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
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)
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 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