# 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)