File size: 8,703 Bytes
50261d7 | 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 | import gradio as gr
import torch
import dlib
import numpy as np
from PIL import Image
from diffusers import UNet2DConditionModel as OriginalUNet2DConditionModel
from pipeline_sd15 import StableDiffusionControlNetPipeline
from diffusers import DPMSolverMultistepScheduler, ControlNetModel
from detail_encoder.encoder_plus import detail_encoder
from spiga_draw import *
from spiga.inference.config import ModelConfig
from spiga.inference.framework import SPIGAFramework
from facelib import FaceDetector
from modules import script_callbacks
import os
from huggingface_hub import snapshot_download
torch.cuda.set_device(0)
processor = SPIGAFramework(ModelConfig("300wpublic"))
detector = FaceDetector(weight_path="./models/mobilenet0.25_Final.pth")
def get_draw(pil_img, size):
spigas = spiga_process(pil_img, detector)
if not spigas:
width, height = pil_img.size
black_image_pil = Image.new('RGB', (width, height), color=(0, 0, 0))
return black_image_pil
else:
spigas_faces = spiga_segmentation(spigas, size=size)
return spigas_faces
#to download model
model_id = "runwayml/stable-diffusion-v1-5"
base_path = os.path.join(os.path.dirname(__file__), "..", "models")
makeup_encoder_path = os.path.join(base_path, "pytorch_model.bin")
id_encoder_path = os.path.join(base_path, "pytorch_model_1.bin")
pose_encoder_path = os.path.join(base_path, "pytorch_model_2.bin")
# URL for manual download
manual_download_url = "https://huggingface.co/kigy1/Stable-Makeup"
# Check if the file pytorch_model.bin exists
if not os.path.isfile(makeup_encoder_path):
try:
# Try downloading the model
snapshot_download(repo_id="kigy1/Stable-Makeup", local_dir=base_path)
except Exception as e:
print("First download attempt failed, trying alternative method.")
try:
# Try the alternative method
snapshot_download(repo_id="kigy1/Stable-Makeup", local_dir=base_path, use_symlinks=False)
except Exception as e:
print("Both download attempts failed. Please download the model manually from the following URL put the three model inside sd-webui-Stable-Makeup\models:")
print(manual_download_url)
traceback.print_exc()
else:
print(f"The file {makeup_encoder_path} already exists. Skipping download.")
Unet = OriginalUNet2DConditionModel.from_pretrained(model_id, subfolder="unet").to("cuda")
id_encoder = ControlNetModel.from_unet(Unet)
pose_encoder = ControlNetModel.from_unet(Unet)
makeup_encoder = detail_encoder(Unet, "openai/clip-vit-large-patch14", "cuda", dtype=torch.float32)
makeup_state_dict = torch.load(makeup_encoder_path)
id_state_dict = torch.load(id_encoder_path)
id_encoder.load_state_dict(id_state_dict, strict=False)
pose_state_dict = torch.load(pose_encoder_path)
pose_encoder.load_state_dict(pose_state_dict, strict=False)
makeup_encoder.load_state_dict(makeup_state_dict, strict=False)
pose_encoder.to("cuda")
id_encoder.to("cuda")
makeup_encoder.to("cuda")
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id,
safety_checker=None,
unet=Unet,
controlnet=[id_encoder, pose_encoder],
torch_dtype=torch.float32).to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
def model_call(id_image, makeup_image, num):
try:
detector = dlib.get_frontal_face_detector()
image_array = np.array(id_image)
face_locations = detector(image_array)
if len(face_locations) > 0:
top, right, bottom, left = face_locations[0].top(), face_locations[0].right(), face_locations[0].bottom(), face_locations[0].left()
margin = 50
sstop = 50
top = max(0, top - margin)
right = min(image_array.shape[1], right + margin)
bottom = min(image_array.shape[0], bottom + margin)
left = max(0, left - margin)
ttop = 0
tright = 0
tbottom = 0
tleft = 0
while True:
if (top <= 0) or (left <= 0) or (right >= image_array.shape[1]) or (bottom >= image_array.shape[0]):
top = ttop
right = tright
bottom = tbottom
left = tleft
break
else:
margin = 20
sstop += 20
if sstop >= 200:
break
else:
ttop = top
tright = right
tbottom = bottom
tleft = left
top = max(0, top - margin)
right = min(image_array.shape[1], right + margin)
bottom = min(image_array.shape[0], bottom + margin)
left = max(0, left - margin)
face_image_pil = Image.fromarray(id_image.astype('uint8'), 'RGB')
head_image = face_image_pil.crop((left, top, right, bottom))
back_img_org = Image.fromarray(id_image.astype('uint8'), 'RGB')
makeup_image_pil = Image.fromarray(makeup_image.astype('uint8'), 'RGB')
head_image = head_image.resize((512, 512))
makeup_image_pil = makeup_image_pil.resize((512, 512))
back_img = back_img_org.resize((512, 512))
pose_image = get_draw(id_image, size=512)
result_img = makeup_encoder.generate(id_image=[head_image, pose_image], makeup_image=makeup_image_pil, guidance_scale=num, pipe=pipe)
backgr = makeup_encoder.generate(id_image=[back_img, pose_image], makeup_image=makeup_image_pil, guidance_scale=num, pipe=pipe)
width, height = right - left, bottom - top
head_image_original_size = result_img.resize((width, height))
finalsize = np.array(id_image)
heightx, widthx, channelsx = finalsize.shape
backgr_size = backgr.resize((widthx, heightx))
backgr_array = np.array(backgr_size)
face_with_makeup = Image.fromarray(backgr_array.astype('uint8'), 'RGB')
face_with_makeup.paste(head_image_original_size, (left, top))
return face_with_makeup
except:
id_image = Image.fromarray(id_image.astype('uint8'), 'RGB')
org_size = id_image
makeup_image = Image.fromarray(makeup_image.astype('uint8'), 'RGB')
id_image = id_image.resize((512, 512))
makeup_image = makeup_image.resize((512, 512))
pose_image = get_draw(id_image, size=512)
result_img = makeup_encoder.generate(id_image=[id_image, pose_image], makeup_image=makeup_image, guidance_scale=num, pipe=pipe)
result_img = result_img.resize(org_size.size)
return result_img
css = '''
#face_image_upload, #makeup_image_upload, #output_image {
height: 400px;
max-height: 400px;
max-width: 350px;
}
#face_image_upload [data-testid="image"], #face_image_upload [data-testid="image"] > div,
#makeup_image_upload [data-testid="image"], #makeup_image_upload [data-testid="image"] > div,
#output_image [data-testid="image"], #output_image [data-testid="image"] > div {
height: 400px;
max-height: 400px;
max-width: 350px;
}
#slider_column {
width: 20%; /* Adjust the width as needed */
}
'''
def create_ui():
with gr.Blocks(css=css, analytics_enabled=False) as ui_component:
with gr.Row():
with gr.Column():
with gr.Row():
image1 = gr.Image(label="Face Image", elem_id="face_image_upload").style(height=400)
image2 = gr.Image(label="Makeup Image", elem_id="makeup_image_upload").style(height=400)
with gr.Column():
number = gr.Slider(minimum=1.01, maximum=5, value=1.5, label="Makeup Guidance Scale")
button = gr.Button("Run")
with gr.Column():
output = gr.Image(type="pil", label="Output Image", elem_id="output_image").style(height=400)
def process_images(id_image, makeup_image, num):
return model_call(id_image, makeup_image, num)
button.click(process_images, inputs=[image1, image2, number], outputs=output)
ui_component.title = "Stable-Makeup"
ui_component.description = "Upload 2 images to see the model output. 1.05-1.15 is suggested for light makeup and 2 for heavy makeup"
return [(ui_component, "Stable-Makeup", "Stable-Makeup")]
script_callbacks.on_ui_tabs(create_ui)
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