import random
import torch
import sys
from diffusers import Transformer2DModel
from torch import nn
from torch.nn import Parameter
from torch.nn.modules.module import T
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from toolkit.models.clip_pre_processor import CLIPImagePreProcessor
from toolkit.models.zipper_resampler import ZipperResampler
from toolkit.saving import load_ip_adapter_model
from toolkit.train_tools import get_torch_dtype
from toolkit.util.inverse_cfg import inverse_classifier_guidance
from typing import TYPE_CHECKING, Union, Iterator, Mapping, Any, Tuple, List, Optional
from collections import OrderedDict
from toolkit.util.ip_adapter_utils import AttnProcessor2_0, IPAttnProcessor2_0, ImageProjModel
from toolkit.resampler import Resampler
from toolkit.config_modules import AdapterConfig
from toolkit.prompt_utils import PromptEmbeds
import weakref
from diffusers import FluxTransformer2DModel
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
from transformers import (
CLIPImageProcessor,
CLIPVisionModelWithProjection,
AutoImageProcessor,
ConvNextV2ForImageClassification,
ConvNextForImageClassification,
ConvNextImageProcessor
)
from toolkit.models.size_agnostic_feature_encoder import SAFEImageProcessor, SAFEVisionModel
import torch.nn.functional as F
class MLPProjModelClipFace(torch.nn.Module):
def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.num_tokens = num_tokens
self.norm = torch.nn.LayerNorm(id_embeddings_dim)
self.proj = torch.nn.Sequential(
torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2),
torch.nn.GELU(),
torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens),
)
# Initialize the last linear layer weights near zero
torch.nn.init.uniform_(self.proj[2].weight, a=-0.01, b=0.01)
torch.nn.init.zeros_(self.proj[2].bias)
# # Custom initialization for LayerNorm to output near zero
# torch.nn.init.constant_(self.norm.weight, 0.1) # Small weights near zero
# torch.nn.init.zeros_(self.norm.bias) # Bias to zero
def forward(self, x):
x = self.norm(x)
x = self.proj(x)
x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
return x
class CustomIPAttentionProcessor(IPAttnProcessor2_0):
def __init__(self, hidden_size, cross_attention_dim, scale=1.0, num_tokens=4, adapter=None, train_scaler=False, full_token_scaler=False):
super().__init__(hidden_size, cross_attention_dim, scale=scale, num_tokens=num_tokens)
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.train_scaler = train_scaler
if train_scaler:
if full_token_scaler:
self.ip_scaler = torch.nn.Parameter(torch.ones([num_tokens], dtype=torch.float32) * 0.999)
else:
self.ip_scaler = torch.nn.Parameter(torch.ones([1], dtype=torch.float32) * 0.999)
# self.ip_scaler = torch.nn.Parameter(torch.ones([1], dtype=torch.float32) * 0.9999)
self.ip_scaler.requires_grad_(True)
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
):
is_active = self.adapter_ref().is_active
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if is_active:
# since we are removing tokens, we need to adjust the sequence length
sequence_length = sequence_length - self.num_tokens
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
# will be none if disabled
if not is_active:
ip_hidden_states = None
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
else:
# get encoder_hidden_states, ip_hidden_states
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
encoder_hidden_states, ip_hidden_states = (
encoder_hidden_states[:, :end_pos, :],
encoder_hidden_states[:, end_pos:, :],
)
if attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
try:
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
except Exception as e:
print(e)
raise e
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# will be none if disabled
if ip_hidden_states is not None:
# apply scaler
if self.train_scaler:
weight = self.ip_scaler
# reshape to (1, self.num_tokens, 1)
weight = weight.view(1, -1, 1)
ip_hidden_states = ip_hidden_states * weight
# for ip-adapter
ip_key = self.to_k_ip(ip_hidden_states)
ip_value = self.to_v_ip(ip_hidden_states)
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
ip_hidden_states = F.scaled_dot_product_attention(
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
ip_hidden_states = ip_hidden_states.to(query.dtype)
scale = self.scale
hidden_states = hidden_states + scale * ip_hidden_states
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
# this ensures that the ip_scaler is not changed when we load the model
# def _apply(self, fn):
# if hasattr(self, "ip_scaler"):
# # Overriding the _apply method to prevent the special_parameter from changing dtype
# self.ip_scaler = fn(self.ip_scaler)
# # Temporarily set the special_parameter to None to exclude it from default _apply processing
# ip_scaler = self.ip_scaler
# self.ip_scaler = None
# super(CustomIPAttentionProcessor, self)._apply(fn)
# # Restore the special_parameter after the default _apply processing
# self.ip_scaler = ip_scaler
# return self
# else:
# return super(CustomIPAttentionProcessor, self)._apply(fn)
class CustomIPFluxAttnProcessor2_0(torch.nn.Module):
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self, hidden_size, cross_attention_dim, scale=1.0, num_tokens=4, adapter=None, train_scaler=False,
full_token_scaler=False):
super().__init__()
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
self.scale = scale
self.num_tokens = num_tokens
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.train_scaler = train_scaler
self.num_tokens = num_tokens
if train_scaler:
if full_token_scaler:
self.ip_scaler = torch.nn.Parameter(torch.ones([num_tokens], dtype=torch.float32) * 0.999)
else:
self.ip_scaler = torch.nn.Parameter(torch.ones([1], dtype=torch.float32) * 0.999)
# self.ip_scaler = torch.nn.Parameter(torch.ones([1], dtype=torch.float32) * 0.9999)
self.ip_scaler.requires_grad_(True)
def __call__(
self,
attn,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.FloatTensor:
is_active = self.adapter_ref().is_active
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from diffusers.models.embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# begin ip adapter
if not is_active:
ip_hidden_states = None
else:
# get ip hidden states. Should be stored
ip_hidden_states = self.adapter_ref().last_conditional
# add unconditional to front if it exists
if ip_hidden_states.shape[0] * 2 == batch_size:
if self.adapter_ref().last_unconditional is None:
raise ValueError("Unconditional is None but should not be")
ip_hidden_states = torch.cat([self.adapter_ref().last_unconditional, ip_hidden_states], dim=0)
if ip_hidden_states is not None:
# apply scaler
if self.train_scaler:
weight = self.ip_scaler
# reshape to (1, self.num_tokens, 1)
weight = weight.view(1, -1, 1)
ip_hidden_states = ip_hidden_states * weight
# for ip-adapter
ip_key = self.to_k_ip(ip_hidden_states)
ip_value = self.to_v_ip(ip_hidden_states)
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_hidden_states = F.scaled_dot_product_attention(
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
ip_hidden_states = ip_hidden_states.to(query.dtype)
scale = self.scale
hidden_states = hidden_states + scale * ip_hidden_states
# end ip adapter
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states
# loosely based on # ref https://github.com/tencent-ailab/IP-Adapter/blob/main/tutorial_train.py
class IPAdapter(torch.nn.Module):
"""IP-Adapter"""
def __init__(self, sd: 'StableDiffusion', adapter_config: 'AdapterConfig'):
super().__init__()
self.config = adapter_config
self.sd_ref: weakref.ref = weakref.ref(sd)
self.device = self.sd_ref().unet.device
self.preprocessor: Optional[CLIPImagePreProcessor] = None
self.input_size = 224
self.clip_noise_zero = True
self.unconditional: torch.Tensor = None
self.last_conditional: torch.Tensor = None
self.last_unconditional: torch.Tensor = None
self.additional_loss = None
if self.config.image_encoder_arch.startswith("clip"):
try:
self.clip_image_processor = CLIPImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
self.clip_image_processor = CLIPImageProcessor()
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
adapter_config.image_encoder_path,
ignore_mismatched_sizes=True).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'siglip':
from transformers import SiglipImageProcessor, SiglipVisionModel
try:
self.clip_image_processor = SiglipImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
self.clip_image_processor = SiglipImageProcessor()
self.image_encoder = SiglipVisionModel.from_pretrained(
adapter_config.image_encoder_path,
ignore_mismatched_sizes=True).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'safe':
try:
self.clip_image_processor = SAFEImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
self.clip_image_processor = SAFEImageProcessor()
self.image_encoder = SAFEVisionModel(
in_channels=3,
num_tokens=self.config.safe_tokens,
num_vectors=sd.unet.config['cross_attention_dim'],
reducer_channels=self.config.safe_reducer_channels,
channels=self.config.safe_channels,
downscale_factor=8
).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'convnext':
try:
self.clip_image_processor = ConvNextImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
print(f"could not load image processor from {adapter_config.image_encoder_path}")
self.clip_image_processor = ConvNextImageProcessor(
size=320,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
)
self.image_encoder = ConvNextForImageClassification.from_pretrained(
adapter_config.image_encoder_path,
use_safetensors=True,
).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
elif self.config.image_encoder_arch == 'convnextv2':
try:
self.clip_image_processor = AutoImageProcessor.from_pretrained(adapter_config.image_encoder_path)
except EnvironmentError:
print(f"could not load image processor from {adapter_config.image_encoder_path}")
self.clip_image_processor = ConvNextImageProcessor(
size=512,
image_mean=[0.485, 0.456, 0.406],
image_std=[0.229, 0.224, 0.225],
)
self.image_encoder = ConvNextV2ForImageClassification.from_pretrained(
adapter_config.image_encoder_path,
use_safetensors=True,
).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
else:
raise ValueError(f"unknown image encoder arch: {adapter_config.image_encoder_arch}")
if not self.config.train_image_encoder:
# compile it
print('Compiling image encoder')
#torch.compile(self.image_encoder, fullgraph=True)
self.input_size = self.image_encoder.config.image_size
if self.config.quad_image: # 4x4 image
# self.clip_image_processor.config
# We do a 3x downscale of the image, so we need to adjust the input size
preprocessor_input_size = self.image_encoder.config.image_size * 2
# update the preprocessor so images come in at the right size
if 'height' in self.clip_image_processor.size:
self.clip_image_processor.size['height'] = preprocessor_input_size
self.clip_image_processor.size['width'] = preprocessor_input_size
elif hasattr(self.clip_image_processor, 'crop_size'):
self.clip_image_processor.size['shortest_edge'] = preprocessor_input_size
self.clip_image_processor.crop_size['height'] = preprocessor_input_size
self.clip_image_processor.crop_size['width'] = preprocessor_input_size
if self.config.image_encoder_arch == 'clip+':
# self.clip_image_processor.config
# We do a 3x downscale of the image, so we need to adjust the input size
preprocessor_input_size = self.image_encoder.config.image_size * 4
# update the preprocessor so images come in at the right size
self.clip_image_processor.size['shortest_edge'] = preprocessor_input_size
self.clip_image_processor.crop_size['height'] = preprocessor_input_size
self.clip_image_processor.crop_size['width'] = preprocessor_input_size
self.preprocessor = CLIPImagePreProcessor(
input_size=preprocessor_input_size,
clip_input_size=self.image_encoder.config.image_size,
)
if not self.config.image_encoder_arch == 'safe':
if 'height' in self.clip_image_processor.size:
self.input_size = self.clip_image_processor.size['height']
elif hasattr(self.clip_image_processor, 'crop_size'):
self.input_size = self.clip_image_processor.crop_size['height']
elif 'shortest_edge' in self.clip_image_processor.size.keys():
self.input_size = self.clip_image_processor.size['shortest_edge']
else:
raise ValueError(f"unknown image processor size: {self.clip_image_processor.size}")
self.current_scale = 1.0
self.is_active = True
is_pixart = sd.is_pixart
is_flux = sd.is_flux
if adapter_config.type == 'ip':
# ip-adapter
image_proj_model = ImageProjModel(
cross_attention_dim=sd.unet.config['cross_attention_dim'],
clip_embeddings_dim=self.image_encoder.config.projection_dim,
clip_extra_context_tokens=self.config.num_tokens, # usually 4
)
elif adapter_config.type == 'ip_clip_face':
cross_attn_dim = 4096 if is_pixart else sd.unet.config['cross_attention_dim']
image_proj_model = MLPProjModelClipFace(
cross_attention_dim=cross_attn_dim,
id_embeddings_dim=self.image_encoder.config.projection_dim,
num_tokens=self.config.num_tokens, # usually 4
)
elif adapter_config.type == 'ip+':
heads = 12 if not sd.is_xl else 20
if is_flux:
dim = 1280
else:
dim = sd.unet.config['cross_attention_dim'] if not sd.is_xl else 1280
embedding_dim = self.image_encoder.config.hidden_size if not self.config.image_encoder_arch.startswith(
'convnext') else \
self.image_encoder.config.hidden_sizes[-1]
image_encoder_state_dict = self.image_encoder.state_dict()
# max_seq_len = CLIP tokens + CLS token
max_seq_len = 257
if "vision_model.embeddings.position_embedding.weight" in image_encoder_state_dict:
# clip
max_seq_len = int(
image_encoder_state_dict["vision_model.embeddings.position_embedding.weight"].shape[0])
if is_pixart:
heads = 20
dim = 1280
output_dim = 4096
elif is_flux:
heads = 20
dim = 1280
output_dim = 3072
else:
output_dim = sd.unet.config['cross_attention_dim']
if self.config.image_encoder_arch.startswith('convnext'):
in_tokens = 16 * 16
embedding_dim = self.image_encoder.config.hidden_sizes[-1]
# ip-adapter-plus
image_proj_model = Resampler(
dim=dim,
depth=4,
dim_head=64,
heads=heads,
num_queries=self.config.num_tokens if self.config.num_tokens > 0 else max_seq_len,
embedding_dim=embedding_dim,
max_seq_len=max_seq_len,
output_dim=output_dim,
ff_mult=4
)
elif adapter_config.type == 'ipz':
dim = sd.unet.config['cross_attention_dim']
if hasattr(self.image_encoder.config, 'hidden_sizes'):
embedding_dim = self.image_encoder.config.hidden_sizes[-1]
else:
embedding_dim = self.image_encoder.config.target_hidden_size
image_encoder_state_dict = self.image_encoder.state_dict()
# max_seq_len = CLIP tokens + CLS token
in_tokens = 257
if "vision_model.embeddings.position_embedding.weight" in image_encoder_state_dict:
# clip
in_tokens = int(image_encoder_state_dict["vision_model.embeddings.position_embedding.weight"].shape[0])
if self.config.image_encoder_arch.startswith('convnext'):
in_tokens = 16 * 16
embedding_dim = self.image_encoder.config.hidden_sizes[-1]
is_conv_next = self.config.image_encoder_arch.startswith('convnext')
out_tokens = self.config.num_tokens if self.config.num_tokens > 0 else in_tokens
# ip-adapter-plus
image_proj_model = ZipperResampler(
in_size=embedding_dim,
in_tokens=in_tokens,
out_size=dim,
out_tokens=out_tokens,
hidden_size=embedding_dim,
hidden_tokens=in_tokens,
# num_blocks=1 if not is_conv_next else 2,
num_blocks=1 if not is_conv_next else 2,
is_conv_input=is_conv_next
)
elif adapter_config.type == 'ilora':
# we apply the clip encodings to the LoRA
image_proj_model = None
else:
raise ValueError(f"unknown adapter type: {adapter_config.type}")
# init adapter modules
attn_procs = {}
unet_sd = sd.unet.state_dict()
attn_processor_keys = []
if is_pixart:
transformer: Transformer2DModel = sd.unet
for i, module in transformer.transformer_blocks.named_children():
attn_processor_keys.append(f"transformer_blocks.{i}.attn1")
# cross attention
attn_processor_keys.append(f"transformer_blocks.{i}.attn2")
elif is_flux:
transformer: FluxTransformer2DModel = sd.unet
for i, module in transformer.transformer_blocks.named_children():
attn_processor_keys.append(f"transformer_blocks.{i}.attn")
# single transformer blocks do not have cross attn, but we will do them anyway
for i, module in transformer.single_transformer_blocks.named_children():
attn_processor_keys.append(f"single_transformer_blocks.{i}.attn")
else:
attn_processor_keys = list(sd.unet.attn_processors.keys())
attn_processor_names = []
blocks = []
transformer_blocks = []
for name in attn_processor_keys:
name_split = name.split(".")
block_name = f"{name_split[0]}.{name_split[1]}"
transformer_idx = name_split.index("transformer_blocks") if "transformer_blocks" in name_split else -1
if transformer_idx >= 0:
transformer_name = ".".join(name_split[:2])
transformer_name += "." + ".".join(name_split[transformer_idx:transformer_idx + 2])
if transformer_name not in transformer_blocks:
transformer_blocks.append(transformer_name)
if block_name not in blocks:
blocks.append(block_name)
if is_flux:
cross_attention_dim = None
else:
cross_attention_dim = None if name.endswith("attn1.processor") or name.endswith("attn.1") or name.endswith("attn1") else \
sd.unet.config['cross_attention_dim']
if name.startswith("mid_block"):
hidden_size = sd.unet.config['block_out_channels'][-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(sd.unet.config['block_out_channels']))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = sd.unet.config['block_out_channels'][block_id]
elif name.startswith("transformer") or name.startswith("single_transformer"):
if is_flux:
hidden_size = 3072
else:
hidden_size = sd.unet.config['cross_attention_dim']
else:
# they didnt have this, but would lead to undefined below
raise ValueError(f"unknown attn processor name: {name}")
if cross_attention_dim is None and not is_flux:
attn_procs[name] = AttnProcessor2_0()
else:
layer_name = name.split(".processor")[0]
# if quantized, we need to scale the weights
if f"{layer_name}.to_k.weight._data" in unet_sd and is_flux:
# is quantized
k_weight = torch.randn(hidden_size, hidden_size) * 0.01
v_weight = torch.randn(hidden_size, hidden_size) * 0.01
k_weight = k_weight.to(self.sd_ref().torch_dtype)
v_weight = v_weight.to(self.sd_ref().torch_dtype)
else:
k_weight = unet_sd[layer_name + ".to_k.weight"]
v_weight = unet_sd[layer_name + ".to_v.weight"]
weights = {
"to_k_ip.weight": k_weight,
"to_v_ip.weight": v_weight
}
if is_flux:
attn_procs[name] = CustomIPFluxAttnProcessor2_0(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
num_tokens=self.config.num_tokens,
adapter=self,
train_scaler=self.config.train_scaler or self.config.merge_scaler,
full_token_scaler=False
)
else:
attn_procs[name] = CustomIPAttentionProcessor(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
num_tokens=self.config.num_tokens,
adapter=self,
train_scaler=self.config.train_scaler or self.config.merge_scaler,
# full_token_scaler=self.config.train_scaler # full token cannot be merged in, only use if training an actual scaler
full_token_scaler=False
)
if self.sd_ref().is_pixart or self.sd_ref().is_flux:
# pixart is much more sensitive
weights = {
"to_k_ip.weight": weights["to_k_ip.weight"] * 0.01,
"to_v_ip.weight": weights["to_v_ip.weight"] * 0.01,
}
attn_procs[name].load_state_dict(weights, strict=False)
attn_processor_names.append(name)
print(f"Attn Processors")
print(attn_processor_names)
if self.sd_ref().is_pixart:
# we have to set them ourselves
transformer: Transformer2DModel = sd.unet
for i, module in transformer.transformer_blocks.named_children():
module.attn1.processor = attn_procs[f"transformer_blocks.{i}.attn1"]
module.attn2.processor = attn_procs[f"transformer_blocks.{i}.attn2"]
self.adapter_modules = torch.nn.ModuleList(
[
transformer.transformer_blocks[i].attn2.processor for i in
range(len(transformer.transformer_blocks))
])
elif self.sd_ref().is_flux:
# we have to set them ourselves
transformer: FluxTransformer2DModel = sd.unet
for i, module in transformer.transformer_blocks.named_children():
module.attn.processor = attn_procs[f"transformer_blocks.{i}.attn"]
# do single blocks too even though they dont have cross attn
for i, module in transformer.single_transformer_blocks.named_children():
module.attn.processor = attn_procs[f"single_transformer_blocks.{i}.attn"]
self.adapter_modules = torch.nn.ModuleList(
[
transformer.transformer_blocks[i].attn.processor for i in
range(len(transformer.transformer_blocks))
] + [
transformer.single_transformer_blocks[i].attn.processor for i in
range(len(transformer.single_transformer_blocks))
]
)
else:
sd.unet.set_attn_processor(attn_procs)
self.adapter_modules = torch.nn.ModuleList(sd.unet.attn_processors.values())
sd.adapter = self
self.unet_ref: weakref.ref = weakref.ref(sd.unet)
self.image_proj_model = image_proj_model
# load the weights if we have some
if self.config.name_or_path:
loaded_state_dict = load_ip_adapter_model(
self.config.name_or_path,
device='cpu',
dtype=sd.torch_dtype
)
self.load_state_dict(loaded_state_dict)
self.set_scale(1.0)
if self.config.train_image_encoder:
self.image_encoder.train()
self.image_encoder.requires_grad_(True)
# premake a unconditional
zerod = torch.zeros(1, 3, self.input_size, self.input_size, device=self.device, dtype=torch.float16)
self.unconditional = self.clip_image_processor(
images=zerod,
return_tensors="pt",
do_resize=True,
do_rescale=False,
).pixel_values
def to(self, *args, **kwargs):
super().to(*args, **kwargs)
self.image_encoder.to(*args, **kwargs)
self.image_proj_model.to(*args, **kwargs)
self.adapter_modules.to(*args, **kwargs)
if self.preprocessor is not None:
self.preprocessor.to(*args, **kwargs)
return self
# def load_ip_adapter(self, state_dict: Union[OrderedDict, dict]):
# self.image_proj_model.load_state_dict(state_dict["image_proj"])
# ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
# ip_layers.load_state_dict(state_dict["ip_adapter"])
# if self.config.train_image_encoder and 'image_encoder' in state_dict:
# self.image_encoder.load_state_dict(state_dict["image_encoder"])
# if self.preprocessor is not None and 'preprocessor' in state_dict:
# self.preprocessor.load_state_dict(state_dict["preprocessor"])
# def load_state_dict(self, state_dict: Union[OrderedDict, dict]):
# self.load_ip_adapter(state_dict)
def state_dict(self) -> OrderedDict:
state_dict = OrderedDict()
if self.config.train_only_image_encoder:
return self.image_encoder.state_dict()
if self.config.train_scaler:
state_dict["ip_scale"] = self.adapter_modules.state_dict()
# remove items that are not scalers
for key in list(state_dict["ip_scale"].keys()):
if not key.endswith("ip_scaler"):
del state_dict["ip_scale"][key]
return state_dict
state_dict["image_proj"] = self.image_proj_model.state_dict()
state_dict["ip_adapter"] = self.adapter_modules.state_dict()
# handle merge scaler training
if self.config.merge_scaler:
for key in list(state_dict["ip_adapter"].keys()):
if key.endswith("ip_scaler"):
# merge in the scaler so we dont have to save it and it will be compatible with other ip adapters
scale = state_dict["ip_adapter"][key].clone()
key_start = key.split(".")[-2]
# reshape to (1, 1)
scale = scale.view(1, 1)
del state_dict["ip_adapter"][key]
# find the to_k_ip and to_v_ip keys
for key2 in list(state_dict["ip_adapter"].keys()):
if key2.endswith(f"{key_start}.to_k_ip.weight"):
state_dict["ip_adapter"][key2] = state_dict["ip_adapter"][key2].clone() * scale
if key2.endswith(f"{key_start}.to_v_ip.weight"):
state_dict["ip_adapter"][key2] = state_dict["ip_adapter"][key2].clone() * scale
if self.config.train_image_encoder:
state_dict["image_encoder"] = self.image_encoder.state_dict()
if self.preprocessor is not None:
state_dict["preprocessor"] = self.preprocessor.state_dict()
return state_dict
def get_scale(self):
return self.current_scale
def set_scale(self, scale):
self.current_scale = scale
if not self.sd_ref().is_pixart and not self.sd_ref().is_flux:
for attn_processor in self.sd_ref().unet.attn_processors.values():
if isinstance(attn_processor, CustomIPAttentionProcessor):
attn_processor.scale = scale
# @torch.no_grad()
# def get_clip_image_embeds_from_pil(self, pil_image: Union[Image.Image, List[Image.Image]],
# drop=False) -> torch.Tensor:
# # todo: add support for sdxl
# if isinstance(pil_image, Image.Image):
# pil_image = [pil_image]
# clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
# clip_image = clip_image.to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
# if drop:
# clip_image = clip_image * 0
# clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
# return clip_image_embeds
def to(self, *args, **kwargs):
super().to(*args, **kwargs)
self.image_encoder.to(*args, **kwargs)
self.image_proj_model.to(*args, **kwargs)
self.adapter_modules.to(*args, **kwargs)
if self.preprocessor is not None:
self.preprocessor.to(*args, **kwargs)
return self
def parse_clip_image_embeds_from_cache(
self,
image_embeds_list: List[dict], # has ['last_hidden_state', 'image_embeds', 'penultimate_hidden_states']
quad_count=4,
):
with torch.no_grad():
device = self.sd_ref().unet.device
clip_image_embeds = torch.cat([x[self.config.clip_layer] for x in image_embeds_list], dim=0)
if self.config.quad_image:
# get the outputs of the quat
chunks = clip_image_embeds.chunk(quad_count, dim=0)
chunk_sum = torch.zeros_like(chunks[0])
for chunk in chunks:
chunk_sum = chunk_sum + chunk
# get the mean of them
clip_image_embeds = chunk_sum / quad_count
clip_image_embeds = clip_image_embeds.to(device, dtype=get_torch_dtype(self.sd_ref().dtype)).detach()
return clip_image_embeds
def get_empty_clip_image(self, batch_size: int) -> torch.Tensor:
with torch.no_grad():
tensors_0_1 = torch.rand([batch_size, 3, self.input_size, self.input_size], device=self.device)
noise_scale = torch.rand([tensors_0_1.shape[0], 1, 1, 1], device=self.device,
dtype=get_torch_dtype(self.sd_ref().dtype))
tensors_0_1 = tensors_0_1 * noise_scale
# tensors_0_1 = tensors_0_1 * 0
mean = torch.tensor(self.clip_image_processor.image_mean).to(
self.device, dtype=get_torch_dtype(self.sd_ref().dtype)
).detach()
std = torch.tensor(self.clip_image_processor.image_std).to(
self.device, dtype=get_torch_dtype(self.sd_ref().dtype)
).detach()
tensors_0_1 = torch.clip((255. * tensors_0_1), 0, 255).round() / 255.0
clip_image = (tensors_0_1 - mean.view([1, 3, 1, 1])) / std.view([1, 3, 1, 1])
return clip_image.detach()
def get_clip_image_embeds_from_tensors(
self,
tensors_0_1: torch.Tensor,
drop=False,
is_training=False,
has_been_preprocessed=False,
quad_count=4,
cfg_embed_strength=None, # perform CFG on embeds with unconditional as negative
) -> torch.Tensor:
if self.sd_ref().unet.device != self.device:
self.to(self.sd_ref().unet.device)
if self.sd_ref().unet.device != self.image_encoder.device:
self.to(self.sd_ref().unet.device)
if not self.config.train:
is_training = False
uncond_clip = None
with torch.no_grad():
# on training the clip image is created in the dataloader
if not has_been_preprocessed:
# tensors should be 0-1
if tensors_0_1.ndim == 3:
tensors_0_1 = tensors_0_1.unsqueeze(0)
# training tensors are 0 - 1
tensors_0_1 = tensors_0_1.to(self.device, dtype=torch.float16)
# if images are out of this range throw error
if tensors_0_1.min() < -0.3 or tensors_0_1.max() > 1.3:
raise ValueError("image tensor values must be between 0 and 1. Got min: {}, max: {}".format(
tensors_0_1.min(), tensors_0_1.max()
))
# unconditional
if drop:
if self.clip_noise_zero:
tensors_0_1 = torch.rand_like(tensors_0_1).detach()
noise_scale = torch.rand([tensors_0_1.shape[0], 1, 1, 1], device=self.device,
dtype=get_torch_dtype(self.sd_ref().dtype))
tensors_0_1 = tensors_0_1 * noise_scale
else:
tensors_0_1 = torch.zeros_like(tensors_0_1).detach()
# tensors_0_1 = tensors_0_1 * 0
clip_image = self.clip_image_processor(
images=tensors_0_1,
return_tensors="pt",
do_resize=True,
do_rescale=False,
).pixel_values
else:
if drop:
# scale the noise down
if self.clip_noise_zero:
tensors_0_1 = torch.rand_like(tensors_0_1).detach()
noise_scale = torch.rand([tensors_0_1.shape[0], 1, 1, 1], device=self.device,
dtype=get_torch_dtype(self.sd_ref().dtype))
tensors_0_1 = tensors_0_1 * noise_scale
else:
tensors_0_1 = torch.zeros_like(tensors_0_1).detach()
# tensors_0_1 = tensors_0_1 * 0
mean = torch.tensor(self.clip_image_processor.image_mean).to(
self.device, dtype=get_torch_dtype(self.sd_ref().dtype)
).detach()
std = torch.tensor(self.clip_image_processor.image_std).to(
self.device, dtype=get_torch_dtype(self.sd_ref().dtype)
).detach()
tensors_0_1 = torch.clip((255. * tensors_0_1), 0, 255).round() / 255.0
clip_image = (tensors_0_1 - mean.view([1, 3, 1, 1])) / std.view([1, 3, 1, 1])
else:
clip_image = tensors_0_1
clip_image = clip_image.to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype)).detach()
if self.config.quad_image:
# split the 4x4 grid and stack on batch
ci1, ci2 = clip_image.chunk(2, dim=2)
ci1, ci3 = ci1.chunk(2, dim=3)
ci2, ci4 = ci2.chunk(2, dim=3)
to_cat = []
for i, ci in enumerate([ci1, ci2, ci3, ci4]):
if i < quad_count:
to_cat.append(ci)
else:
break
clip_image = torch.cat(to_cat, dim=0).detach()
# if drop:
# clip_image = clip_image * 0
with torch.set_grad_enabled(is_training):
if is_training and self.config.train_image_encoder:
self.image_encoder.train()
clip_image = clip_image.requires_grad_(True)
if self.preprocessor is not None:
clip_image = self.preprocessor(clip_image)
clip_output = self.image_encoder(
clip_image,
output_hidden_states=True
)
else:
self.image_encoder.eval()
if self.preprocessor is not None:
clip_image = self.preprocessor(clip_image)
clip_output = self.image_encoder(
clip_image, output_hidden_states=True
)
if self.config.clip_layer == 'penultimate_hidden_states':
# they skip last layer for ip+
# https://github.com/tencent-ailab/IP-Adapter/blob/f4b6742db35ea6d81c7b829a55b0a312c7f5a677/tutorial_train_plus.py#L403C26-L403C26
clip_image_embeds = clip_output.hidden_states[-2]
elif self.config.clip_layer == 'last_hidden_state':
clip_image_embeds = clip_output.hidden_states[-1]
else:
clip_image_embeds = clip_output.image_embeds
if self.config.adapter_type == "clip_face":
l2_norm = torch.norm(clip_image_embeds, p=2)
clip_image_embeds = clip_image_embeds / l2_norm
if self.config.image_encoder_arch.startswith('convnext'):
# flatten the width height layers to make the token space
clip_image_embeds = clip_image_embeds.view(clip_image_embeds.size(0), clip_image_embeds.size(1), -1)
# rearrange to (batch, tokens, size)
clip_image_embeds = clip_image_embeds.permute(0, 2, 1)
# apply unconditional if doing cfg on embeds
with torch.no_grad():
if cfg_embed_strength is not None:
uncond_clip = self.get_empty_clip_image(tensors_0_1.shape[0]).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
if self.config.quad_image:
# split the 4x4 grid and stack on batch
ci1, ci2 = uncond_clip.chunk(2, dim=2)
ci1, ci3 = ci1.chunk(2, dim=3)
ci2, ci4 = ci2.chunk(2, dim=3)
to_cat = []
for i, ci in enumerate([ci1, ci2, ci3, ci4]):
if i < quad_count:
to_cat.append(ci)
else:
break
uncond_clip = torch.cat(to_cat, dim=0).detach()
uncond_clip_output = self.image_encoder(
uncond_clip, output_hidden_states=True
)
if self.config.clip_layer == 'penultimate_hidden_states':
uncond_clip_output_embeds = uncond_clip_output.hidden_states[-2]
elif self.config.clip_layer == 'last_hidden_state':
uncond_clip_output_embeds = uncond_clip_output.hidden_states[-1]
else:
uncond_clip_output_embeds = uncond_clip_output.image_embeds
if self.config.adapter_type == "clip_face":
l2_norm = torch.norm(uncond_clip_output_embeds, p=2)
uncond_clip_output_embeds = uncond_clip_output_embeds / l2_norm
uncond_clip_output_embeds = uncond_clip_output_embeds.detach()
# apply inverse cfg
clip_image_embeds = inverse_classifier_guidance(
clip_image_embeds,
uncond_clip_output_embeds,
cfg_embed_strength
)
if self.config.quad_image:
# get the outputs of the quat
chunks = clip_image_embeds.chunk(quad_count, dim=0)
if self.config.train_image_encoder and is_training:
# perform a loss across all chunks this will teach the vision encoder to
# identify similarities in our pairs of images and ignore things that do not make them similar
num_losses = 0
total_loss = None
for chunk in chunks:
for chunk2 in chunks:
if chunk is not chunk2:
loss = F.mse_loss(chunk, chunk2)
if total_loss is None:
total_loss = loss
else:
total_loss = total_loss + loss
num_losses += 1
if total_loss is not None:
total_loss = total_loss / num_losses
total_loss = total_loss * 1e-2
if self.additional_loss is not None:
total_loss = total_loss + self.additional_loss
self.additional_loss = total_loss
chunk_sum = torch.zeros_like(chunks[0])
for chunk in chunks:
chunk_sum = chunk_sum + chunk
# get the mean of them
clip_image_embeds = chunk_sum / quad_count
if not is_training or not self.config.train_image_encoder:
clip_image_embeds = clip_image_embeds.detach()
return clip_image_embeds
# use drop for prompt dropout, or negatives
def forward(self, embeddings: PromptEmbeds, clip_image_embeds: torch.Tensor, is_unconditional=False) -> PromptEmbeds:
clip_image_embeds = clip_image_embeds.to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
if self.sd_ref().is_flux:
# do not attach to text embeds for flux, we will save and grab them as it messes
# with the RoPE to have them in the same tensor
if is_unconditional:
self.last_unconditional = image_prompt_embeds
else:
self.last_conditional = image_prompt_embeds
else:
embeddings.text_embeds = torch.cat([embeddings.text_embeds, image_prompt_embeds], dim=1)
return embeddings
def train(self: T, mode: bool = True) -> T:
if self.config.train_image_encoder:
self.image_encoder.train(mode)
if not self.config.train_only_image_encoder:
for attn_processor in self.adapter_modules:
attn_processor.train(mode)
if self.image_proj_model is not None:
self.image_proj_model.train(mode)
return super().train(mode)
def get_parameter_groups(self, adapter_lr):
param_groups = []
# when training just scaler, we do not train anything else
if not self.config.train_scaler:
param_groups.append({
"params": list(self.get_non_scaler_parameters()),
"lr": adapter_lr,
})
if self.config.train_scaler or self.config.merge_scaler:
scaler_lr = adapter_lr if self.config.scaler_lr is None else self.config.scaler_lr
param_groups.append({
"params": list(self.get_scaler_parameters()),
"lr": scaler_lr,
})
return param_groups
def get_scaler_parameters(self):
# only get the scalera from the adapter modules
for attn_processor in self.adapter_modules:
# only get the scaler
# check if it has ip_scaler attribute
if hasattr(attn_processor, "ip_scaler"):
scaler_param = attn_processor.ip_scaler
yield scaler_param
def get_non_scaler_parameters(self, recurse: bool = True) -> Iterator[Parameter]:
if self.config.train_only_image_encoder:
if self.config.train_only_image_encoder_positional_embedding:
yield from self.image_encoder.vision_model.embeddings.position_embedding.parameters(recurse)
else:
yield from self.image_encoder.parameters(recurse)
return
if self.config.train_scaler:
# no params
return
for attn_processor in self.adapter_modules:
if self.config.train_scaler or self.config.merge_scaler:
# todo remove scaler
if hasattr(attn_processor, "to_k_ip"):
# yield the linear layer
yield from attn_processor.to_k_ip.parameters(recurse)
if hasattr(attn_processor, "to_v_ip"):
# yield the linear layer
yield from attn_processor.to_v_ip.parameters(recurse)
else:
yield from attn_processor.parameters(recurse)
yield from self.image_proj_model.parameters(recurse)
if self.config.train_image_encoder:
yield from self.image_encoder.parameters(recurse)
if self.preprocessor is not None:
yield from self.preprocessor.parameters(recurse)
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
yield from self.get_non_scaler_parameters(recurse)
if self.config.train_scaler or self.config.merge_scaler:
yield from self.get_scaler_parameters()
def merge_in_weights(self, state_dict: Mapping[str, Any]):
# merge in img_proj weights
current_img_proj_state_dict = self.image_proj_model.state_dict()
for key, value in state_dict["image_proj"].items():
if key in current_img_proj_state_dict:
current_shape = current_img_proj_state_dict[key].shape
new_shape = value.shape
if current_shape != new_shape:
try:
# merge in what we can and leave the other values as they are
if len(current_shape) == 1:
current_img_proj_state_dict[key][:new_shape[0]] = value
elif len(current_shape) == 2:
current_img_proj_state_dict[key][:new_shape[0], :new_shape[1]] = value
elif len(current_shape) == 3:
current_img_proj_state_dict[key][:new_shape[0], :new_shape[1], :new_shape[2]] = value
elif len(current_shape) == 4:
current_img_proj_state_dict[key][:new_shape[0], :new_shape[1], :new_shape[2],
:new_shape[3]] = value
else:
raise ValueError(f"unknown shape: {current_shape}")
except RuntimeError as e:
print(e)
print(
f"could not merge in {key}: {list(current_shape)} <<< {list(new_shape)}. Trying other way")
if len(current_shape) == 1:
current_img_proj_state_dict[key][:current_shape[0]] = value[:current_shape[0]]
elif len(current_shape) == 2:
current_img_proj_state_dict[key][:current_shape[0], :current_shape[1]] = value[
:current_shape[0],
:current_shape[1]]
elif len(current_shape) == 3:
current_img_proj_state_dict[key][:current_shape[0], :current_shape[1],
:current_shape[2]] = value[:current_shape[0], :current_shape[1], :current_shape[2]]
elif len(current_shape) == 4:
current_img_proj_state_dict[key][:current_shape[0], :current_shape[1], :current_shape[2],
:current_shape[3]] = value[:current_shape[0], :current_shape[1], :current_shape[2],
:current_shape[3]]
else:
raise ValueError(f"unknown shape: {current_shape}")
print(f"Force merged in {key}: {list(current_shape)} <<< {list(new_shape)}")
else:
current_img_proj_state_dict[key] = value
self.image_proj_model.load_state_dict(current_img_proj_state_dict)
# merge in ip adapter weights
current_ip_adapter_state_dict = self.adapter_modules.state_dict()
for key, value in state_dict["ip_adapter"].items():
if key in current_ip_adapter_state_dict:
current_shape = current_ip_adapter_state_dict[key].shape
new_shape = value.shape
if current_shape != new_shape:
try:
# merge in what we can and leave the other values as they are
if len(current_shape) == 1:
current_ip_adapter_state_dict[key][:new_shape[0]] = value
elif len(current_shape) == 2:
current_ip_adapter_state_dict[key][:new_shape[0], :new_shape[1]] = value
elif len(current_shape) == 3:
current_ip_adapter_state_dict[key][:new_shape[0], :new_shape[1], :new_shape[2]] = value
elif len(current_shape) == 4:
current_ip_adapter_state_dict[key][:new_shape[0], :new_shape[1], :new_shape[2],
:new_shape[3]] = value
else:
raise ValueError(f"unknown shape: {current_shape}")
print(f"Force merged in {key}: {list(current_shape)} <<< {list(new_shape)}")
except RuntimeError as e:
print(e)
print(
f"could not merge in {key}: {list(current_shape)} <<< {list(new_shape)}. Trying other way")
if (len(current_shape) == 1):
current_ip_adapter_state_dict[key][:current_shape[0]] = value[:current_shape[0]]
elif (len(current_shape) == 2):
current_ip_adapter_state_dict[key][:current_shape[0], :current_shape[1]] = value[
:current_shape[
0],
:current_shape[
1]]
elif (len(current_shape) == 3):
current_ip_adapter_state_dict[key][:current_shape[0], :current_shape[1],
:current_shape[2]] = value[:current_shape[0], :current_shape[1], :current_shape[2]]
elif (len(current_shape) == 4):
current_ip_adapter_state_dict[key][:current_shape[0], :current_shape[1], :current_shape[2],
:current_shape[3]] = value[:current_shape[0], :current_shape[1], :current_shape[2],
:current_shape[3]]
else:
raise ValueError(f"unknown shape: {current_shape}")
print(f"Force merged in {key}: {list(current_shape)} <<< {list(new_shape)}")
else:
current_ip_adapter_state_dict[key] = value
self.adapter_modules.load_state_dict(current_ip_adapter_state_dict)
def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
strict = False
if self.config.train_scaler and 'ip_scale' in state_dict:
self.adapter_modules.load_state_dict(state_dict["ip_scale"], strict=False)
if 'ip_adapter' in state_dict:
try:
self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=strict)
self.adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=strict)
except Exception as e:
print(e)
print("could not load ip adapter weights, trying to merge in weights")
self.merge_in_weights(state_dict)
if self.config.train_image_encoder and 'image_encoder' in state_dict:
self.image_encoder.load_state_dict(state_dict["image_encoder"], strict=strict)
if self.preprocessor is not None and 'preprocessor' in state_dict:
self.preprocessor.load_state_dict(state_dict["preprocessor"], strict=strict)
if self.config.train_only_image_encoder and 'ip_adapter' not in state_dict:
# we are loading pure clip weights.
self.image_encoder.load_state_dict(state_dict, strict=strict)
def enable_gradient_checkpointing(self):
if hasattr(self.image_encoder, "enable_gradient_checkpointing"):
self.image_encoder.enable_gradient_checkpointing()
elif hasattr(self.image_encoder, 'gradient_checkpointing'):
self.image_encoder.gradient_checkpointing = True
import random
import torch
import sys
from diffusers import Transformer2DModel
from torch import nn
from torch.nn import Parameter
from torch.nn.modules.module import T
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from toolkit.models.clip_pre_processor import CLIPImagePreProcessor
from toolkit.models.zipper_resampler import ZipperResampler
from toolkit.saving import load_ip_adapter_model
from toolkit.train_tools import get_torch_dtype
from toolkit.util.inverse_cfg import inverse_classifier_guidance
from typing import TYPE_CHECKING, Union, Iterator, Mapping, Any, Tuple, List, Optional
from collections import OrderedDict
from toolkit.util.ip_adapter_utils import AttnProcessor2_0, IPAttnProcessor2_0, ImageProjModel
from toolkit.resampler import Resampler
from toolkit.config_modules import AdapterConfig
from toolkit.prompt_utils import PromptEmbeds
import weakref
from diffusers import FluxTransformer2DModel
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
from transformers import (
CLIPImageProcessor,
CLIPVisionModelWithProjection,
AutoImageProcessor,
ConvNextV2ForImageClassification,
ConvNextForImageClassification,
ConvNextImageProcessor
)
from toolkit.models.size_agnostic_feature_encoder import SAFEImageProcessor, SAFEVisionModel
import torch.nn.functional as F
class MLPProjModelClipFace(torch.nn.Module):
def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.num_tokens = num_tokens
self.norm = torch.nn.LayerNorm(id_embeddings_dim)
self.proj = torch.nn.Sequential(
torch.nn.Linear(id_embeddings_dim, id_embeddings_dim * 2),
torch.nn.GELU(),
torch.nn.Linear(id_embeddings_dim * 2, cross_attention_dim * num_tokens),
)
# Initialize the last linear layer weights near zero
torch.nn.init.uniform_(self.proj[2].weight, a=-0.01, b=0.01)
torch.nn.init.zeros_(self.proj[2].bias)
# # Custom initialization for LayerNorm to output near zero
# torch.nn.init.constant_(self.norm.weight, 0.1) # Small weights near zero
# torch.nn.init.zeros_(self.norm.bias) # Bias to zero
def forward(self, x):
x = self.norm(x)
x = self.proj(x)
x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
return x
class CustomIPAttentionProcessor(IPAttnProcessor2_0):
def __init__(self, hidden_size, cross_attention_dim, scale=1.0, num_tokens=4, adapter=None, train_scaler=False, full_token_scaler=False):
super().__init__(hidden_size, cross_attention_dim, scale=scale, num_tokens=num_tokens)
self.adapter_ref: weakref.ref = weakref.ref(adapter)
self.train_scaler = train_scaler
if train_scaler:
if full_token_scaler:
self.ip_scaler = torch.nn.Parameter(torch.ones([num_tokens], dtype=torch.float32) * 0.999)
else:
self.ip_scaler = torch.nn.Parameter(torch.ones([1], dtype=torch.float32) * 0.999)
# self.ip_scaler = torch.nn.Parameter(torch.ones([1], dtype=torch.float32) * 0.9999)
self.ip_scaler.requires_grad_(True)
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
):
is_active = self.adapter_ref().is_active
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape