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, pipeline 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, ) # 번역 모델 초기화 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en") # 프롬프트 처리 함수 추가 def process_prompt(prompt): if any('\u3131' <= char <= '\u3163' or '\uac00' <= char <= '\ud7a3' for char in prompt): translated = translator(prompt)[0]['translation_text'] return prompt, translated return prompt, prompt 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 ): original_prompt, english_prompt = process_prompt(prompt) 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 = english_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, original_prompt, english_prompt examples = [ ["wearing a full suit sitting in a restaurant with candle lights", "image/test0.png"], ["Wild cowboy hat with western town and horses in the background", "image/test1.png"] ] def load_description(fp): with open(fp, 'r', encoding='utf-8') as f: content = f.read() return content css = """ footer { visibility: hidden; } """ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as SAK: with gr.Row(): with gr.Column(elem_id="col-left"): with gr.Row(): prompt = gr.Textbox( label="프롬프트", placeholder="프롬프트를 입력하세요 (한글 또는 영어)", lines=2 ) with gr.Row(): image = gr.Image(label="이미지", type="pil") with gr.Accordion("고급 설정", open=False): negative_prompt = gr.Textbox( label="네거티브 프롬프트", placeholder="네거티브 프롬프트를 입력하세요", visible=True, value="nsfw, 얼굴 그림자, 저해상도, jpeg 아티팩트, 흐릿함, 열악함, 네온 조명" ) seed = gr.Slider( label="시드", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="시드 무작위화", value=True) with gr.Row(): guidance_scale = gr.Slider( label="가이던스 스케일", minimum=0.0, maximum=10.0, step=0.1, value=5.0, ) num_inference_steps = gr.Slider( label="추론 단계 수", minimum=10, maximum=50, step=1, value=25, ) with gr.Row(): button = gr.Button("실행", elem_id="button") with gr.Column(elem_id="col-right"): result = gr.Image(label="결과", show_label=False) seed_used = gr.Number(label="사용된 시드") original_prompt_display = gr.Textbox(label="원본 프롬프트") english_prompt_display = gr.Textbox(label="영어 프롬프트") with gr.Row(): gr.Examples( fn = infer, examples = examples, inputs = [prompt, image], outputs = [result, seed_used, original_prompt_display, english_prompt_display], ) button.click( fn = infer, inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps], outputs = [result, seed_used, original_prompt_display, english_prompt_display] ) SAK.queue().launch(debug=True, share=True)