app.py

import random
import torch
import cv2
import insightface
import gradio as gr
import numpy as np
import os
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 = "Kwai-Kolors/Kolors"
ckpt_dir_faceid = "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="./.insightface/"):
        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
face_info_generator = FaceInfoGenerator()


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=5.0,
          num_inference_steps=50
          ):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    global pipe
    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 = 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(server_name="0.0.0.0", share=True)