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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
399 lines
17 KiB
Python
399 lines
17 KiB
Python
import math
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from dataclasses import dataclass
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import torch
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from einops import rearrange
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from torch import Tensor, nn
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from .xflux.src.flux.math import attention, rope
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from .xflux.src.flux.modules.layers import LoRALinearLayer
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from torch.nn import functional as F
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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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)
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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 DoubleStreamBlockLorasMixerProcessor(nn.Module):
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def __init__(self,):
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super().__init__()
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self.qkv_lora1 = []
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self.proj_lora1 = []
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self.qkv_lora2 = []
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self.proj_lora2 = []
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self.lora_weight = []
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self.names = []
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def add_lora(self, processor):
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if isinstance(processor, DoubleStreamBlockLorasMixerProcessor):
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self.qkv_lora1+=processor.qkv_lora1
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self.qkv_lora2+=processor.qkv_lora2
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self.proj_lora1+=processor.proj_lora1
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self.proj_lora2+=processor.proj_lora2
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self.lora_weight+=processor.lora_weight
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else:
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if hasattr(processor, "qkv_lora1"):
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self.qkv_lora1.append(processor.qkv_lora1)
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if hasattr(processor, "proj_lora1"):
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self.proj_lora1.append(processor.proj_lora1)
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if hasattr(processor, "qkv_lora2"):
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self.qkv_lora2.append(processor.qkv_lora2)
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if hasattr(processor, "proj_lora2"):
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self.proj_lora2.append(processor.proj_lora2)
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if hasattr(processor, "lora_weight"):
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self.lora_weight.append(processor.lora_weight)
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def get_loras(self):
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return (
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self.qkv_lora1, self.qkv_lora2,
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self.proj_lora1, self.proj_lora2,
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self.lora_weight
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)
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def set_loras(self, qkv1s, qkv2s, proj1s, proj2s, w8s):
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for el in qkv1s:
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self.qkv_lora1.append(el)
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for el in qkv2s:
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self.qkv_lora2.append(el)
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for el in proj1s:
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self.proj_lora1.append(el)
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for el in proj2s:
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self.proj_lora2.append(el)
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for el in w8s:
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self.lora_weight.append(el)
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def add_shift(self, layer, origin, inputs, gating = 1.0):
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#shift = torch.zeros_like(origin)
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count = len(layer)
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for i in range(count):
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origin += layer[i](inputs)*self.lora_weight[i]*gating
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def forward(self, attn, img, txt, vec, pe, **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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# prepare image for attention
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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_qkv = attn.img_attn.qkv(img_modulated)
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#print(self.qkv_lora1)
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self.add_shift(self.qkv_lora1, img_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)
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img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v)
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# prepare txt for attention
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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_qkv = attn.txt_attn.qkv(txt_modulated)
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self.add_shift(self.qkv_lora2, txt_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)
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txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v)
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# run actual attention
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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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# calculate the img bloks
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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_mod1.gate * attn.img_attn.proj(img_attn)
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self.add_shift(self.proj_lora1, img, img_attn, img_mod1.gate)
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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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# calculate the txt bloks
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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_mod1.gate * attn.txt_attn.proj(txt_attn)
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self.add_shift(self.proj_lora2, txt, txt_attn, txt_mod1.gate)
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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 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 forward(self, attn, img, txt, vec, pe, **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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# prepare image for attention
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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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# prepare txt for attention
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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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# run actual attention
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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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# calculate the img bloks
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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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# calculate the txt bloks
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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 DoubleStreamBlockProcessor(nn.Module):
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def __init__(self):
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super().__init__()
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def __call__(self, attn, img, txt, vec, pe, **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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# prepare image for attention
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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)
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img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v)
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# prepare txt for attention
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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)
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txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v)
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# run actual attention
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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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# calculate the img bloks
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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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# calculate the txt bloks
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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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def forward(self, attn, img, txt, vec, pe, **attention_kwargs):
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self.__call__(attn, img, txt, vec, pe, **attention_kwargs)
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class IPProcessor(nn.Module):
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def __init__(self, context_dim, hidden_dim, ip_hidden_states=None, ip_scale=None, text_scale=None):
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super().__init__()
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self.ip_hidden_states = ip_hidden_states
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self.ip_scale = ip_scale
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self.text_scale = text_scale
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self.in_hidden_states_neg = None
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self.in_hidden_states_pos = ip_hidden_states
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# Ensure context_dim matches the dimension of ip_hidden_states
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self.context_dim = context_dim
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self.hidden_dim = hidden_dim
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if text_scale is None:
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self.text_scale=1.0
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if self.text_scale is None:
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self.text_scale=1.0
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if self.ip_scale is None:
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self.ip_scale=1.0
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if self.text_scale == 0:
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self.text_scale = 0.0001
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# Initialize projections for IP-adapter
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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 forward(self, img_q, attn):
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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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# IP-adapter processing
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ip_query = img_q # latent sample query
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ip_key = self.ip_adapter_double_stream_k_proj(self.ip_hidden_states)
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ip_value = self.ip_adapter_double_stream_v_proj(self.ip_hidden_states)
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# Reshape projections for multi-head attention
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ip_key = rearrange(ip_key, 'B L (H D) -> B H L D', H=attn.num_heads)
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ip_value = rearrange(ip_value, 'B L (H D) -> B H L D', H=attn.num_heads)
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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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# Compute attention between IP projections and the latent query
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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)
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return ip_attention*self.ip_scale
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class ImageProjModel(torch.nn.Module):
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"""Projection Model
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https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/ip_adapter.py#L28
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"""
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def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
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super().__init__()
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self.generator = None
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self.cross_attention_dim = cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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def forward(self, image_embeds):
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embeds = image_embeds
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clip_extra_context_tokens = self.proj(embeds).reshape(
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-1, self.clip_extra_context_tokens, self.cross_attention_dim
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)
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clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
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return clip_extra_context_tokens
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class DoubleStreamMixerProcessor(DoubleStreamBlockLorasMixerProcessor):
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def __init__(self,):
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super().__init__()
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self.ip_adapters = nn.ModuleList()
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def add_ipadapter(self, ip_adapter):
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self.ip_adapters.append(ip_adapter)
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def get_ip_adapters(self):
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return self.ip_adapters
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def set_ip_adapters(self, ip_adapters):
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self.ip_adapters = ip_adapters
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def shift_ip(self, img_qkv, attn, x):
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for block in self.ip_adapters:
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#x = x*block.text_scale
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x += torch.mean(block(img_qkv, attn), dim=0, keepdim=True)
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return x
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def scale_txt(self, txt):
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for block in self.ip_adapters:
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txt = txt * block.text_scale
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return txt
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def add_lora(self, processor):
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if isinstance(processor, DoubleStreamBlockLorasMixerProcessor):
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self.qkv_lora1+=processor.qkv_lora1
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self.qkv_lora2+=processor.qkv_lora2
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self.proj_lora1+=processor.proj_lora1
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self.proj_lora2+=processor.proj_lora2
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self.lora_weight+=processor.lora_weight
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elif isinstance(processor, DoubleStreamMixerProcessor):
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self.qkv_lora1+=processor.qkv_lora1
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self.qkv_lora2+=processor.qkv_lora2
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self.proj_lora1+=processor.proj_lora1
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self.proj_lora2+=processor.proj_lora2
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self.lora_weight+=processor.lora_weight
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else:
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if hasattr(processor, "qkv_lora1"):
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self.qkv_lora1.append(processor.qkv_lora1)
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if hasattr(processor, "proj_lora1"):
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self.proj_lora1.append(processor.proj_lora1)
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if hasattr(processor, "qkv_lora2"):
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self.qkv_lora2.append(processor.qkv_lora2)
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if hasattr(processor, "proj_lora2"):
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self.proj_lora2.append(processor.proj_lora2)
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if hasattr(processor, "lora_weight"):
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self.lora_weight.append(processor.lora_weight)
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def forward(self, attn, img, txt, vec, pe, **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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# prepare image for attention
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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_qkv = attn.img_attn.qkv(img_modulated)
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#print(self.qkv_lora1)
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self.add_shift(self.qkv_lora1, img_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)
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img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v)
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# prepare txt for attention
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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_qkv = attn.txt_attn.qkv(txt_modulated)
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self.add_shift(self.qkv_lora2, txt_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)
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txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v)
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# run actual attention
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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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# calculate the img bloks
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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_mod1.gate * attn.img_attn.proj(img_attn)
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self.add_shift(self.proj_lora1, img, img_attn, img_mod1.gate)
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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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img = self.shift_ip(img_q, attn, img)
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# calculate the txt bloks
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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_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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#txt = self.scale_txt(txt)
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self.add_shift(self.proj_lora2, txt, txt_attn, txt_mod1.gate)
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return img, txt
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