mirror of
https://github.com/finegrain-ai/refiners.git
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make IP-Adapter generic for SD1 and SDXL
This commit is contained in:
Cédric Deltheil
Cédric Deltheil
parent
61858d9371
commit
e5425e2968
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@ -4,8 +4,7 @@ import argparse
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import torch
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from refiners.foundationals.latent_diffusion import SD1UNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1IPAdapter
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from refiners.foundationals.latent_diffusion import SD1UNet, SD1IPAdapter
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from refiners.fluxion.utils import save_to_safetensors
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335
src/refiners/foundationals/latent_diffusion/image_prompt.py
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335
src/refiners/foundationals/latent_diffusion/image_prompt.py
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@ -0,0 +1,335 @@
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from enum import IntEnum
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from functools import partial
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from typing import Generic, TypeVar, Any
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from torch import Tensor, as_tensor, cat, zeros_like, device as Device, dtype as DType
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from PIL import Image
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from refiners.fluxion.adapters.adapter import Adapter
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from refiners.foundationals.clip.image_encoder import CLIPImageEncoder
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
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from refiners.fluxion.layers.attentions import ScaledDotProductAttention
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from refiners.fluxion.utils import image_to_tensor
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import refiners.fluxion.layers as fl
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T = TypeVar("T", bound=SD1UNet | SDXLUNet)
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TIPAdapter = TypeVar("TIPAdapter", bound="IPAdapter[Any]") # Self (see PEP 673)
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class ImageProjection(fl.Chain):
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structural_attrs = ["clip_image_embedding_dim", "clip_text_embedding_dim", "sequence_length"]
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def __init__(
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self,
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clip_image_embedding_dim: int = 1024,
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clip_text_embedding_dim: int = 768,
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sequence_length: int = 4,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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self.clip_image_embedding_dim = clip_image_embedding_dim
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self.clip_text_embedding_dim = clip_text_embedding_dim
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self.sequence_length = sequence_length
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super().__init__(
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fl.Linear(
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in_features=clip_image_embedding_dim,
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out_features=clip_text_embedding_dim * sequence_length,
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device=device,
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dtype=dtype,
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),
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fl.Reshape(sequence_length, clip_text_embedding_dim),
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fl.LayerNorm(normalized_shape=clip_text_embedding_dim, device=device, dtype=dtype),
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)
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class _CrossAttnIndex(IntEnum):
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TXT_CROSS_ATTN = 0 # text cross-attention
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IMG_CROSS_ATTN = 1 # image cross-attention
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# Fluxion's Attention layer drop-in replacement implementing Decoupled Cross-Attention
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class IPAttention(fl.Chain):
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structural_attrs = [
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"embedding_dim",
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"text_sequence_length",
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"image_sequence_length",
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"scale",
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"num_heads",
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"heads_dim",
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"key_embedding_dim",
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"value_embedding_dim",
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"inner_dim",
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"use_bias",
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"is_causal",
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]
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def __init__(
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self,
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embedding_dim: int,
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text_sequence_length: int = 77,
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image_sequence_length: int = 4,
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scale: float = 1.0,
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num_heads: int = 1,
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key_embedding_dim: int | None = None,
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value_embedding_dim: int | None = None,
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inner_dim: int | None = None,
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use_bias: bool = True,
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is_causal: bool | None = None,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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assert (
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embedding_dim % num_heads == 0
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), f"embedding_dim {embedding_dim} must be divisible by num_heads {num_heads}"
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self.embedding_dim = embedding_dim
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self.text_sequence_length = text_sequence_length
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self.image_sequence_length = image_sequence_length
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self.scale = scale
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self.num_heads = num_heads
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self.heads_dim = embedding_dim // num_heads
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self.key_embedding_dim = key_embedding_dim or embedding_dim
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self.value_embedding_dim = value_embedding_dim or embedding_dim
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self.inner_dim = inner_dim or embedding_dim
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self.use_bias = use_bias
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self.is_causal = is_causal
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super().__init__(
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fl.Distribute(
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# Note: the same query is used for image cross-attention as for text cross-attention
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fl.Linear(
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in_features=self.embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wq
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fl.Parallel(
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fl.Chain(
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fl.Slicing(dim=1, start=0, length=text_sequence_length),
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fl.Linear(
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in_features=self.key_embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wk
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),
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fl.Chain(
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fl.Slicing(dim=1, start=text_sequence_length, length=image_sequence_length),
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fl.Linear(
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in_features=self.key_embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wk'
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),
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),
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fl.Parallel(
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fl.Chain(
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fl.Slicing(dim=1, start=0, length=text_sequence_length),
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fl.Linear(
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in_features=self.key_embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wv
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),
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fl.Chain(
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fl.Slicing(dim=1, start=text_sequence_length, length=image_sequence_length),
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fl.Linear(
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in_features=self.key_embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wv'
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),
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),
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),
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fl.Sum(
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fl.Chain(
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fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.TXT_CROSS_ATTN)),
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ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal),
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),
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fl.Chain(
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fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.IMG_CROSS_ATTN)),
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ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal),
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fl.Lambda(func=self.scale_outputs),
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),
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),
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fl.Linear(
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in_features=self.inner_dim,
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out_features=self.embedding_dim,
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bias=True,
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device=device,
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dtype=dtype,
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),
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)
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def select_qkv(
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self, query: Tensor, keys: tuple[Tensor, Tensor], values: tuple[Tensor, Tensor], index: _CrossAttnIndex
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) -> tuple[Tensor, Tensor, Tensor]:
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return (query, keys[index.value], values[index.value])
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def scale_outputs(self, x: Tensor) -> Tensor:
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return x * self.scale
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class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
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structural_attrs = ["text_sequence_length", "image_sequence_length", "scale"]
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def __init__(
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self,
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target: fl.Attention,
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text_sequence_length: int = 77,
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image_sequence_length: int = 4,
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scale: float = 1.0,
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) -> None:
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self.text_sequence_length = text_sequence_length
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self.image_sequence_length = image_sequence_length
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self.scale = scale
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with self.setup_adapter(target):
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super().__init__(
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IPAttention(
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embedding_dim=target.embedding_dim,
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text_sequence_length=text_sequence_length,
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image_sequence_length=image_sequence_length,
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scale=scale,
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num_heads=target.num_heads,
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key_embedding_dim=target.key_embedding_dim,
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value_embedding_dim=target.value_embedding_dim,
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inner_dim=target.inner_dim,
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use_bias=target.use_bias,
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is_causal=target.is_causal,
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device=target.device,
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dtype=target.dtype,
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)
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)
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def get_parameter_name(self, matrix: str, bias: bool = False) -> str:
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match matrix:
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case "wq":
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index = 0
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case "wk":
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index = 1
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case "wk_prime":
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index = 2
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case "wv":
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index = 3
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case "wv_prime":
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index = 4
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case "proj":
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index = 5
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case _:
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raise ValueError(f"Unexpected matrix name {matrix}")
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linear = list(self.IPAttention.layers(fl.Linear))[index]
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param = getattr(linear, "bias" if bias else "weight")
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name = next((n for n, p in self.named_parameters() if id(p) == id(param)), None)
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assert name is not None
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return name
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class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
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def __init__(
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self,
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target: T,
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clip_image_encoder: CLIPImageEncoder,
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scale: float = 1.0,
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weights: dict[str, Tensor] | None = None,
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) -> None:
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with self.setup_adapter(target):
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super().__init__(target)
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self.clip_image_encoder = clip_image_encoder
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self.image_proj = ImageProjection(device=target.device, dtype=target.dtype)
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self.sub_adapters = [
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CrossAttentionAdapter(target=cross_attn, scale=scale)
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for cross_attn in filter(lambda attn: type(attn) != fl.SelfAttention, target.layers(fl.Attention))
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]
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if weights is not None:
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image_proj_state_dict: dict[str, Tensor] = {
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k.removeprefix("image_proj."): v for k, v in weights.items() if k.startswith("image_proj.")
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}
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self.image_proj.load_state_dict(image_proj_state_dict)
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for i, cross_attn in enumerate(self.sub_adapters):
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cross_attn_state_dict: dict[str, Tensor] = {}
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for k, v in weights.items():
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prefix = f"ip_adapter.{i:03d}."
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if not k.startswith(prefix):
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continue
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cross_attn_state_dict[k.removeprefix(prefix)] = v
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# Retrieve original (frozen) cross-attention weights
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# Note: this assumes the target UNet has already loaded weights
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cross_attn_linears = list(cross_attn.target.layers(fl.Linear))
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assert len(cross_attn_linears) == 4 # Wq, Wk, Wv and Proj
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cross_attn_state_dict[cross_attn.get_parameter_name("wq")] = cross_attn_linears[0].weight
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cross_attn_state_dict[cross_attn.get_parameter_name("wk")] = cross_attn_linears[1].weight
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cross_attn_state_dict[cross_attn.get_parameter_name("wv")] = cross_attn_linears[2].weight
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cross_attn_state_dict[cross_attn.get_parameter_name("proj")] = cross_attn_linears[3].weight
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cross_attn_state_dict[cross_attn.get_parameter_name("proj", bias=True)] = cross_attn_linears[3].bias
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cross_attn.load_state_dict(state_dict=cross_attn_state_dict)
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def inject(self: "TIPAdapter", parent: fl.Chain | None = None) -> "TIPAdapter":
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for adapter in self.sub_adapters:
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adapter.inject()
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return super().inject(parent)
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def eject(self) -> None:
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for adapter in self.sub_adapters:
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adapter.eject()
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super().eject()
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# These should be concatenated to the CLIP text embedding before setting the UNet context
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def compute_clip_image_embedding(self, image_prompt: Tensor | None) -> Tensor:
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clip_embedding = self.clip_image_encoder(image_prompt)
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conditional_embedding = self.image_proj(clip_embedding)
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negative_embedding = self.image_proj(zeros_like(clip_embedding))
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return cat((negative_embedding, conditional_embedding))
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def preprocess_image(
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self,
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image: Image.Image,
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size: tuple[int, int] = (224, 224),
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mean: list[float] | None = None,
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std: list[float] | None = None,
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) -> Tensor:
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# Default mean and std are parameters from https://github.com/openai/CLIP
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return self._normalize(
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image_to_tensor(image.resize(size), device=self.target.device, dtype=self.target.dtype),
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mean=[0.48145466, 0.4578275, 0.40821073] if mean is None else mean,
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std=[0.26862954, 0.26130258, 0.27577711] if std is None else std,
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)
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# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py
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@staticmethod
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def _normalize(tensor: Tensor, mean: list[float], std: list[float], inplace: bool = False) -> Tensor:
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assert tensor.is_floating_point()
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assert tensor.ndim >= 3
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if not inplace:
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tensor = tensor.clone()
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dtype = tensor.dtype
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mean_tensor = as_tensor(mean, dtype=tensor.dtype, device=tensor.device)
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std_tensor = as_tensor(std, dtype=tensor.dtype, device=tensor.device)
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if (std_tensor == 0).any():
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raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
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if mean_tensor.ndim == 1:
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mean_tensor = mean_tensor.view(-1, 1, 1)
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if std_tensor.ndim == 1:
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std_tensor = std_tensor.view(-1, 1, 1)
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return tensor.sub_(mean_tensor).div_(std_tensor)
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@ -1,234 +1,12 @@
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from enum import IntEnum
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from pathlib import Path
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from functools import partial
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from torch import Tensor
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from torch import Tensor, as_tensor, cat, zeros_like, device as Device, dtype as DType
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from PIL import Image
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from refiners.foundationals.latent_diffusion.image_prompt import IPAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1UNet
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from refiners.fluxion.adapters.adapter import Adapter
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from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
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from refiners.fluxion.layers.attentions import ScaledDotProductAttention
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from refiners.fluxion.utils import image_to_tensor, load_from_safetensors
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import refiners.fluxion.layers as fl
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class ImageProjection(fl.Chain):
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structural_attrs = ["clip_image_embedding_dim", "clip_text_embedding_dim", "sequence_length"]
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def __init__(
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self,
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clip_image_embedding_dim: int = 1024,
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clip_text_embedding_dim: int = 768,
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sequence_length: int = 4,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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self.clip_image_embedding_dim = clip_image_embedding_dim
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self.clip_text_embedding_dim = clip_text_embedding_dim
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self.sequence_length = sequence_length
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super().__init__(
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fl.Linear(
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in_features=clip_image_embedding_dim,
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out_features=clip_text_embedding_dim * sequence_length,
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device=device,
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dtype=dtype,
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),
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fl.Reshape(sequence_length, clip_text_embedding_dim),
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fl.LayerNorm(normalized_shape=clip_text_embedding_dim, device=device, dtype=dtype),
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)
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class _CrossAttnIndex(IntEnum):
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TXT_CROSS_ATTN = 0 # text cross-attention
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IMG_CROSS_ATTN = 1 # image cross-attention
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# Fluxion's Attention layer drop-in replacement implementing Decoupled Cross-Attention
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class IPAttention(fl.Chain):
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structural_attrs = [
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"embedding_dim",
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"text_sequence_length",
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"image_sequence_length",
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"scale",
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"num_heads",
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"heads_dim",
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"key_embedding_dim",
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"value_embedding_dim",
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"inner_dim",
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"use_bias",
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"is_causal",
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]
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def __init__(
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self,
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embedding_dim: int,
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text_sequence_length: int = 77,
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image_sequence_length: int = 4,
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scale: float = 1.0,
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num_heads: int = 1,
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key_embedding_dim: int | None = None,
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value_embedding_dim: int | None = None,
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inner_dim: int | None = None,
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use_bias: bool = True,
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is_causal: bool | None = None,
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device: Device | str | None = None,
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dtype: DType | None = None,
|
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) -> None:
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assert (
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embedding_dim % num_heads == 0
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), f"embedding_dim {embedding_dim} must be divisible by num_heads {num_heads}"
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self.embedding_dim = embedding_dim
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self.text_sequence_length = text_sequence_length
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self.image_sequence_length = image_sequence_length
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self.scale = scale
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self.num_heads = num_heads
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self.heads_dim = embedding_dim // num_heads
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self.key_embedding_dim = key_embedding_dim or embedding_dim
|
||||
self.value_embedding_dim = value_embedding_dim or embedding_dim
|
||||
self.inner_dim = inner_dim or embedding_dim
|
||||
self.use_bias = use_bias
|
||||
self.is_causal = is_causal
|
||||
super().__init__(
|
||||
fl.Distribute(
|
||||
# Note: the same query is used for image cross-attention as for text cross-attention
|
||||
fl.Linear(
|
||||
in_features=self.embedding_dim,
|
||||
out_features=self.inner_dim,
|
||||
bias=self.use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
), # Wq
|
||||
fl.Parallel(
|
||||
fl.Chain(
|
||||
fl.Slicing(dim=1, start=0, length=text_sequence_length),
|
||||
fl.Linear(
|
||||
in_features=self.key_embedding_dim,
|
||||
out_features=self.inner_dim,
|
||||
bias=self.use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
), # Wk
|
||||
),
|
||||
fl.Chain(
|
||||
fl.Slicing(dim=1, start=text_sequence_length, length=image_sequence_length),
|
||||
fl.Linear(
|
||||
in_features=self.key_embedding_dim,
|
||||
out_features=self.inner_dim,
|
||||
bias=self.use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
), # Wk'
|
||||
),
|
||||
),
|
||||
fl.Parallel(
|
||||
fl.Chain(
|
||||
fl.Slicing(dim=1, start=0, length=text_sequence_length),
|
||||
fl.Linear(
|
||||
in_features=self.key_embedding_dim,
|
||||
out_features=self.inner_dim,
|
||||
bias=self.use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
), # Wv
|
||||
),
|
||||
fl.Chain(
|
||||
fl.Slicing(dim=1, start=text_sequence_length, length=image_sequence_length),
|
||||
fl.Linear(
|
||||
in_features=self.key_embedding_dim,
|
||||
out_features=self.inner_dim,
|
||||
bias=self.use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
), # Wv'
|
||||
),
|
||||
),
|
||||
),
|
||||
fl.Sum(
|
||||
fl.Chain(
|
||||
fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.TXT_CROSS_ATTN)),
|
||||
ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal),
|
||||
),
|
||||
fl.Chain(
|
||||
fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.IMG_CROSS_ATTN)),
|
||||
ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal),
|
||||
fl.Lambda(func=self.scale_outputs),
|
||||
),
|
||||
),
|
||||
fl.Linear(
|
||||
in_features=self.inner_dim,
|
||||
out_features=self.embedding_dim,
|
||||
bias=True,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
),
|
||||
)
|
||||
|
||||
def select_qkv(
|
||||
self, query: Tensor, keys: tuple[Tensor, Tensor], values: tuple[Tensor, Tensor], index: _CrossAttnIndex
|
||||
) -> tuple[Tensor, Tensor, Tensor]:
|
||||
return (query, keys[index.value], values[index.value])
|
||||
|
||||
def scale_outputs(self, x: Tensor) -> Tensor:
|
||||
return x * self.scale
|
||||
|
||||
|
||||
class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
|
||||
structural_attrs = ["text_sequence_length", "image_sequence_length", "scale"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
target: fl.Attention,
|
||||
text_sequence_length: int = 77,
|
||||
image_sequence_length: int = 4,
|
||||
scale: float = 1.0,
|
||||
) -> None:
|
||||
self.text_sequence_length = text_sequence_length
|
||||
self.image_sequence_length = image_sequence_length
|
||||
self.scale = scale
|
||||
with self.setup_adapter(target):
|
||||
super().__init__(
|
||||
IPAttention(
|
||||
embedding_dim=target.embedding_dim,
|
||||
text_sequence_length=text_sequence_length,
|
||||
image_sequence_length=image_sequence_length,
|
||||
scale=scale,
|
||||
num_heads=target.num_heads,
|
||||
key_embedding_dim=target.key_embedding_dim,
|
||||
value_embedding_dim=target.value_embedding_dim,
|
||||
inner_dim=target.inner_dim,
|
||||
use_bias=target.use_bias,
|
||||
is_causal=target.is_causal,
|
||||
device=target.device,
|
||||
dtype=target.dtype,
|
||||
)
|
||||
)
|
||||
|
||||
def get_parameter_name(self, matrix: str, bias: bool = False) -> str:
|
||||
match matrix:
|
||||
case "wq":
|
||||
index = 0
|
||||
case "wk":
|
||||
index = 1
|
||||
case "wk_prime":
|
||||
index = 2
|
||||
case "wv":
|
||||
index = 3
|
||||
case "wv_prime":
|
||||
index = 4
|
||||
case "proj":
|
||||
index = 5
|
||||
case _:
|
||||
raise ValueError(f"Unexpected matrix name {matrix}")
|
||||
|
||||
linear = list(self.IPAttention.layers(fl.Linear))[index]
|
||||
param = getattr(linear, "bias" if bias else "weight")
|
||||
name = next((n for n, p in self.named_parameters() if id(p) == id(param)), None)
|
||||
assert name is not None
|
||||
return name
|
||||
|
||||
|
||||
class SD1IPAdapter(fl.Chain, Adapter[SD1UNet]):
|
||||
class SD1IPAdapter(IPAdapter[SD1UNet]):
|
||||
def __init__(
|
||||
self,
|
||||
target: SD1UNet,
|
||||
|
|
@ -236,113 +14,9 @@ class SD1IPAdapter(fl.Chain, Adapter[SD1UNet]):
|
|||
scale: float = 1.0,
|
||||
weights: dict[str, Tensor] | None = None,
|
||||
) -> None:
|
||||
with self.setup_adapter(target):
|
||||
super().__init__(target)
|
||||
|
||||
self.clip_image_encoder = clip_image_encoder or CLIPImageEncoderH(device=target.device, dtype=target.dtype)
|
||||
self.image_proj = ImageProjection(device=target.device, dtype=target.dtype)
|
||||
|
||||
self.sub_adapters = [
|
||||
CrossAttentionAdapter(target=cross_attn, scale=scale)
|
||||
for cross_attn in filter(lambda attn: type(attn) != fl.SelfAttention, target.layers(fl.Attention))
|
||||
]
|
||||
|
||||
if weights is not None:
|
||||
image_proj_state_dict: dict[str, Tensor] = {
|
||||
k.removeprefix("image_proj."): v for k, v in weights.items() if k.startswith("image_proj.")
|
||||
}
|
||||
self.image_proj.load_state_dict(image_proj_state_dict)
|
||||
|
||||
for i, cross_attn in enumerate(self.sub_adapters):
|
||||
cross_attn_state_dict: dict[str, Tensor] = {}
|
||||
for k, v in weights.items():
|
||||
prefix = f"ip_adapter.{i:03d}."
|
||||
if not k.startswith(prefix):
|
||||
continue
|
||||
cross_attn_state_dict[k.removeprefix(prefix)] = v
|
||||
|
||||
# Retrieve original (frozen) cross-attention weights
|
||||
# Note: this assumes the target UNet has already loaded weights
|
||||
cross_attn_linears = list(cross_attn.target.layers(fl.Linear))
|
||||
assert len(cross_attn_linears) == 4 # Wq, Wk, Wv and Proj
|
||||
|
||||
cross_attn_state_dict[cross_attn.get_parameter_name("wq")] = cross_attn_linears[0].weight
|
||||
cross_attn_state_dict[cross_attn.get_parameter_name("wk")] = cross_attn_linears[1].weight
|
||||
cross_attn_state_dict[cross_attn.get_parameter_name("wv")] = cross_attn_linears[2].weight
|
||||
cross_attn_state_dict[cross_attn.get_parameter_name("proj")] = cross_attn_linears[3].weight
|
||||
cross_attn_state_dict[cross_attn.get_parameter_name("proj", bias=True)] = cross_attn_linears[3].bias
|
||||
|
||||
cross_attn.load_state_dict(state_dict=cross_attn_state_dict)
|
||||
|
||||
@classmethod
|
||||
def from_safetensors(
|
||||
cls,
|
||||
target: SD1UNet,
|
||||
checkpoint_path: Path | str,
|
||||
clip_image_encoder: CLIPImageEncoderH | None = None,
|
||||
scale: float = 1.0,
|
||||
):
|
||||
weights = load_from_safetensors(checkpoint_path, device=target.device if target.device is not None else "cpu")
|
||||
|
||||
return cls(
|
||||
super().__init__(
|
||||
target=target,
|
||||
clip_image_encoder=clip_image_encoder,
|
||||
clip_image_encoder=clip_image_encoder or CLIPImageEncoderH(device=target.device, dtype=target.dtype),
|
||||
scale=scale,
|
||||
weights=weights,
|
||||
)
|
||||
|
||||
def inject(self: "SD1IPAdapter", parent: fl.Chain | None = None) -> "SD1IPAdapter":
|
||||
for adapter in self.sub_adapters:
|
||||
adapter.inject()
|
||||
return super().inject(parent)
|
||||
|
||||
def eject(self) -> None:
|
||||
for adapter in self.sub_adapters:
|
||||
adapter.eject()
|
||||
super().eject()
|
||||
|
||||
# These should be concatenated to the CLIP text embedding before setting the UNet context
|
||||
def compute_clip_image_embedding(self, image_prompt: Tensor | None) -> Tensor:
|
||||
clip_embedding = self.clip_image_encoder(image_prompt)
|
||||
conditional_embedding = self.image_proj(clip_embedding)
|
||||
negative_embedding = self.image_proj(zeros_like(clip_embedding))
|
||||
return cat((negative_embedding, conditional_embedding))
|
||||
|
||||
def preprocess_image(
|
||||
self,
|
||||
image: Image.Image,
|
||||
size: tuple[int, int] = (224, 224),
|
||||
mean: list[float] | None = None,
|
||||
std: list[float] | None = None,
|
||||
) -> Tensor:
|
||||
# Default mean and std are parameters from https://github.com/openai/CLIP
|
||||
return self._normalize(
|
||||
image_to_tensor(image.resize(size), device=self.target.device, dtype=self.target.dtype),
|
||||
mean=[0.48145466, 0.4578275, 0.40821073] if mean is None else mean,
|
||||
std=[0.26862954, 0.26130258, 0.27577711] if std is None else std,
|
||||
)
|
||||
|
||||
# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py
|
||||
@staticmethod
|
||||
def _normalize(tensor: Tensor, mean: list[float], std: list[float], inplace: bool = False) -> Tensor:
|
||||
assert tensor.is_floating_point()
|
||||
assert tensor.ndim >= 3
|
||||
|
||||
if not inplace:
|
||||
tensor = tensor.clone()
|
||||
|
||||
dtype = tensor.dtype
|
||||
|
||||
mean_tensor = as_tensor(mean, dtype=tensor.dtype, device=tensor.device)
|
||||
std_tensor = as_tensor(std, dtype=tensor.dtype, device=tensor.device)
|
||||
|
||||
if (std_tensor == 0).any():
|
||||
raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
|
||||
|
||||
if mean_tensor.ndim == 1:
|
||||
mean_tensor = mean_tensor.view(-1, 1, 1)
|
||||
|
||||
if std_tensor.ndim == 1:
|
||||
std_tensor = std_tensor.view(-1, 1, 1)
|
||||
|
||||
return tensor.sub_(mean_tensor).div_(std_tensor)
|
||||
|
|
|
|||
|
|
@ -974,7 +974,7 @@ def test_diffusion_ip_adapter(
|
|||
prompt = "best quality, high quality"
|
||||
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
||||
|
||||
ip_adapter = SD1IPAdapter.from_safetensors(target=sd15.unet, checkpoint_path=ip_adapter_weights)
|
||||
ip_adapter = SD1IPAdapter(target=sd15.unet, weights=load_from_safetensors(ip_adapter_weights))
|
||||
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
|
||||
ip_adapter.inject()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue