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"""
modified from from https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/ip_adapter.py
"""
import os
from typing import List
import torch
from PIL import Image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection
import alpha_clip
from .utils import get_generator
from .attention_processor import AttnProcessor, IPAttnProcessor
from safetensors import safe_open
from safetensors.torch import load_model
import numpy as np
import torch.nn as nn
class ImageProjModel(torch.nn.Module):
"""Projection Model of IP-Adapter"""
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
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 CLIPAway:
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, alpha_clip_path, config, device, alpha_clip_id="ViT-L/14", num_tokens=4):
super().__init__()
self.device = device
self.ipadapter_image_encoder_path = image_encoder_path
self.ipadapter_ckpt = ip_ckpt
self.num_tokens = num_tokens
self.pipe = sd_pipe.to(self.device)
self.set_ip_adapter()
alpha_clip_model, alpha_clip_preprocess = alpha_clip.load(alpha_clip_id, alpha_vision_ckpt_pth=alpha_clip_path, device=device)
# load image encoder
self.image_encoder = alpha_clip_model.visual.to(self.device, dtype=torch.float32)
self.clip_proj = CLIPVisionModelWithProjection.from_pretrained(self.ipadapter_image_encoder_path).to(
self.device, dtype=torch.float32
)
self.alpha_clip_image_processor = alpha_clip_preprocess
# preprocess mask transformation for alpha clip
if "@336" in alpha_clip_id:
self.mask_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((336, 336)), # change to (336,336) when using ViT-L/14@336px
transforms.Normalize(0.5, 0.26)
])
else:
self.mask_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((224, 224)), # change to (336,336) when using ViT-L/14@336px
transforms.Normalize(0.5, 0.26)
])
# image proj model
self.image_proj_model = self.init_proj()
self.load_ip_adapter()
self.mlp_projection_layer = self.generate_projection_layer(config)
print(config.mlp_projection_layer_ckpt_path, type(config.mlp_projection_layer_ckpt_path) )
if config.mlp_projection_layer_ckpt_path is not None:
self.load_projection_layer(config.mlp_projection_layer_ckpt_path)
def load_projection_layer(self, path):
load_model(self.mlp_projection_layer, path)
print("Projection layer loaded from", path)
def generate_projection_layer(self, config):
projection_layer = nn.ModuleList()
for i in range(config.number_of_hidden_layers):
if i < config.number_of_hidden_layers // 2:
projection_layer.append(nn.Linear(config.alpha_clip_embed_dim, config.alpha_clip_embed_dim))
projection_layer.append(nn.LayerNorm(config.alpha_clip_embed_dim))
elif i == config.number_of_hidden_layers // 2:
projection_layer.append(nn.Linear(config.alpha_clip_embed_dim, config.ip_adapter_embed_dim))
projection_layer.append(nn.LayerNorm(config.ip_adapter_embed_dim))
else:
projection_layer.append(nn.Linear(config.ip_adapter_embed_dim, config.ip_adapter_embed_dim))
projection_layer.append(nn.LayerNorm(config.ip_adapter_embed_dim))
projection_layer.append(nn.GELU())
projection_layer.append(nn.Linear(config.ip_adapter_embed_dim, config.ip_adapter_embed_dim))
return nn.Sequential(*projection_layer).to(self.device).to(torch.float32)
def init_proj(self):
image_proj_model = ImageProjModel(
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
clip_embeddings_dim=self.clip_proj.config.projection_dim,
clip_extra_context_tokens=self.num_tokens,
).to(self.device, dtype=torch.float32)
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().to(self.device)
else:
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.float32)
unet.set_attn_processor(attn_procs)
def get_alpha_clip_embeds(self, pil_image, alpha):
clip_image = [self.alpha_clip_image_processor(image) for image in pil_image]
clip_image = torch.stack(clip_image).to(self.device, dtype=torch.float32)
masks = [self.mask_transform(mask) for mask in alpha]
masks = torch.stack(masks).to(self.device, dtype=torch.float32)
return self.image_encoder(clip_image, masks)
def load_ip_adapter(self):
if os.path.splitext(self.ipadapter_ckpt)[-1] == ".safetensors":
state_dict = {"image_proj": {}, "ip_adapter": {}}
with safe_open(self.ipadapter_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.ipadapter_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"])
def get_complement_of_mask(self, mask):
return Image.fromarray((255 - np.array(mask[0])).astype(np.uint8))
def clipaway_projection_block(self, bg_embeds, fg_embeds):
projected_vector_magnitude = bg_embeds[0].dot(fg_embeds[0]) / fg_embeds[0].norm()
projected_vector = projected_vector_magnitude * fg_embeds / fg_embeds.norm()
return bg_embeds - projected_vector
def get_focused_embeddings(self, pil_image, alpha, use_projection_block=False):
# get focused alpha clip embeds
clip_image_embeds_fg = self.get_alpha_clip_embeds(pil_image, alpha)
clip_image_embeds_bg = self.get_alpha_clip_embeds(pil_image, [self.get_complement_of_mask(alpha)])
# mlp projection
projected_alpha_clip_embeds_fg = self.mlp_projection_layer(clip_image_embeds_fg)
projected_alpha_clip_embeds_bg = self.mlp_projection_layer(clip_image_embeds_bg)
# ip adapter logic
image_prompt_embeds_fg = self.image_proj_model(projected_alpha_clip_embeds_fg)
image_prompt_embeds_bg = self.image_proj_model(projected_alpha_clip_embeds_bg)
uncond_image_prompt_embeds = self.image_proj_model(self.mlp_projection_layer(torch.zeros_like(clip_image_embeds_fg)))
if use_projection_block:
# clipaway projection block
projected_alpha_clip_embeds = self.clipaway_projection_block(projected_alpha_clip_embeds_bg, projected_alpha_clip_embeds_fg)
image_prompt_embeds = self.image_proj_model(projected_alpha_clip_embeds)
return image_prompt_embeds, image_prompt_embeds_fg, image_prompt_embeds_bg, uncond_image_prompt_embeds
return image_prompt_embeds_fg, image_prompt_embeds_bg, uncond_image_prompt_embeds
def get_ipadapter_embeds(self, pil_image=None, alpha=None):
# get focused alpha clip embeds
clip_image_embeds_fg = self.get_alpha_clip_embeds(pil_image, alpha)
clip_image_embeds_bg = self.get_alpha_clip_embeds(pil_image, [self.get_complement_of_mask(alpha)])
# mlp projection
projected_alpha_clip_embeds_fg = self.mlp_projection_layer(clip_image_embeds_fg)
projected_alpha_clip_embeds_bg = self.mlp_projection_layer(clip_image_embeds_bg)
# clipaway projection block
projected_alpha_clip_embeds = self.clipaway_projection_block(projected_alpha_clip_embeds_bg, projected_alpha_clip_embeds_fg)
# ip adapter logic
image_prompt_embeds = self.image_proj_model(projected_alpha_clip_embeds)
uncond_image_prompt_embeds = self.image_proj_model(self.mlp_projection_layer(torch.zeros_like(clip_image_embeds_fg)))
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
@torch.inference_mode()
def generate(
self,
pil_image=None,
alpha=None,
prompt=None,
negative_prompt=None,
image_prompt_embeds=None,
uncond_image_prompt_embeds=None,
scale=1.0,
num_samples=1,
seed=None,
guidance_scale=7.5,
num_inference_steps=50,
**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 image_prompt_embeds is None or uncond_image_prompt_embeds is None:
image_prompt_embeds, uncond_image_prompt_embeds= self.get_ipadapter_embeds(pil_image=pil_image, alpha=alpha)
else:
image_prompt_embeds = image_prompt_embeds.to(self.device)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.to(self.device)
bs_embed, seq_len, _ = image_prompt_embeds.shape
image_prompt_embeds = image_prompt_embeds.view(bs_embed, seq_len, -1)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed, 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,
image=pil_image,
mask_image=alpha,
**kwargs,
).images
return images
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