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Zero
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import torch
from collections import namedtuple, OrderedDict
from safetensors import safe_open
from .attention_processor import init_attn_proc
from .ip_adapter import MultiIPAdapterImageProjection
from .resampler import Resampler
from transformers import (
AutoModel, AutoImageProcessor,
CLIPVisionModelWithProjection, CLIPImageProcessor)
def init_adapter_in_unet(
unet,
image_proj_model=None,
pretrained_model_path_or_dict=None,
adapter_tokens=64,
embedding_dim=None,
use_lcm=False,
use_adaln=True,
):
device = unet.device
dtype = unet.dtype
if image_proj_model is None:
assert embedding_dim is not None, "embedding_dim must be provided if image_proj_model is None."
image_proj_model = Resampler(
embedding_dim=embedding_dim,
output_dim=unet.config.cross_attention_dim,
num_queries=adapter_tokens,
)
if pretrained_model_path_or_dict is not None:
if not isinstance(pretrained_model_path_or_dict, dict):
if pretrained_model_path_or_dict.endswith(".safetensors"):
state_dict = {"image_proj": {}, "ip_adapter": {}}
with safe_open(pretrained_model_path_or_dict, framework="pt", device=unet.device) as f:
for key in f.keys():
if key.startswith("image_proj."):
state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
elif key.startswith("ip_adapter."):
state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
else:
state_dict = torch.load(pretrained_model_path_or_dict, map_location=unet.device)
else:
state_dict = pretrained_model_path_or_dict
keys = list(state_dict.keys())
if "image_proj" not in keys and "ip_adapter" not in keys:
state_dict = revise_state_dict(state_dict)
# Creat IP cross-attention in unet.
attn_procs = init_attn_proc(unet, adapter_tokens, use_lcm, use_adaln)
unet.set_attn_processor(attn_procs)
# Load pretrinaed model if needed.
if pretrained_model_path_or_dict is not None:
if "ip_adapter" in state_dict.keys():
adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
missing, unexpected = adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=False)
for mk in missing:
if "ln" not in mk:
raise ValueError(f"Missing keys in adapter_modules: {missing}")
if "image_proj" in state_dict.keys():
image_proj_model.load_state_dict(state_dict["image_proj"])
# Load image projectors into iterable ModuleList.
image_projection_layers = []
image_projection_layers.append(image_proj_model)
unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
# Adjust unet config to handle addtional ip hidden states.
unet.config.encoder_hid_dim_type = "ip_image_proj"
unet.to(dtype=dtype, device=device)
def load_adapter_to_pipe(
pipe,
pretrained_model_path_or_dict,
image_encoder_or_path=None,
feature_extractor_or_path=None,
use_clip_encoder=False,
adapter_tokens=64,
use_lcm=False,
use_adaln=True,
):
if not isinstance(pretrained_model_path_or_dict, dict):
if pretrained_model_path_or_dict.endswith(".safetensors"):
state_dict = {"image_proj": {}, "ip_adapter": {}}
with safe_open(pretrained_model_path_or_dict, framework="pt", device=pipe.device) as f:
for key in f.keys():
if key.startswith("image_proj."):
state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
elif key.startswith("ip_adapter."):
state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
else:
state_dict = torch.load(pretrained_model_path_or_dict, map_location=pipe.device)
else:
state_dict = pretrained_model_path_or_dict
keys = list(state_dict.keys())
if "image_proj" not in keys and "ip_adapter" not in keys:
state_dict = revise_state_dict(state_dict)
# load CLIP image encoder here if it has not been registered to the pipeline yet
if image_encoder_or_path is not None:
if isinstance(image_encoder_or_path, str):
feature_extractor_or_path = image_encoder_or_path if feature_extractor_or_path is None else feature_extractor_or_path
image_encoder_or_path = (
CLIPVisionModelWithProjection.from_pretrained(
image_encoder_or_path
) if use_clip_encoder else
AutoModel.from_pretrained(image_encoder_or_path)
)
if feature_extractor_or_path is not None:
if isinstance(feature_extractor_or_path, str):
feature_extractor_or_path = (
CLIPImageProcessor() if use_clip_encoder else
AutoImageProcessor.from_pretrained(feature_extractor_or_path)
)
# create image encoder if it has not been registered to the pipeline yet
if hasattr(pipe, "image_encoder") and getattr(pipe, "image_encoder", None) is None:
image_encoder = image_encoder_or_path.to(pipe.device, dtype=pipe.dtype)
pipe.register_modules(image_encoder=image_encoder)
else:
image_encoder = pipe.image_encoder
# create feature extractor if it has not been registered to the pipeline yet
if hasattr(pipe, "feature_extractor") and getattr(pipe, "feature_extractor", None) is None:
feature_extractor = feature_extractor_or_path
pipe.register_modules(feature_extractor=feature_extractor)
else:
feature_extractor = pipe.feature_extractor
# load adapter into unet
unet = getattr(pipe, pipe.unet_name) if not hasattr(pipe, "unet") else pipe.unet
attn_procs = init_attn_proc(unet, adapter_tokens, use_lcm, use_adaln)
unet.set_attn_processor(attn_procs)
image_proj_model = Resampler(
embedding_dim=image_encoder.config.hidden_size,
output_dim=unet.config.cross_attention_dim,
num_queries=adapter_tokens,
)
# Load pretrinaed model if needed.
if "ip_adapter" in state_dict.keys():
adapter_modules = torch.nn.ModuleList(unet.attn_processors.values())
missing, unexpected = adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=False)
for mk in missing:
if "ln" not in mk:
raise ValueError(f"Missing keys in adapter_modules: {missing}")
if "image_proj" in state_dict.keys():
image_proj_model.load_state_dict(state_dict["image_proj"])
# convert IP-Adapter Image Projection layers to diffusers
image_projection_layers = []
image_projection_layers.append(image_proj_model)
unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
# Adjust unet config to handle addtional ip hidden states.
unet.config.encoder_hid_dim_type = "ip_image_proj"
unet.to(dtype=pipe.dtype, device=pipe.device)
def revise_state_dict(old_state_dict_or_path, map_location="cpu"):
new_state_dict = OrderedDict()
new_state_dict["image_proj"] = OrderedDict()
new_state_dict["ip_adapter"] = OrderedDict()
if isinstance(old_state_dict_or_path, str):
old_state_dict = torch.load(old_state_dict_or_path, map_location=map_location)
else:
old_state_dict = old_state_dict_or_path
for name, weight in old_state_dict.items():
if name.startswith("image_proj_model."):
new_state_dict["image_proj"][name[len("image_proj_model."):]] = weight
elif name.startswith("adapter_modules."):
new_state_dict["ip_adapter"][name[len("adapter_modules."):]] = weight
return new_state_dict
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(image_encoder, feature_extractor, image, device, num_images_per_prompt, output_hidden_states=None):
dtype = next(image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
return image_enc_hidden_states
else:
if isinstance(image_encoder, CLIPVisionModelWithProjection):
# CLIP image encoder.
image_embeds = image_encoder(image).image_embeds
else:
# DINO image encoder.
image_embeds = image_encoder(image).last_hidden_state
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
return image_embeds
def prepare_training_image_embeds(
image_encoder, feature_extractor,
ip_adapter_image, ip_adapter_image_embeds,
device, drop_rate, output_hidden_state, idx_to_replace=None
):
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
# if len(ip_adapter_image) != len(unet.encoder_hid_proj.image_projection_layers):
# raise ValueError(
# f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
# )
image_embeds = []
for single_ip_adapter_image in ip_adapter_image:
if idx_to_replace is None:
idx_to_replace = torch.rand(len(single_ip_adapter_image)) < drop_rate
zero_ip_adapter_image = torch.zeros_like(single_ip_adapter_image)
single_ip_adapter_image[idx_to_replace] = zero_ip_adapter_image[idx_to_replace]
single_image_embeds = encode_image(
image_encoder, feature_extractor, single_ip_adapter_image, device, 1, output_hidden_state
)
single_image_embeds = torch.stack([single_image_embeds], dim=1) # FIXME
image_embeds.append(single_image_embeds)
else:
repeat_dims = [1]
image_embeds = []
for single_image_embeds in ip_adapter_image_embeds:
if do_classifier_free_guidance:
single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
single_image_embeds = single_image_embeds.repeat(
num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
)
single_negative_image_embeds = single_negative_image_embeds.repeat(
num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
)
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
else:
single_image_embeds = single_image_embeds.repeat(
num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
)
image_embeds.append(single_image_embeds)
return image_embeds |