Spaces:
Running
on
Zero
Running
on
Zero
File size: 15,221 Bytes
28acd08 5c38014 28acd08 e89aac5 28acd08 2438e6b 28acd08 2438e6b 28acd08 2438e6b 28acd08 1fc6ba2 2438e6b 1fc6ba2 28acd08 8701b82 28acd08 89bbbd2 8961d26 28acd08 8961d26 d9e3d60 8961d26 28acd08 86b3ed9 301b68a 86b3ed9 28acd08 a8007c5 86b3ed9 aaed44b 28acd08 6996882 28acd08 a8007c5 28acd08 aaed44b 28acd08 aaed44b 28acd08 86b3ed9 c5c93cb 97422e4 717b099 86b3ed9 301b68a 9faeb57 a8007c5 86b3ed9 a8007c5 86b3ed9 a8007c5 86b3ed9 c5c93cb 86b3ed9 a8007c5 86b3ed9 c5c93cb 86b3ed9 a8007c5 86b3ed9 76f396c 717b099 17aca73 067016d 717b099 17aca73 717b099 86b3ed9 28acd08 a8007c5 1798e4f 28acd08 b66268c 28acd08 6996882 28acd08 2438e6b 28acd08 2438e6b 28acd08 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 |
import os
from typing import List
import torch
from diffusers import StableDiffusionPipeline
from diffusers.pipelines.controlnet import MultiControlNetModel
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from PIL import Image
from .attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor, CNAttnProcessor2_0 as CNAttnProcessor
from .resampler import Resampler
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.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 IPAdapter:
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4):
self.device = device
self.image_encoder_path = image_encoder_path
self.ip_ckpt = ip_ckpt
self.num_tokens = num_tokens
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.bfloat16)
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.bfloat16)
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:
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.bfloat16)
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 update_state_dict(self, state_dict):
image_proj_dict = {}
ip_adapter_dict = {}
for k in state_dict.keys():
if k.startswith("image_proj_model"):
image_proj_dict[k.replace("image_proj_model.", "")] = state_dict[k]
if k.startswith("adapter_modules"):
ip_adapter_dict[k.replace("adapter_modules.", "")] = state_dict[k]
dict = {'image_proj': image_proj_dict,
'ip_adapter' : ip_adapter_dict
}
return dict
def load_ip_adapter(self):
state_dict = torch.load(self.ip_ckpt, map_location="cpu")
if "image_proj_model.proj.weight" in state_dict.keys():
state_dict = self.update_state_dict(state_dict)
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"])
@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
print('clip_image_processor shape:',clip_image.shape)
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.bfloat16)).image_embeds
print('image_encoder shape:',clip_image_embeds.shape)
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
print('image_proj_model shape:',image_prompt_embeds.shape)
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,
prompt=None,
negative_prompt=None,
scale=1.0,
num_samples=4,
seed=-1,
guidance_scale=7.5,
num_inference_steps=30,
**kwargs,
):
self.set_scale(scale)
if isinstance(pil_image, List):
num_prompts = len(pil_image)
else:
num_prompts = 1
# if isinstance(pil_image, Image.Image):
# num_prompts = 1
# else:
# num_prompts = len(pil_image)
# print("num promp", num_prompts)
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 = self.pipe._encode_prompt(
prompt, device=self.device, num_images_per_prompt=num_samples, do_classifier_free_guidance=True, negative_prompt=negative_prompt)
negative_prompt_embeds_, prompt_embeds_ = prompt_embeds.chunk(2)
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 = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
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(IPAdapter):
"""SDXL"""
def get_scale(self):
for attn_processor in self.pipe.unet.attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
print('IP attn_scale:')
print(attn_processor.scale)
for attn_processor in self.pipe.unet.attn_processors.values():
if isinstance(attn_processor, AttnProcessor):
print('UNET attn_scale:')
print(attn_processor.scale)
def generate(
self,
pil_image_1,
pil_image_2=None,
pil_image_3=None,
pil_image_4=None,
pil_image_5=None,
prompt=None,
negative_prompt=None,
text_scale=1.0,
ip_scale=1.0,
scale_1=1.0,
scale_2=1.0,
scale_3=1.0,
scale_4=1.0,
scale_5=1.0,
num_samples=1,
seed=-1,
num_inference_steps=30,
guidance_scale=7.5,
**kwargs,
):
#self.get_scale()
self.set_scale(ip_scale)
if isinstance(pil_image_1, Image.Image):
num_prompts = 1
else:
num_prompts = len(pil_image_1)
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_list = []
uncond_image_prompt_embeds_list = []
print('Using primary image.')
image_prompt_embeds_1, uncond_image_prompt_embeds_1 = self.get_image_embeds(pil_image_1)
image_prompt_embeds_1 = image_prompt_embeds_1 * scale_1
image_prompt_embeds_list.append(image_prompt_embeds_1)
uncond_image_prompt_embeds_list.append(uncond_image_prompt_embeds_1)
if pil_image_2 != None:
print('Using secondary image.')
image_prompt_embeds_2, uncond_image_prompt_embeds_2 = self.get_image_embeds(pil_image_2)
image_prompt_embeds_2 = image_prompt_embeds_2 * scale_2
image_prompt_embeds_list.append(image_prompt_embeds_2)
uncond_image_prompt_embeds_list.append(uncond_image_prompt_embeds_2)
if pil_image_3 != None:
print('Using tertiary image.')
image_prompt_embeds_3, uncond_image_prompt_embeds_3 = self.get_image_embeds(pil_image_3)
image_prompt_embeds_3 = image_prompt_embeds_3 * scale_3
image_prompt_embeds_list.append(image_prompt_embeds_3)
uncond_image_prompt_embeds_list.append(uncond_image_prompt_embeds_3)
if pil_image_4 != None:
print('Using quaternary image.')
image_prompt_embeds_4, uncond_image_prompt_embeds_4 = self.get_image_embeds(pil_image_4)
image_prompt_embeds_4 = image_prompt_embeds_4 * scale_4
image_prompt_embeds_list.append(image_prompt_embeds_4)
uncond_image_prompt_embeds_list.append(uncond_image_prompt_embeds_4)
if pil_image_5 != None:
print('Using quinary image.')
image_prompt_embeds_5, uncond_image_prompt_embeds_5 = self.get_image_embeds(pil_image_5)
image_prompt_embeds_5 = image_prompt_embeds_5 * scale_5
image_prompt_embeds_list.append(image_prompt_embeds_5)
uncond_image_prompt_embeds_list.append(uncond_image_prompt_embeds_5)
image_prompt_embeds = torch.cat(image_prompt_embeds_list).mean(dim=0).unsqueeze(0)
print('catted embeds list with mean and unsqueeze shape: ',image_prompt_embeds.shape)
bs_embed, seq_len, _ = image_prompt_embeds.shape
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
print('catted embeds repeat: ',image_prompt_embeds.shape)
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
print('viewed embeds: ',image_prompt_embeds.shape)
uncond_image_prompt_embeds = torch.cat(uncond_image_prompt_embeds_list).mean(dim=0).unsqueeze(0)
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 = prompt_embeds * text_scale
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 = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
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,
guidance_scale=guidance_scale,
generator=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.bfloat16)
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.bfloat16)
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
|