# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
# and modified as needed
from contextlib import contextmanager
from typing import Optional, Union
import torch
from diffusers.models import UNet2DConditionModel
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from invokeai.backend.model_management.models.base import calc_model_size_by_data
from .attention_processor import AttnProcessor2_0, IPAttnProcessor2_0
from .resampler import Resampler
class ImageProjModel(torch.nn.Module):
"""Image Projection Model"""
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, 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)
@classmethod
def from_state_dict(cls, state_dict: dict[torch.Tensor], clip_extra_context_tokens=4):
"""Initialize an ImageProjModel from a state_dict.
The cross_attention_dim and clip_embeddings_dim are inferred from the shape of the tensors in the state_dict.
Args:
state_dict (dict[torch.Tensor]): The state_dict of model weights.
clip_extra_context_tokens (int, optional): Defaults to 4.
Returns:
ImageProjModel
"""
cross_attention_dim = state_dict["norm.weight"].shape[0]
clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
model = cls(cross_attention_dim, clip_embeddings_dim, clip_extra_context_tokens)
model.load_state_dict(state_dict)
return model
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 IPAdapter:
"""IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
def __init__(
self,
state_dict: dict[torch.Tensor],
device: torch.device,
dtype: torch.dtype = torch.float16,
num_tokens: int = 4,
):
self.device = device
self.dtype = dtype
self._num_tokens = num_tokens
self._clip_image_processor = CLIPImageProcessor()
self._state_dict = state_dict
self._image_proj_model = self._init_image_proj_model(self._state_dict["image_proj"])
# The _attn_processors will be initialized later when we have access to the UNet.
self._attn_processors = None
def to(self, device: torch.device, dtype: Optional[torch.dtype] = None):
self.device = device
if dtype is not None:
self.dtype = dtype
self._image_proj_model.to(device=self.device, dtype=self.dtype)
if self._attn_processors is not None:
torch.nn.ModuleList(self._attn_processors.values()).to(device=self.device, dtype=self.dtype)
def calc_size(self):
if self._state_dict is not None:
image_proj_size = sum(
[tensor.nelement() * tensor.element_size() for tensor in self._state_dict["image_proj"].values()]
)
ip_adapter_size = sum(
[tensor.nelement() * tensor.element_size() for tensor in self._state_dict["ip_adapter"].values()]
)
return image_proj_size + ip_adapter_size
else:
return calc_model_size_by_data(self._image_proj_model) + calc_model_size_by_data(
torch.nn.ModuleList(self._attn_processors.values())
)
def _init_image_proj_model(self, state_dict):
return ImageProjModel.from_state_dict(state_dict, self._num_tokens).to(self.device, dtype=self.dtype)
def _prepare_attention_processors(self, unet: UNet2DConditionModel):
"""Prepare a dict of attention processors that can later be injected into a unet, and load the IP-Adapter
attention weights into them.
Note that the `unet` param is only used to determine attention block dimensions and naming.
TODO(ryand): As a future improvement, this could all be inferred from the state_dict when the IPAdapter is
intialized.
"""
attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
attn_procs[name] = AttnProcessor2_0()
else:
attn_procs[name] = IPAttnProcessor2_0(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
).to(self.device, dtype=self.dtype)
ip_layers = torch.nn.ModuleList(attn_procs.values())
ip_layers.load_state_dict(self._state_dict["ip_adapter"])
self._attn_processors = attn_procs
self._state_dict = None
# @genomancer: pushed scaling back out into its own method (like original Tencent implementation)
# which makes implementing begin_step_percent and end_step_percent easier
# but based on self._attn_processors (ala @Ryan) instead of original Tencent unet.attn_processors,
# which should make it easier to implement multiple IPAdapters
def set_scale(self, scale):
if self._attn_processors is not None:
for attn_processor in self._attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor2_0):
attn_processor.scale = scale
@contextmanager
def apply_ip_adapter_attention(self, unet: UNet2DConditionModel, scale: float):
"""A context manager that patches `unet` with this IP-Adapter's attention processors while it is active.
Yields:
None
"""
if self._attn_processors is None:
# We only have to call _prepare_attention_processors(...) once, and then the result is cached and can be
# used on any UNet model (with the same dimensions).
self._prepare_attention_processors(unet)
# Set scale
self.set_scale(scale)
# for attn_processor in self._attn_processors.values():
# if isinstance(attn_processor, IPAttnProcessor2_0):
# attn_processor.scale = scale
orig_attn_processors = unet.attn_processors
# Make a (moderately-) shallow copy of the self._attn_processors dict, because unet.set_attn_processor(...)
# actually pops elements from the passed dict.
ip_adapter_attn_processors = {k: v for k, v in self._attn_processors.items()}
try:
unet.set_attn_processor(ip_adapter_attn_processors)
yield None
finally:
unet.set_attn_processor(orig_attn_processors)
@torch.inference_mode()
def get_image_embeds(self, pil_image, image_encoder: CLIPVisionModelWithProjection):
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_embeds = image_encoder(clip_image.to(self.device, dtype=self.dtype)).image_embeds
image_prompt_embeds = self._image_proj_model(clip_image_embeds)
uncond_image_prompt_embeds = self._image_proj_model(torch.zeros_like(clip_image_embeds))
return image_prompt_embeds, uncond_image_prompt_embeds
class IPAdapterPlus(IPAdapter):
"""IP-Adapter with fine-grained features"""
def _init_image_proj_model(self, state_dict):
return Resampler.from_state_dict(
state_dict=state_dict,
depth=4,
dim_head=64,
heads=12,
num_queries=self._num_tokens,
ff_mult=4,
).to(self.device, dtype=self.dtype)
@torch.inference_mode()
def get_image_embeds(self, pil_image, image_encoder: CLIPVisionModelWithProjection):
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=self.dtype)
clip_image_embeds = image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
image_prompt_embeds = self._image_proj_model(clip_image_embeds)
uncond_clip_image_embeds = image_encoder(torch.zeros_like(clip_image), output_hidden_states=True).hidden_states[
-2
]
uncond_image_prompt_embeds = self._image_proj_model(uncond_clip_image_embeds)
return image_prompt_embeds, uncond_image_prompt_embeds
def build_ip_adapter(
ip_adapter_ckpt_path: str, device: torch.device, dtype: torch.dtype = torch.float16
) -> Union[IPAdapter, IPAdapterPlus]:
state_dict = torch.load(ip_adapter_ckpt_path, map_location="cpu")
# Determine if the state_dict is from an IPAdapter or IPAdapterPlus based on the image_proj weights that it
# contains.
is_plus = "proj.weight" not in state_dict["image_proj"]
if is_plus:
return IPAdapterPlus(state_dict, device=device, dtype=dtype)
else:
return IPAdapter(state_dict, device=device, dtype=dtype)