import spaces
import random
import torch
import cv2
import insightface
import gradio as gr
import numpy as np
from huggingface_hub import snapshot_download
from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor
from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter_FaceID import StableDiffusionXLPipeline
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from diffusers import AutoencoderKL
from kolors.models.unet_2d_condition import UNet2DConditionModel
from diffusers import EulerDiscreteScheduler
from PIL import Image
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
device = "cuda"
ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors")
ckpt_dir_faceid = snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus")
text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device)
tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device)
scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(f'{ckpt_dir_faceid}/clip-vit-large-patch14-336', ignore_mismatched_sizes=True)
clip_image_encoder.to(device)
clip_image_processor = CLIPImageProcessor(size = 336, crop_size = 336)
pipe = StableDiffusionXLPipeline(
vae = vae,
text_encoder = text_encoder,
tokenizer = tokenizer,
unet = unet,
scheduler = scheduler,
face_clip_encoder = clip_image_encoder,
face_clip_processor = clip_image_processor,
force_zeros_for_empty_prompt = False,
)
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]
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU
def infer(prompt,
image = None,
negative_prompt = "nsfw，脸部阴影，低分辨率，jpeg伪影、模糊、糟糕，黑脸，霓虹灯",
seed = 66,
randomize_seed = False,
guidance_scale = 6.0,
num_inference_steps = 50
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
pipe = pipe.to(device)
pipe.load_ip_adapter_faceid_plus(f'{ckpt_dir_faceid}/ipa-faceid-plus.bin', device = device)
scale = 0.8
pipe.set_face_fidelity_scale(scale)
face_info_generator = FaceInfoGenerator(root_dir = "./")
face_info = face_info_generator.get_faceinfo_one_img(image)
face_bbox_square = face_bbox_to_square(face_info["bbox"])
crop_image = image.crop(face_bbox_square)
crop_image = crop_image.resize((336, 336))
crop_image = [crop_image]
face_embeds = torch.from_numpy(np.array([face_info["embedding"]]))
face_embeds = face_embeds.to(device, dtype = torch.float16)
image = pipe(
prompt = prompt,
negative_prompt = negative_prompt,
height = 1024,
width = 1024,
num_inference_steps= num_inference_steps,
guidance_scale = guidance_scale,
num_images_per_prompt = 1,
generator = generator,
face_crop_image = crop_image,
face_insightface_embeds = face_embeds
).images[0]
return image, seed
examples = [
["穿着晚礼服，在星光下的晚宴场景中，烛光闪闪，整个场景洋溢着浪漫而奢华的氛围", "image/image1.png"],
["西部牛仔，牛仔帽，荒野大镖客，背景是西部小镇，仙人掌，,日落余晖, 暖色调, 使用XT4胶片拍摄, 噪点, 晕影, 柯达胶卷，复古", "image/image2.png"]
]
css="""
#col-left {
margin: 0 auto;
max-width: 600px;
}
#col-right {
margin: 0 auto;
max-width: 750px;
}
#button {
color: blue;
}
"""
def load_description(fp):
with open(fp, 'r', encoding='utf-8') as f:
content = f.read()
return content
with gr.Blocks(css=css) as Kolors:
gr.HTML(load_description("assets/title.md"))
with gr.Row():
with gr.Column(elem_id="col-left"):
with gr.Row():
prompt = gr.Textbox(
label="Prompt",
placeholder="Enter your prompt",
lines=2
)
with gr.Row():
image = gr.Image(label="Image", type="pil")
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Textbox(
label="Negative prompt",
placeholder="Enter a negative prompt",
visible=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=5.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=10,
maximum=50,
step=1,
value=25,
)
with gr.Row():
button = gr.Button("Run", elem_id="button")
with gr.Column(elem_id="col-right"):
result = gr.Image(label="Result", show_label=False)
seed_used = gr.Number(label="Seed Used")
with gr.Row():
gr.Examples(
fn = infer,
examples = examples,
inputs = [prompt, image],
outputs = [result, seed_used],
)
button.click(
fn = infer,
inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps],
outputs = [result, seed_used]
)
Kolors.queue().launch(debug=True)