import os import gradio as gr import numpy as np from enum import Enum import db_examples import cv2 from demo_utils1 import * from misc_utils.train_utils import unit_test_create_model from misc_utils.image_utils import save_tensor_to_gif, save_tensor_to_images import os from PIL import Image import torch import torchvision from torchvision import transforms from einops import rearrange import imageio import time from torchvision.transforms import functional as F from torch.hub import download_url_to_file import os import spaces # 推理设置 from pl_trainer.inference.inference import InferenceIP2PVideo from tqdm import tqdm # if not os.path.exists(filename): # original_path = os.getcwd() # base_path = './models' # os.makedirs(base_path, exist_ok=True) # # 直接在代码中写入 Token（注意安全风险） # GIT_TOKEN = "955b8ea91095840b76fe38b90a088c200d4c813c" # repo_url = f"https://YeFang:{GIT_TOKEN}@code.openxlab.org.cn/YeFang/RIV_models.git" # try: # if os.system(f'git clone {repo_url} {base_path}') != 0: # raise RuntimeError("Git 克隆失败") # os.chdir(base_path) # if os.system('git lfs pull') != 0: # raise RuntimeError("Git LFS 拉取失败") # finally: # os.chdir(original_path) def tensor_to_pil_image(x): """ 将 4D PyTorch 张量转换为 PIL 图像。 """ x = x.float() # 确保张量类型为 float grid_img = torchvision.utils.make_grid(x, nrow=4).permute(1, 2, 0).detach().cpu().numpy() grid_img = (grid_img * 255).clip(0, 255).astype("uint8") # 将 [0, 1] 范围转换为 [0, 255] return Image.fromarray(grid_img) def frame_to_batch(x): """ 将帧维度转换为批次维度。 """ return rearrange(x, 'b f c h w -> (b f) c h w') def clip_image(x, min=0., max=1.): """ 将图像张量裁剪到指定的最小和最大值。 """ return torch.clamp(x, min=min, max=max) def unnormalize(x): """ 将张量范围从 [-1, 1] 转换到 [0, 1]。 """ return (x + 1) / 2 # 读取图像文件 def read_images_from_directory(directory, num_frames=16): images = [] for i in range(num_frames): img_path = os.path.join(directory, f'{i:04d}.png') img = imageio.imread(img_path) images.append(torch.tensor(img).permute(2, 0, 1)) # Convert to Tensor (C, H, W) return images def load_and_process_images(folder_path): """ 读取文件夹中的所有图片，将它们转换为 [-1, 1] 范围的张量并返回一个 4D 张量。 """ processed_images = [] transform = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: x * 2 - 1) # 将 [0, 1] 转换为 [-1, 1] ]) for filename in sorted(os.listdir(folder_path)): if filename.endswith(".png"): img_path = os.path.join(folder_path, filename) image = Image.open(img_path).convert("RGB") processed_image = transform(image) processed_images.append(processed_image) return torch.stack(processed_images) # 返回 4D 张量 def load_and_process_video(video_path, num_frames=16, crop_size=512): """ 读取视频文件中的前 num_frames 帧，将每一帧转换为 [-1, 1] 范围的张量， 并进行中心裁剪至 crop_size x crop_size，返回一个 4D 张量。 """ processed_frames = [] transform = transforms.Compose([ transforms.CenterCrop(crop_size), # 中心裁剪 transforms.ToTensor(), transforms.Lambda(lambda x: x * 2 - 1) # 将 [0, 1] 转换为 [-1, 1] ]) # 使用 OpenCV 读取视频 cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise ValueError(f"无法打开视频文件: {video_path}") frame_count = 0 while frame_count < num_frames: ret, frame = cap.read() if not ret: break # 视频帧读取完毕或视频帧不足 # 转换为 RGB 格式 frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) image = Image.fromarray(frame) # 应用转换 processed_frame = transform(image) processed_frames.append(processed_frame) frame_count += 1 cap.release() # 释放视频资源 if len(processed_frames) < num_frames: raise ValueError(f"视频帧不足 {num_frames} 帧，仅找到 {len(processed_frames)} 帧。") return torch.stack(processed_frames) # 返回 4D 张量 (帧数, 通道数, 高度, 宽度) def clear_cache(output_path): if os.path.exists(output_path): os.remove(output_path) return None #! 加载模型 # 配置路径和加载模型 config_path = 'configs/instruct_v2v_ic_gradio.yaml' diffusion_model = unit_test_create_model(config_path) diffusion_model = diffusion_model.to('cuda') # 加载模型检查点 # ckpt_path = 'models/relvid_mm_sd15_fbc_unet.pth' #! change # ckpt_path = 'tmp/pytorch_model.bin' # 下载文件 os.makedirs('models', exist_ok=True) model_path = "models/relvid_mm_sd15_fbc_unet.pth" if not os.path.exists(model_path): download_url_to_file(url='https://huggingface.co/aleafy/RelightVid/resolve/main/relvid_mm_sd15_fbc_unet.pth', dst=model_path) ckpt = torch.load(model_path, map_location='cpu') diffusion_model.load_state_dict(ckpt, strict=False) # import pdb; pdb.set_trace() # 更改全局临时目录 new_tmp_dir = "./demo/gradio_bg" os.makedirs(new_tmp_dir, exist_ok=True) # import pdb; pdb.set_trace() def save_video_from_frames(image_pred, save_pth, fps=8): """ 将 image_pred 中的帧保存为视频文件。 参数: - image_pred: Tensor，形状为 (1, 16, 3, 512, 512) - save_pth: 保存视频的路径，例如 "output_video.mp4" - fps: 视频的帧率 """ # 视频参数 num_frames = image_pred.shape[1] frame_height, frame_width = 512, 512 # 目标尺寸 fourcc = cv2.VideoWriter_fourcc(*'mp4v') # 使用 mp4 编码格式 # 创建 VideoWriter 对象 out = cv2.VideoWriter(save_pth, fourcc, fps, (frame_width, frame_height)) for i in range(num_frames): # 反归一化 + 转换为 0-255 范围 pred_frame = clip_image(unnormalize(image_pred[0][i].unsqueeze(0))) * 255 pred_frame_resized = pred_frame.squeeze(0).detach().cpu() # (3, 512, 512) pred_frame_resized = pred_frame_resized.permute(1, 2, 0).numpy().astype("uint8") # (512, 512, 3) # Resize 到 256x256 pred_frame_resized = cv2.resize(pred_frame_resized, (frame_width, frame_height)) # 将 RGB 转为 BGR（因为 OpenCV 使用 BGR 格式） pred_frame_bgr = cv2.cvtColor(pred_frame_resized, cv2.COLOR_RGB2BGR) # 写入帧到视频 out.write(pred_frame_bgr) # 释放 VideoWriter 资源 out.release() print(f"视频已保存至 {save_pth}") inf_pipe = InferenceIP2PVideo( diffusion_model.unet, scheduler='ddpm', num_ddim_steps=20 ) def process_example(*args): v_index = args[0] select_e = db_examples.background_conditioned_examples[int(v_index)-1] input_fg_path = select_e[1] input_bg_path = select_e[2] result_video_path = select_e[-1] # input_fg_img = args[1] # 第 0 个参数 # input_bg_img = args[2] # 第 1 个参数 # result_video_img = args[-1] # 最后一个参数 input_fg = input_fg_path.replace("frames/0000.png", "cropped_video.mp4") input_bg = input_bg_path.replace("frames/0000.png", "cropped_video.mp4") result_video = result_video_path.replace(".png", ".mp4") return input_fg, input_bg, result_video # 伪函数占位（生成空白视频） @spaces.GPU def dummy_process(input_fg, input_bg, prompt): # import pdb; pdb.set_trace() diffusion_model.to(torch.float16) fg_tensor = load_and_process_video(input_fg).cuda().unsqueeze(0).to(dtype=torch.float16) bg_tensor = load_and_process_video(input_bg).cuda().unsqueeze(0).to(dtype=torch.float16) # (1, 16, 4, 64, 64) cond_fg_tensor = diffusion_model.encode_image_to_latent(fg_tensor) # (1, 16, 4, 64, 64) cond_bg_tensor = diffusion_model.encode_image_to_latent(bg_tensor) cond_tensor = torch.cat((cond_fg_tensor, cond_bg_tensor), dim=2) # 初始化潜变量 init_latent = torch.randn_like(cond_fg_tensor) # EDIT_PROMPT = 'change the background' EDIT_PROMPT = prompt VIDEO_CFG = 1.2 TEXT_CFG = 7.5 text_cond = diffusion_model.encode_text([EDIT_PROMPT]) # (1, 77, 768) text_uncond = diffusion_model.encode_text(['']) # to float16 print('------------to float 16----------------') init_latent, text_cond, text_uncond, cond_tensor = ( init_latent.to(dtype=torch.float16), text_cond.to(dtype=torch.float16), text_uncond.to(dtype=torch.float16), cond_tensor.to(dtype=torch.float16) ) inf_pipe.unet.to(torch.float16) latent_pred = inf_pipe( latent=init_latent, text_cond=text_cond, text_uncond=text_uncond, img_cond=cond_tensor, text_cfg=TEXT_CFG, img_cfg=VIDEO_CFG, )['latent'] image_pred = diffusion_model.decode_latent_to_image(latent_pred) # (1,16,3,512,512) output_path = os.path.join(new_tmp_dir, f"output_{int(time.time())}.mp4") # clear_cache(output_path) save_video_from_frames(image_pred, output_path) # import pdb; pdb.set_trace() # fps = 8 # frames = [] # for i in range(16): # pred_frame = clip_image(unnormalize(image_pred[0][i].unsqueeze(0))) * 255 # pred_frame_resized = pred_frame.squeeze(0).detach().cpu() #(3,512,512) # pred_frame_resized = pred_frame_resized.permute(1, 2, 0).detach().cpu().numpy().astype("uint8") #(512,512,3) np # Image.fromarray(pred_frame_resized).save(save_pth) # # 生成一个简单的黑色视频作为示例 # output_path = os.path.join(new_tmp_dir, "output.mp4") # fourcc = cv2.VideoWriter_fourcc(*'mp4v') # out = cv2.VideoWriter(output_path, fourcc, 20.0, (512, 512)) # for _ in range(60): # 生成 3 秒的视频（20fps） # frame = np.zeros((512, 512, 3), dtype=np.uint8) # out.write(frame) # out.release() torch.cuda.empty_cache() return output_path # 枚举类用于背景选择 class BGSource(Enum): UPLOAD = "Use Background Video" UPLOAD_FLIP = "Use Flipped Background Video" UPLOAD_REVERSE = "Use Reversed Background Video" # Quick prompts 示例 # quick_prompts = [ # 'beautiful woman, fantasy setting', # 'beautiful woman, neon dynamic lighting', # 'man in suit, tunel lighting', # 'animated mouse, aesthetic lighting', # 'robot warrior, a sunset background', # 'yellow cat, reflective wet beach', # 'camera, dock, calm sunset', # 'astronaut, dim lighting', # 'astronaut, colorful balloons', # 'astronaut, desert landscape' # ] # quick_prompts = [ # 'beautiful woman', # 'handsome man', # 'beautiful woman, cinematic lighting', # 'handsome man, cinematic lighting', # 'beautiful woman, natural lighting', # 'handsome man, natural lighting', # 'beautiful woman, neo punk lighting, cyberpunk', # 'handsome man, neo punk lighting, cyberpunk', # ] quick_prompts = [ 'beautiful woman', 'handsome man', # 'beautiful woman, cinematic lighting', 'handsome man, cinematic lighting', 'beautiful woman, natural lighting', 'handsome man, natural lighting', 'beautiful woman, warm lighting', 'handsome man, soft lighting', 'change the background lighting', ] quick_prompts = [[x] for x in quick_prompts] # css = """ # #foreground-gallery { # width: 700 !important; /* 限制最大宽度 */ # max-width: 700px !important; /* 避免它自动变宽 */ # flex: none !important; /* 让它不自动扩展 */ # } # """ # css = """ # #prompt-box, #bg-source, #quick-list, #relight-btn { # width: 750px !important; # } # """ # Gradio UI 结构 block = gr.Blocks().queue() with block: with gr.Row(): # gr.Markdown("## RelightVid (Relighting with Foreground and Background Video Condition)") gr.Markdown("# 💡RelightVid \n### Relighting with Foreground and Background Video Condition") with gr.Row(): with gr.Column(): with gr.Row(): input_fg = gr.Video(label="Foreground Video", height=380, width=420, visible=True) input_bg = gr.Video(label="Background Video", height=380, width=420, visible=True) segment_button = gr.Button(value="Video Segmentation") with gr.Accordion("Segmentation Options", open=False): # 如果用户不使用 point_prompt，而是直接提供坐标，则使用 x, y with gr.Row(): x_coord = gr.Slider(label="X Coordinate (Point Prompt Ratio)", minimum=0.0, maximum=1.0, value=0.5, step=0.01) y_coord = gr.Slider(label="Y Coordinate (Point Prompt Ratio)", minimum=0.0, maximum=1.0, value=0.5, step=0.01) fg_gallery = gr.Gallery(height=150, object_fit='contain', label='Foreground Quick List', value=db_examples.fg_samples, columns=5, allow_preview=False) bg_gallery = gr.Gallery(height=450, object_fit='contain', label='Background Quick List', value=db_examples.bg_samples, columns=5, allow_preview=False) with gr.Group(): # with gr.Row(): # num_samples = gr.Slider(label="Videos", minimum=1, maximum=12, value=1, step=1) # seed = gr.Number(label="Seed", value=12345, precision=0) with gr.Row(): video_width = gr.Slider(label="Video Width", minimum=256, maximum=1024, value=512, step=64, visible=False) video_height = gr.Slider(label="Video Height", minimum=256, maximum=1024, value=512, step=64, visible=False) # with gr.Accordion("Advanced options", open=False): # steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) # cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=7.0, step=0.01) # highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=3.0, value=1.5, step=0.01) # highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=0.9, value=0.5, step=0.01) # a_prompt = gr.Textbox(label="Added Prompt", value='best quality') # n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality') # normal_button = gr.Button(value="Compute Normal (4x Slower)") with gr.Column(): result_video = gr.Video(label='Output Video', height=750, visible=True) prompt = gr.Textbox(label="Prompt") bg_source = gr.Radio(choices=[e.value for e in BGSource], value=BGSource.UPLOAD.value, label="Background Source", type='value') example_prompts = gr.Dataset(samples=quick_prompts, label='Prompt Quick List', components=[prompt]) relight_button = gr.Button(value="Relight") # prompt = gr.Textbox(label="Prompt") # bg_source = gr.Radio(choices=[e.value for e in BGSource], # value=BGSource.UPLOAD.value, # label="Background Source", type='value') # example_prompts = gr.Dataset(samples=quick_prompts, label='Prompt Quick List', components=[prompt]) # relight_button = gr.Button(value="Relight") # fg_gallery = gr.Gallery(witdth=400, object_fit='contain', label='Foreground Quick List', value=db_examples.bg_samples, columns=4, allow_preview=False) # fg_gallery = gr.Gallery( # height=380, # object_fit='contain', # label='Foreground Quick List', # value=db_examples.fg_samples, # columns=4, # allow_preview=False, # elem_id="foreground-gallery" # 👈 添加 elem_id # ) # 输入列表 # ips = [input_fg, input_bg, prompt, video_width, video_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source] ips = [input_fg, input_bg, prompt] # 按钮绑定处理函数 # relight_button.click(fn=lambda: None, inputs=[], outputs=[result_video]) relight_button.click(fn=dummy_process, inputs=ips, outputs=[result_video]) # normal_button.click(fn=dummy_process, inputs=ips, outputs=[result_video]) # 背景库选择 def bg_gallery_selected(gal, evt: gr.SelectData): # import pdb; pdb.set_trace() # img_path = gal[evt.index][0] img_path = db_examples.bg_samples[evt.index] video_path = img_path.replace('frames/0000.png', 'cropped_video.mp4') return video_path bg_gallery.select(bg_gallery_selected, inputs=bg_gallery, outputs=input_bg) def fg_gallery_selected(gal, evt: gr.SelectData): # import pdb; pdb.set_trace() # img_path = gal[evt.index][0] img_path = db_examples.fg_samples[evt.index] video_path = img_path.replace('frames/0000.png', 'cropped_video.mp4') return video_path fg_gallery.select(fg_gallery_selected, inputs=fg_gallery, outputs=input_fg) input_fg_img = gr.Image(label="Foreground Video", visible=False) input_bg_img = gr.Image(label="Background Video", visible=False) result_video_img = gr.Image(label="Output Video", visible=False) v_index = gr.Textbox(label="ID", visible=False) example_prompts.click(lambda x: x[0], inputs=example_prompts, outputs=prompt, show_progress=False, queue=False) # 示例 # dummy_video_for_outputs = gr.Video(visible=False, label='Result') gr.Examples( # fn=lambda *args: args[-1], fn=process_example, examples=db_examples.background_conditioned_examples, # inputs=[v_index, input_fg_img, input_bg_img, prompt, bg_source, video_width, video_height, result_video_img], inputs=[v_index, input_fg_img, input_bg_img, prompt, bg_source, result_video_img], outputs=[input_fg, input_bg, result_video], run_on_click=True, examples_per_page=1024 ) # 启动 Gradio 应用 # block.launch(server_name='0.0.0.0', server_port=10002, share=True) block.launch()