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Zero
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import os
import torch
from typing import List
from collections import namedtuple, OrderedDict
def is_torch2_available():
return hasattr(torch.nn.functional, "scaled_dot_product_attention")
if is_torch2_available():
from .attention_processor import (
AttnProcessor2_0 as AttnProcessor,
)
from .attention_processor import (
CNAttnProcessor2_0 as CNAttnProcessor,
)
from .attention_processor import (
IPAttnProcessor2_0 as IPAttnProcessor,
)
from .attention_processor import (
TA_IPAttnProcessor2_0 as TA_IPAttnProcessor,
)
else:
from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor, TA_IPAttnProcessor
class ImageProjModel(torch.nn.Module):
"""Projection Model"""
def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.clip_extra_context_tokens = clip_extra_context_tokens
self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
self.norm = torch.nn.LayerNorm(cross_attention_dim)
def forward(self, image_embeds):
embeds = image_embeds
clip_extra_context_tokens = self.proj(embeds).reshape(
-1, self.clip_extra_context_tokens, self.cross_attention_dim
)
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
return clip_extra_context_tokens
class MLPProjModel(torch.nn.Module):
"""SD model with image prompt"""
def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280):
super().__init__()
self.proj = torch.nn.Sequential(
torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
torch.nn.GELU(),
torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
torch.nn.LayerNorm(cross_attention_dim)
)
def forward(self, image_embeds):
clip_extra_context_tokens = self.proj(image_embeds)
return clip_extra_context_tokens
class MultiIPAdapterImageProjection(torch.nn.Module):
def __init__(self, IPAdapterImageProjectionLayers):
super().__init__()
self.image_projection_layers = torch.nn.ModuleList(IPAdapterImageProjectionLayers)
def forward(self, image_embeds: List[torch.FloatTensor]):
projected_image_embeds = []
# currently, we accept `image_embeds` as
# 1. a tensor (deprecated) with shape [batch_size, embed_dim] or [batch_size, sequence_length, embed_dim]
# 2. list of `n` tensors where `n` is number of ip-adapters, each tensor can hae shape [batch_size, num_images, embed_dim] or [batch_size, num_images, sequence_length, embed_dim]
if not isinstance(image_embeds, list):
image_embeds = [image_embeds.unsqueeze(1)]
if len(image_embeds) != len(self.image_projection_layers):
raise ValueError(
f"image_embeds must have the same length as image_projection_layers, got {len(image_embeds)} and {len(self.image_projection_layers)}"
)
for image_embed, image_projection_layer in zip(image_embeds, self.image_projection_layers):
batch_size, num_images = image_embed.shape[0], image_embed.shape[1]
image_embed = image_embed.reshape((batch_size * num_images,) + image_embed.shape[2:])
image_embed = image_projection_layer(image_embed)
# image_embed = image_embed.reshape((batch_size, num_images) + image_embed.shape[1:])
projected_image_embeds.append(image_embed)
return projected_image_embeds
class IPAdapter(torch.nn.Module):
"""IP-Adapter"""
def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
super().__init__()
self.unet = unet
self.image_proj = image_proj_model
self.ip_adapter = adapter_modules
if ckpt_path is not None:
self.load_from_checkpoint(ckpt_path)
def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds):
ip_tokens = self.image_proj(image_embeds)
encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample
return noise_pred
def load_from_checkpoint(self, ckpt_path: str):
# Calculate original checksums
orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()]))
orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()]))
state_dict = torch.load(ckpt_path, map_location="cpu")
keys = list(state_dict.keys())
if keys != ["image_proj", "ip_adapter"]:
state_dict = revise_state_dict(state_dict)
# Load state dict for image_proj_model and adapter_modules
self.image_proj.load_state_dict(state_dict["image_proj"], strict=True)
self.ip_adapter.load_state_dict(state_dict["ip_adapter"], strict=True)
# Calculate new checksums
new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()]))
new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()]))
# Verify if the weights have changed
assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
class IPAdapterPlus(torch.nn.Module):
"""IP-Adapter"""
def __init__(self, unet, image_proj_model, adapter_modules, ckpt_path=None):
super().__init__()
self.unet = unet
self.image_proj = image_proj_model
self.ip_adapter = adapter_modules
if ckpt_path is not None:
self.load_from_checkpoint(ckpt_path)
def forward(self, noisy_latents, timesteps, encoder_hidden_states, image_embeds):
ip_tokens = self.image_proj(image_embeds)
encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states).sample
return noise_pred
def load_from_checkpoint(self, ckpt_path: str):
# Calculate original checksums
orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()]))
orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()]))
org_unet_sum = []
for attn_name, attn_proc in self.unet.attn_processors.items():
if isinstance(attn_proc, (TA_IPAttnProcessor, IPAttnProcessor)):
org_unet_sum.append(torch.sum(torch.stack([torch.sum(p) for p in attn_proc.parameters()])))
org_unet_sum = torch.sum(torch.stack(org_unet_sum))
state_dict = torch.load(ckpt_path, map_location="cpu")
keys = list(state_dict.keys())
if keys != ["image_proj", "ip_adapter"]:
state_dict = revise_state_dict(state_dict)
# Check if 'latents' exists in both the saved state_dict and the current model's state_dict
strict_load_image_proj_model = True
if "latents" in state_dict["image_proj"] and "latents" in self.image_proj.state_dict():
# Check if the shapes are mismatched
if state_dict["image_proj"]["latents"].shape != self.image_proj.state_dict()["latents"].shape:
print(f"Shapes of 'image_proj.latents' in checkpoint {ckpt_path} and current model do not match.")
print("Removing 'latents' from checkpoint and loading the rest of the weights.")
del state_dict["image_proj"]["latents"]
strict_load_image_proj_model = False
# Load state dict for image_proj_model and adapter_modules
self.image_proj.load_state_dict(state_dict["image_proj"], strict=strict_load_image_proj_model)
missing_key, unexpected_key = self.ip_adapter.load_state_dict(state_dict["ip_adapter"], strict=False)
if len(missing_key) > 0:
for ms in missing_key:
if "ln" not in ms:
raise ValueError(f"Missing key in adapter_modules: {len(missing_key)}")
if len(unexpected_key) > 0:
raise ValueError(f"Unexpected key in adapter_modules: {len(unexpected_key)}")
# Calculate new checksums
new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj.parameters()]))
new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.ip_adapter.parameters()]))
# Verify if the weights loaded to unet
unet_sum = []
for attn_name, attn_proc in self.unet.attn_processors.items():
if isinstance(attn_proc, (TA_IPAttnProcessor, IPAttnProcessor)):
unet_sum.append(torch.sum(torch.stack([torch.sum(p) for p in attn_proc.parameters()])))
unet_sum = torch.sum(torch.stack(unet_sum))
assert org_unet_sum != unet_sum, "Weights of adapter_modules in unet did not change!"
assert (unet_sum - new_adapter_sum < 1e-4), "Weights of adapter_modules did not load to unet!"
# Verify if the weights have changed
assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_mod`ules did not change!"
class IPAdapterXL(IPAdapter):
"""SDXL"""
def forward(self, noisy_latents, timesteps, encoder_hidden_states, unet_added_cond_kwargs, image_embeds):
ip_tokens = self.image_proj(image_embeds)
encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=unet_added_cond_kwargs).sample
return noise_pred
class IPAdapterPlusXL(IPAdapterPlus):
"""IP-Adapter with fine-grained features"""
def forward(self, noisy_latents, timesteps, encoder_hidden_states, unet_added_cond_kwargs, image_embeds):
ip_tokens = self.image_proj(image_embeds)
encoder_hidden_states = torch.cat([encoder_hidden_states, ip_tokens], dim=1)
# Predict the noise residual
noise_pred = self.unet(noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=unet_added_cond_kwargs).sample
return noise_pred
class IPAdapterFull(IPAdapterPlus):
"""IP-Adapter with full features"""
def init_proj(self):
image_proj_model = MLPProjModel(
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
clip_embeddings_dim=self.image_encoder.config.hidden_size,
).to(self.device, dtype=torch.float16)
return image_proj_model
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