mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
94e8d1b6d5
- Replace legacy model manager service with the v2 manager. - Update invocations to use new load interface. - Fixed many but not all type checking errors in the invocations. Most were unrelated to model manager - Updated routes. All the new routes live under the route tag `model_manager_v2`. To avoid confusion with the old routes, they have the URL prefix `/api/v2/models`. The old routes have been de-registered. - Added a pytest for the loader. - Updated documentation in contributing/MODEL_MANAGER.md
215 lines
8.2 KiB
Python
215 lines
8.2 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 typing import Optional, Union
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import torch
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from PIL import Image
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from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionWeights
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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 MLPProjModel(torch.nn.Module):
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"""SD model with image prompt"""
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def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
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super().__init__()
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self.proj = torch.nn.Sequential(
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torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
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torch.nn.GELU(),
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torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
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torch.nn.LayerNorm(cross_attention_dim),
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)
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@classmethod
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def from_state_dict(cls, state_dict: dict[torch.Tensor]):
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"""Initialize an MLPProjModel 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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Returns:
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MLPProjModel
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"""
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cross_attention_dim = state_dict["proj.3.weight"].shape[0]
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clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
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model = cls(cross_attention_dim, clip_embeddings_dim)
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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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clip_extra_context_tokens = self.proj(image_embeds)
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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[str, 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._image_proj_model = self._init_image_proj_model(state_dict["image_proj"])
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self.attn_weights = IPAttentionWeights.from_state_dict(state_dict["ip_adapter"]).to(
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self.device, dtype=self.dtype
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)
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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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self.attn_weights.to(device=self.device, dtype=self.dtype)
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def calc_size(self):
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# workaround for circular import
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from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
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return calc_model_size_by_data(self._image_proj_model) + calc_model_size_by_data(self.attn_weights)
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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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@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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class IPAdapterFull(IPAdapterPlus):
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"""IP-Adapter Plus with full features."""
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def _init_image_proj_model(self, state_dict: dict[torch.Tensor]):
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return MLPProjModel.from_state_dict(state_dict).to(self.device, dtype=self.dtype)
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class IPAdapterPlusXL(IPAdapterPlus):
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"""IP-Adapter Plus for SDXL."""
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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=20,
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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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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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if "proj.weight" in state_dict["image_proj"]: # IPAdapter (with ImageProjModel).
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return IPAdapter(state_dict, device=device, dtype=dtype)
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elif "proj_in.weight" in state_dict["image_proj"]: # IPAdaterPlus or IPAdapterPlusXL (with Resampler).
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cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
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if cross_attention_dim == 768:
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# SD1 IP-Adapter Plus
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return IPAdapterPlus(state_dict, device=device, dtype=dtype)
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elif cross_attention_dim == 2048:
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# SDXL IP-Adapter Plus
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return IPAdapterPlusXL(state_dict, device=device, dtype=dtype)
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else:
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raise Exception(f"Unsupported IP-Adapter Plus cross-attention dimension: {cross_attention_dim}.")
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elif "proj.0.weight" in state_dict["image_proj"]: # IPAdapterFull (with MLPProjModel).
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return IPAdapterFull(state_dict, device=device, dtype=dtype)
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else:
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raise ValueError(f"'{ip_adapter_ckpt_path}' has an unrecognized IP-Adapter model architecture.")
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