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| import spaces | |
| import argparse | |
| import os | |
| import shutil | |
| import cv2 | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from facexlib.utils.face_restoration_helper import FaceRestoreHelper | |
| import huggingface_hub | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from torchvision.transforms.functional import normalize | |
| from dreamo.dreamo_pipeline import DreamOPipeline | |
| from dreamo.utils import img2tensor, resize_numpy_image_area, tensor2img, resize_numpy_image_long | |
| from tools import BEN2 | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--port', type=int, default=8080) | |
| parser.add_argument('--no_turbo', action='store_true') | |
| args = parser.parse_args() | |
| huggingface_hub.login(os.getenv('HF_TOKEN')) | |
| try: | |
| shutil.rmtree('gradio_cached_examples') | |
| except FileNotFoundError: | |
| print("cache folder not exist") | |
| class Generator: | |
| def __init__(self): | |
| device = torch.device('cuda') | |
| # preprocessing models | |
| # background remove model: BEN2 | |
| self.bg_rm_model = BEN2.BEN_Base().to(device).eval() | |
| hf_hub_download(repo_id='PramaLLC/BEN2', filename='BEN2_Base.pth', local_dir='models') | |
| self.bg_rm_model.loadcheckpoints('models/BEN2_Base.pth') | |
| # face crop and align tool: facexlib | |
| self.face_helper = FaceRestoreHelper( | |
| upscale_factor=1, | |
| face_size=512, | |
| crop_ratio=(1, 1), | |
| det_model='retinaface_resnet50', | |
| save_ext='png', | |
| device=device, | |
| ) | |
| # load dreamo | |
| model_root = 'black-forest-labs/FLUX.1-dev' | |
| dreamo_pipeline = DreamOPipeline.from_pretrained(model_root, torch_dtype=torch.bfloat16) | |
| dreamo_pipeline.load_dreamo_model(device, use_turbo=not args.no_turbo) | |
| self.dreamo_pipeline = dreamo_pipeline.to(device) | |
| def get_align_face(self, img): | |
| # the face preprocessing code is same as PuLID | |
| self.face_helper.clean_all() | |
| image_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) | |
| self.face_helper.read_image(image_bgr) | |
| self.face_helper.get_face_landmarks_5(only_center_face=True) | |
| self.face_helper.align_warp_face() | |
| if len(self.face_helper.cropped_faces) == 0: | |
| return None | |
| align_face = self.face_helper.cropped_faces[0] | |
| input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0 | |
| input = input.to(torch.device("cuda")) | |
| parsing_out = self.face_helper.face_parse(normalize(input, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0] | |
| parsing_out = parsing_out.argmax(dim=1, keepdim=True) | |
| bg_label = [0, 16, 18, 7, 8, 9, 14, 15] | |
| bg = sum(parsing_out == i for i in bg_label).bool() | |
| white_image = torch.ones_like(input) | |
| # only keep the face features | |
| face_features_image = torch.where(bg, white_image, input) | |
| face_features_image = tensor2img(face_features_image, rgb2bgr=False) | |
| return face_features_image | |
| generator = Generator() | |
| def generate_image( | |
| ref_image1, | |
| ref_image2, | |
| ref_task1, | |
| ref_task2, | |
| prompt, | |
| seed, | |
| width=1024, | |
| height=1024, | |
| ref_res=512, | |
| num_steps=12, | |
| guidance=3.5, | |
| true_cfg=1, | |
| cfg_start_step=0, | |
| cfg_end_step=0, | |
| neg_prompt='', | |
| neg_guidance=3.5, | |
| first_step_guidance=0, | |
| ): | |
| print(prompt) | |
| ref_conds = [] | |
| debug_images = [] | |
| ref_images = [ref_image1, ref_image2] | |
| ref_tasks = [ref_task1, ref_task2] | |
| for idx, (ref_image, ref_task) in enumerate(zip(ref_images, ref_tasks)): | |
| if ref_image is not None: | |
| if ref_task == "id": | |
| ref_image = resize_numpy_image_long(ref_image, 1024) | |
| ref_image = generator.get_align_face(ref_image) | |
| elif ref_task != "style": | |
| ref_image = generator.bg_rm_model.inference(Image.fromarray(ref_image)) | |
| if ref_task != "id": | |
| ref_image = resize_numpy_image_area(np.array(ref_image), ref_res * ref_res) | |
| debug_images.append(ref_image) | |
| ref_image = img2tensor(ref_image, bgr2rgb=False).unsqueeze(0) / 255.0 | |
| ref_image = 2 * ref_image - 1.0 | |
| ref_conds.append( | |
| { | |
| 'img': ref_image, | |
| 'task': ref_task, | |
| 'idx': idx + 1, | |
| } | |
| ) | |
| seed = int(seed) | |
| if seed == -1: | |
| seed = torch.Generator(device="cpu").seed() | |
| image = generator.dreamo_pipeline( | |
| prompt=prompt, | |
| width=width, | |
| height=height, | |
| num_inference_steps=num_steps, | |
| guidance_scale=guidance, | |
| ref_conds=ref_conds, | |
| generator=torch.Generator(device="cpu").manual_seed(seed), | |
| true_cfg_scale=true_cfg, | |
| true_cfg_start_step=cfg_start_step, | |
| true_cfg_end_step=cfg_end_step, | |
| negative_prompt=neg_prompt, | |
| neg_guidance_scale=neg_guidance, | |
| first_step_guidance_scale=first_step_guidance if first_step_guidance > 0 else guidance, | |
| ).images[0] | |
| return image, debug_images, seed | |
| # ----------------------------- | |
| # (1) 여기에 영상 API 호출을 위한 추가 코드 | |
| # ----------------------------- | |
| import requests | |
| import random | |
| import tempfile | |
| import subprocess | |
| from gradio_client import Client, handle_file | |
| # 예시: 원격 서버 Endpoint (필요하다면 수정) | |
| REMOTE_ENDPOINT = os.getenv("H100_URL") | |
| client = Client(REMOTE_ENDPOINT) | |
| def run_process_video_api(image_path: str, prompt: str, video_length: float = 2.0): | |
| """ | |
| 원격 /process 엔드포인트 호출하여 영상을 생성. | |
| (예시: prompt, negative_prompt, seed 등은 하드코딩하거나 원하는대로 조정 가능) | |
| """ | |
| # 랜덤 시드 | |
| seed_val = random.randint(0, 9999999) | |
| # negative_prompt = "" 등 고정 | |
| negative_prompt = "" | |
| use_teacache = True | |
| # /process 호출 (gradio_client) | |
| result = client.predict( | |
| input_image=handle_file(image_path), | |
| prompt=prompt, | |
| n_prompt=negative_prompt, | |
| seed=seed_val, | |
| use_teacache=use_teacache, | |
| video_length=video_length, | |
| api_name="/process", | |
| ) | |
| # result는 (video_dict, preview_dict, md_text, html_text) 구조 | |
| video_dict, preview_dict, md_text, html_text = result | |
| video_path = video_dict.get("video") if isinstance(video_dict, dict) else None | |
| return video_path | |
| def add_watermark_to_video(input_video_path: str, watermark_text="Ginigen.com") -> str: | |
| """ | |
| FFmpeg로 영상에 오른쪽 하단 워터마크를 추가한 새 영상을 리턴 | |
| """ | |
| if not os.path.exists(input_video_path): | |
| raise FileNotFoundError(f"Input video not found: {input_video_path}") | |
| # 출력 경로 | |
| base, ext = os.path.splitext(input_video_path) | |
| watermarked_path = base + "_wm" + ext | |
| # ffmpeg 명령어 구성 | |
| # - y: 덮어쓰기 | |
| # drawtext 필터로 오른쪽 하단(x=w-tw-10, y=h-th-10)에 boxcolor=black 반투명 박스 | |
| cmd = [ | |
| "ffmpeg", "-y", | |
| "-i", input_video_path, | |
| "-vf", f"drawtext=fontsize=20:fontcolor=white:text='{watermark_text}':x=w-tw-10:y=h-th-10:box=1:boxcolor=black@0.5:boxborderw=5", | |
| "-codec:a", "copy", | |
| watermarked_path | |
| ] | |
| try: | |
| subprocess.run(cmd, check=True) | |
| except Exception as e: | |
| print(f"[WARN] FFmpeg watermark failed: {e}") | |
| return input_video_path # 실패 시 원본 반환 | |
| return watermarked_path | |
| def generate_video_from_image(image_array: np.ndarray): | |
| """ | |
| 1) Numpy 이미지를 임시 파일로 저장 | |
| 2) 원격 API로 2초 영상 생성 (기본 prompt 고정) | |
| 3) FFmpeg로 'Ginigen.com' 워터마크 추가 | |
| 4) 최종 mp4 경로 반환 | |
| """ | |
| if image_array is None: | |
| raise gr.Error("이미지가 없습니다.") | |
| # (1) 임시 파일로 저장 | |
| with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as fp: | |
| temp_img_path = fp.name | |
| Image.fromarray(image_array).save(temp_img_path, format="PNG") | |
| # (2) 원격 API 호출 | |
| default_video_prompt = "Generate a video with smooth and natural movement. Objects should have visible motion while maintaining fluid transitions." | |
| result_video_path = run_process_video_api( | |
| image_path=temp_img_path, | |
| prompt=default_video_prompt, | |
| video_length=2.0, | |
| ) | |
| if result_video_path is None: | |
| raise gr.Error("영상 API 호출 실패 또는 결과 없음") | |
| # (3) FFmpeg 워터마크 추가 | |
| final_video = add_watermark_to_video(result_video_path, watermark_text="Ginigen.com") | |
| return final_video | |
| # ----------------------------- | |
| # Custom CSS, Headers, etc. | |
| # ----------------------------- | |
| _CUSTOM_CSS_ = """ | |
| :root { | |
| --primary-color: #f8c3cd; /* Sakura pink - primary accent */ | |
| --secondary-color: #b3e5fc; /* Pastel blue - secondary accent */ | |
| --background-color: #f5f5f7; /* Very light gray background */ | |
| --card-background: #ffffff; /* White for cards */ | |
| --text-color: #424242; /* Dark gray for text */ | |
| --accent-color: #ffb6c1; /* Light pink for accents */ | |
| --success-color: #c8e6c9; /* Pastel green for success */ | |
| --warning-color: #fff9c4; /* Pastel yellow for warnings */ | |
| --shadow-color: rgba(0, 0, 0, 0.1); /* Shadow color */ | |
| --border-radius: 12px; /* Rounded corners */ | |
| } | |
| body { | |
| background-color: var(--background-color) !important; | |
| font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important; | |
| } | |
| .gradio-container { | |
| max-width: 1200px !important; | |
| margin: 0 auto !important; | |
| } | |
| /* Header styling */ | |
| h1 { | |
| color: #9c27b0 !important; | |
| font-weight: 800 !important; | |
| text-shadow: 2px 2px 4px rgba(156, 39, 176, 0.2) !important; | |
| letter-spacing: -0.5px !important; | |
| } | |
| /* Card styling for panels */ | |
| .panel-box { | |
| border-radius: var(--border-radius) !important; | |
| box-shadow: 0 8px 16px var(--shadow-color) !important; | |
| background-color: var(--card-background) !important; | |
| border: none !important; | |
| overflow: hidden !important; | |
| padding: 20px !important; | |
| margin-bottom: 20px !important; | |
| } | |
| /* Button styling */ | |
| button.gr-button { | |
| background: linear-gradient(135deg, var(--primary-color), #e1bee7) !important; | |
| border-radius: var(--border-radius) !important; | |
| color: #4a148c !important; | |
| font-weight: 600 !important; | |
| border: none !important; | |
| padding: 10px 20px !important; | |
| transition: all 0.3s ease !important; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1) !important; | |
| } | |
| button.gr-button:hover { | |
| transform: translateY(-2px) !important; | |
| box-shadow: 0 6px 10px rgba(0, 0, 0, 0.15) !important; | |
| background: linear-gradient(135deg, #e1bee7, var(--primary-color)) !important; | |
| } | |
| /* Input fields styling */ | |
| input, select, textarea, .gr-input { | |
| border-radius: 8px !important; | |
| border: 2px solid #e0e0e0 !important; | |
| padding: 10px 15px !important; | |
| transition: all 0.3s ease !important; | |
| background-color: #fafafa !important; | |
| } | |
| input:focus, select:focus, textarea:focus, .gr-input:focus { | |
| border-color: var(--primary-color) !important; | |
| box-shadow: 0 0 0 3px rgba(248, 195, 205, 0.3) !important; | |
| } | |
| /* Slider styling */ | |
| .gr-form input[type=range] { | |
| appearance: none !important; | |
| width: 100% !important; | |
| height: 6px !important; | |
| background: #e0e0e0 !important; | |
| border-radius: 5px !important; | |
| outline: none !important; | |
| } | |
| .gr-form input[type=range]::-webkit-slider-thumb { | |
| appearance: none !important; | |
| width: 16px !important; | |
| height: 16px !important; | |
| background: var(--primary-color) !important; | |
| border-radius: 50% !important; | |
| cursor: pointer !important; | |
| border: 2px solid white !important; | |
| box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1) !important; | |
| } | |
| /* Dropdown styling */ | |
| .gr-form select { | |
| background-color: white !important; | |
| border: 2px solid #e0e0e0 !important; | |
| border-radius: 8px !important; | |
| padding: 10px 15px !important; | |
| } | |
| .gr-form select option { | |
| padding: 10px !important; | |
| } | |
| /* Image upload area */ | |
| .gr-image-input { | |
| border: 2px dashed #b39ddb !important; | |
| border-radius: var(--border-radius) !important; | |
| background-color: #f3e5f5 !important; | |
| padding: 20px !important; | |
| display: flex !important; | |
| flex-direction: column !important; | |
| align-items: center !important; | |
| justify-content: center !important; | |
| transition: all 0.3s ease !important; | |
| } | |
| .gr-image-input:hover { | |
| background-color: #ede7f6 !important; | |
| border-color: #9575cd !important; | |
| } | |
| /* Add a nice pattern to the background */ | |
| body::before { | |
| content: "" !important; | |
| position: fixed !important; | |
| top: 0 !important; | |
| left: 0 !important; | |
| width: 100% !important; | |
| height: 100% !important; | |
| background: | |
| radial-gradient(circle at 10% 20%, rgba(248, 195, 205, 0.1) 0%, rgba(245, 245, 247, 0) 20%), | |
| radial-gradient(circle at 80% 70%, rgba(179, 229, 252, 0.1) 0%, rgba(245, 245, 247, 0) 20%) !important; | |
| pointer-events: none !important; | |
| z-index: -1 !important; | |
| } | |
| /* Gallery styling */ | |
| .gr-gallery { | |
| grid-gap: 15px !important; | |
| } | |
| .gr-gallery-item { | |
| border-radius: var(--border-radius) !important; | |
| overflow: hidden !important; | |
| box-shadow: 0 4px 8px var(--shadow-color) !important; | |
| transition: transform 0.3s ease !important; | |
| } | |
| .gr-gallery-item:hover { | |
| transform: scale(1.02) !important; | |
| } | |
| /* Label styling */ | |
| .gr-form label { | |
| font-weight: 600 !important; | |
| color: #673ab7 !important; | |
| margin-bottom: 5px !important; | |
| } | |
| /* Improve spacing */ | |
| .gr-padded { | |
| padding: 20px !important; | |
| } | |
| .gr-compact { | |
| gap: 15px !important; | |
| } | |
| .gr-form > div { | |
| margin-bottom: 16px !important; | |
| } | |
| /* Headings */ | |
| .gr-form h3 { | |
| color: #7b1fa2 !important; | |
| margin-top: 5px !important; | |
| margin-bottom: 15px !important; | |
| border-bottom: 2px solid #e1bee7 !important; | |
| padding-bottom: 8px !important; | |
| } | |
| /* Examples section */ | |
| #examples-panel { | |
| background-color: #f3e5f5 !important; | |
| border-radius: var(--border-radius) !important; | |
| padding: 15px !important; | |
| box-shadow: 0 4px 8px rgba(0, 0, 0, 0.05) !important; | |
| } | |
| #examples-panel h2 { | |
| color: #7b1fa2 !important; | |
| font-size: 1.5rem !important; | |
| margin-bottom: 15px !important; | |
| } | |
| /* Accordion styling */ | |
| .gr-accordion { | |
| border: 1px solid #e0e0e0 !important; | |
| border-radius: var(--border-radius) !important; | |
| overflow: hidden !important; | |
| } | |
| .gr-accordion summary { | |
| padding: 12px 16px !important; | |
| background-color: #f9f9f9 !important; | |
| cursor: pointer !important; | |
| font-weight: 600 !important; | |
| color: #673ab7 !important; | |
| } | |
| /* Generate button special styling */ | |
| #generate-btn { | |
| background: linear-gradient(135deg, #ff9a9e, #fad0c4) !important; | |
| font-size: 1.1rem !important; | |
| padding: 12px 24px !important; | |
| margin-top: 10px !important; | |
| margin-bottom: 15px !important; | |
| width: 100% !important; | |
| } | |
| #generate-btn:hover { | |
| background: linear-gradient(135deg, #fad0c4, #ff9a9e) !important; | |
| } | |
| """ | |
| _HEADER_ = ''' | |
| <div style="text-align: center; max-width: 850px; margin: 0 auto; padding: 25px 0;"> | |
| <div style="background: linear-gradient(135deg, #f8c3cd, #e1bee7, #b3e5fc); color: white; padding: 15px; border-radius: 15px; box-shadow: 0 10px 20px rgba(0,0,0,0.1); margin-bottom: 20px;"> | |
| <h1 style="font-size: 3rem; font-weight: 800; margin: 0; color: white; text-shadow: 2px 2px 4px rgba(0,0,0,0.2);">✨ DreamO Video ✨</h1> | |
| <p style="font-size: 1.2rem; margin: 10px 0 0;">Create customized images with advanced AI</p> | |
| </div> | |
| <div style="background: white; padding: 15px; border-radius: 12px; box-shadow: 0 5px 15px rgba(0,0,0,0.05);"> | |
| <p style="font-size: 1rem; margin: 0;">In the current demo version, due to ZeroGPU limitations, video generation is restricted to 2 seconds only. (The full version supports generation of up to 60 seconds)</p> | |
| </div> | |
| </div> | |
| <div style="background: #fff9c4; padding: 15px; border-radius: 12px; margin-bottom: 20px; border-left: 5px solid #ffd54f; box-shadow: 0 5px 15px rgba(0,0,0,0.05);"> | |
| <h3 style="margin-top: 0; color: #ff6f00;">🚩 Update Notes:</h3> | |
| <ul style="margin-bottom: 0; padding-left: 20px;"> | |
| <li><b>2025.05.11:</b> We have updated the model to mitigate over-saturation and plastic-face issues. The new version shows consistent improvements over the previous release.</li> | |
| <li><b>2025.05.13:</b> 'DreamO Video' Integration version Release</li> | |
| </ul> | |
| </div> | |
| ''' | |
| _CITE_ = r""" | |
| <div style="background: white; padding: 20px; border-radius: 12px; margin-top: 20px; box-shadow: 0 5px 15px rgba(0,0,0,0.05);"> | |
| <p style="margin: 0; font-size: 1.1rem;">If DreamO is helpful, please help to ⭐ the <a href='https://discord.gg/openfreeai' target='_blank' style="color: #9c27b0; font-weight: 600;">community</a>. Thanks!</p> | |
| <hr style="border: none; height: 1px; background-color: #e0e0e0; margin: 15px 0;"> | |
| <h4 style="margin: 0 0 10px; color: #7b1fa2;">📧 Contact</h4> | |
| <p style="margin: 0;">If you have any questions or feedback, feel free to open a discussion or contact <b>arxivgpt@gmail.com</b></p> | |
| </div> | |
| """ | |
| def create_demo(): | |
| with gr.Blocks(css=_CUSTOM_CSS_) as demo: | |
| gr.HTML(_HEADER_) | |
| with gr.Row(): | |
| with gr.Column(scale=6): | |
| with gr.Group(elem_id="input-panel", elem_classes="panel-box"): | |
| gr.Markdown("### 📸 Reference Images") | |
| with gr.Row(): | |
| with gr.Column(): | |
| ref_image1 = gr.Image(label="Reference Image 1", type="numpy", height=256, elem_id="ref-image-1") | |
| ref_task1 = gr.Dropdown(choices=["ip", "id", "style"], value="ip", label="Task for Reference Image 1", elem_id="ref-task-1") | |
| with gr.Column(): | |
| ref_image2 = gr.Image(label="Reference Image 2", type="numpy", height=256, elem_id="ref-image-2") | |
| ref_task2 = gr.Dropdown(choices=["ip", "id", "style"], value="ip", label="Task for Reference Image 2", elem_id="ref-task-2") | |
| gr.Markdown("### ✏️ Generation Parameters") | |
| prompt = gr.Textbox(label="Prompt", value="a person playing guitar in the street", elem_id="prompt-input") | |
| with gr.Row(): | |
| width = gr.Slider(768, 1024, 1024, step=16, label="Width", elem_id="width-slider") | |
| height = gr.Slider(768, 1024, 1024, step=16, label="Height", elem_id="height-slider") | |
| with gr.Row(): | |
| num_steps = gr.Slider(8, 30, 12, step=1, label="Number of Steps", elem_id="steps-slider") | |
| guidance = gr.Slider(1.0, 10.0, 3.5, step=0.1, label="Guidance Scale", elem_id="guidance-slider") | |
| seed = gr.Textbox(label="Seed (-1 for random)", value="-1", elem_id="seed-input") | |
| with gr.Accordion("Advanced Options", open=False): | |
| ref_res = gr.Slider(512, 1024, 512, step=16, label="Resolution for Reference Image") | |
| neg_prompt = gr.Textbox(label="Negative Prompt", value="") | |
| neg_guidance = gr.Slider(1.0, 10.0, 3.5, step=0.1, label="Negative Guidance") | |
| with gr.Row(): | |
| true_cfg = gr.Slider(1, 5, 1, step=0.1, label="True CFG") | |
| first_step_guidance = gr.Slider(0, 10, 0, step=0.1, label="First Step Guidance") | |
| with gr.Row(): | |
| cfg_start_step = gr.Slider(0, 30, 0, step=1, label="CFG Start Step") | |
| cfg_end_step = gr.Slider(0, 30, 0, step=1, label="CFG End Step") | |
| generate_btn = gr.Button("✨ Generate Image", elem_id="generate-btn") | |
| gr.HTML(_CITE_) | |
| with gr.Column(scale=6): | |
| with gr.Group(elem_id="output-panel", elem_classes="panel-box"): | |
| gr.Markdown("### 🖼️ Generated Result") | |
| output_image = gr.Image(label="Generated Image", elem_id="output-image", format='png') | |
| seed_output = gr.Textbox(label="Used Seed", elem_id="seed-output") | |
| # (2) 영상 생성 버튼 & 출력 영역 추가 | |
| generate_video_btn = gr.Button("🎬 Generate Video from Image") | |
| output_video = gr.Video(label="Generated Video", elem_id="video-output") | |
| gr.Markdown("### 🔍 Preprocessing") | |
| debug_image = gr.Gallery( | |
| label="Preprocessing Results (including face crop and background removal)", | |
| elem_id="debug-gallery", | |
| ) | |
| with gr.Group(elem_id="examples-panel", elem_classes="panel-box"): | |
| gr.Markdown("## 📚 Examples") | |
| example_inps = [ | |
| [ | |
| 'example_inputs/choi.jpg', | |
| None, | |
| 'ip', | |
| 'ip', | |
| 'a woman sitting on the cloud, playing guitar', | |
| 1206523688721442817, | |
| ], | |
| [ | |
| 'example_inputs/choi.jpg', | |
| None, | |
| 'id', | |
| 'ip', | |
| 'a woman holding a sign saying "TOP", on the mountain', | |
| 10441727852953907380, | |
| ], | |
| [ | |
| 'example_inputs/perfume.png', | |
| None, | |
| 'ip', | |
| 'ip', | |
| 'a perfume under spotlight', | |
| 116150031980664704, | |
| ], | |
| [ | |
| 'example_inputs/choi.jpg', | |
| None, | |
| 'id', | |
| 'ip', | |
| 'portrait, in alps', | |
| 5443415087540486371, | |
| ], | |
| [ | |
| 'example_inputs/mickey.png', | |
| None, | |
| 'style', | |
| 'ip', | |
| 'generate a same style image. A rooster wearing overalls.', | |
| 6245580464677124951, | |
| ], | |
| [ | |
| 'example_inputs/mountain.png', | |
| None, | |
| 'style', | |
| 'ip', | |
| 'generate a same style image. A pavilion by the river, and the distant mountains are endless', | |
| 5248066378927500767, | |
| ], | |
| [ | |
| 'example_inputs/shirt.png', | |
| 'example_inputs/skirt.jpeg', | |
| 'ip', | |
| 'ip', | |
| 'A girl is wearing a short-sleeved shirt and a short skirt on the beach.', | |
| 9514069256241143615, | |
| ], | |
| [ | |
| 'example_inputs/woman2.png', | |
| 'example_inputs/dress.png', | |
| 'id', | |
| 'ip', | |
| 'the woman wearing a dress, In the banquet hall', | |
| 7698454872441022867, | |
| ], | |
| [ | |
| 'example_inputs/dog1.png', | |
| 'example_inputs/dog2.png', | |
| 'ip', | |
| 'ip', | |
| 'two dogs in the jungle', | |
| 6187006025405083344, | |
| ], | |
| ] | |
| gr.Examples( | |
| examples=example_inps, | |
| inputs=[ref_image1, ref_image2, ref_task1, ref_task2, prompt, seed], | |
| label='Examples by category: IP task (rows 1-4), ID task (row 5), Style task (rows 6-7), Try-On task (rows 8-9)', | |
| cache_examples='lazy', | |
| outputs=[output_image, debug_image, seed_output], | |
| fn=generate_image, | |
| ) | |
| # 기존 이미지 생성 함수와 연결 | |
| generate_btn.click( | |
| fn=generate_image, | |
| inputs=[ | |
| ref_image1, | |
| ref_image2, | |
| ref_task1, | |
| ref_task2, | |
| prompt, | |
| seed, | |
| width, | |
| height, | |
| ref_res, | |
| num_steps, | |
| guidance, | |
| true_cfg, | |
| cfg_start_step, | |
| cfg_end_step, | |
| neg_prompt, | |
| neg_guidance, | |
| first_step_guidance, | |
| ], | |
| outputs=[output_image, debug_image, seed_output], | |
| ) | |
| # (3) 영상 생성 버튼 클릭 -> generate_video_from_image() 호출 | |
| def on_click_generate_video(img): | |
| if img is None: | |
| raise gr.Error("먼저 이미지를 생성해주세요.") | |
| video_path = generate_video_from_image(img) | |
| return video_path | |
| generate_video_btn.click( | |
| fn=on_click_generate_video, | |
| inputs=[output_image], | |
| outputs=[output_video], | |
| ) | |
| return demo | |
| if __name__ == '__main__': | |
| demo = create_demo() | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| share=True, | |
| ssr_mode=False | |
| ) | |