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