mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
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261 lines
10 KiB
Python
261 lines
10 KiB
Python
# Based on:
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# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
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# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
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import torch
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import torch.nn as nn
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from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder, Mlp
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from .pixart import PixArt, get_2d_sincos_pos_embed
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class PatchEmbed(nn.Module):
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"""
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2D Image to Patch Embedding
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"""
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def __init__(
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self,
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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norm_layer=None,
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flatten=True,
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bias=True,
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dtype=None,
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device=None,
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operations=None
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):
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super().__init__()
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patch_size = (patch_size, patch_size)
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self.patch_size = patch_size
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self.flatten = flatten
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self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
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def forward(self, x):
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x = self.proj(x)
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if self.flatten:
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x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
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x = self.norm(x)
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return x
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class PixArtMSBlock(nn.Module):
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"""
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A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
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"""
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def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
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sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
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super().__init__()
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self.hidden_size = hidden_size
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self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.attn = AttentionKVCompress(
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hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
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qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
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)
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self.cross_attn = MultiHeadCrossAttention(
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hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
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)
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self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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# to be compatible with lower version pytorch
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.mlp = Mlp(
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in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,
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dtype=dtype, device=device, operations=operations
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)
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self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
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def forward(self, x, y, t, mask=None, HW=None, **kwargs):
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B, N, C = x.shape
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(x.dtype) + t.reshape(B, 6, -1)).chunk(6, dim=1)
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x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
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x = x + self.cross_attn(x, y, mask)
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x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
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return x
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### Core PixArt Model ###
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class PixArtMS(PixArt):
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"""
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Diffusion model with a Transformer backbone.
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"""
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def __init__(
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self,
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input_size=32,
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patch_size=2,
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in_channels=4,
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hidden_size=1152,
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depth=28,
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num_heads=16,
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mlp_ratio=4.0,
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class_dropout_prob=0.1,
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learn_sigma=True,
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pred_sigma=True,
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drop_path: float = 0.,
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caption_channels=4096,
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pe_interpolation=None,
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pe_precision=None,
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config=None,
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model_max_length=120,
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micro_condition=True,
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qk_norm=False,
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kv_compress_config=None,
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dtype=None,
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device=None,
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operations=None,
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**kwargs,
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):
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nn.Module.__init__(self)
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self.dtype = dtype
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self.pred_sigma = pred_sigma
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self.in_channels = in_channels
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self.out_channels = in_channels * 2 if pred_sigma else in_channels
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self.patch_size = patch_size
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self.num_heads = num_heads
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self.pe_interpolation = pe_interpolation
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self.pe_precision = pe_precision
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self.hidden_size = hidden_size
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self.depth = depth
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self.h = self.w = 0
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.t_block = nn.Sequential(
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nn.SiLU(),
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operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
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)
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self.x_embedder = PatchEmbed(
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patch_size, in_channels, hidden_size, bias=True,
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dtype=dtype, device=device, operations=operations
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)
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self.t_embedder = TimestepEmbedder(
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hidden_size, dtype=dtype, device=device, operations=operations,
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)
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self.y_embedder = CaptionEmbedder(
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in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
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act_layer=approx_gelu, token_num=model_max_length,
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dtype=dtype, device=device, operations=operations,
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)
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self.micro_conditioning = micro_condition
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if self.micro_conditioning:
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self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
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self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
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# Will use fixed sin-cos embedding:
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num_patches = (input_size // patch_size) * (input_size // patch_size)
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self.base_size = input_size // self.patch_size
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self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
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drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule
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if kv_compress_config is None:
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kv_compress_config = {
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'sampling': None,
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'scale_factor': 1,
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'kv_compress_layer': [],
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}
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self.blocks = nn.ModuleList([
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PixArtMSBlock(
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hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
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input_size=(input_size // patch_size, input_size // patch_size),
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sampling=kv_compress_config['sampling'],
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sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
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qk_norm=qk_norm,
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dtype=dtype,
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device=device,
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operations=operations,
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)
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for i in range(depth)
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])
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self.final_layer = T2IFinalLayer(
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hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
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)
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def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs):
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"""
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Original forward pass of PixArt.
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x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
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t: (N,) tensor of diffusion timesteps
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y: (N, 1, 120, C) conditioning
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ar: (N, 1): aspect ratio
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cs: (N ,2) size conditioning for height/width
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"""
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pe_interpolation = self.pe_interpolation
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if pe_interpolation is None or self.pe_precision is not None:
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# calculate pe_interpolation on-the-fly
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pe_interpolation = round((x.shape[-1]+x.shape[-2])/2.0 / (512/8.0), self.pe_precision or 0)
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self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
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pos_embed = torch.from_numpy(
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get_2d_sincos_pos_embed(
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self.hidden_size, (self.h, self.w), pe_interpolation=pe_interpolation,
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base_size=self.base_size
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)
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).to(device=x.device, dtype=x.dtype).unsqueeze(0)
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x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
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t = self.t_embedder(timestep, x.dtype) # (N, D)
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if self.micro_conditioning and (c_size is not None and c_ar is not None):
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bs = x.shape[0]
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c_size = self.csize_embedder(c_size, bs) # (N, D)
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c_ar = self.ar_embedder(c_ar, bs) # (N, D)
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t = t + torch.cat([c_size, c_ar], dim=1)
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t0 = self.t_block(t)
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y = self.y_embedder(y, self.training) # (N, D)
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if mask is not None:
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if mask.shape[0] != y.shape[0]:
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mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
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mask = mask.squeeze(1).squeeze(1)
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y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
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y_lens = mask.sum(dim=1).tolist()
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else:
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y_lens = [y.shape[2]] * y.shape[0]
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y = y.squeeze(1).view(1, -1, x.shape[-1])
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for block in self.blocks:
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x = block(x, y, t0, y_lens, (self.h, self.w), **kwargs) # (N, T, D)
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x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
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x = self.unpatchify(x) # (N, out_channels, H, W)
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return x
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def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs):
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B, C, H, W = x.shape
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# Fallback for missing microconds
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if self.micro_conditioning:
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if c_size is None:
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c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)
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if c_ar is None:
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c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)
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## Still accepts the input w/o that dim but returns garbage
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if len(context.shape) == 3:
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context = context.unsqueeze(1)
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## run original forward pass
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out = self.forward_orig(x, timesteps, context, c_size=c_size, ar=c_ar)
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## only return EPS
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if self.pred_sigma:
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return out[:, :self.in_channels]
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return out
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def unpatchify(self, x):
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"""
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x: (N, T, patch_size**2 * C)
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imgs: (N, H, W, C)
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"""
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c = self.out_channels
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p = self.x_embedder.patch_size[0]
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assert self.h * self.w == x.shape[1]
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x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
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x = torch.einsum('nhwpqc->nchpwq', x)
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imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
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return imgs
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