample(x, H, W) - Wh, Ww = (H + 1) // 2, (W + 1) // 2 - return x, H, W, x_down, Wh, Ww - else: - return x, H, W, x, H, W - - -class PatchEmbed(nn.Module): - """ Image to Patch Embedding - Args: - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - patch_size = to_2tuple(patch_size) - self.patch_size = patch_size - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - """Forward function.""" - # padding - _, _, H, W = x.size() - if W % self.patch_size[1] != 0: - x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) - if H % self.patch_size[0] != 0: - x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) - - x = self.proj(x) # B C Wh Ww - if self.norm is not None: - Wh, Ww = x.size(2), x.size(3) - x = x.flatten(2).transpose(1, 2) - x = self.norm(x) - x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) - - return x - - -class SwinTransformer(nn.Module): - """ Swin Transformer backbone. - A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - - https://arxiv.org/pdf/2103.14030 - Args: - pretrain_img_size (int): Input image size for training the pretrained model, - used in absolute position embedding. Default 224. - patch_size (int | tuple(int)): Patch size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - depths (tuple[int]): Depths of each Swin Transformer stage. - num_heads (tuple[int]): Number of attention head of each stage. - window_size (int): Window size. Default: 7. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. - drop_rate (float): Dropout rate. - attn_drop_rate (float): Attention dropout rate. Default: 0. - drop_path_rate (float): Stochastic depth rate. Default: 0.2. - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. - patch_norm (bool): If True, add normalization after patch embedding. Default: True. - out_indices (Sequence[int]): Output from which stages. - frozen_stages (int): Stages to be frozen (stop grad and set eval mode). - -1 means not freezing any parameters. - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, - pretrain_img_size=224, - patch_size=4, - in_chans=3, - embed_dim=96, - depths=None, - num_heads=None, - window_size=7, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.2, - norm_layer=nn.LayerNorm, - ape=False, - patch_norm=True, - out_indices=(0, 1, 2, 3), - frozen_stages=-1, - use_checkpoint=False): - if num_heads is None: - num_heads = [3, 6, 12, 24] - if depths is None: - depths = [2, 2, 6, 2] - super().__init__() - - self.pretrain_img_size = pretrain_img_size - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.out_indices = out_indices - self.frozen_stages = frozen_stages - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - - # absolute position embedding - if self.ape: - pretrain_img_size = to_2tuple(pretrain_img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] - - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build layers - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = BasicLayer( - dim=int(embed_dim * 2 ** i_layer), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], - norm_layer=norm_layer, - downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, - use_checkpoint=use_checkpoint) - self.layers.append(layer) - - num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] - self.num_features = num_features - - # add a norm layer for each output - for i_layer in out_indices: - layer = norm_layer(num_features[i_layer]) - layer_name = f'norm{i_layer}' - self.add_module(layer_name, layer) - - self._freeze_stages() - - def _freeze_stages(self): - if self.frozen_stages >= 0: - self.patch_embed.eval() - for param in self.patch_embed.parameters(): - param.requires_grad = False - - if self.frozen_stages >= 1 and self.ape: - self.absolute_pos_embed.requires_grad = False - - if self.frozen_stages >= 2: - self.pos_drop.eval() - for i in range(0, self.frozen_stages - 1): - m = self.layers[i] - m.eval() - for param in m.parameters(): - param.requires_grad = False - - - def forward(self, x): - - x = self.patch_embed(x) - - Wh, Ww = x.size(2), x.size(3) - if self.ape: - # interpolate the position embedding to the corresponding size - absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic') - x = x + absolute_pos_embed # B Wh*Ww C - - outs = [x.contiguous()] - x = x.flatten(2).transpose(1, 2) - x = self.pos_drop(x) - - - for i in range(self.num_layers): - layer = self.layers[i] - x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) - - - if i in self.out_indices: - norm_layer = getattr(self, f'norm{i}') - x_out = norm_layer(x_out) - - out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() - outs.append(out) - - - - return tuple(outs) - - - - - - - - -def get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "gelu": - return F.gelu - - raise RuntimeError(F"activation should be gelu, not {activation}.") - - -def make_cbr(in_dim, out_dim): - return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1), nn.InstanceNorm2d(out_dim), nn.GELU()) - - -def make_cbg(in_dim, out_dim): - return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1), nn.InstanceNorm2d(out_dim), nn.GELU()) - - -def rescale_to(x, scale_factor: float = 2, interpolation='nearest'): - return F.interpolate(x, scale_factor=scale_factor, mode=interpolation) - - -def resize_as(x, y, interpolation='bilinear'): - return F.interpolate(x, size=y.shape[-2:], mode=interpolation) - - -def image2patches(x): - """b c (hg h) (wg w) -> (hg wg b) c h w""" - x = rearrange(x, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2 ) - return x - - -def patches2image(x): - """(hg wg b) c h w -> b c (hg h) (wg w)""" - x = rearrange(x, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2) - return x - - - -class PositionEmbeddingSine: - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - self.dim_t = torch.arange(0, self.num_pos_feats, dtype=torch.float32) - - def __call__(self, b, h, w): - device = self.dim_t.device - mask = torch.zeros([b, h, w], dtype=torch.bool, device=device) - assert mask is not None - not_mask = ~mask - y_embed = not_mask.cumsum(dim=1, dtype=torch.float32) - x_embed = not_mask.cumsum(dim=2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = self.temperature ** (2 * (self.dim_t.to(device) // 2) / self.num_pos_feats) - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - - pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) - - return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - - - -class MCLM(nn.Module): - def __init__(self, d_model, num_heads, pool_ratios=None): - if pool_ratios is None: - pool_ratios = [1, 4, 8] - super().__init__() - self.attention = nn.ModuleList([ - nn.MultiheadAttention(d_model, num_heads, dropout=0.1), - nn.MultiheadAttention(d_model, num_heads, dropout=0.1), - nn.MultiheadAttention(d_model, num_heads, dropout=0.1), - nn.MultiheadAttention(d_model, num_heads, dropout=0.1), - nn.MultiheadAttention(d_model, num_heads, dropout=0.1) - ]) - - self.linear1 = nn.Linear(d_model, d_model * 2) - self.linear2 = nn.Linear(d_model * 2, d_model) - self.linear3 = nn.Linear(d_model, d_model * 2) - self.linear4 = nn.Linear(d_model * 2, d_model) - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout = nn.Dropout(0.1) - self.dropout1 = nn.Dropout(0.1) - self.dropout2 = nn.Dropout(0.1) - self.activation = get_activation_fn('gelu') - self.pool_ratios = pool_ratios - self.p_poses = [] - self.g_pos = None - self.positional_encoding = PositionEmbeddingSine(num_pos_feats=d_model // 2, normalize=True) - - def forward(self, l, g): - """ - l: 4,c,h,w - g: 1,c,h,w - """ - self.p_poses = [] - self.g_pos = None - _b, _c, h, w = l.size() - # 4,c,h,w -> 1,c,2h,2w - concated_locs = rearrange(l, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2) - - pools = [] - for pool_ratio in self.pool_ratios: - # b,c,h,w - tgt_hw = (round(h / pool_ratio), round(w / pool_ratio)) - pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw) - pools.append(rearrange(pool, 'b c h w -> (h w) b c')) - if self.g_pos is None: - pos_emb = self.positional_encoding(pool.shape[0], pool.shape[2], pool.shape[3]) - pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c') - self.p_poses.append(pos_emb) - pools = torch.cat(pools, 0) - if self.g_pos is None: - self.p_poses = torch.cat(self.p_poses, dim=0) - pos_emb = self.positional_encoding(g.shape[0], g.shape[2], g.shape[3]) - self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c') - - device = pools.device - self.p_poses = self.p_poses.to(device) - self.g_pos = self.g_pos.to(device) - - - # attention between glb (q) & multisensory concated-locs (k,v) - g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c') - - - g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0]) - g_hw_b_c = self.norm1(g_hw_b_c) - g_hw_b_c = g_hw_b_c + self.dropout2(self.linear2(self.dropout(self.activation(self.linear1(g_hw_b_c)).clone()))) - g_hw_b_c = self.norm2(g_hw_b_c) - - # attention between origin locs (q) & freashed glb (k,v) - l_hw_b_c = rearrange(l, "b c h w -> (h w) b c") - _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w) - _g_hw_b_c = rearrange(_g_hw_b_c, "(ng h) (nw w) b c -> (h w) (ng nw b) c", ng=2, nw=2) - outputs_re = [] - for i, (_l, _g) in enumerate(zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1), strict=False)): - outputs_re.append(self.attention[i + 1](_l, _g, _g)[0]) # (h w) 1 c - outputs_re = torch.cat(outputs_re, 1) # (h w) 4 c - - l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re) - l_hw_b_c = self.norm1(l_hw_b_c) - l_hw_b_c = l_hw_b_c + self.dropout2(self.linear4(self.dropout(self.activation(self.linear3(l_hw_b_c)).clone()))) - l_hw_b_c = self.norm2(l_hw_b_c) - - l = torch.cat((l_hw_b_c, g_hw_b_c), 1) # hw,b(5),c - return rearrange(l, "(h w) b c -> b c h w", h=h, w=w) ## (5,c,h*w) - - - - - - - - - -class MCRM(nn.Module): - def __init__(self, d_model, num_heads, pool_ratios=None, h=None): # pylint: disable=unused-argument - if pool_ratios is None: - pool_ratios = [4, 8, 16] - super().__init__() - self.attention = nn.ModuleList([ - nn.MultiheadAttention(d_model, num_heads, dropout=0.1), - nn.MultiheadAttention(d_model, num_heads, dropout=0.1), - nn.MultiheadAttention(d_model, num_heads, dropout=0.1), - nn.MultiheadAttention(d_model, num_heads, dropout=0.1) - ]) - self.linear3 = nn.Linear(d_model, d_model * 2) - self.linear4 = nn.Linear(d_model * 2, d_model) - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout = nn.Dropout(0.1) - self.dropout1 = nn.Dropout(0.1) - self.dropout2 = nn.Dropout(0.1) - self.sigmoid = nn.Sigmoid() - self.activation = get_activation_fn('gelu') - self.sal_conv = nn.Conv2d(d_model, 1, 1) - self.pool_ratios = pool_ratios - - def forward(self, x): - # device = x.device - _b, c, h, w = x.size() - loc, glb = x.split([4, 1], dim=0) # 4,c,h,w; 1,c,h,w - - patched_glb = rearrange(glb, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2) - - token_attention_map = self.sigmoid(self.sal_conv(glb)) - token_attention_map = F.interpolate(token_attention_map, size=patches2image(loc).shape[-2:], mode='nearest') - loc = loc * rearrange(token_attention_map, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2) - - pools = [] - for pool_ratio in self.pool_ratios: - tgt_hw = (round(h / pool_ratio), round(w / pool_ratio)) - pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw) - pools.append(rearrange(pool, 'nl c h w -> nl c (h w)')) # nl(4),c,hw - - pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c") - loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c') - - outputs = [] - for i, q in enumerate(loc_.unbind(dim=0)): # traverse all local patches - v = pools[i] - k = v - outputs.append(self.attention[i](q, k, v)[0]) - - outputs = torch.cat(outputs, 1) - src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs) - src = self.norm1(src) - src = src + self.dropout2(self.linear4(self.dropout(self.activation(self.linear3(src)).clone()))) - src = self.norm2(src) - src = src.permute(1, 2, 0).reshape(4, c, h, w) # freshed loc - glb = glb + F.interpolate(patches2image(src), size=glb.shape[-2:], mode='nearest') # freshed glb - - return torch.cat((src, glb), 0), token_attention_map - - - -class BEN_Base(nn.Module): - def __init__(self): - super().__init__() - - self.backbone = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12) - emb_dim = 128 - self.sideout5 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) - self.sideout4 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) - self.sideout3 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) - self.sideout2 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) - self.sideout1 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) - - self.output5 = make_cbr(1024, emb_dim) - self.output4 = make_cbr(512, emb_dim) - self.output3 = make_cbr(256, emb_dim) - self.output2 = make_cbr(128, emb_dim) - self.output1 = make_cbr(128, emb_dim) - - self.multifieldcrossatt = MCLM(emb_dim, 1, [1, 4, 8]) - self.conv1 = make_cbr(emb_dim, emb_dim) - self.conv2 = make_cbr(emb_dim, emb_dim) - self.conv3 = make_cbr(emb_dim, emb_dim) - self.conv4 = make_cbr(emb_dim, emb_dim) - self.dec_blk1 = MCRM(emb_dim, 1, [2, 4, 8]) - self.dec_blk2 = MCRM(emb_dim, 1, [2, 4, 8]) - self.dec_blk3 = MCRM(emb_dim, 1, [2, 4, 8]) - self.dec_blk4 = MCRM(emb_dim, 1, [2, 4, 8]) - - self.insmask_head = nn.Sequential( - nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1), - nn.InstanceNorm2d(384), - nn.GELU(), - nn.Conv2d(384, 384, kernel_size=3, padding=1), - nn.InstanceNorm2d(384), - nn.GELU(), - nn.Conv2d(384, emb_dim, kernel_size=3, padding=1) - ) - - self.shallow = nn.Sequential(nn.Conv2d(3, emb_dim, kernel_size=3, padding=1)) - self.upsample1 = make_cbg(emb_dim, emb_dim) - self.upsample2 = make_cbg(emb_dim, emb_dim) - self.output = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) - - for m in self.modules(): - if isinstance(m, nn.GELU) or isinstance(m, nn.Dropout): - m.inplace = True - - - @torch.inference_mode() - @torch.autocast(device_type="cuda",dtype=torch.float16) - def forward(self, x): - real_batch = x.size(0) - - shallow_batch = self.shallow(x) - glb_batch = rescale_to(x, scale_factor=0.5, interpolation='bilinear') - - - - final_input = None - for i in range(real_batch): - start = i * 4 - end = (i + 1) * 4 - loc_batch = image2patches(x[i,:,:,:].unsqueeze(dim=0)) - input_ = torch.cat((loc_batch, glb_batch[i,:,:,:].unsqueeze(dim=0)), dim=0) - if final_input is None: - final_input= input_ - else: - final_input = torch.cat((final_input, input_), dim=0) - - features = self.backbone(final_input) - outputs = [] - for i in range(real_batch): - - start = i * 5 - end = (i + 1) * 5 - f4 = features[4][start:end, :, :, :] # shape: [5, C, H, W] - f3 = features[3][start:end, :, :, :] - f2 = features[2][start:end, :, :, :] - f1 = features[1][start:end, :, :, :] - f0 = features[0][start:end, :, :, :] - e5 = self.output5(f4) - e4 = self.output4(f3) - e3 = self.output3(f2) - e2 = self.output2(f1) - e1 = self.output1(f0) - loc_e5, glb_e5 = e5.split([4, 1], dim=0) - e5 = self.multifieldcrossatt(loc_e5, glb_e5) # (4,128,16,16) - - - e4, _tokenattmap4 = self.dec_blk4(e4 + resize_as(e5, e4)) - e4 = self.conv4(e4) - e3, _tokenattmap3 = self.dec_blk3(e3 + resize_as(e4, e3)) - e3 = self.conv3(e3) - e2, _tokenattmap2 = self.dec_blk2(e2 + resize_as(e3, e2)) - e2 = self.conv2(e2) - e1, _tokenattmap1 = self.dec_blk1(e1 + resize_as(e2, e1)) - e1 = self.conv1(e1) - - loc_e1, glb_e1 = e1.split([4, 1], dim=0) - - output1_cat = patches2image(loc_e1) # (1,128,256,256) - - # add glb feat in - output1_cat = output1_cat + resize_as(glb_e1, output1_cat) - # merge - final_output = self.insmask_head(output1_cat) # (1,128,256,256) - # shallow feature merge - shallow = shallow_batch[i,:,:,:].unsqueeze(dim=0) - final_output = final_output + resize_as(shallow, final_output) - final_output = self.upsample1(rescale_to(final_output)) - final_output = rescale_to(final_output + resize_as(shallow, final_output)) - final_output = self.upsample2(final_output) - final_output = self.output(final_output) - mask = final_output.sigmoid() - outputs.append(mask) - - return torch.cat(outputs, dim=0) - - - def loadcheckpoints(self,model_path): - model_dict = torch.load(model_path, map_location="cpu", weights_only=True) - self.load_state_dict(model_dict['model_state_dict'], strict=True) - del model_path - - def inference(self,image,refine_foreground=False): - # image = ImageOps.exif_transpose(image) - if isinstance(image, Image.Image): - image, h, w,original_image = rgb_loader_refiner(image) - if torch.cuda.is_available(): - - img_tensor = img_transform(image).unsqueeze(0).to(next(self.parameters()).device) - else: - img_tensor = img_transform32(image).unsqueeze(0).to(next(self.parameters()).device) - - with torch.no_grad(): - res = self.forward(img_tensor) - - # Show Results - if refine_foreground: - - pred_pil = transforms.ToPILImage()(res.squeeze()) - image_masked = refine_foreground_process(original_image, pred_pil) - image_masked.putalpha(pred_pil.resize(original_image.size)) - return image_masked - - else: - alpha = postprocess_image(res, im_size=[w,h]) - pred_pil = transforms.ToPILImage()(alpha) - mask = pred_pil.resize(original_image.size) - original_image.putalpha(mask) - # mask = Image.fromarray(alpha) - - return original_image - - - else: - foregrounds = [] - for batch in image: - image, h, w,original_image = rgb_loader_refiner(batch) - if torch.cuda.is_available(): - - img_tensor = img_transform(image).unsqueeze(0).to(next(self.parameters()).device) - else: - img_tensor = img_transform32(image).unsqueeze(0).to(next(self.parameters()).device) - - with torch.no_grad(): - res = self.forward(img_tensor) - - if refine_foreground: - - pred_pil = transforms.ToPILImage()(res.squeeze()) - image_masked = refine_foreground_process(original_image, pred_pil) - image_masked.putalpha(pred_pil.resize(original_image.size)) - - foregrounds.append(image_masked) - else: - alpha = postprocess_image(res, im_size=[w,h]) - pred_pil = transforms.ToPILImage()(alpha) - mask = pred_pil.resize(original_image.size) - original_image.putalpha(mask) - # mask = Image.fromarray(alpha) - foregrounds.append(original_image) - - return foregrounds - - - - - def segment_video(self, video_path, output_path="./", fps=0, refine_foreground=False, batch=1, print_frames_processed=True, webm = False, rgb_value= (0, 255, 0)): - """ - Segments the given video to extract the foreground (with alpha) from each frame - and saves the result as either a WebM video (with alpha channel) or MP4 (with a - color background). - - Args: - video_path (str): - Path to the input video file. - - output_path (str, optional): - Directory (or full path) where the output video and/or files will be saved. - Defaults to "./". - - fps (int, optional): - The frames per second (FPS) to use for the output video. If 0 (default), the - original FPS of the input video is used. Otherwise, overrides it. - - refine_foreground (bool, optional): - Whether to run an additional “refine foreground” process on each frame. - Defaults to False. - - batch (int, optional): - Number of frames to process at once (inference batch size). Large batch sizes - may require more GPU memory. Defaults to 1. - - print_frames_processed (bool, optional): - If True (default), prints progress (how many frames have been processed) to - the console. - - webm (bool, optional): - If True (default), exports a WebM video with alpha channel (VP9 / yuva420p). - If False, exports an MP4 video composited over a solid color background. - - rgb_value (tuple, optional): - The RGB background color (e.g., green screen) used to composite frames when - saving to MP4. Defaults to (0, 255, 0). - - Returns: - None. Writes the output video(s) to disk in the specified format. - """ - cap = cv2.VideoCapture(video_path) - if not cap.isOpened(): - raise OSError(f"Cannot open video: {video_path}") - - original_fps = cap.get(cv2.CAP_PROPFPS) - original_fps = 30 if original_fps == 0 else original_fps - fps = original_fps if fps == 0 else fps - - ret, first_frame = cap.read() - if not ret: - raise ValueError("No frames found in the video.") - _height, _width = first_frame.shape[:2] - cap.set(cv2.CAP_PROP_POSFRAMES, 0) - - foregrounds = [] - frame_idx = 0 - processed_count = 0 - batch_frames = [] - total_frames = int(cap.get(cv2.CAP_PROPFRAME_COUNT)) - - while True: - ret, frame = cap.read() - if not ret: - if batch_frames: - batch_results = self.inference(batch_frames, refine_foreground) - if isinstance(batch_results, Image.Image): - foregrounds.append(batch_results) - else: - foregrounds.extend(batch_results) - if print_frames_processed: - print(f"Processed frames {frame_idx-len(batch_frames)+1} to {frame_idx} of {total_frames}") - break - - # Process every frame instead of using intervals - frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - pil_frame = Image.fromarray(frame_rgb) - batch_frames.append(pil_frame) - if len(batch_frames) == batch: - batch_results = self.inference(batch_frames, refine_foreground) - if isinstance(batch_results, Image.Image): - foregrounds.append(batch_results) - else: - foregrounds.extend(batch_results) - if print_frames_processed: - print(f"Processed frames {frame_idx-batch+1} to {frame_idx} of {total_frames}") - batch_frames = [] - processed_count += batch - - frame_idx += 1 - - - if webm: - alpha_webm_path = os.path.join(output_path, "foreground.webm") - pil_images_to_webm_alpha(foregrounds, alpha_webm_path, fps=original_fps) - - else: - cap.release() - fg_output = os.path.join(output_path, 'foreground.mp4') - pil_images_to_mp4(foregrounds, fg_output, fps=original_fps,rgb_value=rgb_value) - cv2.destroyAllWindows() - try: - fg_audio_output = os.path.join(output_path, 'foreground_output_with_audio.mp4') - add_audio_to_video(fg_output, video_path, fg_audio_output) - except Exception as e: - print("No audio found in the original video") - print(e) - - -def rgb_loader_refiner( original_image): - h, w = original_image.size - image = original_image - # Convert to RGB if necessary - if image.mode != 'RGB': - image = image.convert('RGB') - # Resize the image - image = image.resize((1024, 1024), resample=Image.Resampling.LANCZOS) - return image.convert('RGB'), h, w,original_image - - -# Define the image transformation -img_transform = transforms.Compose([ - transforms.ToTensor(), - transforms.ConvertImageDtype(torch.float16), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) -]) - -img_transform32 = transforms.Compose([ - transforms.ToTensor(), - transforms.ConvertImageDtype(torch.float32), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) -]) - - -def pil_images_to_mp4(images, output_path, fps=24, rgb_value=(0, 255, 0)): - """ - Converts an array of PIL images to an MP4 video. - Args: - images: List of PIL images - output_path: Path to save the MP4 file - fps: Frames per second (default: 24) - rgb_value: Background RGB color tuple (default: green (0, 255, 0)) - """ - if not images: - raise ValueError("No images provided to convert to MP4.") - - width, height = images[0].size - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - video_writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) - - for image in images: - # If image has alpha channel, composite onto the specified background color - if image.mode == 'RGBA': - # Create background image with specified RGB color - background = Image.new('RGB', image.size, rgb_value) - background = background.convert('RGBA') - # Composite the image onto the background - image = Image.alpha_composite(background, image) - image = image.convert('RGB') - else: - # Ensure RGB format for non-alpha images - image = image.convert('RGB') - - # Convert to OpenCV format and write - open_cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) - video_writer.write(open_cv_image) - video_writer.release() - -def pil_images_to_webm_alpha(images, output_path, fps=30): - """ - Converts a list of PIL RGBA images to a VP9 .webm video with alpha channel. - - NOTE: Not all players will display alpha in WebM. - Browsers like Chrome/Firefox typically do support VP9 alpha. - """ - if not images: - raise ValueError("No images provided for WebM with alpha.") - - # Ensure output directory exists - os.makedirs(os.path.dirname(output_path), exist_ok=True) - - with tempfile.TemporaryDirectory() as tmpdir: - # Save frames as PNG (with alpha) - for idx, img in enumerate(images): - if img.mode != "RGBA": - img = img.convert("RGBA") - out_path = os.path.join(tmpdir, f"{idx:06d}.png") - img.save(out_path, "PNG") - - # Construct ffmpeg command - # -c:v libvpx-vp9 => VP9 encoder - # -pix_fmt yuva420p => alpha-enabled pixel format - # -auto-alt-ref 0 => helps preserve alpha frames (libvpx quirk) - ffmpeg_cmd = [ - "ffmpeg", "-y", - "-framerate", str(fps), - "-i", os.path.join(tmpdir, "%06d.png"), - "-c:v", "libvpx-vp9", - "-pix_fmt", "yuva420p", - "-auto-alt-ref", "0", - output_path - ] - - subprocess.run(ffmpeg_cmd, check=True) - - print(f"WebM with alpha saved to {output_path}") - -def add_audio_to_video(video_without_audio_path, original_video_path, output_path): - """ - Check if the original video has an audio stream. If yes, add it. If not, skip. - """ - # 1) Probe original video for audio streams - probe_command = [ - 'ffprobe', '-v', 'error', - '-select_streams', 'a:0', - '-show_entries', 'stream=index', - '-of', 'csv=p=0', - original_video_path - ] - result = subprocess.run(probe_command, capture_output=True, text=True, check=False) - - # result.stdout is empty if no audio stream found - if not result.stdout.strip(): - print("No audio track found in original video, skipping audio addition.") - return - print("Audio track detected; proceeding to mux audio.") - # 2) If audio found, run ffmpeg to add it - command = [ - 'ffmpeg', '-y', - '-i', video_without_audio_path, - '-i', original_video_path, - '-c', 'copy', - '-map', '0:v:0', - '-map', '1:a:0', # we know there's an audio track now - output_path - ] - subprocess.run(command, check=True) - print(f"Audio added successfully => {output_path}") - - -### Thanks to the source: https://huggingface.co/ZhengPeng7/BiRefNet/blob/main/handler.py -def refine_foreground_process(image, mask, r=90): - if mask.size != image.size: - mask = mask.resize(image.size) - image = np.array(image) / 255.0 - mask = np.array(mask) / 255.0 - estimated_foreground = FB_blur_fusion_foreground_estimator_2(image, mask, r=r) - image_masked = Image.fromarray((estimated_foreground * 255.0).astype(np.uint8)) - return image_masked - - -def FB_blur_fusion_foreground_estimator_2(image, alpha, r=90): - # Thanks to the source: https://github.com/Photoroom/fast-foreground-estimation - alpha = alpha[:, :, None] - F, blur_B = FB_blur_fusion_foreground_estimator(image, image, image, alpha, r) - return FB_blur_fusion_foreground_estimator(image, F, blur_B, alpha, r=6)[0] - - -def FB_blur_fusion_foreground_estimator(image, F, B, alpha, r=90): - if isinstance(image, Image.Image): - image = np.array(image) / 255.0 - blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None] - - blurredFA = cv2.blur(F * alpha, (r, r)) - blurredF = blurredFA / (blurred_alpha + 1e-5) - - blurred_B1A = cv2.blur(B * (1 - alpha), (r, r)) - blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5) - F = blurredF + alpha * \ - (image - alpha * blurredF - (1 - alpha) * blurred_B) - F = np.clip(F, 0, 1) - return F, blurred_B - - -def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray: - result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear'), 0) - ma = torch.max(result) - mi = torch.min(result) - result = (result - mi) / (ma - mi) - im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8) - im_array = np.squeeze(im_array) - return im_array diff --git a/modules/masking.py b/modules/masking.py index 4f2689758..0767f79a7 100644 --- a/modules/masking.py +++ b/modules/masking.py @@ -263,7 +263,7 @@ def run_rembg(input_image: Image.Image, input_mask: np.ndarray): if "U2NET_HOME" not in os.environ: os.environ["U2NET_HOME"] = os.path.join(paths.models_path, "Rembg") if opts.model == 'ben2': - from modules import ben2 + from modules.rembg import ben2 args = { 'image': input_image, 'refine': True, diff --git a/modules/rembg/rembg_api.py b/modules/rembg/rembg_api.py index 0bb30f4dd..c100377b7 100644 --- a/modules/rembg/rembg_api.py +++ b/modules/rembg/rembg_api.py @@ -3,7 +3,7 @@ from fastapi import Body def dependencies(): from installer import install - for pkg in ["dctorch==0.1.2", "pymatting", "pooch", "rembg", "numba"]: + for pkg in ["dctorch==0.1.2", "pymatting", "pooch", "rembg", "numba", "kornia", "kornia-rs"]: install(pkg, no_deps=True, ignore=False) @@ -25,6 +25,7 @@ async def post_rembg( if input_image is None: return {} + dependencies() if model == "ben2": from modules.rembg import ben2 image = ben2.remove(input_image, refine=refine) @@ -32,7 +33,6 @@ async def post_rembg( from modules.rembg import lucida image = lucida.remove(input_image) else: - dependencies() import rembg image = rembg.remove( # pylint: disable=c-extension-no-member input_image, diff --git a/requirements.txt b/requirements.txt index 89a95dbdf..5d77e7432 100644 --- a/requirements.txt +++ b/requirements.txt @@ -34,7 +34,6 @@ accelerate==1.14.0 einops==0.8.2 huggingface_hub==1.30.0 hf_xet==1.6.0 -numpy==2.5.2 pandas==2.3.1 protobuf==7.36.1 pytorch_lightning==2.6.5 diff --git a/scripts/postprocessing_rembg.py b/scripts/postprocessing_rembg.py index 4068f958b..61825e5db 100644 --- a/scripts/postprocessing_rembg.py +++ b/scripts/postprocessing_rembg.py @@ -20,7 +20,7 @@ models = [ def dependencies(): from installer import install - for pkg in ["dctorch==0.1.2", "pymatting", "pooch", "rembg", "numba"]: + for pkg in ["dctorch==0.1.2", "pymatting", "pooch", "rembg", "numba", "kornia", "kornia-rs"]: install(pkg, no_deps=True, ignore=False) @@ -78,6 +78,7 @@ class ScriptPostprocessingRembg(scripts_postprocessing.ScriptPostprocessing): if image is None: return pp log.info(f'RemoveBackground: model={model} merge_alpha={merge_alpha} refine={refine} mask_only={mask_only} postprocess_mask={postprocess_mask} alpha_matting={alpha_matting} alpha_matting_foreground_threshold={alpha_matting_foreground_threshold} alpha_matting_background_threshold={alpha_matting_background_threshold} alpha_matting_erode_size={alpha_matting_erode_size}') + dependencies() if model == 'ben2': try: from modules.rembg import ben2 @@ -96,7 +97,6 @@ class ScriptPostprocessingRembg(scripts_postprocessing.ScriptPostprocessing): return pp else: try: - dependencies() import rembg if "U2NET_HOME" not in os.environ: from modules.paths import models_path