app.py

import spaces
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 SAK.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter_FaceID import StableDiffusionXLPipeline
from SAK.models.modeling_chatglm import ChatGLMModel
from SAK.models.tokenization_chatglm import ChatGLMTokenizer
from diffusers import AutoencoderKL
from SAK.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="SunderAli17/SAK")
ckpt_dir_faceid = snapshot_download(repo_id="SunderAli17/SAK-IP-Adapter-FaceTransform-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()

@spaces.GPU
def infer(prompt, 
          image = None, 
          negative_prompt = "nsfw，Face shadows，Low resolution，JPEG artifacts、Vague、bad，Neon lights", 
          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 = [
    ["wearing a full suit sitting in a restaurant with candle lights ", "image/image1.png"]
    # ["Cowboy, cowboy hat, Wild Cowboy, background is a western town, cactus, sunset, warm colors, shot with XT4 film, noise, vignette, Kodak film, vintage", "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]
    )


SAK.queue().launch(debug=True)