Spaces:
Runtime error
Runtime error
File size: 10,646 Bytes
c19ca42 |
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 |
from typing import List
import os
import cv2
import torch
import numpy as np
import diffusers
import huggingface_hub as hf
from PIL import Image
from modules import processing, shared, devices, extra_networks, sd_models, sd_hijack_freeu, script_callbacks, ipadapter
from modules.sd_hijack_hypertile import context_hypertile_vae, context_hypertile_unet
FACEID_MODELS = {
"FaceID Base": "h94/IP-Adapter-FaceID/ip-adapter-faceid_sd15.bin",
"FaceID Plus v1": "h94/IP-Adapter-FaceID/ip-adapter-faceid-plus_sd15.bin",
"FaceID Plus v2": "h94/IP-Adapter-FaceID/ip-adapter-faceid-plusv2_sd15.bin",
"FaceID XL": "h94/IP-Adapter-FaceID/ip-adapter-faceid_sdxl.bin",
# "FaceID Portrait v10": "h94/IP-Adapter-FaceID/ip-adapter-faceid-portrait_sd15.bin",
# "FaceID Portrait v11": "h94/IP-Adapter-FaceID/ip-adapter-faceid-portrait-v11_sd15.bin",
# "FaceID XL Plus v2": "h94/IP-Adapter-FaceID/ip-adapter-faceid_sdxl.bin",
}
faceid_model_weights = None
faceid_model_name = None
debug = shared.log.trace if os.environ.get("SD_FACE_DEBUG", None) is not None else lambda *args, **kwargs: None
def hijack_load_ip_adapter(self):
self.image_proj_model.load_state_dict(faceid_model_weights["image_proj"])
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
ip_layers.load_state_dict(faceid_model_weights["ip_adapter"], strict=False)
def face_id(
p: processing.StableDiffusionProcessing,
app,
source_images: List[Image.Image],
model: str,
override: bool,
cache: bool,
scale: float,
structure: float,
):
global faceid_model_weights, faceid_model_name # pylint: disable=global-statement
if source_images is None or len(source_images) == 0:
shared.log.warning('FaceID: no input images')
return None
from insightface.utils import face_align
try:
from ip_adapter.ip_adapter_faceid import (
IPAdapterFaceID,
IPAdapterFaceIDPlus,
IPAdapterFaceIDXL,
IPAdapterFaceIDPlusXL,
)
from ip_adapter.ip_adapter_faceid_separate import (
IPAdapterFaceID as IPAdapterFaceIDPortrait,
)
except Exception as e:
shared.log.error(f"FaceID incorrect version of ip_adapter: {e}")
return None
processed_images = []
faceid_model = None
original_load_ip_adapter = None
try:
shared.prompt_styles.apply_styles_to_extra(p)
if not shared.opts.cuda_compile:
sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
sd_hijack_freeu.apply_freeu(p, shared.backend == shared.Backend.ORIGINAL)
script_callbacks.before_process_callback(p)
with context_hypertile_vae(p), context_hypertile_unet(p), devices.inference_context():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
ip_ckpt = FACEID_MODELS[model]
folder, filename = os.path.split(ip_ckpt)
basename, _ext = os.path.splitext(filename)
model_path = hf.hf_hub_download(repo_id=folder, filename=filename, cache_dir=shared.opts.diffusers_dir)
if model_path is None:
shared.log.error(f"FaceID download failed: model={model} file={ip_ckpt}")
return None
if override:
shared.sd_model.scheduler = diffusers.DDIMScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
steps_offset=1,
)
if faceid_model_weights is None or faceid_model_name != model or not cache:
shared.log.debug(f"FaceID load: model={model} file={ip_ckpt}")
faceid_model_weights = torch.load(model_path, map_location="cpu")
else:
shared.log.debug(f"FaceID cached: model={model} file={ip_ckpt}")
if "XL Plus" in model:
image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
original_load_ip_adapter = IPAdapterFaceIDPlusXL.load_ip_adapter
IPAdapterFaceIDPlusXL.load_ip_adapter = hijack_load_ip_adapter
faceid_model = IPAdapterFaceIDPlusXL(
sd_pipe=shared.sd_model,
image_encoder_path=image_encoder_path,
ip_ckpt=model_path,
lora_rank=128,
num_tokens=4,
device=devices.device,
torch_dtype=devices.dtype,
)
elif "XL" in model:
original_load_ip_adapter = IPAdapterFaceIDXL.load_ip_adapter
IPAdapterFaceIDXL.load_ip_adapter = hijack_load_ip_adapter
faceid_model = IPAdapterFaceIDXL(
sd_pipe=shared.sd_model,
ip_ckpt=model_path,
lora_rank=128,
num_tokens=4,
device=devices.device,
torch_dtype=devices.dtype,
)
elif "Plus" in model:
original_load_ip_adapter = IPAdapterFaceIDPlus.load_ip_adapter
IPAdapterFaceIDPlus.load_ip_adapter = hijack_load_ip_adapter
image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
faceid_model = IPAdapterFaceIDPlus(
sd_pipe=shared.sd_model,
image_encoder_path=image_encoder_path,
ip_ckpt=model_path,
lora_rank=128,
num_tokens=4,
device=devices.device,
torch_dtype=devices.dtype,
)
elif "Portrait" in model:
original_load_ip_adapter = IPAdapterFaceIDPortrait.load_ip_adapter
IPAdapterFaceIDPortrait.load_ip_adapter = hijack_load_ip_adapter
faceid_model = IPAdapterFaceIDPortrait(
sd_pipe=shared.sd_model,
ip_ckpt=model_path,
num_tokens=16,
n_cond=5,
device=devices.device,
torch_dtype=devices.dtype,
)
else:
original_load_ip_adapter = IPAdapterFaceID.load_ip_adapter
IPAdapterFaceID.load_ip_adapter = hijack_load_ip_adapter
faceid_model = IPAdapterFaceID(
sd_pipe=shared.sd_model,
ip_ckpt=model_path,
lora_rank=128,
num_tokens=4,
device=devices.device,
torch_dtype=devices.dtype,
)
shortcut = "v2" in model
faceid_model_name = model
face_embeds = []
face_images = []
for i, source_image in enumerate(source_images):
np_image = cv2.cvtColor(np.array(source_image), cv2.COLOR_RGB2BGR)
faces = app.get(np_image)
if len(faces) == 0:
shared.log.error("FaceID: no faces found")
break
face_embeds.append(torch.from_numpy(faces[0].normed_embedding).unsqueeze(0))
face_images.append(face_align.norm_crop(np_image, landmark=faces[0].kps, image_size=224))
shared.log.debug(f'FaceID face: i={i+1} score={faces[0].det_score:.2f} gender={"female" if faces[0].gender==0 else "male"} age={faces[0].age} bbox={faces[0].bbox}')
p.extra_generation_params[f"FaceID {i+1}"] = f'{faces[0].det_score:.2f} {"female" if faces[0].gender==0 else "male"} {faces[0].age}y'
if len(face_embeds) == 0:
shared.log.error("FaceID: no faces found")
return None
face_embeds = torch.cat(face_embeds, dim=0)
ip_model_dict = { # main generate dict
"num_samples": p.batch_size,
"width": p.width,
"height": p.height,
"num_inference_steps": p.steps,
"scale": scale,
"guidance_scale": p.cfg_scale,
"faceid_embeds": face_embeds.shape, # placeholder
}
# optional generate dict
if shortcut is not None:
ip_model_dict["shortcut"] = shortcut
if "Plus" in model:
ip_model_dict["s_scale"] = structure
shared.log.debug(f"FaceID args: {ip_model_dict}")
if "Plus" in model:
ip_model_dict["face_image"] = face_images
ip_model_dict["faceid_embeds"] = face_embeds # overwrite placeholder
faceid_model.set_scale(scale)
extra_network_data = None
for i in range(p.n_iter):
p.iteration = i
p.prompts = p.all_prompts[i * p.batch_size:(i + 1) * p.batch_size]
p.negative_prompts = p.all_negative_prompts[i * p.batch_size:(i + 1) * p.batch_size]
p.prompts, extra_network_data = extra_networks.parse_prompts(p.prompts)
p.seeds = p.all_seeds[i * p.batch_size:(i + 1) * p.batch_size]
if not p.disable_extra_networks:
with devices.autocast():
extra_networks.activate(p, extra_network_data)
ip_model_dict.update({
"prompt": p.prompts,
"negative_prompt": p.negative_prompts,
"seed": int(p.seeds[0]),
})
debug(f"FaceID: {ip_model_dict}")
res = faceid_model.generate(**ip_model_dict)
if isinstance(res, list):
processed_images += res
faceid_model.set_scale(0)
faceid_model = None
if not cache:
faceid_model_weights = None
faceid_model_name = None
devices.torch_gc()
ipadapter.unapply(p.sd_model)
if not p.disable_extra_networks:
extra_networks.deactivate(p, extra_network_data)
p.extra_generation_params["IP Adapter"] = f"{basename}:{scale}"
finally:
if faceid_model is not None and original_load_ip_adapter is not None:
faceid_model.__class__.load_ip_adapter = original_load_ip_adapter
if not shared.opts.cuda_compile:
sd_models.apply_token_merging(p.sd_model, 0)
script_callbacks.after_process_callback(p)
return processed_images
|