1687 lines
79 KiB
Python
1687 lines
79 KiB
Python
import pdb
|
|
import cv2
|
|
import torch.nn.functional as F
|
|
import os
|
|
from typing import List
|
|
|
|
import torch
|
|
from diffusers import StableDiffusionPipeline
|
|
from diffusers.pipelines.controlnet import MultiControlNetModel
|
|
from PIL import Image
|
|
from safetensors import safe_open
|
|
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
|
|
from torchvision import transforms
|
|
from .utils import is_torch2_available, get_generator
|
|
|
|
# import torchvision.transforms.functional as Func
|
|
|
|
# from .clip_style_models import CSD_CLIP, convert_state_dict
|
|
|
|
if is_torch2_available():
|
|
from .attention_processor import (
|
|
AttnProcessor2_0 as AttnProcessor,
|
|
)
|
|
from .attention_processor import (
|
|
CNAttnProcessor2_0 as CNAttnProcessor,
|
|
)
|
|
from .attention_processor import (
|
|
IPAttnProcessor2_0 as IPAttnProcessor,
|
|
)
|
|
from .attention_processor import IP_CS_AttnProcessor2_0 as IP_CS_AttnProcessor
|
|
from .attention_processor import IP_FuAd_AttnProcessor2_0_exp as IP_FuAd_AttnProcessor_exp
|
|
from .attention_processor import AttnProcessor2_0_hijack as AttnProcessor_hijack
|
|
from .attention_processor import IPAttnProcessor2_0_cross_modal as IPAttnProcessor_cross_modal
|
|
else:
|
|
from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor
|
|
from .resampler import Resampler
|
|
|
|
from transformers import AutoImageProcessor, AutoModel
|
|
|
|
|
|
class ImageProjModel(torch.nn.Module):
|
|
"""Projection Model"""
|
|
|
|
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
|
|
super().__init__()
|
|
|
|
self.generator = None
|
|
self.cross_attention_dim = cross_attention_dim
|
|
self.clip_extra_context_tokens = clip_extra_context_tokens
|
|
# print(clip_embeddings_dim, self.clip_extra_context_tokens, cross_attention_dim)
|
|
self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
|
|
self.norm = torch.nn.LayerNorm(cross_attention_dim)
|
|
|
|
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 MLPProjModel(torch.nn.Module):
|
|
"""SD model with image prompt"""
|
|
|
|
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
|
|
super().__init__()
|
|
|
|
self.proj = torch.nn.Sequential(
|
|
torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
|
|
torch.nn.GELU(),
|
|
torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
|
|
torch.nn.LayerNorm(cross_attention_dim)
|
|
)
|
|
|
|
def forward(self, image_embeds):
|
|
clip_extra_context_tokens = self.proj(image_embeds)
|
|
return clip_extra_context_tokens
|
|
|
|
|
|
class IPAdapter:
|
|
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, target_blocks=["block"]):
|
|
self.device = device
|
|
self.image_encoder_path = image_encoder_path
|
|
self.ip_ckpt = ip_ckpt
|
|
self.num_tokens = num_tokens
|
|
self.target_blocks = target_blocks
|
|
|
|
self.pipe = sd_pipe.to(self.device)
|
|
self.set_ip_adapter()
|
|
|
|
# load image encoder
|
|
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
|
self.device, dtype=torch.float16
|
|
)
|
|
self.clip_image_processor = CLIPImageProcessor()
|
|
# image proj model
|
|
self.image_proj_model = self.init_proj()
|
|
|
|
self.load_ip_adapter()
|
|
|
|
def init_proj(self):
|
|
image_proj_model = ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
|
clip_extra_context_tokens=self.num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
def set_ip_adapter(self):
|
|
unet = self.pipe.unet
|
|
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] = AttnProcessor()
|
|
else:
|
|
selected = False
|
|
for block_name in self.target_blocks:
|
|
if block_name in name:
|
|
selected = True
|
|
break
|
|
if selected:
|
|
attn_procs[name] = IPAttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
scale=1.0,
|
|
num_tokens=self.num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
else:
|
|
attn_procs[name] = IPAttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
scale=1.0,
|
|
num_tokens=self.num_tokens,
|
|
skip=True
|
|
).to(self.device, dtype=torch.float16)
|
|
unet.set_attn_processor(attn_procs)
|
|
if hasattr(self.pipe, "controlnet"):
|
|
if isinstance(self.pipe.controlnet, MultiControlNetModel):
|
|
for controlnet in self.pipe.controlnet.nets:
|
|
controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
|
|
else:
|
|
self.pipe.controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
|
|
|
|
def load_ip_adapter(self):
|
|
if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
|
|
state_dict = {"image_proj": {}, "ip_adapter": {}}
|
|
with safe_open(self.ip_ckpt, framework="pt", device="cpu") 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(self.ip_ckpt, map_location="cpu")
|
|
self.image_proj_model.load_state_dict(state_dict["image_proj"])
|
|
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
|
|
ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)
|
|
|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None):
|
|
if pil_image is not None:
|
|
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 = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
else:
|
|
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
|
|
|
if content_prompt_embeds is not None:
|
|
clip_image_embeds = clip_image_embeds - content_prompt_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
|
|
|
|
def set_scale(self, scale):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, IPAttnProcessor):
|
|
attn_processor.scale = scale
|
|
|
|
def generate(
|
|
self,
|
|
pil_image=None,
|
|
clip_image_embeds=None,
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
scale=1.0,
|
|
num_samples=4,
|
|
seed=None,
|
|
guidance_scale=7.5,
|
|
num_inference_steps=30,
|
|
neg_content_emb=None,
|
|
**kwargs,
|
|
):
|
|
self.set_scale(scale)
|
|
|
|
if pil_image is not None:
|
|
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
|
else:
|
|
num_prompts = clip_image_embeds.size(0)
|
|
|
|
if prompt is None:
|
|
prompt = "best quality, high quality"
|
|
if negative_prompt is None:
|
|
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
|
negative_prompt = [negative_prompt] * num_prompts
|
|
|
|
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
|
|
pil_image=pil_image, clip_image_embeds=clip_image_embeds, content_prompt_embeds=neg_content_emb
|
|
)
|
|
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
|
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
|
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
|
|
with torch.inference_mode():
|
|
prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
|
|
prompt,
|
|
device=self.device,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
|
|
|
|
generator = get_generator(seed, self.device)
|
|
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
guidance_scale=guidance_scale,
|
|
num_inference_steps=num_inference_steps,
|
|
generator=generator,
|
|
**kwargs,
|
|
).images
|
|
|
|
return images
|
|
|
|
|
|
class IPAdapter_CS:
|
|
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_content_tokens=4,
|
|
num_style_tokens=4,
|
|
target_content_blocks=["block"], target_style_blocks=["block"], content_image_encoder_path=None,
|
|
controlnet_adapter=False,
|
|
controlnet_target_content_blocks=None,
|
|
controlnet_target_style_blocks=None,
|
|
content_model_resampler=False,
|
|
style_model_resampler=False,
|
|
):
|
|
self.device = device
|
|
self.image_encoder_path = image_encoder_path
|
|
self.ip_ckpt = ip_ckpt
|
|
self.num_content_tokens = num_content_tokens
|
|
self.num_style_tokens = num_style_tokens
|
|
self.content_target_blocks = target_content_blocks
|
|
self.style_target_blocks = target_style_blocks
|
|
|
|
self.content_model_resampler = content_model_resampler
|
|
self.style_model_resampler = style_model_resampler
|
|
|
|
self.controlnet_adapter = controlnet_adapter
|
|
self.controlnet_target_content_blocks = controlnet_target_content_blocks
|
|
self.controlnet_target_style_blocks = controlnet_target_style_blocks
|
|
|
|
self.pipe = sd_pipe.to(self.device)
|
|
self.set_ip_adapter()
|
|
self.content_image_encoder_path = content_image_encoder_path
|
|
|
|
|
|
# load image encoder
|
|
if content_image_encoder_path is not None:
|
|
self.content_image_encoder = AutoModel.from_pretrained(content_image_encoder_path).to(self.device,
|
|
dtype=torch.float16)
|
|
self.content_image_processor = AutoImageProcessor.from_pretrained(content_image_encoder_path)
|
|
else:
|
|
self.content_image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
|
self.device, dtype=torch.float16
|
|
)
|
|
self.content_image_processor = CLIPImageProcessor()
|
|
# model.requires_grad_(False)
|
|
|
|
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
|
self.device, dtype=torch.float16
|
|
)
|
|
# if self.use_CSD is not None:
|
|
# self.style_image_encoder = CSD_CLIP("vit_large", "default",self.use_CSD+"/ViT-L-14.pt")
|
|
# model_path = self.use_CSD+"/checkpoint.pth"
|
|
# checkpoint = torch.load(model_path, map_location="cpu")
|
|
# state_dict = convert_state_dict(checkpoint['model_state_dict'])
|
|
# self.style_image_encoder.load_state_dict(state_dict, strict=False)
|
|
#
|
|
# normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
|
|
# self.style_preprocess = transforms.Compose([
|
|
# transforms.Resize(size=224, interpolation=Func.InterpolationMode.BICUBIC),
|
|
# transforms.CenterCrop(224),
|
|
# transforms.ToTensor(),
|
|
# normalize,
|
|
# ])
|
|
|
|
self.clip_image_processor = CLIPImageProcessor()
|
|
# image proj model
|
|
self.content_image_proj_model = self.init_proj(self.num_content_tokens, content_or_style_='content',
|
|
model_resampler=self.content_model_resampler)
|
|
self.style_image_proj_model = self.init_proj(self.num_style_tokens, content_or_style_='style',
|
|
model_resampler=self.style_model_resampler)
|
|
|
|
self.load_ip_adapter()
|
|
|
|
def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False):
|
|
|
|
# print('@@@@',self.pipe.unet.config.cross_attention_dim,self.image_encoder.config.projection_dim)
|
|
if content_or_style_ == 'content' and self.content_image_encoder_path is not None:
|
|
image_proj_model = ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.content_image_encoder.config.projection_dim,
|
|
clip_extra_context_tokens=num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
image_proj_model = ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
|
clip_extra_context_tokens=num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
def set_ip_adapter(self):
|
|
unet = self.pipe.unet
|
|
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] = AttnProcessor()
|
|
else:
|
|
# layername_id += 1
|
|
selected = False
|
|
for block_name in self.style_target_blocks:
|
|
if block_name in name:
|
|
selected = True
|
|
# print(name)
|
|
attn_procs[name] = IP_CS_AttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
style_scale=1.0,
|
|
style=True,
|
|
num_content_tokens=self.num_content_tokens,
|
|
num_style_tokens=self.num_style_tokens,
|
|
)
|
|
for block_name in self.content_target_blocks:
|
|
if block_name in name:
|
|
# selected = True
|
|
if selected is False:
|
|
attn_procs[name] = IP_CS_AttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
content_scale=1.0,
|
|
content=True,
|
|
num_content_tokens=self.num_content_tokens,
|
|
num_style_tokens=self.num_style_tokens,
|
|
)
|
|
else:
|
|
attn_procs[name].set_content_ipa(content_scale=1.0)
|
|
# attn_procs[name].content=True
|
|
|
|
if selected is False:
|
|
attn_procs[name] = IP_CS_AttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
num_content_tokens=self.num_content_tokens,
|
|
num_style_tokens=self.num_style_tokens,
|
|
skip=True,
|
|
)
|
|
|
|
attn_procs[name].to(self.device, dtype=torch.float16)
|
|
unet.set_attn_processor(attn_procs)
|
|
if hasattr(self.pipe, "controlnet"):
|
|
if self.controlnet_adapter is False:
|
|
if isinstance(self.pipe.controlnet, MultiControlNetModel):
|
|
for controlnet in self.pipe.controlnet.nets:
|
|
controlnet.set_attn_processor(CNAttnProcessor(
|
|
num_tokens=self.num_content_tokens + self.num_style_tokens))
|
|
else:
|
|
self.pipe.controlnet.set_attn_processor(CNAttnProcessor(
|
|
num_tokens=self.num_content_tokens + self.num_style_tokens))
|
|
|
|
else:
|
|
controlnet_attn_procs = {}
|
|
controlnet_style_target_blocks = self.controlnet_target_style_blocks
|
|
controlnet_content_target_blocks = self.controlnet_target_content_blocks
|
|
for name in self.pipe.controlnet.attn_processors.keys():
|
|
# print(name)
|
|
cross_attention_dim = None if name.endswith(
|
|
"attn1.processor") else self.pipe.controlnet.config.cross_attention_dim
|
|
if name.startswith("mid_block"):
|
|
hidden_size = self.pipe.controlnet.config.block_out_channels[-1]
|
|
elif name.startswith("up_blocks"):
|
|
block_id = int(name[len("up_blocks.")])
|
|
hidden_size = list(reversed(self.pipe.controlnet.config.block_out_channels))[block_id]
|
|
elif name.startswith("down_blocks"):
|
|
block_id = int(name[len("down_blocks.")])
|
|
hidden_size = self.pipe.controlnet.config.block_out_channels[block_id]
|
|
if cross_attention_dim is None:
|
|
# layername_id += 1
|
|
controlnet_attn_procs[name] = AttnProcessor()
|
|
|
|
else:
|
|
# layername_id += 1
|
|
selected = False
|
|
for block_name in controlnet_style_target_blocks:
|
|
if block_name in name:
|
|
selected = True
|
|
# print(name)
|
|
controlnet_attn_procs[name] = IP_CS_AttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
style_scale=1.0,
|
|
style=True,
|
|
num_content_tokens=self.num_content_tokens,
|
|
num_style_tokens=self.num_style_tokens,
|
|
)
|
|
|
|
for block_name in controlnet_content_target_blocks:
|
|
if block_name in name:
|
|
if selected is False:
|
|
controlnet_attn_procs[name] = IP_CS_AttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
content_scale=1.0,
|
|
content=True,
|
|
num_content_tokens=self.num_content_tokens,
|
|
num_style_tokens=self.num_style_tokens,
|
|
)
|
|
|
|
selected = True
|
|
elif selected is True:
|
|
controlnet_attn_procs[name].set_content_ipa(content_scale=1.0)
|
|
|
|
# if args.content_image_encoder_type !='dinov2':
|
|
# weights = {
|
|
# "to_k_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_k_ip.weight"],
|
|
# "to_v_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_v_ip.weight"],
|
|
# }
|
|
# attn_procs[name].load_state_dict(weights)
|
|
if selected is False:
|
|
controlnet_attn_procs[name] = IP_CS_AttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
num_content_tokens=self.num_content_tokens,
|
|
num_style_tokens=self.num_style_tokens,
|
|
skip=True,
|
|
)
|
|
controlnet_attn_procs[name].to(self.device, dtype=torch.float16)
|
|
# layer_name = name.split(".processor")[0]
|
|
# # print(state_dict["ip_adapter"].keys())
|
|
# weights = {
|
|
# "to_k_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_k_ip.weight"],
|
|
# "to_v_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_v_ip.weight"],
|
|
# }
|
|
# attn_procs[name].load_state_dict(weights)
|
|
self.pipe.controlnet.set_attn_processor(controlnet_attn_procs)
|
|
|
|
def load_ip_adapter(self):
|
|
if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
|
|
state_dict = {"content_image_proj": {}, "style_image_proj": {}, "ip_adapter": {}}
|
|
with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
|
|
for key in f.keys():
|
|
if key.startswith("content_image_proj."):
|
|
state_dict["content_image_proj"][key.replace("content_image_proj.", "")] = f.get_tensor(key)
|
|
elif key.startswith("style_image_proj."):
|
|
state_dict["style_image_proj"][key.replace("style_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(self.ip_ckpt, map_location="cpu")
|
|
self.content_image_proj_model.load_state_dict(state_dict["content_image_proj"])
|
|
self.style_image_proj_model.load_state_dict(state_dict["style_image_proj"])
|
|
|
|
if 'conv_in_unet_sd' in state_dict.keys():
|
|
self.pipe.unet.conv_in.load_state_dict(state_dict["conv_in_unet_sd"], strict=True)
|
|
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
|
|
ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)
|
|
|
|
if self.controlnet_adapter is True:
|
|
print('loading controlnet_adapter')
|
|
self.pipe.controlnet.load_state_dict(state_dict["controlnet_adapter_modules"], strict=False)
|
|
|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None,
|
|
content_or_style_=''):
|
|
# if pil_image is not None:
|
|
# 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 = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
# else:
|
|
# clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
|
|
|
# if content_prompt_embeds is not None:
|
|
# clip_image_embeds = clip_image_embeds - content_prompt_embeds
|
|
|
|
if content_or_style_ == 'content':
|
|
if pil_image is not None:
|
|
if isinstance(pil_image, Image.Image):
|
|
pil_image = [pil_image]
|
|
if self.content_image_proj_model is not None:
|
|
clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.content_image_encoder(
|
|
clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
else:
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
else:
|
|
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
|
|
|
image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
|
uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
return image_prompt_embeds, uncond_image_prompt_embeds
|
|
if content_or_style_ == 'style':
|
|
if pil_image is not None:
|
|
if self.use_CSD is not None:
|
|
clip_image = self.style_preprocess(pil_image).unsqueeze(0).to(self.device, dtype=torch.float32)
|
|
clip_image_embeds = self.style_image_encoder(clip_image)
|
|
else:
|
|
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 = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
|
|
|
|
else:
|
|
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
|
image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
|
uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
return image_prompt_embeds, uncond_image_prompt_embeds
|
|
|
|
def set_scale(self, content_scale, style_scale):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, IP_CS_AttnProcessor):
|
|
if attn_processor.content is True:
|
|
attn_processor.content_scale = content_scale
|
|
|
|
if attn_processor.style is True:
|
|
attn_processor.style_scale = style_scale
|
|
# print('style_scale:',style_scale)
|
|
if self.controlnet_adapter is not None:
|
|
for attn_processor in self.pipe.controlnet.attn_processors.values():
|
|
|
|
if isinstance(attn_processor, IP_CS_AttnProcessor):
|
|
if attn_processor.content is True:
|
|
attn_processor.content_scale = content_scale
|
|
# print(content_scale)
|
|
|
|
if attn_processor.style is True:
|
|
attn_processor.style_scale = style_scale
|
|
|
|
def generate(
|
|
self,
|
|
pil_content_image=None,
|
|
pil_style_image=None,
|
|
clip_content_image_embeds=None,
|
|
clip_style_image_embeds=None,
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
content_scale=1.0,
|
|
style_scale=1.0,
|
|
num_samples=4,
|
|
seed=None,
|
|
guidance_scale=7.5,
|
|
num_inference_steps=30,
|
|
neg_content_emb=None,
|
|
**kwargs,
|
|
):
|
|
self.set_scale(content_scale, style_scale)
|
|
|
|
if pil_content_image is not None:
|
|
num_prompts = 1 if isinstance(pil_content_image, Image.Image) else len(pil_content_image)
|
|
else:
|
|
num_prompts = clip_content_image_embeds.size(0)
|
|
|
|
if prompt is None:
|
|
prompt = "best quality, high quality"
|
|
if negative_prompt is None:
|
|
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
|
negative_prompt = [negative_prompt] * num_prompts
|
|
|
|
content_image_prompt_embeds, uncond_content_image_prompt_embeds = self.get_image_embeds(
|
|
pil_image=pil_content_image, clip_image_embeds=clip_content_image_embeds
|
|
)
|
|
style_image_prompt_embeds, uncond_style_image_prompt_embeds = self.get_image_embeds(
|
|
pil_image=pil_style_image, clip_image_embeds=clip_style_image_embeds
|
|
)
|
|
|
|
bs_embed, seq_len, _ = content_image_prompt_embeds.shape
|
|
content_image_prompt_embeds = content_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
content_image_prompt_embeds = content_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.view(bs_embed * num_samples, seq_len,
|
|
-1)
|
|
|
|
bs_style_embed, seq_style_len, _ = content_image_prompt_embeds.shape
|
|
style_image_prompt_embeds = style_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
style_image_prompt_embeds = style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len, -1)
|
|
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len,
|
|
-1)
|
|
|
|
with torch.inference_mode():
|
|
prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
|
|
prompt,
|
|
device=self.device,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds_, content_image_prompt_embeds, style_image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat([negative_prompt_embeds_,
|
|
uncond_content_image_prompt_embeds, uncond_style_image_prompt_embeds],
|
|
dim=1)
|
|
|
|
generator = get_generator(seed, self.device)
|
|
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
guidance_scale=guidance_scale,
|
|
num_inference_steps=num_inference_steps,
|
|
generator=generator,
|
|
**kwargs,
|
|
).images
|
|
|
|
return images
|
|
|
|
|
|
class IPAdapterXL_CS(IPAdapter_CS):
|
|
"""SDXL"""
|
|
|
|
def generate(
|
|
self,
|
|
pil_content_image,
|
|
pil_style_image,
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
content_scale=1.0,
|
|
style_scale=1.0,
|
|
num_samples=4,
|
|
seed=None,
|
|
content_image_embeds=None,
|
|
style_image_embeds=None,
|
|
num_inference_steps=30,
|
|
neg_content_emb=None,
|
|
neg_content_prompt=None,
|
|
neg_content_scale=1.0,
|
|
|
|
**kwargs,
|
|
):
|
|
self.set_scale(content_scale, style_scale)
|
|
|
|
num_prompts = 1 if isinstance(pil_content_image, Image.Image) else len(pil_content_image)
|
|
|
|
if prompt is None:
|
|
prompt = "best quality, high quality"
|
|
if negative_prompt is None:
|
|
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
|
negative_prompt = [negative_prompt] * num_prompts
|
|
|
|
content_image_prompt_embeds, uncond_content_image_prompt_embeds = self.get_image_embeds(pil_content_image,
|
|
content_image_embeds,
|
|
content_or_style_='content')
|
|
|
|
|
|
|
|
style_image_prompt_embeds, uncond_style_image_prompt_embeds = self.get_image_embeds(pil_style_image,
|
|
style_image_embeds,
|
|
content_or_style_='style')
|
|
|
|
|
|
bs_embed, seq_len, _ = content_image_prompt_embeds.shape
|
|
|
|
content_image_prompt_embeds = content_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
content_image_prompt_embeds = content_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
|
|
uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.view(bs_embed * num_samples, seq_len,
|
|
-1)
|
|
bs_style_embed, seq_style_len, _ = style_image_prompt_embeds.shape
|
|
style_image_prompt_embeds = style_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
style_image_prompt_embeds = style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len, -1)
|
|
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len,
|
|
-1)
|
|
|
|
with torch.inference_mode():
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.pipe.encode_prompt(
|
|
prompt,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds, content_image_prompt_embeds, style_image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat([negative_prompt_embeds,
|
|
uncond_content_image_prompt_embeds, uncond_style_image_prompt_embeds],
|
|
dim=1)
|
|
|
|
# self.generator = get_generator(seed, self.device)
|
|
# latents = torch.randn((1, 4, 128, 128), generator=self.generator, device="cuda", dtype=torch.float16).to("cuda")
|
|
# latents = latents.repeat(2, 1, 1, 1)
|
|
# print(latents.shape)
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
num_inference_steps=num_inference_steps,
|
|
# generator=self.generator,
|
|
**kwargs,
|
|
).images
|
|
return images
|
|
|
|
|
|
class CSGO(IPAdapterXL_CS):
|
|
"""SDXL"""
|
|
|
|
def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False):
|
|
if content_or_style_ == 'content':
|
|
if model_resampler:
|
|
image_proj_model = Resampler(
|
|
dim=self.pipe.unet.config.cross_attention_dim,
|
|
depth=4,
|
|
dim_head=64,
|
|
heads=12,
|
|
num_queries=num_tokens,
|
|
embedding_dim=self.content_image_encoder.config.hidden_size,
|
|
output_dim=self.pipe.unet.config.cross_attention_dim,
|
|
ff_mult=4,
|
|
).to(self.device, dtype=torch.float16)
|
|
else:
|
|
image_proj_model = ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
|
clip_extra_context_tokens=num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
if content_or_style_ == 'style':
|
|
if model_resampler:
|
|
image_proj_model = Resampler(
|
|
dim=self.pipe.unet.config.cross_attention_dim,
|
|
depth=4,
|
|
dim_head=64,
|
|
heads=12,
|
|
num_queries=num_tokens,
|
|
embedding_dim=self.content_image_encoder.config.hidden_size,
|
|
output_dim=self.pipe.unet.config.cross_attention_dim,
|
|
ff_mult=4,
|
|
).to(self.device, dtype=torch.float16)
|
|
else:
|
|
image_proj_model = ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
|
clip_extra_context_tokens=num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_or_style_=''):
|
|
if isinstance(pil_image, Image.Image):
|
|
pil_image = [pil_image]
|
|
if content_or_style_ == 'style':
|
|
|
|
if self.style_model_resampler:
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16),
|
|
output_hidden_states=True).hidden_states[-2]
|
|
image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
|
uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
else:
|
|
|
|
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
|
uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
return image_prompt_embeds, uncond_image_prompt_embeds
|
|
|
|
|
|
else:
|
|
|
|
if self.content_image_encoder_path is not None:
|
|
clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
outputs = self.content_image_encoder(clip_image.to(self.device, dtype=torch.float16),
|
|
output_hidden_states=True)
|
|
clip_image_embeds = outputs.last_hidden_state
|
|
image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
|
|
|
# uncond_clip_image_embeds = self.image_encoder(
|
|
# torch.zeros_like(clip_image), output_hidden_states=True
|
|
# ).last_hidden_state
|
|
uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
return image_prompt_embeds, uncond_image_prompt_embeds
|
|
|
|
else:
|
|
if self.content_model_resampler:
|
|
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
|
|
clip_image = clip_image.to(self.device, dtype=torch.float16)
|
|
clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
|
# clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
|
image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
|
# uncond_clip_image_embeds = self.image_encoder(
|
|
# torch.zeros_like(clip_image), output_hidden_states=True
|
|
# ).hidden_states[-2]
|
|
uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
else:
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
|
uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
|
|
return image_prompt_embeds, uncond_image_prompt_embeds
|
|
|
|
# # clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
# clip_image = clip_image.to(self.device, dtype=torch.float16)
|
|
# clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
|
# image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
|
# uncond_clip_image_embeds = self.image_encoder(
|
|
# torch.zeros_like(clip_image), output_hidden_states=True
|
|
# ).hidden_states[-2]
|
|
# uncond_image_prompt_embeds = self.content_image_proj_model(uncond_clip_image_embeds)
|
|
# return image_prompt_embeds, uncond_image_prompt_embeds
|
|
|
|
|
|
class StyleStudio_Adapter(CSGO):
|
|
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device,
|
|
num_style_tokens=4,
|
|
target_style_blocks=["block"],
|
|
controlnet_adapter=False,
|
|
controlnet_target_content_blocks=None,
|
|
controlnet_target_style_blocks=None,
|
|
style_model_resampler=False,
|
|
fuAttn=False,
|
|
fuSAttn=False,
|
|
fuIPAttn=False,
|
|
fuScale=0,
|
|
adainIP=False,
|
|
end_fusion=0,
|
|
save_attn_map=False,
|
|
):
|
|
self.fuAttn = fuAttn
|
|
self.fuSAttn = fuSAttn
|
|
self.fuIPAttn = fuIPAttn
|
|
self.adainIP = adainIP
|
|
self.fuScale = fuScale
|
|
# if self.adainIP:
|
|
# print("use the cross modal adain")
|
|
if self.fuSAttn:
|
|
print(f"hijack Self AttnMap in {end_fusion} steps")
|
|
if self.fuAttn:
|
|
print(f"hijack Cross AttnMap in {end_fusion} steps")
|
|
if self.fuIPAttn:
|
|
print(f"hijack IP AttnMap in {end_fusion} steps")
|
|
self.end_fusion = end_fusion
|
|
self.save_attn_map = save_attn_map
|
|
|
|
self.device = device
|
|
self.image_encoder_path = image_encoder_path
|
|
self.ip_ckpt = ip_ckpt
|
|
self.num_style_tokens = num_style_tokens
|
|
self.style_target_blocks = target_style_blocks
|
|
|
|
self.style_model_resampler = style_model_resampler
|
|
|
|
self.controlnet_adapter = controlnet_adapter
|
|
self.controlnet_target_content_blocks = controlnet_target_content_blocks
|
|
self.controlnet_target_style_blocks = controlnet_target_style_blocks
|
|
|
|
self.pipe = sd_pipe.to(self.device)
|
|
self.set_ip_adapter()
|
|
|
|
|
|
# load image encoder
|
|
# model.requires_grad_(False)
|
|
|
|
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
|
self.device, dtype=torch.float16
|
|
)
|
|
|
|
self.clip_image_processor = CLIPImageProcessor()
|
|
# image proj model
|
|
self.style_image_proj_model = self.init_proj(self.num_style_tokens, content_or_style_='style',
|
|
model_resampler=self.style_model_resampler)
|
|
self.load_ip_adapter()
|
|
|
|
def set_ip_adapter(self):
|
|
unet = self.pipe.unet
|
|
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] = AttnProcessor_hijack(
|
|
fuSAttn=self.fuSAttn,
|
|
end_fusion=self.end_fusion,
|
|
attn_name=name)
|
|
else:
|
|
# layername_id += 1
|
|
selected = False
|
|
for block_name in self.style_target_blocks:
|
|
if block_name in name:
|
|
selected = True
|
|
# print(name)
|
|
attn_procs[name] = IPAttnProcessor_cross_modal(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
num_tokens=self.num_style_tokens,
|
|
fuAttn=self.fuAttn,
|
|
fuIPAttn=self.fuIPAttn,
|
|
adainIP=self.adainIP,
|
|
end_fusion=self.end_fusion,
|
|
attn_name=name,
|
|
)
|
|
if selected is False:
|
|
attn_procs[name] = IPAttnProcessor_cross_modal(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
num_tokens=self.num_style_tokens,
|
|
skip=True,
|
|
fuAttn=self.fuAttn,
|
|
fuIPAttn=self.fuIPAttn,
|
|
adainIP=self.adainIP,
|
|
end_fusion=self.end_fusion,
|
|
attn_name=name,
|
|
)
|
|
|
|
attn_procs[name].to(self.device, dtype=torch.float16)
|
|
unet.set_attn_processor(attn_procs)
|
|
if hasattr(self.pipe, "controlnet"):
|
|
if self.controlnet_adapter is False:
|
|
if isinstance(self.pipe.controlnet, MultiControlNetModel):
|
|
for controlnet in self.pipe.controlnet.nets:
|
|
controlnet.set_attn_processor(CNAttnProcessor(
|
|
num_tokens=self.num_content_tokens + self.num_style_tokens))
|
|
else:
|
|
self.pipe.controlnet.set_attn_processor(CNAttnProcessor(
|
|
num_tokens=self.num_content_tokens + self.num_style_tokens))
|
|
|
|
def load_ip_adapter(self):
|
|
if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
|
|
state_dict = {"content_image_proj": {}, "style_image_proj": {}, "ip_adapter": {}}
|
|
with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
|
|
for key in f.keys():
|
|
if key.startswith("content_image_proj."):
|
|
state_dict["content_image_proj"][key.replace("content_image_proj.", "")] = f.get_tensor(key)
|
|
elif key.startswith("style_image_proj."):
|
|
state_dict["style_image_proj"][key.replace("style_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(self.ip_ckpt, map_location="cpu")
|
|
self.style_image_proj_model.load_state_dict(state_dict["style_image_proj"])
|
|
|
|
if 'conv_in_unet_sd' in state_dict.keys():
|
|
self.pipe.unet.conv_in.load_state_dict(state_dict["conv_in_unet_sd"], strict=True)
|
|
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
|
|
ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)
|
|
|
|
def set_scale(self, style_scale):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, IP_FuAd_AttnProcessor):
|
|
if attn_processor.style is True:
|
|
attn_processor.style_scale = style_scale
|
|
# print('style_scale:',style_scale)
|
|
|
|
def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False):
|
|
if content_or_style_ == 'content':
|
|
if model_resampler:
|
|
image_proj_model = Resampler(
|
|
dim=self.pipe.unet.config.cross_attention_dim,
|
|
depth=4,
|
|
dim_head=64,
|
|
heads=12,
|
|
num_queries=num_tokens,
|
|
embedding_dim=self.image_encoder.config.hidden_size,
|
|
output_dim=self.pipe.unet.config.cross_attention_dim,
|
|
ff_mult=4,
|
|
).to(self.device, dtype=torch.float16)
|
|
else:
|
|
image_proj_model = ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
|
clip_extra_context_tokens=num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
if content_or_style_ == 'style':
|
|
if model_resampler:
|
|
image_proj_model = Resampler(
|
|
dim=self.pipe.unet.config.cross_attention_dim,
|
|
depth=4,
|
|
dim_head=64,
|
|
heads=12,
|
|
num_queries=num_tokens,
|
|
embedding_dim=self.image_encoder.config.hidden_size,
|
|
output_dim=self.pipe.unet.config.cross_attention_dim,
|
|
ff_mult=4,
|
|
).to(self.device, dtype=torch.float16)
|
|
else:
|
|
image_proj_model = ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
|
clip_extra_context_tokens=num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
|
|
if isinstance(pil_image, Image.Image):
|
|
pil_image = [pil_image]
|
|
if self.style_model_resampler:
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16),
|
|
output_hidden_states=True).hidden_states[-2]
|
|
image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
|
uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
else:
|
|
|
|
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
|
uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
|
|
return image_prompt_embeds, uncond_image_prompt_embeds
|
|
|
|
@torch.inference_mode()
|
|
def get_neg_image_embeds(self, pil_image=None, clip_image_embeds=None):
|
|
if isinstance(pil_image, Image.Image):
|
|
pil_image = [pil_image]
|
|
|
|
if self.style_model_resampler:
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16),
|
|
output_hidden_states=True).hidden_states[-2]
|
|
neg_image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
|
else:
|
|
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
|
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
neg_image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
|
return neg_image_prompt_embeds
|
|
|
|
def set_endFusion(self, end_T):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, AttnProcessor_hijack):
|
|
attn_processor.end_fusion = end_T
|
|
|
|
def set_SAttn(self, use_SAttn):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, AttnProcessor_hijack):
|
|
attn_processor.fuSAttn = use_SAttn
|
|
|
|
def set_num_inference_step(self, num_T):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, AttnProcessor_hijack) or isinstance(attn_processor, IPAttnProcessor_cross_modal):
|
|
attn_processor.num_inference_step = num_T
|
|
attn_processor.denoise_step = 0
|
|
|
|
def set_adain(self, use_CMA):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, IPAttnProcessor_cross_modal):
|
|
attn_processor.adainIP = use_CMA
|
|
|
|
def generate(
|
|
self,
|
|
pil_style_image,
|
|
|
|
neg_pil_style_image=None,
|
|
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
num_samples=2,
|
|
style_image_embeds=None,
|
|
num_inference_steps=30,
|
|
end_fusion=20,
|
|
cross_modal_adain=True,
|
|
use_SAttn=True,
|
|
**kwargs,
|
|
):
|
|
|
|
self.set_endFusion(end_T = end_fusion)
|
|
self.set_adain(use_CMA=cross_modal_adain)
|
|
self.set_SAttn(use_SAttn=use_SAttn)
|
|
self.set_num_inference_step(num_T=num_inference_steps)
|
|
|
|
num_prompts = 1 if isinstance(pil_style_image, Image.Image) else len(pil_style_image)
|
|
|
|
if prompt is None:
|
|
prompt = "best quality, high quality"
|
|
if negative_prompt is None:
|
|
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
|
negative_prompt = [negative_prompt] * num_prompts
|
|
|
|
style_image_prompt_embeds, uncond_style_image_prompt_embeds = self.get_image_embeds(
|
|
pil_style_image,
|
|
style_image_embeds,
|
|
)
|
|
|
|
if neg_pil_style_image is not None:
|
|
print("using neg style image")
|
|
neg_style_image_prompt_embeds = self.get_neg_image_embeds(neg_pil_style_image,
|
|
style_image_embeds,)
|
|
cos_sim_neg = F.cosine_similarity(style_image_prompt_embeds, neg_style_image_prompt_embeds.squeeze(0).unsqueeze(1), dim=-1)
|
|
cos_sim_uncond = F.cosine_similarity(style_image_prompt_embeds, uncond_style_image_prompt_embeds.squeeze(0).unsqueeze(1), dim=-1)
|
|
print(f"neg cos sim is: {cos_sim_neg.diagonal()}")
|
|
print(f"uncond cos sim is: {cos_sim_uncond.diagonal()}")
|
|
uncond_style_image_prompt_embeds = neg_style_image_prompt_embeds
|
|
|
|
bs_embed, seq_style_len, _ = style_image_prompt_embeds.shape
|
|
style_image_prompt_embeds = style_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
style_image_prompt_embeds = style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len, -1)
|
|
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len,
|
|
-1)
|
|
|
|
with torch.inference_mode():
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.pipe.encode_prompt(
|
|
prompt,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds, style_image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat([negative_prompt_embeds,
|
|
uncond_style_image_prompt_embeds],
|
|
dim=1)
|
|
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
num_inference_steps=num_inference_steps,
|
|
**kwargs,
|
|
).images
|
|
return images
|
|
|
|
class IPAdapterXL(IPAdapter):
|
|
"""SDXL"""
|
|
|
|
def generate(
|
|
self,
|
|
pil_image,
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
scale=1.0,
|
|
num_samples=4,
|
|
seed=None,
|
|
num_inference_steps=30,
|
|
neg_content_emb=None,
|
|
neg_content_prompt=None,
|
|
neg_content_scale=1.0,
|
|
**kwargs,
|
|
):
|
|
self.set_scale(scale)
|
|
|
|
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
|
|
|
if prompt is None:
|
|
prompt = "best quality, high quality"
|
|
if negative_prompt is None:
|
|
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
|
negative_prompt = [negative_prompt] * num_prompts
|
|
|
|
if neg_content_emb is None:
|
|
if neg_content_prompt is not None:
|
|
with torch.inference_mode():
|
|
(
|
|
prompt_embeds_, # torch.Size([1, 77, 2048])
|
|
negative_prompt_embeds_,
|
|
pooled_prompt_embeds_, # torch.Size([1, 1280])
|
|
negative_pooled_prompt_embeds_,
|
|
) = self.pipe.encode_prompt(
|
|
neg_content_prompt,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
pooled_prompt_embeds_ *= neg_content_scale
|
|
else:
|
|
pooled_prompt_embeds_ = neg_content_emb
|
|
else:
|
|
pooled_prompt_embeds_ = None
|
|
|
|
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image,
|
|
content_prompt_embeds=pooled_prompt_embeds_)
|
|
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
|
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
|
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
|
|
with torch.inference_mode():
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.pipe.encode_prompt(
|
|
prompt,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
|
|
|
|
self.generator = get_generator(seed, self.device)
|
|
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
num_inference_steps=num_inference_steps,
|
|
generator=self.generator,
|
|
**kwargs,
|
|
).images
|
|
|
|
return images
|
|
|
|
class IPAdapterPlus(IPAdapter):
|
|
"""IP-Adapter with fine-grained features"""
|
|
|
|
def init_proj(self):
|
|
image_proj_model = Resampler(
|
|
dim=self.pipe.unet.config.cross_attention_dim,
|
|
depth=4,
|
|
dim_head=64,
|
|
heads=12,
|
|
num_queries=self.num_tokens,
|
|
embedding_dim=self.image_encoder.config.hidden_size,
|
|
output_dim=self.pipe.unet.config.cross_attention_dim,
|
|
ff_mult=4,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
|
|
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=torch.float16)
|
|
clip_image_embeds = self.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 = self.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
|
|
|
|
|
|
class IPAdapterFull(IPAdapterPlus):
|
|
"""IP-Adapter with full features"""
|
|
|
|
def init_proj(self):
|
|
image_proj_model = MLPProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.image_encoder.config.hidden_size,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
|
|
class IPAdapterPlusXL(IPAdapter):
|
|
"""SDXL"""
|
|
|
|
def init_proj(self):
|
|
image_proj_model = Resampler(
|
|
dim=1280,
|
|
depth=4,
|
|
dim_head=64,
|
|
heads=20,
|
|
num_queries=self.num_tokens,
|
|
embedding_dim=self.image_encoder.config.hidden_size,
|
|
output_dim=self.pipe.unet.config.cross_attention_dim,
|
|
ff_mult=4,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, pil_image):
|
|
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=torch.float16)
|
|
clip_image_embeds = self.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 = self.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 generate(
|
|
self,
|
|
pil_image,
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
scale=1.0,
|
|
num_samples=4,
|
|
seed=None,
|
|
num_inference_steps=30,
|
|
**kwargs,
|
|
):
|
|
self.set_scale(scale)
|
|
|
|
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
|
|
|
if prompt is None:
|
|
prompt = "best quality, high quality"
|
|
if negative_prompt is None:
|
|
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
|
negative_prompt = [negative_prompt] * num_prompts
|
|
|
|
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
|
|
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
|
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
|
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
|
|
with torch.inference_mode():
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.pipe.encode_prompt(
|
|
prompt,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
|
|
|
|
generator = get_generator(seed, self.device)
|
|
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
num_inference_steps=num_inference_steps,
|
|
generator=generator,
|
|
**kwargs,
|
|
).images
|
|
|
|
return images
|
|
|
|
|
|
class IPAdapterXL_cross_modal(IPAdapterXL):
|
|
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4,
|
|
target_blocks=["block"],
|
|
fuAttn=False,
|
|
fuSAttn=False,
|
|
fuIPAttn=False,
|
|
fuScale=0,
|
|
adainIP=False,
|
|
end_fusion=0,
|
|
save_attn_map=False,):
|
|
self.fuAttn = fuAttn
|
|
self.fuSAttn = fuSAttn
|
|
self.fuIPAttn = fuIPAttn
|
|
self.adainIP = adainIP
|
|
self.fuScale = fuScale
|
|
|
|
if self.fuSAttn:
|
|
print(f"hijack Self AttnMap in {end_fusion} steps")
|
|
if self.fuAttn:
|
|
print(f"hijack Cross AttnMap in {end_fusion} steps")
|
|
if self.fuIPAttn:
|
|
print(f"hijack IP AttnMap in {end_fusion} steps")
|
|
self.end_fusion = end_fusion
|
|
self.save_attn_map = save_attn_map
|
|
|
|
self.device = device
|
|
self.image_encoder_path = image_encoder_path
|
|
self.ip_ckpt = ip_ckpt
|
|
self.num_tokens = num_tokens
|
|
self.target_blocks = target_blocks
|
|
|
|
self.pipe = sd_pipe.to(self.device)
|
|
self.set_ip_adapter()
|
|
|
|
# load image encoder
|
|
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
|
self.device, dtype=torch.float16
|
|
)
|
|
self.clip_image_processor = CLIPImageProcessor()
|
|
# image proj model
|
|
self.image_proj_model = self.init_proj()
|
|
|
|
self.load_ip_adapter()
|
|
|
|
def init_proj(self):
|
|
image_proj_model = ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
|
clip_extra_context_tokens=self.num_tokens,
|
|
).to(self.device, dtype=torch.float16)
|
|
return image_proj_model
|
|
|
|
def set_ip_adapter(self):
|
|
unet = self.pipe.unet
|
|
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] = AttnProcessor_hijack(
|
|
fuSAttn=self.fuSAttn,
|
|
end_fusion=self.end_fusion,
|
|
attn_name=name) # Self Attention
|
|
else: # Cross Attention
|
|
selected = False
|
|
for block_name in self.target_blocks:
|
|
if block_name in name:
|
|
selected = True
|
|
break
|
|
if selected:
|
|
attn_procs[name] = IPAttnProcessor_cross_modal(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
scale=1.0,
|
|
num_tokens=self.num_tokens,
|
|
fuAttn=self.fuAttn,
|
|
fuIPAttn=self.fuIPAttn,
|
|
adainIP=self.adainIP,
|
|
end_fusion=self.end_fusion,
|
|
).to(self.device, dtype=torch.float16)
|
|
else:
|
|
attn_procs[name] = IPAttnProcessor_cross_modal(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
scale=1.0,
|
|
num_tokens=self.num_tokens,
|
|
skip=True,
|
|
fuAttn=self.fuAttn,
|
|
fuIPAttn=self.fuIPAttn,
|
|
adainIP=self.adainIP,
|
|
end_fusion=self.end_fusion,
|
|
).to(self.device, dtype=torch.float16)
|
|
unet.set_attn_processor(attn_procs)
|
|
if hasattr(self.pipe, "controlnet"):
|
|
if isinstance(self.pipe.controlnet, MultiControlNetModel):
|
|
for controlnet in self.pipe.controlnet.nets:
|
|
controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
|
|
else:
|
|
self.pipe.controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
|
|
|
|
def load_ip_adapter(self):
|
|
if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
|
|
state_dict = {"image_proj": {}, "ip_adapter": {}}
|
|
with safe_open(self.ip_ckpt, framework="pt", device="cpu") 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(self.ip_ckpt, map_location="cpu")
|
|
self.image_proj_model.load_state_dict(state_dict["image_proj"])
|
|
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
|
|
ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)
|
|
|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None):
|
|
if pil_image is not None:
|
|
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 = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
else:
|
|
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
|
|
|
if content_prompt_embeds is not None:
|
|
clip_image_embeds = clip_image_embeds - content_prompt_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
|
|
|
|
def set_scale(self, scale):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, IPAttnProcessor_cross_modal):
|
|
attn_processor.scale = scale
|
|
|
|
@torch.inference_mode()
|
|
def get_neg_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None):
|
|
if pil_image is not None:
|
|
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 = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
|
else:
|
|
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
|
|
|
if content_prompt_embeds is not None:
|
|
clip_image_embeds = clip_image_embeds - content_prompt_embeds
|
|
|
|
neg_image_prompt_embeds = self.image_proj_model(clip_image_embeds)
|
|
return neg_image_prompt_embeds
|
|
|
|
def set_endFusion(self, end_T):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, AttnProcessor_hijack):
|
|
attn_processor.end_fusion = end_T
|
|
|
|
def set_SAttn(self, use_SAttn):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, AttnProcessor_hijack):
|
|
attn_processor.fuSAttn = use_SAttn
|
|
|
|
def set_num_inference_step(self, num_T):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, AttnProcessor_hijack) or isinstance(attn_processor, IPAttnProcessor_cross_modal):
|
|
attn_processor.num_inference_step = num_T
|
|
attn_processor.denoise_step = 0
|
|
|
|
def set_adain(self, use_CMA):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, IPAttnProcessor_cross_modal):
|
|
attn_processor.adainIP = use_CMA
|
|
|
|
def generate(
|
|
self,
|
|
pil_image,
|
|
neg_pil_image=None,
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
scale=1.0,
|
|
num_samples=4,
|
|
seed=None,
|
|
num_inference_steps=30,
|
|
neg_content_emb=None,
|
|
neg_content_prompt=None,
|
|
neg_content_scale=1.0,
|
|
**kwargs,
|
|
):
|
|
self.set_scale(scale)
|
|
|
|
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
|
|
|
if prompt is None:
|
|
prompt = "best quality, high quality"
|
|
if negative_prompt is None:
|
|
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
|
negative_prompt = [negative_prompt] * num_prompts
|
|
|
|
if neg_content_emb is None:
|
|
if neg_content_prompt is not None:
|
|
with torch.inference_mode():
|
|
(
|
|
prompt_embeds_, # torch.Size([1, 77, 2048])
|
|
negative_prompt_embeds_,
|
|
pooled_prompt_embeds_, # torch.Size([1, 1280])
|
|
negative_pooled_prompt_embeds_,
|
|
) = self.pipe.encode_prompt(
|
|
neg_content_prompt,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
pooled_prompt_embeds_ *= neg_content_scale
|
|
else:
|
|
pooled_prompt_embeds_ = neg_content_emb
|
|
else:
|
|
pooled_prompt_embeds_ = None
|
|
|
|
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image, content_prompt_embeds=pooled_prompt_embeds_)
|
|
|
|
if neg_pil_image is not None:
|
|
neg_image_prompt_embeds = self.get_neg_image_embeds(neg_pil_image)
|
|
cos_sim_neg = F.cosine_similarity(image_prompt_embeds, neg_image_prompt_embeds.squeeze(0).unsqueeze(1), dim=-1)
|
|
cos_sim_uncond = F.cosine_similarity(image_prompt_embeds, uncond_image_prompt_embeds.squeeze(0).unsqueeze(1), dim=-1)
|
|
print(f"neg cos sim is: {cos_sim_neg.diagonal()}")
|
|
print(f"uncond cos sim is: {cos_sim_uncond.diagonal()}")
|
|
uncond_image_prompt_embeds = neg_image_prompt_embeds
|
|
|
|
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
|
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
|
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
|
|
|
with torch.inference_mode():
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.pipe.encode_prompt(
|
|
prompt,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
|
|
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
num_inference_steps=num_inference_steps,
|
|
**kwargs,
|
|
).images
|
|
|
|
return images
|