Spaces:
Running
on
Zero
Running
on
Zero
File size: 9,428 Bytes
02685a6 33934d8 02685a6 33934d8 02685a6 33934d8 02685a6 33934d8 014db32 948e9f7 014db32 33934d8 98e0801 33934d8 948e9f7 33934d8 02685a6 33934d8 02685a6 33934d8 014db32 |
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 |
import torch
from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor
from diffusers.utils import load_image
import os,sys
import gradio as gr
from kolors.pipelines.pipeline_controlnet_xl_kolors_img2img_face import StableDiffusionXLControlNetImg2ImgPipeline
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from kolors.models.controlnet import ControlNetModel
from diffusers import AutoencoderKL
from kolors.models.unet_2d_condition import UNet2DConditionModel
from diffusers import EulerDiscreteScheduler
from PIL import Image
import numpy as np
import cv2
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
example_path = os.path.join(os.path.dirname(__file__), 'examples')
class FaceInfoGenerator():
def __init__(self, root_dir = "./"):
self.app = FaceAnalysis(name = 'antelopev2', root = root_dir, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
self.app.prepare(ctx_id = 0, det_size = (640, 640))
def get_faceinfo_one_img(self, face_image):
face_info = self.app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
if len(face_info) == 0:
face_info = None
else:
face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face
return face_info
def face_bbox_to_square(bbox):
## l, t, r, b to square l, t, r, b
l,t,r,b = bbox
cent_x = (l + r) / 2
cent_y = (t + b) / 2
w, h = r - l, b - t
r = max(w, h) / 2
l0 = cent_x - r
r0 = cent_x + r
t0 = cent_y - r
b0 = cent_y + r
return [l0, t0, r0, b0]
text_encoder = ChatGLMModel.from_pretrained("Kwai-Kolors/Kolors",subfolder="text_encoder").to(dtype=torch.bfloat16)
tokenizer = ChatGLMTokenizer.from_pretrained("Kwai-Kolors/Kolors",subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained("Kwai-Kolors/Kolors",subfolder="vae", revision=None).to(dtype=torch.bfloat16)
scheduler = EulerDiscreteScheduler.from_pretrained("Kwai-Kolors/Kolors",subfolder="scheduler")
unet = UNet2DConditionModel.from_pretrained("Kwai-Kolors/Kolors",subfolder="unet", revision=None).to(dtype=torch.bfloat16)
control_path = "haowu11/Kolors-Controlnet-Pose-Tryon"
controlnet = ControlNetModel.from_pretrained( control_path , revision=None).to(dtype=torch.bfloat16)
face_info_generator = FaceInfoGenerator(root_dir = "./")
clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained("Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus",cache_dir='./',subfolder="clip-vit-large-patch14-336", ignore_mismatched_sizes=True)
clip_image_encoder.to('cuda')
clip_image_processor = CLIPImageProcessor(size = 336, crop_size = 336)
pipe = StableDiffusionXLControlNetImg2ImgPipeline(
vae=vae,
controlnet = controlnet,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
# image_encoder=image_encoder,
# feature_extractor=clip_image_processor,
force_zeros_for_empty_prompt=False,
face_clip_encoder=clip_image_encoder,
face_clip_processor=clip_image_processor,
)
if hasattr(pipe.unet, 'encoder_hid_proj'):
pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj
ip_scale = 0.5
pipe.load_ip_adapter_faceid_plus('ipa-faceid-plus.bin', device = 'cuda')
pipe.set_face_fidelity_scale(ip_scale)
pipe = pipe.to("cuda")
pipe.enable_model_cpu_offload()
def infer(face_img,pose_img, garm_img, prompt,negative_prompt, n_samples, n_steps, seed):
face_img = Image.open(face_img)
pose_img = Image.open(pose_img)
garm_img = Image.open(garm_img)
face_img = face_img.resize((336, 336))
pose_img = pose_img.resize((768, 1024))
garm_img = garm_img.resize((768, 1024))
background = Image.new("RGB", (768, 768), (255, 255, 255))
#将face_img粘贴到background中心
background.paste(face_img, (int((768 - 336) / 2), int((768 - 336) / 2)))
face_info = face_info_generator.get_faceinfo_one_img(background)
face_embeds = torch.from_numpy(np.array([face_info["embedding"]]))
face_embeds = face_embeds.to('cuda', dtype = torch.bfloat16)
controlnet_conditioning_scale = 1.0
control_guidance_end = 0.9
#strength 越是小，则生成图片越是依赖原始图片。
strength = 1.0
im1 = np.array(pose_img)
im2 = np.array(garm_img)
condi_img = Image.fromarray( np.concatenate( (im1, im2), axis=1 ) )
generator = torch.Generator(device="cpu").manual_seed(seed)
image = pipe(
prompt= prompt ,
# image = init_image,
controlnet_conditioning_scale = controlnet_conditioning_scale,
control_guidance_end = control_guidance_end,
# ip_adapter_image=[ ip_adapter_img ],
face_crop_image = face_img,
face_insightface_embeds = face_embeds,
strength= strength ,
control_image = condi_img,
negative_prompt= negative_prompt ,
num_inference_steps=n_steps ,
guidance_scale= 5.0,
num_images_per_prompt=n_samples,
generator=generator,
).images
return image
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.Markdown("# KolorsControlnerTryon Demo")
with gr.Row():
with gr.Column():
pose_img = gr.Image(label="Pose", sources='upload', type="filepath", height=768, value=os.path.join(example_path, 'pose/1.jpg'))
example = gr.Examples(
inputs=pose_img,
examples_per_page=10,
examples=[
os.path.join(example_path, 'pose/1.jpg'),
os.path.join(example_path, 'pose/2.jpg'),
os.path.join(example_path, 'pose/3.jpg'),
os.path.join(example_path, 'pose/4.jpg'),
os.path.join(example_path, 'pose/5.jpg'),
os.path.join(example_path, 'pose/6.jpg'),
os.path.join(example_path, 'pose/7.jpg'),
os.path.join(example_path, 'pose/8.jpg'),
os.path.join(example_path, 'pose/9.jpg'),
os.path.join(example_path, 'pose/10.jpg'),
])
with gr.Column():
garm_img = gr.Image(label="Garment", sources='upload', type="filepath", height=768, value=os.path.join(example_path, 'garment/1.jpg'),)
example = gr.Examples(
inputs=garm_img,
examples_per_page=10,
examples=[
os.path.join(example_path, 'garment/1.jpg'),
os.path.join(example_path, 'garment/2.jpg'),
os.path.join(example_path, 'garment/3.jpg'),
os.path.join(example_path, 'garment/4.jpg'),
os.path.join(example_path, 'garment/5.jpg'),
os.path.join(example_path, 'garment/6.jpg'),
os.path.join(example_path, 'garment/7.jpg'),
os.path.join(example_path, 'garment/8.jpg'),
os.path.join(example_path, 'garment/9.jpg'),
os.path.join(example_path, 'garment/10.jpg'),
])
with gr.Row():
with gr.Column():
face_img = gr.Image(label="Face", sources='upload', type="filepath", height=336, value=os.path.join(example_path, 'face/1.png'),)
example = gr.Examples(
inputs=face_img,
examples_per_page=10,
examples=[
os.path.join(example_path, 'face/1.png'),
os.path.join(example_path, 'face/2.png'),
os.path.join(example_path, 'face/3.png'),
os.path.join(example_path, 'face/4.png'),
os.path.join(example_path, 'face/5.png'),
os.path.join(example_path, 'face/6.png'),
os.path.join(example_path, 'face/7.png'),
os.path.join(example_path, 'face/8.png'),
os.path.join(example_path, 'face/9.png'),
os.path.join(example_path, 'face/10.png'),
])
with gr.Column():
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", preview=True, scale=1)
with gr.Column():
prompt = gr.Textbox(value="这张图片上的模特穿着一件黑色的长袖T恤，T恤上印着彩色的字母'OBEY'。她还穿着一条牛仔裤。", show_label=False, elem_id="prompt")
negative_prompt = gr.Textbox(value="nsfw，脸部阴影，低分辨率，糟糕的解剖结构、糟糕的手，缺失手指、质量最差、低质量、jpeg伪影、模糊、糟糕，黑脸，霓虹灯", show_label=False, elem_id="negative_prompt")
n_samples = gr.Slider(label="Images", minimum=1, maximum=4, value=1, step=1)
n_steps = gr.Slider(label="Steps", minimum=20, maximum=40, value=20, step=1)
seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=-1)
run_button = gr.Button(value="Run")
ips = [face_img,pose_img, garm_img, prompt,negative_prompt, n_samples, n_steps, seed]
run_button.click(fn=infer, inputs=ips, outputs=[result_gallery])
if __name__ == "__main__":
block.launch(server_name='0.0.0.0') |