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import math |
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from dataclasses import dataclass |
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import torch |
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from einops import rearrange, repeat |
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from torch import Tensor, nn |
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import torch.nn.functional as F |
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import torch |
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from einops import rearrange |
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def attention(q, k, v, pe): |
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q, k = apply_rope(q, k, pe) |
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x = torch.nn.functional.scaled_dot_product_attention(q, k, v) |
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x = rearrange(x, "B H L D -> B L (H D)") |
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return x |
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def rope(pos, dim: int, theta: int): |
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assert dim % 2 == 0 |
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scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim |
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omega = 1.0 / (theta**scale) |
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out = torch.einsum("...n,d->...nd", pos, omega) |
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out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) |
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out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) |
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return out.float() |
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def apply_rope(xq, xk, freqs_cis): |
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xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) |
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xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) |
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xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] |
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xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] |
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return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) |
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class EmbedND(nn.Module): |
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def __init__(self, dim: int, theta: int, axes_dim: list[int]): |
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super().__init__() |
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self.dim = dim |
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self.theta = theta |
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self.axes_dim = axes_dim |
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def forward(self, ids: Tensor): |
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n_axes = ids.shape[-1] |
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emb = torch.cat( |
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[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], |
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dim=-3, |
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) |
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return emb.unsqueeze(1) |
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def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): |
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""" |
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Create sinusoidal timestep embeddings. |
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:param t: a 1-D Tensor of N indices, one per batch element. |
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These may be fractional. |
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:param dim: the dimension of the output. |
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:param max_period: controls the minimum frequency of the embeddings. |
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:return: an (N, D) Tensor of positional embeddings. |
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""" |
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t = time_factor * t |
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half = dim // 2 |
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freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( |
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t.device) |
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args = t[:, None].float() * freqs[None] |
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) |
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if dim % 2: |
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embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) |
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if torch.is_floating_point(t): |
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embedding = embedding.to(t) |
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return embedding |
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class MLPEmbedder(nn.Module): |
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def __init__(self, in_dim: int, hidden_dim: int): |
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super().__init__() |
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self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True) |
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self.silu = nn.SiLU() |
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self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True) |
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def forward(self, x: Tensor): |
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return self.out_layer(self.silu(self.in_layer(x))) |
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class RMSNorm(torch.nn.Module): |
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def __init__(self, dim: int): |
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super().__init__() |
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self.scale = nn.Parameter(torch.ones(dim)) |
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def forward(self, x: Tensor): |
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x_dtype = x.dtype |
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x = x.float() |
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rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6) |
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return (x * rrms).to(dtype=x_dtype) * self.scale |
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class QKNorm(torch.nn.Module): |
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def __init__(self, dim: int): |
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super().__init__() |
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self.query_norm = RMSNorm(dim) |
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self.key_norm = RMSNorm(dim) |
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def forward(self, q: Tensor, k: Tensor, v: Tensor): |
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q = self.query_norm(q) |
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k = self.key_norm(k) |
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return q.to(v), k.to(v) |
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class LoRALinearLayer(nn.Module): |
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def __init__(self, in_features, out_features, rank=4, network_alpha=None, device=None, dtype=None): |
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super().__init__() |
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self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype) |
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self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype) |
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self.network_alpha = network_alpha |
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self.rank = rank |
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nn.init.normal_(self.down.weight, std=1 / rank) |
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nn.init.zeros_(self.up.weight) |
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def forward(self, hidden_states): |
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orig_dtype = hidden_states.dtype |
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dtype = self.down.weight.dtype |
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down_hidden_states = self.down(hidden_states.to(dtype)) |
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up_hidden_states = self.up(down_hidden_states) |
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if self.network_alpha is not None: |
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up_hidden_states *= self.network_alpha / self.rank |
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return up_hidden_states.to(orig_dtype) |
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class FLuxSelfAttnProcessor: |
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def __call__(self, attn, x, pe, **attention_kwargs): |
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print('2' * 30) |
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qkv = attn.qkv(x) |
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q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) |
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q, k = attn.norm(q, k, v) |
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x = attention(q, k, v, pe=pe) |
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x = attn.proj(x) |
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return x |
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class LoraFluxAttnProcessor(nn.Module): |
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def __init__(self, dim: int, rank=4, network_alpha=None, lora_weight=1): |
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super().__init__() |
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self.qkv_lora = LoRALinearLayer(dim, dim * 3, rank, network_alpha) |
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self.proj_lora = LoRALinearLayer(dim, dim, rank, network_alpha) |
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self.lora_weight = lora_weight |
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def __call__(self, attn, x, pe, **attention_kwargs): |
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qkv = attn.qkv(x) + self.qkv_lora(x) * self.lora_weight |
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q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) |
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q, k = attn.norm(q, k, v) |
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x = attention(q, k, v, pe=pe) |
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x = attn.proj(x) + self.proj_lora(x) * self.lora_weight |
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print('1' * 30) |
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print(x.norm(), (self.proj_lora(x) * self.lora_weight).norm(), 'norm') |
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return x |
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class SelfAttention(nn.Module): |
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def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False): |
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super().__init__() |
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self.num_heads = num_heads |
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head_dim = dim // num_heads |
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) |
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self.norm = QKNorm(head_dim) |
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self.proj = nn.Linear(dim, dim) |
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def forward(): |
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pass |
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@dataclass |
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class ModulationOut: |
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shift: Tensor |
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scale: Tensor |
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gate: Tensor |
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class Modulation(nn.Module): |
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def __init__(self, dim: int, double: bool): |
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super().__init__() |
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self.is_double = double |
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self.multiplier = 6 if double else 3 |
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self.lin = nn.Linear(dim, self.multiplier * dim, bias=True) |
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def forward(self, vec: Tensor): |
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out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) |
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return ( |
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ModulationOut(*out[:3]), |
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ModulationOut(*out[3:]) if self.is_double else None, |
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) |
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class DoubleStreamBlockLoraProcessor(nn.Module): |
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def __init__(self, dim: int, rank=4, network_alpha=None, lora_weight=1): |
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super().__init__() |
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self.qkv_lora1 = LoRALinearLayer(dim, dim * 3, rank, network_alpha) |
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self.proj_lora1 = LoRALinearLayer(dim, dim, rank, network_alpha) |
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self.qkv_lora2 = LoRALinearLayer(dim, dim * 3, rank, network_alpha) |
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self.proj_lora2 = LoRALinearLayer(dim, dim, rank, network_alpha) |
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self.lora_weight = lora_weight |
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def __call__(self, attn, img, txt, vec, pe): |
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img_mod1, img_mod2 = attn.img_mod(vec) |
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txt_mod1, txt_mod2 = attn.txt_mod(vec) |
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img_modulated = attn.img_norm1(img) |
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img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift |
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img_qkv = attn.img_attn.qkv(img_modulated) + self.qkv_lora1(img_modulated) * self.lora_weight |
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img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) |
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img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) |
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txt_modulated = attn.txt_norm1(txt) |
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txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift |
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txt_qkv = attn.txt_attn.qkv(txt_modulated) + self.qkv_lora2(txt_modulated) * self.lora_weight |
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txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) |
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txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) |
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q = torch.cat((txt_q, img_q), dim=2) |
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k = torch.cat((txt_k, img_k), dim=2) |
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v = torch.cat((txt_v, img_v), dim=2) |
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attn1 = attention(q, k, v, pe=pe) |
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txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :] |
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img = img + img_mod1.gate * attn.img_attn.proj(img_attn) + img_mod1.gate * self.proj_lora1(img_attn) * self.lora_weight |
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img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) |
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txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) + txt_mod1.gate * self.proj_lora2(txt_attn) * self.lora_weight |
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txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) |
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return img, txt |
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class IPDoubleStreamBlockProcessor(nn.Module): |
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"""Attention processor for handling IP-adapter with double stream block.""" |
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def __init__(self, context_dim, hidden_dim): |
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super().__init__() |
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if not hasattr(F, "scaled_dot_product_attention"): |
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raise ImportError( |
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"IPDoubleStreamBlockProcessor requires PyTorch 2.0 or higher. Please upgrade PyTorch." |
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) |
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self.context_dim = context_dim |
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self.hidden_dim = hidden_dim |
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self.ip_adapter_double_stream_k_proj = nn.Linear(context_dim, hidden_dim, bias=True) |
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self.ip_adapter_double_stream_v_proj = nn.Linear(context_dim, hidden_dim, bias=True) |
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nn.init.zeros_(self.ip_adapter_double_stream_k_proj.weight) |
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nn.init.zeros_(self.ip_adapter_double_stream_k_proj.bias) |
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nn.init.zeros_(self.ip_adapter_double_stream_v_proj.weight) |
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nn.init.zeros_(self.ip_adapter_double_stream_v_proj.bias) |
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def __call__(self, attn, img, txt, vec, pe, image_proj, ip_scale=1.0, **attention_kwargs): |
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img_mod1, img_mod2 = attn.img_mod(vec) |
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txt_mod1, txt_mod2 = attn.txt_mod(vec) |
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img_modulated = attn.img_norm1(img) |
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img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift |
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img_qkv = attn.img_attn.qkv(img_modulated) |
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img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim) |
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img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) |
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txt_modulated = attn.txt_norm1(txt) |
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txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift |
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txt_qkv = attn.txt_attn.qkv(txt_modulated) |
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txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim) |
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txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) |
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q = torch.cat((txt_q, img_q), dim=2) |
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k = torch.cat((txt_k, img_k), dim=2) |
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v = torch.cat((txt_v, img_v), dim=2) |
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attn1 = attention(q, k, v, pe=pe) |
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txt_attn, img_attn = attn1[:, :txt.shape[1]], attn1[:, txt.shape[1]:] |
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img = img + img_mod1.gate * attn.img_attn.proj(img_attn) |
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img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) |
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txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) |
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txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) |
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ip_query = img_q |
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ip_key = self.ip_adapter_double_stream_k_proj(image_proj) |
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ip_value = self.ip_adapter_double_stream_v_proj(image_proj) |
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ip_key = rearrange(ip_key, 'B L (H D) -> B H L D', H=attn.num_heads, D=attn.head_dim) |
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ip_value = rearrange(ip_value, 'B L (H D) -> B H L D', H=attn.num_heads, D=attn.head_dim) |
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ip_attention = F.scaled_dot_product_attention( |
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ip_query, |
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ip_key, |
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ip_value, |
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dropout_p=0.0, |
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is_causal=False |
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) |
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ip_attention = rearrange(ip_attention, "B H L D -> B L (H D)", H=attn.num_heads, D=attn.head_dim) |
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img = img + ip_scale * ip_attention |
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return img, txt |
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class DoubleStreamBlockProcessor: |
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def __call__(self, attn, img, txt, vec, pe): |
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img_mod1, img_mod2 = attn.img_mod(vec) |
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txt_mod1, txt_mod2 = attn.txt_mod(vec) |
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img_modulated = attn.img_norm1(img) |
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img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift |
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img_qkv = attn.img_attn.qkv(img_modulated) |
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img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim) |
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img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v) |
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txt_modulated = attn.txt_norm1(txt) |
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txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift |
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txt_qkv = attn.txt_attn.qkv(txt_modulated) |
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txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim) |
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txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v) |
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q = torch.cat((txt_q, img_q), dim=2) |
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k = torch.cat((txt_k, img_k), dim=2) |
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v = torch.cat((txt_v, img_v), dim=2) |
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attn1 = attention(q, k, v, pe=pe) |
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txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :] |
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img = img + img_mod1.gate * attn.img_attn.proj(img_attn) |
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img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift) |
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txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn) |
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txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift) |
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return img, txt |
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class DoubleStreamBlock(nn.Module): |
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def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False): |
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super().__init__() |
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mlp_hidden_dim = int(hidden_size * mlp_ratio) |
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self.num_heads = num_heads |
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self.hidden_size = hidden_size |
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self.head_dim = hidden_size // num_heads |
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self.img_mod = Modulation(hidden_size, double=True) |
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self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
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self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) |
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|
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self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
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self.img_mlp = nn.Sequential( |
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nn.Linear(hidden_size, mlp_hidden_dim, bias=True), |
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nn.GELU(approximate="tanh"), |
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nn.Linear(mlp_hidden_dim, hidden_size, bias=True), |
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) |
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self.txt_mod = Modulation(hidden_size, double=True) |
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self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
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self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) |
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self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
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self.txt_mlp = nn.Sequential( |
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nn.Linear(hidden_size, mlp_hidden_dim, bias=True), |
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nn.GELU(approximate="tanh"), |
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nn.Linear(mlp_hidden_dim, hidden_size, bias=True), |
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) |
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processor = DoubleStreamBlockProcessor() |
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self.set_processor(processor) |
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|
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def set_processor(self, processor): |
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self.processor = processor |
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|
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def get_processor(self): |
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return self.processor |
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|
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def forward( |
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self, |
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img: Tensor, |
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txt: Tensor, |
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vec: Tensor, |
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pe: Tensor, |
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image_proj: Tensor = None, |
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ip_scale: float =1.0, |
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): |
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if image_proj is None: |
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return self.processor(self, img, txt, vec, pe) |
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else: |
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return self.processor(self, img, txt, vec, pe, image_proj, ip_scale) |
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|
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class IPSingleStreamBlockProcessor(nn.Module): |
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"""Attention processor for handling IP-adapter with single stream block.""" |
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def __init__(self, context_dim, hidden_dim): |
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super().__init__() |
|
if not hasattr(F, "scaled_dot_product_attention"): |
|
raise ImportError( |
|
"IPSingleStreamBlockProcessor requires PyTorch 2.0 or higher. Please upgrade PyTorch." |
|
) |
|
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self.context_dim = context_dim |
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self.hidden_dim = hidden_dim |
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self.ip_adapter_single_stream_k_proj = nn.Linear(context_dim, hidden_dim, bias=False) |
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self.ip_adapter_single_stream_v_proj = nn.Linear(context_dim, hidden_dim, bias=False) |
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|
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nn.init.zeros_(self.ip_adapter_single_stream_k_proj.weight) |
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nn.init.zeros_(self.ip_adapter_single_stream_v_proj.weight) |
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|
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def __call__( |
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self, |
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attn: nn.Module, |
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x: Tensor, |
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vec: Tensor, |
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pe: Tensor, |
|
image_proj: Tensor = None, |
|
ip_scale: float = 1.0 |
|
): |
|
|
|
mod, _ = attn.modulation(vec) |
|
x_mod = (1 + mod.scale) * attn.pre_norm(x) + mod.shift |
|
qkv, mlp = torch.split(attn.linear1(x_mod), [3 * attn.hidden_size, attn.mlp_hidden_dim], dim=-1) |
|
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim) |
|
q, k = attn.norm(q, k, v) |
|
|
|
|
|
attn_1 = attention(q, k, v, pe=pe) |
|
|
|
|
|
ip_query = q |
|
ip_key = self.ip_adapter_single_stream_k_proj(image_proj) |
|
ip_value = self.ip_adapter_single_stream_v_proj(image_proj) |
|
|
|
|
|
ip_key = rearrange(ip_key, 'B L (H D) -> B H L D', H=attn.num_heads, D=attn.head_dim) |
|
ip_value = rearrange(ip_value, 'B L (H D) -> B H L D', H=attn.num_heads, D=attn.head_dim) |
|
|
|
|
|
|
|
ip_attention = F.scaled_dot_product_attention( |
|
ip_query, |
|
ip_key, |
|
ip_value |
|
) |
|
ip_attention = rearrange(ip_attention, "B H L D -> B L (H D)") |
|
|
|
attn_out = attn_1 + ip_scale * ip_attention |
|
|
|
|
|
output = attn.linear2(torch.cat((attn_out, attn.mlp_act(mlp)), 2)) |
|
out = x + mod.gate * output |
|
|
|
return out |
|
|
|
|
|
class SingleStreamBlockLoraProcessor(nn.Module): |
|
def __init__(self, dim: int, rank: int = 4, network_alpha = None, lora_weight: float = 1): |
|
super().__init__() |
|
self.qkv_lora = LoRALinearLayer(dim, dim * 3, rank, network_alpha) |
|
self.proj_lora = LoRALinearLayer(dim, dim, rank, network_alpha) |
|
self.lora_weight = lora_weight |
|
|
|
def __call__(self, attn: nn.Module, x: Tensor, vec: Tensor, pe: Tensor): |
|
|
|
mod, _ = attn.modulation(vec) |
|
x_mod = (1 + mod.scale) * attn.pre_norm(x) + mod.shift |
|
qkv, mlp = torch.split(attn.linear1(x_mod), [3 * attn.hidden_size, attn.mlp_hidden_dim], dim=-1) |
|
qkv = qkv + self.qkv_lora(x_mod) * self.lora_weight |
|
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) |
|
q, k = attn.norm(q, k, v) |
|
|
|
|
|
attn_1 = attention(q, k, v, pe=pe) |
|
|
|
|
|
output = attn.linear2(torch.cat((attn_1, attn.mlp_act(mlp)), 2)) |
|
output = output + self.proj_lora(output) * self.lora_weight |
|
output = x + mod.gate * output |
|
|
|
return output |
|
|
|
|
|
class SingleStreamBlockProcessor: |
|
def __call__(self, attn: nn.Module, x: Tensor, vec: Tensor, pe: Tensor): |
|
|
|
mod, _ = attn.modulation(vec) |
|
x_mod = (1 + mod.scale) * attn.pre_norm(x) + mod.shift |
|
qkv, mlp = torch.split(attn.linear1(x_mod), [3 * attn.hidden_size, attn.mlp_hidden_dim], dim=-1) |
|
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads) |
|
q, k = attn.norm(q, k, v) |
|
|
|
|
|
attn_1 = attention(q, k, v, pe=pe) |
|
|
|
|
|
output = attn.linear2(torch.cat((attn_1, attn.mlp_act(mlp)), 2)) |
|
output = x + mod.gate * output |
|
|
|
return output |
|
|
|
|
|
class SingleStreamBlock(nn.Module): |
|
""" |
|
A DiT block with parallel linear layers as described in |
|
https://arxiv.org/abs/2302.05442 and adapted modulation interface. |
|
""" |
|
|
|
def __init__( |
|
self, |
|
hidden_size: int, |
|
num_heads: int, |
|
mlp_ratio: float = 4.0, |
|
qk_scale: float = None, |
|
): |
|
super().__init__() |
|
self.hidden_dim = hidden_size |
|
self.num_heads = num_heads |
|
self.head_dim = hidden_size // num_heads |
|
self.scale = qk_scale or self.head_dim**-0.5 |
|
|
|
self.mlp_hidden_dim = int(hidden_size * mlp_ratio) |
|
|
|
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) |
|
|
|
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) |
|
|
|
self.norm = QKNorm(self.head_dim) |
|
|
|
self.hidden_size = hidden_size |
|
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
|
|
|
self.mlp_act = nn.GELU(approximate="tanh") |
|
self.modulation = Modulation(hidden_size, double=False) |
|
|
|
processor = SingleStreamBlockProcessor() |
|
self.set_processor(processor) |
|
|
|
|
|
def set_processor(self, processor): |
|
self.processor = processor |
|
|
|
def get_processor(self): |
|
return self.processor |
|
|
|
def forward( |
|
self, |
|
x: Tensor, |
|
vec: Tensor, |
|
pe: Tensor, |
|
image_proj: Tensor = None, |
|
ip_scale: float = 1.0 |
|
): |
|
if image_proj is None: |
|
return self.processor(self, x, vec, pe) |
|
else: |
|
return self.processor(self, x, vec, pe, image_proj, ip_scale) |
|
|
|
|
|
class LastLayer(nn.Module): |
|
def __init__(self, hidden_size: int, patch_size: int, out_channels: int): |
|
super().__init__() |
|
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) |
|
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) |
|
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)) |
|
|
|
def forward(self, x: Tensor, vec: Tensor) -> Tensor: |
|
shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) |
|
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] |
|
x = self.linear(x) |
|
return x |
|
|
|
|
|
class ImageProjModel(torch.nn.Module): |
|
"""Projection Model |
|
https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/ip_adapter.py#L28 |
|
""" |
|
|
|
|
|
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4): |
|
super().__init__() |
|
|
|
self.generator = None |
|
self.cross_attention_dim = cross_attention_dim |
|
self.clip_extra_context_tokens = clip_extra_context_tokens |
|
self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) |
|
self.norm = torch.nn.LayerNorm(cross_attention_dim) |
|
|
|
def forward(self, image_embeds): |
|
embeds = image_embeds |
|
clip_extra_context_tokens = self.proj(embeds).reshape( |
|
-1, self.clip_extra_context_tokens, self.cross_attention_dim |
|
) |
|
clip_extra_context_tokens = self.norm(clip_extra_context_tokens) |
|
return clip_extra_context_tokens |
|
|
|
|