Upload folder using huggingface_hub (part 4)

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+video_gen_14d/models/Wan2.1-VACE-1.3B/google/umt5-xxl/tokenizer.json filter=lfs diff=lfs merge=lfs -text

video_gen_14d/data/VACE-Benchmark/data/sample000265_src_mask.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000265_src_video.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000266_src_mask.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000266_src_video.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000267_src_mask.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000267_src_video.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000268_src_mask.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000268_src_video.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000269_src_mask.mp4

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video_gen_14d/data/VACE-Benchmark/data/sample000269_src_video.mp4

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video_gen_14d/data/VACE-Benchmark/indices/composition.txt

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+{"source": "real", "sample_id": "000240", "task": "MV2V-MV2V", "subtask": "firstframe-inpainting", "raw_video": "", "src_video": "data/sample000240_src_video.mp4", "src_mask": "data/sample000240_src_mask.mp4", "src_ref_images": [], "en_prompt": "The video features a red lobster moving on a white background. The lighting is bright and even, highlighting the detailed texture and color of the lobster's shell. The scene is a close-up shot, focusing on the lobster's movements and physical characteristics.", "zh_prompt": "视频中，一只红色龙虾在白色背景上移动。灯光明亮均匀，突出了龙虾壳的细节质地和颜色。这是一个特写镜头，主要关注龙虾的动作和身体特征。"}
+{"source": "real", "sample_id": "000241", "task": "MV2V-MV2V", "subtask": "firstclip-inpainting", "raw_video": "", "src_video": "data/sample000241_src_video.mp4", "src_mask": "data/sample000241_src_mask.mp4", "src_ref_images": [], "en_prompt": "A man in a black suit and purple tie stands outside a modern glass building, looking at his smartphone. The background features tall buildings with reflective windows under a clear blue sky. The camera angle shifts slightly, capturing different perspectives of the man and the surrounding architecture. The lighting is bright and natural. The scene is a real-life footage, likely taken during daytime.", "zh_prompt": "视频展示了一位穿着正式的男士在现代化的办公楼前使用手机。他身穿深色西装，搭配紫色领带，看起来专业而自信。背景是一栋设计现代的玻璃幕墙建筑，周围环境是繁忙的城市景观。视频镜头固定，聚焦于男士的动作和表情，以及他与手机的互动，传达出一种专业和现代的工作氛围。"}
+{"source": "real", "sample_id": "000242", "task": "MV2V-MV2V", "subtask": "inpainting-firstframe", "raw_video": "", "src_video": "data/sample000242_src_video.mp4", "src_mask": "data/sample000242_src_mask.mp4", "src_ref_images": [], "en_prompt": "A person wearing a blue jacket, black helmet, and yellow-tinted ski goggles adjusts their goggles while snow falls around him. The person has a black face mask covering his mouth and nose, and the background is a snowy landscape with a blurred figure in red visible in the reflection of the goggles. The camera remains stationary, capturing the scene from a close-up angle, focusing on the person's upper body and head. The lighting is natural, with the overcast sky casting a soft, diffused light on the scene. The overall tone is cold and wintry, emphasizing the snowy environment.", "zh_prompt": "视频展示了一位穿着蓝色滑雪服、戴着黑色头盔和黄色护目镜的滑雪者，在雪地中进行滑雪活动。视频从一个固定的视角拍摄，滑雪者在镜头前进行一系列动作，包括调整护目镜和手势。背景中可以看到其他滑雪者和模糊的山脉，表明这是在一个滑雪场景中。整个视频传达了滑雪的乐趣和户外运动的氛围。"}
+{"source": "real", "sample_id": "000243", "task": "MV2V-MV2V", "subtask": "inpainting-firstclip", "raw_video": "", "src_video": "data/sample000243_src_video.mp4", "src_mask": "data/sample000243_src_mask.mp4", "src_ref_images": [], "en_prompt": "A basketball is seen in mid-air, approaching a basketball hoop. The ball, with its distinctive orange and white pattern, descends towards the hoop, which has a red net attached to a blue backboard. As the ball nears the hoop, it bounces off the rim and falls through the net, indicating a successful shot. The background features a clear sky with a few scattered clouds and a tall light pole on the left side of the frame. The camera remains stationary, capturing the scene from a low angle, emphasizing the height of the hoop and the trajectory of the ball. The lighting is natural, with soft shadows cast by the setting sun, creating a serene atmosphere. The scene is captured in real-life footage.", "zh_prompt": "视频展示了一个篮球在空中飞行并即将进入篮筐的瞬间。篮球在篮筐上方悬停，随后篮球的位置逐渐下降，最终进入篮筐。整个过程中，摄像机的角度保持不变，聚焦于篮球和篮筐，背景是一片蓝天和几朵散布的云朵。这个镜头可能是在篮球比赛中捕捉到的一个精彩瞬间，展现了运动员的技巧和运动的激烈。"}
+{"source": "real", "sample_id": "000244", "task": "MV2V-V2V", "subtask": "firstframe-depth", "raw_video": "", "src_video": "data/sample000244_src_video.mp4", "src_mask": "data/sample000244_src_mask.mp4", "src_ref_images": [], "en_prompt": "A brown and white dog with a red collar is seen walking through a snowy forest. The dog's tail wags as it moves forward, occasionally looking around. The forest is dense with tall trees covered in snow, creating a serene winter atmosphere. The camera follows the dog from behind, capturing its movements as it walks deeper into the forest. The lighting is natural, with soft shadows cast by the trees, enhancing the tranquil scene. The video appears to be real-life footage, showcasing the beauty of nature and the dog's exploration.", "zh_prompt": "视频展示了一只棕白色的狗在雪地中的森林里行走。狗的尾巴高高翘起，显得活泼而自由。周围的环境被厚厚的雪覆盖，树木高耸，形成了一幅宁静而美丽的冬日景象。视频的拍摄角度固定，紧随狗的后方，捕捉它在雪地中的每一个动作和表情，传达出一种宁静而和谐的氛围。"}
+{"source": "real", "sample_id": "000245", "task": "MV2V-V2V", "subtask": "firstclip-pose", "raw_video": "", "src_video": "data/sample000245_src_video.mp4", "src_mask": "data/sample000245_src_mask.mp4", "src_ref_images": [], "en_prompt": "A woman with long brown hair tied in a ponytail, wearing a black long-sleeve crop top and black leggings, is jogging along the edge of a calm body of water. She has white earphones in her ears and a smartwatch on her left wrist. The background features a serene lake with a clear sky above, and the sun is setting, casting a warm glow over the scene. The camera follows her from behind at a steady pace, capturing her rhythmic movements as she runs. The lighting is soft and natural, enhancing the peaceful atmosphere of the scene. The video is real-life footage.", "zh_prompt": "视频展示了一位女性在宁静的湖边跑步。她穿着黑色的运动装备，包括紧身的上衣和裤子，头发扎成马尾辫。她的表情专注而平静，耳朵里插着耳机，似乎在享受音乐。随着她的跑步，湖水和天空的背景随之移动，营造出一种平和而动态的氛围。整个视频的拍摄角度固定，专注于捕捉女性的运动和表情。"}
+{"source": "real", "sample_id": "000246", "task": "V2V-MV2V", "subtask": "flow-firstframe", "raw_video": "", "src_video": "data/sample000246_src_video.mp4", "src_mask": "data/sample000246_src_mask.mp4", "src_ref_images": [], "en_prompt": "A group of white geese with orange beaks and feet walk in a line across a lush green field dotted with small white flowers. The geese move steadily from left to right, maintaining their formation as they traverse the grassy terrain. The background is filled with dense green trees, creating a serene and natural setting. The camera follows the geese at a steady pace, capturing their movement from a low angle that emphasizes the expanse of the field and the tranquility of the scene. The lighting is soft and diffused, suggesting an overcast day, which adds to the calm and peaceful atmosphere of the video.", "zh_prompt": "视频展示了一群白色的鹅在一个绿色的草地上行走。鹅们排成一列，从画面的左侧向右侧移动。背景是一片茂密的树林，为这个场景增添了一种宁静和自然的氛围。整个视频的拍摄角度是固定的，没有明显的运镜变化，这使得观众可以专注于鹅的动作和它们所处的自然环境。"}
+{"source": "real", "sample_id": "000247", "task": "V2V-MV2V", "subtask": "depth-firstclip", "raw_video": "", "src_video": "data/sample000247_src_video.mp4", "src_mask": "data/sample000247_src_mask.mp4", "src_ref_images": [], "en_prompt": "A man and a woman are running hand in hand through a vast wheat field. The woman, wearing a yellow dress and a white hat, has her other hand raised to her head, while the man, dressed in a blue denim jacket over a gray shirt and dark pants, is smiling broadly. They both appear joyful and carefree as they run together. The background features a cloudy sky with patches of blue and distant trees on the horizon. The camera follows their movement. The lighting is natural, suggesting it's daytime with soft sunlight filtering through the clouds. The scene conveys a sense of freedom and happiness.", "zh_prompt": "视频展示了一对男女在广阔的麦田中手牵手漫步。女士穿着黄色连衣裙和白色帽子，男士穿着蓝色牛仔外套和灰色T恤。他们在金色的麦田中轻松地行走，周围是开阔的田野和远处的地平线。视频通过固定视角捕捉了他们的互动和周围的自然美景，传达出一种轻松愉悦的氛围。"}
+{"source": "real", "sample_id": "000248", "task": "MV2V-V2V", "subtask": "inpainting-scribble", "raw_video": "", "src_video": "data/sample000248_src_video.mp4", "src_mask": "data/sample000248_src_mask.mp4", "src_ref_images": [], "en_prompt": "A young woman with dark hair tied back and minimal makeup is seen holding a small, open container of pink blush in her left hand. She uses a white makeup brush to scoop some blush from the container and then applies it to her cheekbone area with gentle, sweeping motions. After applying the blush, she smiles at the camera, showing off the finished look. The background is plain and white. The lighting is bright and even, highlighting the woman's features and the product she is using. The scene is a close-up shot, focusing on the woman's face and upper body, emphasizing the application process and the product being used.", "zh_prompt": "视频展示了一位女性在固定的镜头前进行化妆的过程。她的妆容精致，眼妆和��妆都很突出。她手持一支化妆刷和一个粉色的化妆盒，仔细地涂抹着粉色的唇膏。整个视频中，女性的动作流畅而专注，表情平静，展现了化妆的细节和技巧。背景是简洁的白色，突出了化妆品和女性的形象。"}
+{"source": "real", "sample_id": "000249", "task": "MV2V-V2V", "subtask": "inpainting-gray", "raw_video": "", "src_video": "data/sample000249_src_video.mp4", "src_mask": "data/sample000249_src_mask.mp4", "src_ref_images": [], "en_prompt": "Two green budgerigars are perched on a tree branch, facing each other. The budgerigars have yellow and black markings on their heads and blue cheek patches. They appear to be interacting with each other, moving their heads and bodies slightly. The background is blurred, showing a forest-like setting with green foliage. The lighting is natural, suggesting the scene is captured during the day. The camera angle remains stationary, focusing on the budgerigars throughout the video.", "zh_prompt": "视频展示了两只绿色的鹦鹉在树枝上的互动。它们的羽毛鲜艳，头部有黄色的斑点，眼睛周围有蓝色的羽毛。视频中，鹦鹉们似乎在进行交流，它们的头部相互倾斜，喙部相接触，显示出亲昵的行为。背景是模糊的绿色植被，突出了鹦鹉的颜色和细节。整个视频采用了固定视角拍摄，聚焦于鹦鹉的自然行为。"}
+{"source": "real", "sample_id": "000250", "task": "V2V-MV2V", "subtask": "scribble-inpainting", "raw_video": "", "src_video": "data/sample000250_src_video.mp4", "src_mask": "data/sample000250_src_mask.mp4", "src_ref_images": [], "en_prompt": "A ladybug is seen crawling on a bright yellow dandelion flower. The ladybug moves around the edges of the flower, occasionally pausing before continuing its journey. The background consists of green grass and other yellow flowers, creating a vibrant and lively scene. The camera remains stationary, focusing on the ladybug's movements on the dandelion. The lighting is natural and bright, highlighting the vivid colors of the flowers and the ladybug. The scene is a close-up shot, capturing the intricate details of the ladybug and the delicate petals of the dandelion.", "zh_prompt": "视频展示了一朵鲜艳的黄色蒲公英花，花心处有一只红色和黑色相间的瓢虫。视频的拍摄角度是固定的，聚焦于这朵花和它的昆虫伙伴。背景是模糊的绿色植物，强调了花朵和瓢虫的细节。整个视频可能是在展示自然界中的一个宁静而美丽的瞬间，突出了生物与环境之间的和谐共存。"}
+{"source": "real", "sample_id": "000251", "task": "V2V-MV2V", "subtask": "pose-inpainting", "raw_video": "", "src_video": "data/sample000251_src_video.mp4", "src_mask": "data/sample000251_src_mask.mp4", "src_ref_images": [], "en_prompt": "A young woman in a red tank top and blue jeans rollerblades through a series of orange cones set up on an empty street. She skillfully maneuvers around the cones, maintaining her balance with outstretched arms. The background features tall buildings and green trees, indicating an urban setting. The camera follows her movements, capturing her from various angles as she glides smoothly between the cones. The lighting is bright and natural, suggesting it's daytime. The scene is real-life footage, showcasing the woman's rollerblading skills.", "zh_prompt": "视频展示了一位女性在宽阔的城市街道上穿着旱冰鞋进行滑行。她穿着红色无袖上衣和蓝色牛仔裤，头发扎成马尾，专注地滑行在一排彩色锥形标记之间。背景是模糊的城市建筑和树木，强调了前景的动作。整个视频采用固定视角拍摄，记录了女性滑行的连续动作和她的表情变化。"}
+{"source": "real", "sample_id": "000252", "task": "V2V-V2V", "subtask": "flow-depth", "raw_video": "", "src_video": "data/sample000252_src_video.mp4", "src_mask": "data/sample000252_src_mask.mp4", "src_ref_images": [], "en_prompt": "A person in a red jacket and black pants is skiing down a snowy mountain slope. They are wearing a helmet and holding ski poles, making smooth turns as they descend. Other skiers can be seen in the background, some closer and others further away, all enjoying the sunny day on the slopes. The camera follows the skier from behind, capturing their movements and the expansive view of the snow-covered mountains under a clear blue sky. The lighting is bright and natural, highlighting the white snow and the vibrant colors of the skiers' clothing. The scene is real-life footage, showcasing the thrill and beauty of skiing in a picturesque winter landscape.", "zh_prompt": "视频展示了一位穿着红色滑雪服的滑雪者在雪地上滑行的场景。摄像机以跟随的方式拍摄，记录了滑雪者从远处滑向镜头的过程。背景中可以看到其他滑雪者和壮丽的山脉，以及滑雪缆车。阳光照耀下的雪地和滑雪者鲜艳的服装形成了鲜明的对比，营造出一种活力四射的滑雪氛围。"}
+{"source": "real", "sample_id": "000253", "task": "V2V-V2V", "subtask": "gary-scribble", "raw_video": "", "src_video": "data/sample000253_src_video.mp4", "src_mask": "data/sample000253_src_mask.mp4", "src_ref_images": [], "en_prompt": "Two women are walking on a beach, each carrying a black trash bag. They are wearing gloves and picking up litter from the sand. The woman on the left is wearing a green plaid shirt and jeans, while the woman on the right is wearing a blue jacket and black pants. The background shows palm trees, buildings, and a cloudy sky. The camera follows them as they walk away from it, capturing their actions from behind. The lighting is natural, with overcast conditions casting a soft light on the scene. The scene is real-life footage.", "zh_prompt": "视频展示了两位女性在海滩上进行清洁活动的场景。她们穿着休闲服装，手里拿着垃圾袋，沿着沙滩行走，并弯腰捡起地上的垃圾。背景是一个典型的海滩环境，有树木、建筑物和停放的车辆。视频通过固定视角捕捉了她们的行动，强调了环保和清洁的重要性。"}
+{"source": "real", "sample_id": "000254", "task": "R2V-R2V", "subtask": "object-object", "raw_video": "", "src_video": "", "src_mask": "", "src_ref_images": ["data/sample000254_src_ref_images_0.png", "data/sample000254_src_ref_images_1.png"], "en_prompt": "In a scene filled with mystery, an elegantly dressed woman in a meticulously crafted black robe sits gracefully in an ornately decorated classical chair. The chair is made of dark wood, featuring intricate carvings and a high-back design, with a seat covered in soft, deep red velvet, exuding a sense of classic charm. The woman's hand gently rests on the armrest, while the other hand moves slowly, as if casting a spell. A soft light filters through the background, enhancing the fantastical atmosphere. Her outfit is detailed, with a light and flowing skirt that showcases her confidence and poise, making the entire scene radiate a magical allure.", "zh_prompt": "在一个充满神秘气息的场景中，一位身穿考究黑色长袍的女士优雅地坐在一把装饰精美的古典椅子上。椅子由深色木材制成，雕花细致，具有高背设计，座面则覆盖着柔软的深红色绒布，散发出古典韵味。女士的手轻柔地搭在椅子的扶手上，另一只手缓缓摆动，仿佛正在施展魔法。背景透出柔和的光线，增添了奇幻的氛围。她的服装细致，裙摆轻盈飘动，展现出自信与从容的气场，让整个画面充满魔法般的魅力。"}
+{"source": "real", "sample_id": "000255", "task": "R2V-R2V", "subtask": "object-object", "raw_video": "", "src_video": "", "src_mask": "", "src_ref_images": ["data/sample000255_src_ref_images_0.png", "data/sample000255_src_ref_images_1.png"], "en_prompt": "In the video, two famous sculptures elegantly hold a unique tea party. On the left is the contemplative Thinker, carved in a simplistic yet powerful form, with his hand slightly raised. On the right is the imposing Sphinx, sitting steadily, with both hands firmly cradling an exquisite tea cup, seemingly focused on listening to the conversation. The surroundings are adorned with exquisite tea sets, and sunlight filters through the leaves, casting a warm and gentle light.", "zh_prompt": "在视频中，两座著名的雕塑优雅地举行着一场独特的茶会，左侧是深思的思想者，身躯雕刻得简约而富有力量，他的手微微抬起。右侧的狮身人面像坐姿稳重，双手稳健地托着一只精致的茶杯，似乎在专注倾听对方的言谈。周围装饰着华丽的茶具，阳光透过树叶洒下温暖而柔和的光线。"}
+{"source": "synthetic", "sample_id": "000256", "task": "MV2V-MV2V", "subtask": "firstframe-inpainting", "raw_video": "", "src_video": "data/sample000256_src_video.mp4", "src_mask": "data/sample000256_src_mask.mp4", "src_ref_images": [], "en_prompt": "A man in a blue work uniform and white hard hat stands with his hands on his hips, listening attentively to another person who is gesturing with their hand. The man is in an industrial setting filled with large green machinery and other workers in the background. The lighting is bright, illuminating the scene clearly. The camera angle remains steady, focusing on the man as he listens.", "zh_prompt": "纪实摄影风格，一位亚洲工人坚定地拒绝他人的错误指挥。他身穿蓝色工作服，头戴安全帽，表情严肃，眼神坚定地看向镜头外的指挥者。工人站在工厂车间内，周围是各种机械设备和工具。背景中可以看到其他工人在忙碌工作。画面充满紧张氛围，工人双手叉腰，姿态坚定。中景全身人像，平视视角。"}
+{"source": "synthetic", "sample_id": "000257", "task": "MV2V-MV2V", "subtask": "firstclip-inpainting", "raw_video": "", "src_video": "data/sample000257_src_video.mp4", "src_mask": "data/sample000257_src_mask.mp4", "src_ref_images": [], "en_prompt": "A person wearing a white chef's coat is seen skillfully slicing a large piece of raw meat on a wooden cutting board. The chef uses a large, sharp knife to make precise cuts, separating the meat into smaller portions. The background shows a professional kitchen setting with stainless steel appliances and tiled walls. Another person in a white chef's coat walks by in the background. The lighting is bright, highlighting the texture and color of the meat. The camera angle remains focused on the chef's hands and the cutting board, capturing the detailed movements of the knife as it slices through the meat.", "zh_prompt": "纪实摄影风格，特写镜头，一位厨师在厨房里专注地用刀切五花肉。他身穿白色厨师服，戴着高帽，双手熟练地操作着。五花肉的纹理清晰可见，肥瘦相间，刀工细腻。背景是忙碌的厨房，隐约可见其他厨具和食材。画面捕捉到厨师专注的神情和流畅的动作。近景特写，强调肉的质感和厨师的专业技能。"}
+{"source": "synthetic", "sample_id": "000258", "task": "MV2V-MV2V", "subtask": "inpainting-firstframe", "raw_video": "", "src_video": "data/sample000258_src_video.mp4", "src_mask": "data/sample000258_src_mask.mp4", "src_ref_images": [], "en_prompt": "A cheerful yellow dog wearing sunglasses and a blue tank top is riding a bicycle down a bustling city street. The dog has its tongue out, appearing to be enjoying the ride. The background features tall buildings, pedestrians, and various vehicles, creating a lively urban atmosphere. The camera follows the dog from behind, capturing the movement as it pedals along. The lighting is bright and colorful, enhancing the vibrant and energetic mood of the scene. The animation style is cartoonish with exaggerated features and expressions.", "zh_prompt": "3D卡通风格，一只戴着酷炫墨镜的金毛犬在繁忙的城市街道上轻松骑行。它身穿蓝色背心，四肢灵活地踏着自行车踏板，尾巴欢快地摇摆。周围是高楼大厦和来往的行人，画面充满活力与动感。近景动态视角，捕捉狗狗自信的表情和周围的都市风光。"}
+{"source": "synthetic", "sample_id": "000259", "task": "MV2V-MV2V", "subtask": "inpainting-firstclip", "raw_video": "", "src_video": "data/sample000259_src_video.mp4", "src_mask": "data/sample000259_src_mask.mp4", "src_ref_images": [], "en_prompt": "A squirrel and a rabbit are on a tree branch in the forest. The squirrel is moving around energetically, jumping up and down, while the rabbit sits calmly observing. The squirrel's fur is reddish-brown with a bushy tail, and the rabbit has light brown fur with long ears. The background is a lush green forest with trees and foliage. The camera angle is static, focusing on the animals on the branch. The lighting is natural, with sunlight filtering through the trees, creating a bright and warm atmosphere. The scene is real-life footage.", "zh_prompt": "森林里的小兔子米米安静地坐在空心木头旁，眼神温柔地注视着前方。一只活泼的小松鼠在木头上跳跃，用力敲击，发出“咚咚咚”的低沉鼓点。小松鼠毛茸茸的尾巴随着动作摇摆，显得非常灵动。背景是郁郁葱葱的森林，阳光透过树梢洒下斑驳的光影。自然风光中的温馨场景。中景，平视视角。"}
+{"source": "synthetic", "sample_id": "000260", "task": "MV2V-V2V", "subtask": "firstframe-pose", "raw_video": "", "src_video": "data/sample000260_src_video.mp4", "src_mask": "data/sample000260_src_mask.mp4", "src_ref_images": [], "en_prompt": "A family is gathered around a round table covered with a red tablecloth, celebrating a festive occasion. The table is set with various dishes in white bowls and plates, and each person has a glass of wine. They are all dressed in red, symbolizing good luck and prosperity. The family members raise their glasses in a toast, smiling and laughing, creating a warm and joyful atmosphere. The background is decorated with traditional Chinese New Year ornaments, including red lanterns and couplets with the character \"福\" (fortune) hanging on the wall. The lighting is warm and inviting, enhancing the celebratory mood. The scene is captured from a static camera angle, focusing on the family's interactions and expressions.", "zh_prompt": "中国传统新年团圆饭场景，一大家人在温馨灯火下围打架。桌上摆满丰盛菜肴和饮料，热气腾腾，色泽诱人。长辈们笑容满面，年轻人举杯敬酒，孩子们嬉笑打闹。墙上挂着中国结和春联，营造浓厚节日氛围。暖色调灯光洒在每个人幸福的脸上，充满家的温暖。中景，家庭聚餐。"}
+{"source": "synthetic", "sample_id": "000261", "task": "MV2V-V2V", "subtask": "firstclip-gray", "raw_video": "", "src_video": "data/sample000261_src_video.mp4", "src_mask": "data/sample000261_src_mask.mp4", "src_ref_images": [], "en_prompt": "A man in a vintage-style suit and hat stands at the open door of a moving train, looking out with a contemplative expression. He is dressed in a grey tweed jacket, white shirt, and black bow tie, with a brown fedora on his head. The interior of the train is dimly lit, with warm lights reflecting off the polished surfaces. Other passengers are visible inside the train, but they are blurred and not the focus. The camera remains stationary, capturing the man from a side angle as he gazes out into the night. The lighting is soft and warm, creating an intimate and nostalgic atmosphere. The scene appears to be from a period drama or film.", "zh_prompt": "一个外国男人站在一辆复古风格的火车车厢内。他穿着一件灰色西装和一顶棕色帽子，他的目光看向窗外。 背景中的其他乘客模糊不清，但可以看到他们坐在座位上或站立着。"}
+{"source": "synthetic", "sample_id": "000262", "task": "V2V-MV2V", "subtask": "scribble-firstframe", "raw_video": "", "src_video": "data/sample000262_src_video.mp4", "src_mask": "data/sample000262_src_mask.mp4", "src_ref_images": [], "en_prompt": "A green frog with black and yellow stripes sits on a large green leaf, its eyes wide open and looking to the right. The frog's skin is smooth and shiny, reflecting light from its surroundings. The background is blurred, showing more green leaves and water, creating a natural habitat atmosphere. The camera remains stationary, focusing on the frog's face and upper body. The lighting is soft and natural, highlighting the frog's vibrant colors and the texture of its skin. The scene appears to be real-life footage captured in a serene outdoor setting.", "zh_prompt": "微距摄影特写，一只绿皮青蛙蹲在荷叶上，双眼鼓胀，凝视镜头。青蛙皮肤湿润，遍布细腻颗粒，背部点缀着深色斑点。荷叶边缘清晰，纹理丰富，水珠散落其上，晶莹剔透。背景模糊处理，隐约可见池塘水波与夏日绿植，营造出宁静而生动的自然氛围。近景特写，聚焦于青蛙炯炯有神的眼睛，展现其灵动生命力。"}
+{"source": "synthetic", "sample_id": "000263", "task": "V2V-MV2V", "subtask": "depth-firstclip", "raw_video": "", "src_video": "data/sample000263_src_video.mp4", "src_mask": "data/sample000263_src_mask.mp4", "src_ref_images": [], "en_prompt": "The video shows a group of people dressed in traditional Chinese clothing, gathered around a display of colorful lotus-shaped lanterns. The scene is set at night with a large, bright moon visible in the sky. The people are admiring and interacting with each other, some holding smaller lanterns. The lanterns are illuminated from within, casting a warm glow on the surroundings. The camera angle remains stationary, capturing the festive atmosphere. The lighting is soft and ambient, highlighting the vibrant colors of the lanterns and the intricate details of the traditional attire. The scene appears to be from a cultural event or festival.", "zh_prompt": "中国古典风格，唐宋朝代元宵节灯会场景。前景是各式各样的精美灯笼，有莲花、鱼形、宫灯等造型，色彩斑斓，光影交错。背景中，身着古代服饰的男女老少赏灯人群来来往往，热闹非凡。天上挂着一轮明亮的圆月，洒下柔和的银光。近景画面，镜头缓慢横移拍摄，捕捉每一个细节和动态。"}
+{"source": "synthetic", "sample_id": "000264", "task": "MV2V-V2V", "subtask": "inpainting-gray", "raw_video": "", "src_video": "data/sample000264_src_video.mp4", "src_mask": "data/sample000264_src_mask.mp4", "src_ref_images": [], "en_prompt": "A ginger cat with white paws and a white chest is sitting on a carpeted floor in a living room. The cat's attention is drawn to a spool of thread that has been placed on the floor. As the cat sniffs and investigates the thread, it becomes entangled in it. The cat then rolls over onto its back, still wrapped in the thread, and appears to be playing with it. The background shows a wooden coffee table with a candle on it and a gray couch. The lighting is warm and soft, creating a cozy atmosphere. The camera angle remains stationary, capturing the cat's movements from a low perspective.", "zh_prompt": "一只活泼的橘色猫咪在客厅地毯上追逐着线团，它的耳朵竖立，眼睛圆睁，尾巴轻轻摇摆。猫咪动作敏捷，时而跳跃，时而扑倒，显得非常快乐。背景是温馨的家庭环境，有沙发、茶几和一些装饰品。光线柔和，营造出一种温暖舒适的氛围。纪实摄影风格，近景动态捕捉。"}
+{"source": "synthetic", "sample_id": "000265", "task": "MV2V-V2V", "subtask": "inpainting-flow", "raw_video": "", "src_video": "data/sample000265_src_video.mp4", "src_mask": "data/sample000265_src_mask.mp4", "src_ref_images": [], "en_prompt": "A white bus with a furry exterior drives slowly down a cobblestone street lined with old European-style buildings. The bus, with its yellow interior lights on and the word \"Moon\" displayed on the front, moves from right to left across the frame. The street is covered with fallen autumn leaves, and the sky has a soft pink hue, indicating either dawn or dusk. The camera remains stationary, capturing the bus from a low angle as it passes by; the lighting is warm and creates a cozy atmosphere. The scene is captured in real-life footage.", "zh_prompt": "一辆毛茸茸的公交车，外观覆盖着柔软的白色绒毛，行驶在欧洲古老城市的街道上。公交车车身圆润，车窗透出温暖的灯光。街道两旁是典型的欧洲建筑，石板路面上洒落着落叶。公交车缓缓前行，车轮轻轻碾过地面。画面背景是黄昏时分，天空呈现出柔和的橙色和紫色。纪实摄影风格，中景动态镜头。"}
+{"source": "synthetic", "sample_id": "000266", "task": "V2V-MV2V", "subtask": "pose-inpainting", "raw_video": "", "src_video": "data/sample000266_src_video.mp4", "src_mask": "data/sample000266_src_mask.mp4", "src_ref_images": [], "en_prompt": "A man wearing a dark green t-shirt with the text \"CARTE SCRICA\" printed on it is dancing energetically in a modern, well-lit living room. He moves his arms and body rhythmically, occasionally pointing and gesturing as if he is performing or expressing himself through dance. The room features light-colored walls, wooden flooring, and several pieces of colorful abstract art on the walls. There are shelves with books and decorative items, and a large window with sheer curtains allowing natural light to fill the space. The camera angle shifts slightly to follow the man's movements, capturing him from different perspectives. The lighting is bright and even. The scene is captured in real-life footage.", "zh_prompt": "穿着“CARTA SCRITTA”品牌T恤的中年绅士在宽敞的客厅里像疯子一样跳舞。他头发微乱，眼神充满欢乐，嘴角上扬，动作夸张而自由。背景是现代简约风格的客厅，墙上挂着几幅抽象画，地板上散落着一些生活用品。画面整体色调温暖，充满活力。中景全身动态人像，镜头跟随他的舞步移动。"}
+{"source": "synthetic", "sample_id": "000267", "task": "V2V-MV2V", "subtask": "depth-inpainting", "raw_video": "", "src_video": "data/sample000267_src_video.mp4", "src_mask": "data/sample000267_src_mask.mp4", "src_ref_images": [], "en_prompt": "A close-up shot of a white flower with yellow stamen in the center, surrounded by green leaves. The flower is in focus while the background is blurred, creating a bokeh effect. The lighting is natural and bright, with sunlight filtering through the leaves, casting a warm glow on the flower. The camera remains stationary, capturing the delicate details of the flower's petals and stamen. The scene is serene and peaceful, highlighting the beauty of nature.", "zh_prompt": "逆光下的白色樱花从花骨朵缓缓绽放，花瓣轻轻舒展，如同细腻的丝绸。阳光透过花瓣，形成柔和的光晕，营造出梦幻般的氛围。背景是模糊的绿色植被，增添了一丝生机。画面整体呈现出清新唯美的风格，仿佛时间在这一刻凝固。中景特写镜头，捕捉花朵绽放的瞬间动态。"}
+{"source": "synthetic", "sample_id": "000268", "task": "V2V-V2V", "subtask": "depth-scribble", "raw_video": "", "src_video": "data/sample000268_src_video.mp4", "src_mask": "data/sample000268_src_mask.mp4", "src_ref_images": [], "en_prompt": "A person stands on a rocky surface, gazing up at a large satellite floating in the sky. The satellite has two long solar panels and a central body with various mechanical components. As the person observes, the satellite begins to emit a bright light from its front, which intensifies and illuminates the surrounding area. The background is filled with stars and a hazy nebula-like formation, creating a cosmic atmosphere. The camera angle shifts slightly upward, focusing more on the satellite as it becomes the central point of light in the scene. The lighting is predominantly yellowish, casting a warm glow on the satellite and the person, enhancing the sense of wonder and discovery. The scene is captured in a realistic animation style.", "zh_prompt": "一个穿着宇航服的人站在一片荒芜的地面上，他背对着镜头，面向着一颗巨大的卫星。这颗卫星是灰色和黑色相间的，并且有多个天线伸出其表面。它位于画面中央的位置，占据了大部分空间。背景是一片深邃而广阔的星空，充满了星星点点的光芒。太阳正在地平线上升起，投射出一道明亮的光线。"}
+{"source": "synthetic", "sample_id": "000269", "task": "V2V-V2V", "subtask": "pose-flow", "raw_video": "", "src_video": "data/sample000269_src_video.mp4", "src_mask": "data/sample000269_src_mask.mp4", "src_ref_images": [], "en_prompt": "A football player in a red jersey with the number 17 is seen running on a field, while another player in a similar uniform with the number 23 runs past him. The player with the number 17 continues to run forward, maintaining his speed and focus. He is wearing white pants, red socks, and white cleats, with a helmet that has a logo on the side. The background shows a well-maintained grass field with white yard lines, indicating it's a professional or college-level game. The lighting is bright. The camera angle is from the side, capturing the players' movements as they run across the field.", "zh_prompt": "一名身穿17号红色球衣的足球运动员在球场上奔跑，而另一名身穿类似球衣的23号球员从他身边跑过。17号球员继续向前跑，保持速度和专注。他穿着白色裤子、红色袜子和白色鞋钉，戴着侧面有标志的头盔。背景显示了一个维护良好的草地，上面有白色的院子线，表明这是一场专业或大学级别的比赛。灯光很亮。摄像机的角度是从侧面拍摄的，捕捉球员在球场上奔跑时的动作。"}
+{"source": "synthetic", "sample_id": "000270", "task": "R2V-R2V", "subtask": "object-object", "raw_video": "", "src_video": "", "src_mask": "", "src_ref_images": ["data/sample000270_src_ref_images_0.png", "data/sample000270_src_ref_images_1.png"], "en_prompt": "In a joyful scene filled with festive atmosphere, a little girl dressed in a bright red traditional outfit is playfully interacting with her cute cartoon snake. Her outfit is adorned with golden auspicious patterns, radiating a celebratory vibe, while her face is lit up with a radiant smile. The snake boasts a vibrant green body, with a rounded shape and large eyes that give it a friendly and humorous appearance. The girl happily strokes the snake's head with her hand, enjoying this warm moment together. The surroundings are decorated with colorful lanterns and ribbons, and sunlight filters through, creating a New Year ambiance filled with love and happiness.", "zh_prompt": "在一个欢乐而充满节日气氛的场景中，穿着鲜艳红色春服的小女孩正与她的可爱卡通蛇嬉戏。她的春服上绣着金色吉祥图案，散发着喜庆的气息，脸上洋溢着灿烂的笑容。蛇身呈现出亮眼的绿色，形状圆润，宽大的眼睛让它显得既友善又幽默。小女孩欢快地用手轻轻抚摸着蛇的头部，共同享受着这温馨的时刻。周围五彩斑斓的灯笼和彩带装饰着环境，阳光透过洒在她们身上，营造出一个充满友爱与幸福的新年氛围。"}
+{"source": "synthetic", "sample_id": "000271", "task": "R2V-R2V", "subtask": "object-object", "raw_video": "", "src_video": "", "src_mask": "", "src_ref_images": ["data/sample000271_src_ref_images_0.png", "data/sample000271_src_ref_images_1.png"], "en_prompt": "The video showcases an ancient woman dressed in elegant traditional attire, intently using a laptop. Her outfit is beautifully designed, combining soft greens and whites, adorned with intricate floral embroidery. One hand gently rests on the laptop's touchpad while the other gracefully holds a fan. The surrounding environment is minimalist yet stylish, with the screen displaying her browsing through historical and contemporary texts. This creates a fascinating sense of time intertwining, perfectly blending elements of tradition and modernity.", "zh_prompt": "视频展示了一位身穿优雅传统服装的古代女子，专注地使用着一台笔记本电脑。她的服装设计华美，柔和的绿色与白色相结合，细节上绣有精致的花纹。她一只手轻轻按着电脑的触控板，另一只手优雅地握着一把扇子。周围环境简约而时尚，屏幕上显示着她在浏览古今文献的画面，使得整个场景散发出奇妙的时间交错感，完美地融合了传统与现代的元素。"}

video_gen_14d/models/Wan2.1-VACE-1.3B/.gitattributes

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+*.npy filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+google/umt5-xxl/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+assets/comp_effic.png filter=lfs diff=lfs merge=lfs -text
+assets/data_for_diff_stage.jpg filter=lfs diff=lfs merge=lfs -text
+assets/i2v_res.png filter=lfs diff=lfs merge=lfs -text
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+assets/video_dit_arch.jpg filter=lfs diff=lfs merge=lfs -text
+assets/video_vae_res.jpg filter=lfs diff=lfs merge=lfs -text

video_gen_14d/models/Wan2.1-VACE-1.3B/LICENSE.txt

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video_gen_14d/models/Wan2.1-VACE-1.3B/README.md

@@ -0,0 +1,678 @@
+---
+license: apache-2.0
+language:
+- en
+- zh
+tags:
+- video generation
+- video-to-video editing
+- refernce-to-video
+
+pipeline_tag: image-to-video
+---
+# Wan2.1
+
+<p align="center">
+    <img src="assets/logo.png" width="400"/>
+<p>
+
+<p align="center">
+    💜 <a href="https://wan.video"><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a> &nbsp&nbsp  | &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2503.20314">Technical Report</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a> &nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp | &nbsp&nbsp 📖 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>&nbsp&nbsp
+<br>
+
+-----
+
+[**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be>
+
+In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
+- 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
+- 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
+- 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
+- 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
+- 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
+
+## Video Demos
+
+<div align="center">
+    <video width="80%" controls>
+        <source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+</div>
+
+## 🔥 Latest News!!
+
+* May 14, 2025: 👋 We introduce **Wan2.1** [VACE](https://github.com/ali-vilab/VACE), an all-in-one model for video creation and editing, along with its [inference code](#run-vace), [weights](#model-download), and [technical report](https://arxiv.org/abs/2503.07598)!
+* Apr 17, 2025: 👋 We introduce **Wan2.1** [FLF2V](#run-first-last-frame-to-video-generation) with its inference code and weights!
+* Mar 21, 2025: 👋 We are excited to announce the release of the **Wan2.1** [technical report](https://files.alicdn.com/tpsservice/5c9de1c74de03972b7aa657e5a54756b.pdf). We welcome discussions and feedback!
+* Mar 3, 2025: 👋 **Wan2.1**'s T2V and I2V have been integrated into Diffusers ([T2V](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanPipeline) | [I2V](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanImageToVideoPipeline)). Feel free to give it a try!
+* Feb 27, 2025: 👋 **Wan2.1** has been integrated into [ComfyUI](https://comfyanonymous.github.io/ComfyUI_examples/wan/). Enjoy!
+* Feb 25, 2025: 👋 We've released the inference code and weights of **Wan2.1**.
+
+## Community Works
+If your work has improved **Wan2.1** and you would like more people to see it, please inform us.
+- [Phantom](https://github.com/Phantom-video/Phantom) has developed a unified video generation framework for single and multi-subject references based on **Wan2.1-T2V-1.3B**. Please refer to [their examples](https://github.com/Phantom-video/Phantom).
+- [UniAnimate-DiT](https://github.com/ali-vilab/UniAnimate-DiT), based on **Wan2.1-14B-I2V**, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it!
+- [CFG-Zero](https://github.com/WeichenFan/CFG-Zero-star) enhances **Wan2.1** (covering both T2V and I2V models) from the perspective of CFG.
+- [TeaCache](https://github.com/ali-vilab/TeaCache) now supports **Wan2.1** acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try!
+- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for **Wan2.1**, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to [their examples](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo).
+
+
+## 📑 Todo List
+- Wan2.1 Text-to-Video
+    - [x] Multi-GPU Inference code of the 14B and 1.3B models
+    - [x] Checkpoints of the 14B and 1.3B models
+    - [x] Gradio demo
+    - [x] ComfyUI integration
+    - [x] Diffusers integration
+    - [ ] Diffusers + Multi-GPU Inference
+- Wan2.1 Image-to-Video
+    - [x] Multi-GPU Inference code of the 14B model
+    - [x] Checkpoints of the 14B model
+    - [x] Gradio demo
+    - [x] ComfyUI integration
+    - [x] Diffusers integration
+    - [ ] Diffusers + Multi-GPU Inference
+- Wan2.1 First-Last-Frame-to-Video
+    - [x] Multi-GPU Inference code of the 14B model
+    - [x] Checkpoints of the 14B model
+    - [x] Gradio demo
+    - [ ] ComfyUI integration
+    - [ ] Diffusers integration
+    - [ ] Diffusers + Multi-GPU Inference
+- Wan2.1 VACE
+    - [x] Multi-GPU Inference code of the 14B and 1.3B models
+    - [x] Checkpoints of the 14B and 1.3B models
+    - [x] Gradio demo
+    - [x] ComfyUI integration
+    - [ ] Diffusers integration
+    - [ ] Diffusers + Multi-GPU Inference
+
+## Quickstart
+
+#### Installation
+Clone the repo:
+```sh
+git clone https://github.com/Wan-Video/Wan2.1.git
+cd Wan2.1
+```
+
+Install dependencies:
+```sh
+# Ensure torch >= 2.4.0
+pip install -r requirements.txt
+```
+
+
+#### Model Download
+
+| Models       | Download Link                                                                                                                                           |    Notes                      |
+|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------|
+| T2V-14B      | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B)      🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)             | Supports both 480P and 720P
+| I2V-14B-720P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)     | Supports 720P
+| I2V-14B-480P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)     | Supports 480P
+| T2V-1.3B     | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B)     🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)            | Supports 480P
+| FLF2V-14B    | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P)     🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | Supports 720P
+| VACE-1.3B    | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B)     🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)          | Supports 480P
+| VACE-14B     | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B)     🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)        | Supports both 480P and 720P
+
+> 💡Note: 
+> * The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution. 
+> * For the first-last frame to video generation, we train our model primarily on Chinese text-video pairs. Therefore, we recommend using Chinese prompt to achieve better results.
+
+
+Download models using huggingface-cli:
+``` sh
+pip install "huggingface_hub[cli]"
+huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B
+```
+
+Download models using modelscope-cli:
+``` sh
+pip install modelscope
+modelscope download Wan-AI/Wan2.1-T2V-14B --local_dir ./Wan2.1-T2V-14B
+```
+#### Run Text-to-Video Generation
+
+This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:
+
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Task</th>
+            <th colspan="2">Resolution</th>
+            <th rowspan="2">Model</th>
+        </tr>
+        <tr>
+            <th>480P</th>
+            <th>720P</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>t2v-14B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: green;">✔️</td>
+            <td>Wan2.1-T2V-14B</td>
+        </tr>
+        <tr>
+            <td>t2v-1.3B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: red;">❌</td>
+            <td>Wan2.1-T2V-1.3B</td>
+        </tr>
+    </tbody>
+</table>
+
+
+##### (1) Without Prompt Extension
+
+To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step.
+
+- Single-GPU inference
+
+``` sh
+python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True` and `--t5_cpu` options to reduce GPU memory usage. For example, on an RTX 4090 GPU:
+
+``` sh
+python generate.py  --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+> 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance.
+
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+  We use FSDP and [xDiT](https://github.com/xdit-project/xDiT) USP to accelerate  inference.
+
+  * Ulysess Strategy
+
+    If you want to use [`Ulysses`](https://arxiv.org/abs/2309.14509) strategy, you should set `--ulysses_size $GPU_NUMS`. Note that the `num_heads` should be divisible by `ulysses_size` if you wish to use `Ulysess` strategy. For the 1.3B model, the `num_heads` is `12` which can't be divided by 8 (as most multi-GPU machines have 8 GPUs). Therefore, it is recommended to use `Ring Strategy` instead.
+
+  * Ring Strategy
+
+    If you want to use [`Ring`](https://arxiv.org/pdf/2310.01889) strategy, you should set `--ring_size $GPU_NUMS`. Note that the `sequence length` should be divisible by `ring_size` when using the `Ring` strategy.
+
+  Of course, you can also combine the use of `Ulysses` and `Ring` strategies.
+
+
+``` sh
+pip install "xfuser>=0.4.1"
+torchrun --nproc_per_node=8 generate.py --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+
+##### (2) Using Prompt Extension
+
+Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension:
+
+- Use the Dashscope API for extension.
+  - Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) | [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)).
+  - Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1).
+  - Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks.
+  - You can modify the model used for extension with the parameter `--prompt_extend_model`. For example:
+```sh
+DASH_API_KEY=your_key python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'zh'
+```
+
+- Using a local model for extension.
+
+  - By default, the Qwen model on HuggingFace is used for this extension. Users can choose Qwen models or other models based on the available GPU memory size.
+  - For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`.
+  - For image-to-video or first-last-frame-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`.
+  - Larger models generally provide better extension results but require more GPU memory.
+  - You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example:
+
+``` sh
+python generate.py  --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'zh'
+```
+
+
+##### (3) Running with Diffusers
+
+You can easily inference **Wan2.1**-T2V using Diffusers with the following command:
+``` python
+import torch
+from diffusers.utils import export_to_video
+from diffusers import AutoencoderKLWan, WanPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+
+# Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+flow_shift = 5.0 # 5.0 for 720P, 3.0 for 480P
+scheduler = UniPCMultistepScheduler(prediction_type='flow_prediction', use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift)
+pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+pipe.scheduler = scheduler
+pipe.to("cuda")
+
+prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
+negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+
+output = pipe(
+     prompt=prompt,
+     negative_prompt=negative_prompt,
+     height=720,
+     width=1280,
+     num_frames=81,
+     guidance_scale=5.0,
+    ).frames[0]
+export_to_video(output, "output.mp4", fps=16)
+```
+> 💡Note: Please note that this example does not integrate Prompt Extension and distributed inference. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers.
+
+
+##### (4) Running local gradio
+
+``` sh
+cd gradio
+# if one uses dashscope’s API for prompt extension
+DASH_API_KEY=your_key python t2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-14B
+
+# if one uses a local model for prompt extension
+python t2v_14B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-14B
+```
+
+
+
+#### Run Image-to-Video Generation
+
+Similar to Text-to-Video, Image-to-Video is also divided into processes with and without the prompt extension step. The specific parameters and their corresponding settings are as follows:
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Task</th>
+            <th colspan="2">Resolution</th>
+            <th rowspan="2">Model</th>
+        </tr>
+        <tr>
+            <th>480P</th>
+            <th>720P</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>i2v-14B</td>
+            <td style="color: green;">❌</td>
+            <td style="color: green;">✔️</td>
+            <td>Wan2.1-I2V-14B-720P</td>
+        </tr>
+        <tr>
+            <td>i2v-14B</td>
+            <td style="color: green;">✔️</td>
+            <td style="color: red;">❌</td>
+            <td>Wan2.1-T2V-14B-480P</td>
+        </tr>
+    </tbody>
+</table>
+
+
+##### (1) Without Prompt Extension
+
+- Single-GPU inference
+```sh
+python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+```
+
+> 💡For the Image-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
+
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```sh
+pip install "xfuser>=0.4.1"
+torchrun --nproc_per_node=8 generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+```
+
+##### (2) Using Prompt Extension
+
+
+The process of prompt extension can be referenced [here](#2-using-prompt-extention).
+
+Run with local prompt extension using `Qwen/Qwen2.5-VL-7B-Instruct`:
+```
+python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+```
+
+Run with remote prompt extension using `dashscope`:
+```
+DASH_API_KEY=your_key python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_method 'dashscope' --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+```
+
+
+##### (3) Running with Diffusers
+
+You can easily inference **Wan2.1**-I2V using Diffusers with the following command:
+``` python
+import torch
+import numpy as np
+from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+from transformers import CLIPVisionModel
+
+# Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
+model_id = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"
+image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+)
+max_area = 720 * 1280
+aspect_ratio = image.height / image.width
+mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+image = image.resize((width, height))
+prompt = (
+    "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
+    "the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
+)
+negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+
+output = pipe(
+    image=image,
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    height=height, width=width,
+    num_frames=81,
+    guidance_scale=5.0
+).frames[0]
+export_to_video(output, "output.mp4", fps=16)
+
+```
+> 💡Note: Please note that this example does not integrate Prompt Extension and distributed inference. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers.
+
+
+##### (4) Running local gradio
+
+```sh
+cd gradio
+# if one only uses 480P model in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P
+
+# if one only uses 720P model in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
+
+# if one uses both 480P and 720P models in gradio
+DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
+```
+
+
+#### Run First-Last-Frame-to-Video Generation
+
+First-Last-Frame-to-Video is also divided into processes with and without the prompt extension step. Currently, only 720P is supported. The specific parameters and corresponding settings are as follows:
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Task</th>
+            <th colspan="2">Resolution</th>
+            <th rowspan="2">Model</th>
+        </tr>
+        <tr>
+            <th>480P</th>
+            <th>720P</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>flf2v-14B</td>
+            <td style="color: green;">❌</td>
+            <td style="color: green;">✔️</td>
+            <td>Wan2.1-FLF2V-14B-720P</td>
+        </tr>
+    </tbody>
+</table>
+
+
+##### (1) Without Prompt Extension
+
+- Single-GPU inference
+```sh
+python generate.py --task flf2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
+```
+
+> 💡Similar to Image-to-Video, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
+
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```sh
+pip install "xfuser>=0.4.1"
+torchrun --nproc_per_node=8 generate.py --task flf2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
+```
+
+##### (2) Using Prompt Extension
+
+
+The process of prompt extension can be referenced [here](#2-using-prompt-extention).
+
+Run with local prompt extension using `Qwen/Qwen2.5-VL-7B-Instruct`:
+```
+python generate.py --task flf2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
+```
+
+Run with remote prompt extension using `dashscope`:
+```
+DASH_API_KEY=your_key python generate.py --task flf2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --use_prompt_extend --prompt_extend_method 'dashscope' --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
+```
+
+
+##### (3) Running local gradio
+
+```sh
+cd gradio
+# use 720P model in gradio
+DASH_API_KEY=your_key python flf2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-FLF2V-14B-720P
+```
+
+
+#### Run VACE
+
+[VACE](https://github.com/ali-vilab/VACE) now supports two models (1.3B and 14B) and two main resolutions (480P and 720P). 
+The input supports any resolution, but to achieve optimal results, the video size should fall within a specific range.
+The parameters and configurations for these models are as follows:
+
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Task</th>
+            <th colspan="2">Resolution</th>
+            <th rowspan="2">Model</th>
+        </tr>
+        <tr>
+            <th>480P(~81x480x832)</th>
+            <th>720P(~81x720x1280)</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>VACE</td>
+            <td style="color: green; text-align: center; vertical-align: middle;">✔️</td>
+            <td style="color: green; text-align: center; vertical-align: middle;">✔️</td>
+            <td>Wan2.1-VACE-14B</td>
+        </tr>
+        <tr>
+            <td>VACE</td>
+            <td style="color: green; text-align: center; vertical-align: middle;">✔️</td>
+            <td style="color: red; text-align: center; vertical-align: middle;">❌</td>
+            <td>Wan2.1-VACE-1.3B</td>
+        </tr>
+    </tbody>
+</table>
+
+In VACE, users can input text prompt and optional video, mask, and image for video generation or editing. Detailed instructions for using VACE can be found in the [User Guide](https://github.com/ali-vilab/VACE/blob/main/UserGuide.md).
+The execution process is as follows:
+
+##### (1) Preprocessing
+
+User-collected materials needs to be preprocessed into VACE-recognizable inputs, including `src_video`, `src_mask`, `src_ref_images`, and `prompt`.
+For R2V (Reference-to-Video Generation), you may skip this preprocessing, but for V2V (Video-to-Video Editing) and MV2V (Masked Video-to-Video Editing) tasks, additional preprocessing is required to obtain video with conditions such as depth, pose or masked regions.
+For more details, please refer to [vace_preproccess](https://github.com/ali-vilab/VACE/blob/main/vace/vace_preproccess.py).
+
+##### (2) cli inference
+
+- Single-GPU inference
+```sh
+python generate.py --task vace-1.3B --size 832*480 --ckpt_dir ./Wan2.1-VACE-1.3B --src_ref_images examples/girl.png,examples/snake.png --prompt "在一个欢乐而充满节日气氛的场景中，穿着鲜艳红色春服的小女孩正与她的可爱��通蛇嬉戏。她的春服上绣着金色吉祥图案，散发着喜庆的气息，脸上洋溢着灿烂的笑容。蛇身呈现出亮眼的绿色，形状圆润，宽大的眼睛让它显得既友善又幽默。小女孩欢快地用手轻轻抚摸着蛇的头部，共同享受着这温馨的时刻。周围五彩斑斓的灯笼和彩带装饰着环境，阳光透过洒在她们身上，营造出一个充满友爱与幸福的新年氛围。"
+```
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```sh
+torchrun --nproc_per_node=8 generate.py --task vace-14B --size 1280*720 --ckpt_dir ./Wan2.1-VACE-14B --dit_fsdp --t5_fsdp --ulysses_size 8 --src_ref_images examples/girl.png,examples/snake.png --prompt "在一个欢乐而充满节日气氛的场景中，穿着鲜艳红色春服的小女孩正与她的可爱卡通蛇嬉戏。她的春服上绣着金色吉祥图案，散发着喜庆的气息，脸上洋溢着灿烂的笑容。蛇身呈现出亮眼的绿色，形状圆润，宽大的眼睛让它显得既友善又幽默。小女孩欢快地用手轻轻抚摸着蛇的头部，共同享受着这温馨的时刻。周围五彩斑斓的灯笼和彩带装饰着环境，阳光透过洒在她们身上，营造出一个充满友爱与幸福的新年氛围。"
+```
+
+##### (3) Running local gradio
+- Single-GPU inference
+```sh
+python gradio/vace.py --ckpt_dir ./Wan2.1-VACE-1.3B
+```
+
+- Multi-GPU inference using FSDP + xDiT USP
+```sh
+python gradio/vace.py --mp --ulysses_size 8 --ckpt_dir ./Wan2.1-VACE-14B/
+```
+
+#### Run Text-to-Image Generation
+
+Wan2.1 is a unified model for both image and video generation. Since it was trained on both types of data, it can also generate images. The command for generating images is similar to video generation, as follows:
+
+##### (1) Without Prompt Extension
+
+- Single-GPU inference
+```sh
+python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B  --prompt '一个朴素端庄的美人'
+```
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```sh
+torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B  --size 1024*1024 --prompt '一个朴素端庄的美人' --ckpt_dir ./Wan2.1-T2V-14B
+```
+
+##### (2) With Prompt Extention
+
+- Single-GPU inference
+```sh
+python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B  --prompt '一个朴素端庄的美人' --use_prompt_extend
+```
+
+- Multi-GPU inference using FSDP + xDiT USP
+```sh
+torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B  --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人' --use_prompt_extend
+```
+
+
+## Manual Evaluation
+
+##### (1) Text-to-Video Evaluation
+
+Through manual evaluation, the results generated after prompt extension are superior to those from both closed-source and open-source models.
+
+<div align="center">
+    <img src="assets/t2v_res.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+##### (2) Image-to-Video Evaluation
+
+We also conducted extensive manual evaluations to evaluate the performance of the Image-to-Video model, and the results are presented in the table below. The results clearly indicate that **Wan2.1** outperforms both closed-source and open-source models.
+
+<div align="center">
+    <img src="assets/i2v_res.png" alt="" style="width: 80%;" />
+</div>
+
+
+## Computational Efficiency on Different GPUs
+
+We test the computational efficiency of different **Wan2.1** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
+
+
+<div align="center">
+    <img src="assets/comp_effic.png" alt="" style="width: 80%;" />
+</div>
+
+> The parameter settings for the tests presented in this table are as follows:
+> (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
+> (2) For the 14B model on 1 GPU, use `--offload_model True`;
+> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
+> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
+
+> 💡Note: T2V-14B is slower than I2V-14B because the former samples 50 steps while the latter uses 40 steps.
+
+
+-------
+
+## Introduction of Wan2.1
+
+**Wan2.1**  is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
+
+
+##### (1) 3D Variational Autoencoders
+We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
+
+
+<div align="center">
+    <img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+##### (2) Video Diffusion DiT
+
+**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
+
+<div align="center">
+    <img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" />
+</div>
+
+
+| Model  | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
+|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
+| 1.3B   | 1536      | 16              | 16               | 8960                  | 256                 | 12              | 30               |
+| 14B   | 5120       | 16              | 16               | 13824                 | 256                 | 40              | 40               |
+
+
+
+##### Data
+
+We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
+
+![figure1](assets/data_for_diff_stage.jpg "figure1")
+
+
+##### Comparisons to SOTA
+We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performance. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. We then compute the total score by performing a weighted calculation on the scores of each dimension, utilizing weights derived from human preferences in the matching process. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
+
+![figure1](assets/vben_vs_sota.png "figure1")
+
+
+## Citation
+If you find our work helpful, please cite us.
+
+```
+@article{wan2025,
+      title={Wan: Open and Advanced Large-Scale Video Generative Models}, 
+      author={Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
+      journal = {arXiv preprint arXiv:2503.20314},
+      year={2025}
+}
+```
+
+## License Agreement
+The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
+
+
+## Acknowledgements
+
+We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
+
+
+
+## Contact Us
+If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!

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+    "<extra_id_251>",
+    "<extra_id_252>",
+    "<extra_id_253>",
+    "<extra_id_254>",
+    "<extra_id_255>",
+    "<extra_id_256>",
+    "<extra_id_257>",
+    "<extra_id_258>",
+    "<extra_id_259>",
+    "<extra_id_260>",
+    "<extra_id_261>",
+    "<extra_id_262>",
+    "<extra_id_263>",
+    "<extra_id_264>",
+    "<extra_id_265>",
+    "<extra_id_266>",
+    "<extra_id_267>",
+    "<extra_id_268>",
+    "<extra_id_269>",
+    "<extra_id_270>",
+    "<extra_id_271>",
+    "<extra_id_272>",
+    "<extra_id_273>",
+    "<extra_id_274>",
+    "<extra_id_275>",
+    "<extra_id_276>",
+    "<extra_id_277>",
+    "<extra_id_278>",
+    "<extra_id_279>",
+    "<extra_id_280>",
+    "<extra_id_281>",
+    "<extra_id_282>",
+    "<extra_id_283>",
+    "<extra_id_284>",
+    "<extra_id_285>",
+    "<extra_id_286>",
+    "<extra_id_287>",
+    "<extra_id_288>",
+    "<extra_id_289>",
+    "<extra_id_290>",
+    "<extra_id_291>",
+    "<extra_id_292>",
+    "<extra_id_293>",
+    "<extra_id_294>",
+    "<extra_id_295>",
+    "<extra_id_296>",
+    "<extra_id_297>",
+    "<extra_id_298>",
+    "<extra_id_299>"
+  ],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 300,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

video_gen_14d/models/Wan2.1-VACE-1.3B/models_t5_umt5-xxl-enc-bf16.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cace0da2b446bbbbc57d031ab6cf163a3d59b366da94e5afe36745b746fd81d
+size 11361920418

video_gen_14d/requirements-runtime.txt

@@ -0,0 +1,27 @@
+# Reuse the NVIDIA container's CUDA 12.8 builds of torch, torchvision,
+# and flash-attn through `.venv --system-site-packages`.
+numpy>=1.26,<2
+diffusers>=0.31,<1
+transformers>=4.49,<5
+tokenizers>=0.20.3
+accelerate>=1.1.1,<2
+gradio>=5,<6
+# OpenCV 4.12+ requires NumPy 2, while the NVIDIA stack requires NumPy <2.
+opencv-python-headless>=4.9,<4.12
+imageio
+imageio-ffmpeg
+easydict
+ftfy
+dashscope
+decord
+scikit-image
+timm
+# 1.27 targets CUDA 13; the host driver/runtime is CUDA 12.8.
+onnxruntime-gpu==1.20.1
+sentencepiece
+safetensors
+protobuf
+modelscope
+peft
+datasets
+pandas

video_gen_14d/scripts/launch_baseline_8gpu.py

@@ -0,0 +1,270 @@
+#!/usr/bin/env python3
+"""Launch one independent VACE baseline sample per H200 GPU."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parents[1]
+PYTHON = ROOT / ".venv" / "bin" / "python"
+VACE_ROOT = ROOT / "third_party" / "VACE"
+INFERENCE = VACE_ROOT / "vace" / "vace_wan_inference.py"
+MODEL = ROOT / "models" / "Wan2.1-VACE-1.3B"
+BENCHMARK = ROOT / "data" / "VACE-Benchmark" / "assets" / "examples"
+
+TASKS = [
+    {
+        "name": "depth",
+        "src_video": "depth/src_video.mp4",
+        "prompt": (
+            "一群年轻人在天空之城拍摄集体照。一对年轻情侣手牵手、相视而笑，"
+            "周围是彩色热气球和闪烁的星星。镜头从近景缓缓拉远，写实摄影风格。"
+        ),
+    },
+    {
+        "name": "flow",
+        "src_video": "flow/src_video.mp4",
+        "prompt": (
+            "纪实摄影风格，一颗鲜红的小番茄缓缓落入盛着牛奶的玻璃杯中，"
+            "慢镜头捕捉水花在空中形成弧线，近景特写，垂直俯视。"
+        ),
+    },
+    {
+        "name": "pose",
+        "src_video": "pose/src_video.mp4",
+        "prompt": (
+            "热带庆祝派对上，一家人围坐在椰子树下的长桌旁，年轻人举杯，"
+            "孩子在沙滩奔跑。动态中景捕捉自然的人体动作，写实风格。"
+        ),
+    },
+    {
+        "name": "scribble",
+        "src_video": "scribble/src_video.mp4",
+        "prompt": (
+            "荧光色无人机从极低空高速掠过超现实主义风格的西安古城墙，"
+            "尘埃反射阳光，镜头流畅切换至砖石特写，画质清晰华丽。"
+        ),
+    },
+    {
+        "name": "layout",
+        "src_video": "layout/src_video.mp4",
+        "prompt": (
+            "一只成鸟在树枝上的巢中喂养幼鸟，随后飞走并再次带回食物。"
+            "固定机位，背景是模糊绿色植被，强调鸟类自然行为。"
+        ),
+    },
+    {
+        "name": "gray",
+        "src_video": "gray/src_video.mp4",
+        "prompt": (
+            "镜头缓缓向右平移，身穿淡黄色长裙的长发女孩面对镜头微笑，"
+            "长发随风轻扬，背景是秋日红黄树叶，清新写实风格。"
+        ),
+    },
+    {
+        "name": "firstframe",
+        "src_video": "firstframe/src_video.mp4",
+        "src_mask": "firstframe/src_mask.mp4",
+        "prompt": (
+            "纪实摄影风格，一位中国越野爱好者坐在越野车上手持车载电台，"
+            "表情专注。镜头从车外缓缓拉近并定格在人物面部。"
+        ),
+    },
+    {
+        "name": "inpainting",
+        "src_video": "inpainting/src_video.mp4",
+        "src_mask": "inpainting/src_mask.mp4",
+        "prompt": (
+            "一只巨大的金色凤凰从繁华城市上空展翅飞过，羽毛像火焰般发光，"
+            "下方人群惊叹、霓虹闪烁，镜头俯视城市街道。"
+        ),
+    },
+]
+
+
+def parse_gpu_status() -> dict[int, dict[str, int]]:
+    output = subprocess.check_output(
+        [
+            "nvidia-smi",
+            "--query-gpu=index,memory.free,utilization.gpu",
+            "--format=csv,noheader,nounits",
+        ],
+        text=True,
+    )
+    status: dict[int, dict[str, int]] = {}
+    for line in output.strip().splitlines():
+        index, free_mib, utilization = (int(value.strip()) for value in line.split(","))
+        status[index] = {"free_mib": free_mib, "utilization": utilization}
+    return status
+
+
+def build_command(
+    task: dict[str, str],
+    output_dir: Path,
+    seed: int,
+    frames: int,
+    steps: int,
+) -> list[str]:
+    command = [
+        str(PYTHON),
+        str(INFERENCE),
+        "--model_name",
+        "vace-1.3B",
+        "--size",
+        "480p",
+        "--frame_num",
+        str(frames),
+        "--ckpt_dir",
+        str(MODEL),
+        "--offload_model",
+        "False",
+        "--sample_steps",
+        str(steps),
+        "--base_seed",
+        str(seed),
+        "--use_prompt_extend",
+        "plain",
+        "--save_dir",
+        str(output_dir),
+        "--prompt",
+        task["prompt"],
+        "--src_video",
+        str(BENCHMARK / task["src_video"]),
+    ]
+    if task.get("src_mask"):
+        command.extend(["--src_mask", str(BENCHMARK / task["src_mask"])])
+    return command
+
+
+def validate_assets() -> None:
+    required = [PYTHON, INFERENCE, MODEL / "diffusion_pytorch_model.safetensors"]
+    missing = [str(path) for path in required if not path.exists()]
+    for task in TASKS:
+        source = BENCHMARK / task["src_video"]
+        if not source.exists():
+            missing.append(str(source))
+        if task.get("src_mask"):
+            mask = BENCHMARK / task["src_mask"]
+            if not mask.exists():
+                missing.append(str(mask))
+    if missing:
+        raise FileNotFoundError("Missing required assets:\n" + "\n".join(missing))
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--gpus", default="0,1,2,3,4,5,6,7")
+    parser.add_argument("--run-id", default=time.strftime("%Y%m%d_%H%M%S"))
+    parser.add_argument("--frames", type=int, default=49)
+    parser.add_argument("--steps", type=int, default=20)
+    parser.add_argument("--seed", type=int, default=2026)
+    parser.add_argument("--min-free-mib", type=int, default=60_000)
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help="Launch even when the selected GPUs do not meet the free-memory guard.",
+    )
+    args = parser.parse_args()
+
+    gpu_ids = [int(value) for value in args.gpus.split(",") if value.strip()]
+    if not gpu_ids or len(gpu_ids) > len(TASKS):
+        parser.error(f"Choose between 1 and {len(TASKS)} GPUs")
+    if args.frames < 1 or (args.frames - 1) % 4 != 0:
+        parser.error("--frames must have the form 4n+1")
+
+    validate_assets()
+    status = parse_gpu_status()
+    blocked = {
+        gpu: status.get(gpu)
+        for gpu in gpu_ids
+        if gpu not in status or status[gpu]["free_mib"] < args.min_free_mib
+    }
+    if blocked and not args.force:
+        print(json.dumps({"status": "blocked", "gpus": blocked}, indent=2))
+        return 2
+
+    run_root = ROOT / "outputs" / "baseline" / args.run_id
+    run_root.mkdir(parents=True, exist_ok=False)
+    processes: list[tuple[int, str, subprocess.Popen[bytes], object]] = []
+    manifest: list[dict[str, object]] = []
+
+    for offset, gpu in enumerate(gpu_ids):
+        task = TASKS[offset]
+        task_dir = run_root / f"gpu{gpu}_{task['name']}"
+        task_dir.mkdir(parents=True)
+        log_path = task_dir / "run.log"
+        command = build_command(
+            task,
+            task_dir,
+            seed=args.seed + offset,
+            frames=args.frames,
+            steps=args.steps,
+        )
+        env = os.environ.copy()
+        env["CUDA_VISIBLE_DEVICES"] = str(gpu)
+        env["TOKENIZERS_PARALLELISM"] = "false"
+        env["PYTHONUNBUFFERED"] = "1"
+        log_handle = log_path.open("wb")
+        process = subprocess.Popen(
+            command,
+            cwd=VACE_ROOT,
+            env=env,
+            stdout=log_handle,
+            stderr=subprocess.STDOUT,
+        )
+        processes.append((gpu, task["name"], process, log_handle))
+        manifest.append(
+            {
+                "gpu": gpu,
+                "task": task["name"],
+                "pid": process.pid,
+                "seed": args.seed + offset,
+                "frames": args.frames,
+                "steps": args.steps,
+                "output_dir": str(task_dir),
+                "command": command,
+            }
+        )
+        print(f"STARTED gpu={gpu} task={task['name']} pid={process.pid}")
+
+    (run_root / "manifest.json").write_text(
+        json.dumps(manifest, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+
+    failed = False
+    for gpu, task_name, process, log_handle in processes:
+        return_code = process.wait()
+        log_handle.close()
+        print(f"FINISHED gpu={gpu} task={task_name} exit={return_code}")
+        failed = failed or return_code != 0
+
+    summary = {
+        "run_id": args.run_id,
+        "failed": failed,
+        "results": [
+            {
+                "gpu": gpu,
+                "task": task_name,
+                "exit_code": process.returncode,
+            }
+            for gpu, task_name, process, _ in processes
+        ],
+    }
+    (run_root / "summary.json").write_text(
+        json.dumps(summary, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+    print("VACE_BASELINE_COMPLETE" if not failed else "VACE_BASELINE_FAILED")
+    return 1 if failed else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

video_gen_14d/scripts/setup_runtime.sh

@@ -0,0 +1,43 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+VENV="${ROOT}/.venv"
+
+if [[ ! -d "${VENV}" ]]; then
+  python -m venv --system-site-packages "${VENV}"
+fi
+
+"${VENV}/bin/python" -m pip install --upgrade pip setuptools wheel
+"${VENV}/bin/python" -m pip install -r "${ROOT}/requirements-runtime.txt"
+
+# Install local repositories without their broad dependency sets. In particular,
+# Wan declares GUI OpenCV, while this headless node requires opencv-python-headless.
+"${VENV}/bin/python" -m pip install --no-deps -e "${ROOT}/third_party/Wan2.1"
+"${VENV}/bin/python" -m pip install --no-deps -e "${ROOT}/third_party/DiffSynth-Studio"
+
+PYTHONPATH="${ROOT}/third_party/VACE" "${VENV}/bin/python" - <<'PY'
+import cv2
+import diffusers
+import diffsynth
+import numpy
+import onnxruntime
+import torch
+import transformers
+import wan
+from vace.models.wan.wan_vace import WanVace
+
+print("VACE_RUNTIME_READY")
+print(
+    {
+        "torch": torch.__version__,
+        "cuda": torch.version.cuda,
+        "gpu_count": torch.cuda.device_count(),
+        "numpy": numpy.__version__,
+        "opencv": cv2.__version__,
+        "onnxruntime": onnxruntime.__version__,
+        "transformers": transformers.__version__,
+        "diffusers": diffusers.__version__,
+    }
+)
+PY

video_gen_14d/scripts/wait_and_bootstrap_8gpu.sh

@@ -0,0 +1,63 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+PYTHON="${ROOT}/.venv/bin/python"
+LAUNCHER="${ROOT}/scripts/launch_baseline_8gpu.py"
+MIN_FREE_MIB="${MIN_FREE_MIB:-60000}"
+POLL_SECONDS="${POLL_SECONDS:-60}"
+RUN_STAMP="$(date +%Y%m%d_%H%M%S)"
+
+all_gpus_ready() {
+  "${PYTHON}" - "${MIN_FREE_MIB}" <<'PY'
+import subprocess
+import sys
+
+threshold = int(sys.argv[1])
+output = subprocess.check_output(
+    [
+        "nvidia-smi",
+        "--query-gpu=index,memory.free",
+        "--format=csv,noheader,nounits",
+    ],
+    text=True,
+)
+free = [int(line.split(",")[1].strip()) for line in output.strip().splitlines()]
+ready = len(free) >= 8 and all(value >= threshold for value in free[:8])
+print("GPU_FREE_MIB=" + ",".join(map(str, free[:8])))
+raise SystemExit(0 if ready else 1)
+PY
+}
+
+echo "WAITING_FOR_8_GPUS min_free_mib=${MIN_FREE_MIB}"
+while ! all_gpus_ready; do
+  sleep "${POLL_SECONDS}"
+done
+
+echo "GPUS_READY"
+
+# Cheap end-to-end gate before occupying all cards.
+"${PYTHON}" "${LAUNCHER}" \
+  --gpus 0 \
+  --run-id "single_smoke_${RUN_STAMP}" \
+  --frames 17 \
+  --steps 8 \
+  --min-free-mib "${MIN_FREE_MIB}"
+
+echo "SINGLE_GPU_SMOKE_COMPLETE"
+
+# Re-check all devices in case another workload started during the smoke test.
+while ! all_gpus_ready; do
+  echo "WAITING_AGAIN_AFTER_SMOKE"
+  sleep "${POLL_SECONDS}"
+done
+
+# One independent condition per GPU maximizes experiment throughput.
+"${PYTHON}" "${LAUNCHER}" \
+  --gpus 0,1,2,3,4,5,6,7 \
+  --run-id "eight_gpu_baseline_${RUN_STAMP}" \
+  --frames 49 \
+  --steps 20 \
+  --min-free-mib "${MIN_FREE_MIB}"
+
+echo "VACE_8GPU_BASELINE_COMPLETE"

video_gen_14d/third_party/CRONOS-benchmark/LICENSE

@@ -0,0 +1,9 @@
+MIT License
+
+Copyright (c) 2026 León Begiristain Ribó
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

video_gen_14d/third_party/CRONOS-benchmark/README.md

@@ -0,0 +1,214 @@
+# CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models
+
+[![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2605.23699)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://genintel.github.io/CRONOS/)
+[![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/genintel/CRONOS-benchmark)
+[![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/GenIntel/CRONOS-benchmark)
+
+Evaluation code for CRONOS-Benchmark. Given a generated video and a reference sequence from the dataset, this repo runs all predictions and computes the six benchmark metrics.
+
+## Repository layout
+
+```
+CRONOS-Benchmark/
+├── config.py                   # model paths and hyperparameters
+├── requirements-cronos.txt     # Python deps for the cronos environment
+├── constraints-cronos.txt      # torch version pins (prevents pip from upgrading)
+├── setup_cronos.sh             # sets up the 'cronos' conda environment
+├── setup_sam3d.sh              # sets up the 'cronos-sam3d' conda environment
+├── scripts/
+│   ├── run_predictions.sh  # orchestrates all 7 prediction steps
+│   ├── run_sam.py          # SAM3 video segmentation
+│   ├── run_cotracker.py    # CoTracker point tracking
+│   ├── run_sam3d.py        # SAM3D per-object 3D reconstruction
+│   ├── run_dino.py         # DINOv2 temporal embeddings
+│   ├── run_dismo.py        # DisMo motion embeddings
+│   └── run_qwen.py         # Qwen VLM task-performance evaluation
+└── metrics/
+    ├── metrics.py          # compute and aggregate all metrics
+    └── utils.py            # shared loading and scaling utilities
+```
+
+## Models
+
+| Model | Role | Source |
+|---|---|---|
+| [SAM3](https://github.com/facebookresearch/sam3) | Video object segmentation (prompt → per-frame masks) | Clone locally; set `SAM3_MODEL_PATH` in `config.py` |
+| [SAM3D-Objects](https://github.com/facebookresearch/sam-3d-objects) | Per-object 3D mesh reconstruction from video | Clone locally for source code (`SAM3D_OBJECTS_DIR`); checkpoints downloaded automatically from HF Hub (`facebook/sam-3d-objects`) |
+| [CoTracker3](https://github.com/facebookresearch/co-tracker) | Dense point tracking across frames | Loaded via PyTorch Hub (`facebookresearch/co-tracker`) |
+| [DisMo](https://github.com/CompVis/DisMo) | Motion representation embeddings | Loaded via PyTorch Hub (`CompVis/DisMo`) |
+| [DINOv2](https://github.com/facebookresearch/dinov2) | Visual feature embeddings for object consistency | Loaded via PyTorch Hub (`facebookresearch/dinov2`) |
+| [Qwen3-VL](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) | VLM task-performance evaluation | HuggingFace model ID or local path; set `QWEN_MODEL_PATH` in `config.py` |
+| [CLIP](https://github.com/openai/CLIP) | Visual features used by SAM3D pipeline | Download locally; set `CLIP_MODEL_PATH` in `config.py` |
+
+## Installation
+
+### Prerequisites
+
+- Linux x86_64 with an NVIDIA GPU (≥24 GB VRAM for Qwen3-VL-32B; ≥16 GB for all other models)
+- CUDA 12.4 driver (`nvidia-smi` must work)
+- [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or Mamba
+- `nvcc` (CUDA 12.4 toolkit) in PATH — required only for the SAM3D environment
+- ~80 GB of free disk space (model weights + environments)
+
+### Step 1 — Clone model repositories
+
+Clone the following repos to a common directory (e.g. `models/`) and update the paths in `config.py`:
+
+```bash
+MODELS_DIR=/path/to/models
+
+# SAM3 (video segmentation)
+git clone https://github.com/facebookresearch/sam3 "$MODELS_DIR/sam3"
+
+# SAM3D-Objects (3D reconstruction — source code only; checkpoints are fetched from HF Hub automatically)
+git clone https://github.com/facebookresearch/sam-3d-objects "$MODELS_DIR/sam-3d-objects"
+
+# Qwen3-VL (VLM evaluation — includes qwen-vl-utils)
+git clone https://github.com/QwenLM/Qwen3-VL "$MODELS_DIR/Qwen3-VL"
+```
+
+Edit `config.py` so the path variables point to your clones:
+
+| Variable | Value |
+|---|---|
+| `SAM3_MODEL_PATH` | `$MODELS_DIR/sam3` |
+| `SAM3D_OBJECTS_DIR` | `$MODELS_DIR/sam-3d-objects` |
+| `SAM3D_HF_CACHE` | Local directory where SAM3D-Objects checkpoints are cached |
+| `CLIP_MODEL_PATH` | Path to a local CLIP checkpoint (used by SAM3D) |
+| `QWEN_MODEL_PATH` | `"Qwen/Qwen3-VL-32B-Instruct"` (HuggingFace) or a local path |
+
+CoTracker, DisMo, and DINOv2 are downloaded automatically via PyTorch Hub at first run.  
+SAM3D-Objects checkpoints are downloaded automatically from `facebook/sam-3d-objects` on HuggingFace Hub at first run and cached at `SAM3D_HF_CACHE`.
+
+### Step 2 — `cronos` environment (all steps except SAM3D)
+
+This environment covers `run_sam.py`, `run_cotracker.py`, `run_dino.py`, `run_dismo.py`, `run_qwen.py`, and `metrics/`.
+
+```bash
+cd CRONOS-benchmark
+
+bash setup_cronos.sh \
+    --sam3-path       /path/to/models/sam3 \
+    --qwen-utils-path /path/to/models/Qwen3-VL/qwen-vl-utils
+
+conda activate cronos
+```
+
+`setup_cronos.sh` installs PyTorch 2.5.1+cu124, all requirements from `requirements-cronos.txt`, and editable installs of SAM3 and `qwen-vl-utils`.
+
+### Step 3 — `cronos-sam3d` environment (`run_sam3d.py` only)
+
+SAM3D-Objects requires older versions of `timm`, `transformers`, and `bitsandbytes` that conflict with the `cronos` environment. It also needs several packages that must be compiled from source (`pytorch3d`, `gsplat`).
+
+> **Note:** `nvcc` (CUDA 12.4 toolkit) must be available before running `setup_sam3d.sh`.
+
+```bash
+cd CRONOS-benchmark
+
+bash setup_sam3d.sh --sam3d-path /path/to/models/sam-3d-objects
+
+# Record the Python binary path printed at the end:
+#   Python binary path:  /path/to/envs/cronos-sam3d/bin/python
+```
+
+> `pytorch3d` and `gsplat` are compiled from source and may take 10–20 minutes.
+
+`setup_sam3d.sh` automatically removes the `dataclasses` backport package from site-packages after installation. This backport is pulled in as a transitive dependency and shadows the Python 3.11 stdlib version, breaking `imageio` at import time.
+
+### Step 4 — Configure `config.py`
+
+Edit `config.py` to point to your local model paths before running anything.
+
+The CoTracker, DisMo, and DINOv2 models are loaded from PyTorch Hub at runtime using the identifiers in `config.py`. SAM3D-Objects checkpoints are fetched from HuggingFace Hub automatically by `run_sam3d.py` at first run.
+
+## Running predictions
+
+`run_predictions.sh` runs all seven steps in order for a single sequence.  
+Because the SAM3D step runs in a separate environment, set `PYTHON_SAM3D` to the Python binary of the `cronos-sam3d` conda environment before calling the script:
+
+```bash
+# Set PYTHON_SAM3D to the path printed at the end of setup_sam3d.sh, then:
+conda activate cronos
+PYTHON_SAM3D=/path/to/envs/cronos-sam3d/bin/python bash scripts/run_predictions.sh \
+    --video  <generated>.mp4 \
+    --ref    <ref_sequence_dir> \
+    --output <output_dir>
+```
+
+- `--video`: the generated `.mp4` file to evaluate
+- `--ref`: a sequence directory from the CRONOS-Benchmark dataset; must contain `metadata.json`, `mask/frame_0000.jpg`, and `movies/complete.mp4`
+- `--output`: directory where all prediction artefacts are written
+
+> If `PYTHON_SAM3D` is not set, the script falls back to `python` and assumes a single environment that satisfies all dependencies.
+
+### Output files
+
+| File | Produced by |
+|---|---|
+| `segmentation_masks.npz` | `run_sam.py` |
+| `tracks.npz` | `run_cotracker.py` |
+| `sam3D/obj{i}/sam3D/mesh/*.ply` | `run_sam3d.py` |
+| `dino_embeddings.npz` | `run_dino.py` |
+| `dismo_embeddings.npz` | `run_dismo.py` (generated video) |
+| `gt_dismo_embeddings.npz` | `run_dismo.py` (reference video) |
+| `vlm_predictions.json` | `run_qwen.py` |
+
+Individual scripts can also be run standalone; each accepts `--help`.
+
+## Computing metrics
+
+```bash
+python metrics/metrics.py \
+    --video   <generated>.mp4 \
+    --ref     <dataset>/{event}/{scene}/{object}/{appearance}/{view} \
+    --output  <output_dir> \
+    --metrics <output_dir>/metrics.json   # optional, defaults to output/metrics.json
+```
+
+`--output` must point to the same directory used with `run_predictions.sh`.
+
+### Metrics
+
+| Metric | Description | Pass threshold |
+|---|---|---|
+| `bg_mse` | Background MSE vs frame 0 — detects unwanted scene changes | ≤ 0.25 |
+| `motion_similarity` | DisMo cosine similarity between generated and reference motion | ≥ 0.70 |
+| `object_consistency` | DINOv2 temporal consistency of tracked objects | ≥ 0.50 |
+| `mean_chamfer_distance` | SAM3D mesh Chamfer distance across frames — measures shape stability | ≤ 0.80 |
+| `vlm_positive_fraction` | Fraction of Qwen VLM task-specific questions answered positively | ≥ 0.35 |
+| `success` | `True` only when every metric above meets its threshold simultaneously and no disappearance is detected| — |
+
+Raw values, linearly- or exponentially-scaled values, and the `success` flag are all written to the output JSON.
+
+## Acknowledgments
+
+CRONOS builds on the following models and codebases:
+
+- **[SAM3](https://github.com/facebookresearch/sam3)** — Carion et al., *SAM 3: Segment Anything with Concepts*, Meta Superintelligence Labs.
+- **[SAM3D-Objects](https://github.com/facebookresearch/sam-3d-objects)** — Chen et al., *SAM 3D: 3Dfy Anything in Images*, Meta AI Research.
+- **[CoTracker3](https://github.com/facebookresearch/co-tracker)** — Karaev et al., *CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos*, Meta AI Research.
+- **[DisMo](https://github.com/CompVis/DisMo)** — Ressler-Antal et al., *DisMo: Disentangled Motion Representations for Open-World Motion Transfer*, CompVis.
+- **[DINOv2](https://github.com/facebookresearch/dinov2)** — Oquab et al., *DINOv2: Learning Robust Visual Features without Supervision*, Meta AI Research.
+- **[Qwen3-VL](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct)** — Qwen Team, *Qwen3-VL*, Alibaba Cloud.
+- **[CLIP](https://github.com/openai/CLIP)** — Radford et al., *Learning Transferable Visual Models From Natural Language Supervision*, OpenAI.
+
+## License
+
+The benchmark code in this repository is released under the [MIT License](LICENSE). Third-party models used by CRONOS (SAM3, CoTracker3, DisMo, DINOv2, Qwen3-VL, CLIP) are subject to their own licenses — please review them before use.
+
+## Citation
+
+If you use CRONOS-Benchmark in your research, please cite:
+
+```bibtex
+@misc{begiristain2026cronos,
+      title={CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models}, 
+      author={Le{\'o}n Begiristain and Olaf D{\"u}nkel and Adam Kortylewski},
+      year={2026},
+      eprint={2605.23699},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2605.23699}, 
+}
+```

video_gen_14d/third_party/CRONOS-benchmark/config.py

@@ -0,0 +1,111 @@
+# ── Model paths ───────────────────────────────────────────────────────────────
+SAM3_MODEL_PATH   = "/path/to/sam3"
+SAM3D_OBJECTS_DIR = "/path/to/sam-3d-objects"
+SAM3D_HF_REPO     = "facebook/sam-3d-objects"
+SAM3D_HF_CACHE    = "/path/to/hf-cache/sam-3d-objects"
+CLIP_MODEL_PATH   = "/path/to/CLIP"
+QWEN_MODEL_PATH   = "Qwen/Qwen3-VL-32B-Instruct"
+
+# ── Hub identifiers (repo, model) ─────────────────────────────────────────────
+COTRACKER_HUB = ("facebookresearch/co-tracker", "cotracker3_offline")
+DISMO_HUB     = ("CompVis/DisMo", "motion_extractor_large")
+DINO_HUB      = ("facebookresearch/dinov2", "dinov2_vitl14")
+
+# ── Inference defaults ────────────────────────────────────────────────────────
+COTRACKER_GRID_SIZE = 50
+DISMO_TIME_WINDOW   = 4
+DINO_MIN_MASK_SIZE          = 16    # min bounding-box side (px) for a valid mask frame
+MIN_OCCLUSION_FRACTION = 0.25  # exclude frames where visible object area < this fraction of frame-0 area
+SAM3D_SEED          = 42
+SAM3D_STRIDE        = 10   # process every N-th frame for 3D reconstruction
+
+# ── Qwen VLM defaults ─────────────────────────────────────────────────────────
+QWEN_FPS            = 1.0
+QWEN_MIN_PIXELS     = 128 * 28 * 28
+QWEN_MAX_PIXELS     = 512 * 28 * 28
+QWEN_MAX_NEW_TOKENS = 1024
+QWEN_TEMPERATURE    = 0.7
+QWEN_TOP_P          = 0.8
+QWEN_TOP_K          = 20
+
+# ── VLM experiment metadata ───────────────────────────────────────────────────
+OBJECT_NAMES = {
+    "Can":        "can",
+    "TennisBall": "tennis ball",
+    "ToyTruck":   "toy SUV",
+    "SoccerBall": "soccer ball",
+    "Bottle":     "water bottle",
+}
+
+OBJECT_APPEARANCES = {
+    "Can":        ["tin", "blue tin", "brown tin"],
+    "TennisBall": ["green", "orange", "dark green"],
+    "ToyTruck":   ["yellow", "red", "black"],
+    "SoccerBall": ["white and black", "blue and black", "red and black"],
+    "Bottle":     ["orange plastic", "red plastic", "yellow plastic"],
+}
+
+SURFACE_DICT = {
+    "fall": {
+        "kitchen":   "kitchen counter",
+        "house":     "wooden table",
+        "apartment": "wooden table",
+        "garden":    "wooden table",
+        "pool":      "wooden table",
+    },
+    "collision": {
+        "kitchen":   "wooden table",
+        "house":     "wooden table",
+        "apartment": "wooden floor",
+        "garden":    "wooden table",
+        "pool":      "stone slabs",
+    },
+    "occlusion": {
+        "kitchen":   "wooden table",
+        "house":     "wooden table",
+        "apartment": "wooden floor",
+        "garden":    "wooden table",
+        "pool":      "stone slabs",
+    },
+}
+
+# ── Metric normalization ──────────────────────────────────────────────────────
+# None → exponential decay (see METRIC_DECAY); explicit tuple → linear [lo, hi]
+METRIC_RANGES = {
+    "bg_mse":                  None,
+    "motion_similarity":       (0.0, 1.0),
+    "object_consistency":      (0.0, 1.0),
+    "mean_chamfer_distance":   None,
+    "vlm_positive_fraction":   (0.0, 1.0),
+}
+METRIC_DECAY = {
+    "bg_mse":                50.0,
+    "mean_chamfer_distance": 50.0,
+}
+
+# ── Success / failure thresholds ──────────────────────────────────────────────
+# ("min", bound) → fail if below bound; ("max", bound) → fail if above bound
+FAILURE_THRESHOLDS = {
+    "bg_mse":                  ("max", 0.25),
+    "motion_similarity":       ("min", 0.70),
+    "object_consistency":      ("min", 0.50),
+    "mean_chamfer_distance":   ("max", 0.80),
+    "vlm_positive_fraction":   ("min", 0.35),
+}
+
+EXTRA_ELEMENTS = {
+    "collision": {
+        "house":     "blue ceramic cup",
+        "apartment": "tennis ball",
+        "garden":    "ceramic flower pot",
+        "pool":      "ceramic jar",
+        "kitchen":   "metallic vase",
+    },
+    "occlusion": {
+        "house":     "ceramic flower pot",
+        "apartment": "flower pot",
+        "garden":    "clay flower pot",
+        "pool":      "tree trunk",
+        "kitchen":   "black water jar",
+    },
+}

video_gen_14d/third_party/CRONOS-benchmark/metrics/metrics.py

@@ -0,0 +1,367 @@
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+from scipy.spatial import cKDTree
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from config import FAILURE_THRESHOLDS, MIN_OCCLUSION_FRACTION
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from utils import (
+    cosine_similarity, robust_min, robust_max,
+    load_video_frames, load_masks_npz, load_dino_embeddings,
+    load_dismo_embeddings, load_vlm_predictions, load_tracks_npz, scale_metric,
+)
+
+
+def object_consistency(dino_path, masks_path=None):
+    """Robust-min temporal DINO consistency across all objects.
+
+    Per object: cosine similarity of each valid frame's embedding to the first
+    valid frame.  Frames where the visible object area drops below
+    MIN_OCCLUSION_FRACTION of its frame-0 area are excluded (matching
+    causal-bench's occlusion masking).  Returns robust min of the per-object
+    means.
+    """
+    embeddings, validities = load_dino_embeddings(dino_path)
+    if embeddings is None:
+        return float("nan")
+
+    occ_fracs = None
+    if masks_path is not None and os.path.isfile(masks_path):
+        masks  = load_masks_npz(masks_path)                                   # (N_obj, T, H, W)
+        counts = masks.reshape(masks.shape[0], masks.shape[1], -1).sum(axis=2).astype(np.float32)
+        first  = counts[:, 0:1]
+        with np.errstate(invalid="ignore", divide="ignore"):
+            occ_fracs = np.where(first > 0, counts / first, 0.0).clip(0.0, 1.0)  # (N_obj, T)
+
+    consistencies = []
+    for i, (emb, valid) in enumerate(zip(embeddings, validities)):
+        if occ_fracs is not None and i < occ_fracs.shape[0]:
+            occ_valid = occ_fracs[i, :emb.shape[0]] >= MIN_OCCLUSION_FRACTION
+            valid = (valid & occ_valid) if valid is not None else occ_valid
+        idx = np.where(valid)[0] if valid is not None else np.arange(len(emb))
+        if len(idx) == 0:
+            continue
+        ref  = emb[idx[0]:idx[0] + 1]
+        sims = cosine_similarity(emb[idx], np.repeat(ref, len(idx), axis=0))
+        consistencies.append(float(np.mean(sims)))
+    return robust_min(consistencies) if consistencies else float("nan")
+
+
+def dismo_similarity(dismo_path, gt_dismo_path):
+    """Robust-min per-frame cosine similarity between generated and GT DisMo embeddings."""
+    gen_emb = load_dismo_embeddings(dismo_path)
+    gt_emb  = load_dismo_embeddings(gt_dismo_path)
+    n = min(len(gen_emb), len(gt_emb))
+    if n == 0:
+        return float("nan")
+    return robust_min(cosine_similarity(gen_emb[:n], gt_emb[:n]))
+
+
+def chamfer_distance(output_dir, masks_path=None):
+    """Robust-max per-frame Chamfer distance (each frame vs frame 0), over all objects.
+
+    For each obj{i}/sam3D/mesh/ directory, aligns every mesh to the first mesh
+    using the best axis-aligned 90° rotation + uniform scale (RANSAC-style),
+    then computes symmetric Chamfer distance.  Frames where any object's visible
+    area drops below MIN_OCCLUSION_FRACTION of its frame-0 area are excluded
+    (matching causal-bench's occlusion masking).  Returns robust max over all
+    per-frame distances across all objects (worst-case shape change).
+
+    PLY filenames encode the source video frame index as a zero-padded integer
+    (e.g. ``000000010.ply`` = frame 10), which is used to look up the occlusion
+    fraction in the full-resolution mask array.
+    """
+    try:
+        import trimesh
+    except ImportError:
+        print("trimesh not available; skipping Chamfer distance.")
+        return float("nan")
+
+    _ROTS = [
+        np.linalg.matrix_power(base, k).astype(np.float32)
+        for base in (
+            np.array([[0., -1., 0.], [1.,  0., 0.], [0., 0.,  1.]], dtype=np.float32),
+            np.array([[1.,  0., 0.], [0.,  0., -1.], [0., 1., 0.]], dtype=np.float32),
+            np.array([[0.,  0., 1.], [0.,  1., 0.], [-1., 0., 0.]], dtype=np.float32),
+        )
+        for k in range(4)
+    ]
+
+    def _load_verts(path):
+        try:
+            return np.asarray(trimesh.load(str(path), process=False, force="mesh").vertices,
+                              dtype=np.float32)
+        except Exception:
+            return None
+
+    def _chamfer(a, b):
+        d_ab, _ = cKDTree(b).query(a, workers=-1)
+        d_ba, _ = cKDTree(a).query(b, workers=-1)
+        return float(np.mean(d_ab ** 2) + np.mean(d_ba ** 2))
+
+    def _align_and_chamfer(ref, verts):
+        """Best-fit axis-aligned rotation + scale, then Chamfer distance."""
+        rng = np.random.default_rng(0)
+        c_ref  = ref   - ref.mean(axis=0)
+        c_vert = verts - verts.mean(axis=0)
+        n_sc   = min(500, len(c_ref), len(c_vert))
+        a_sc   = c_ref [rng.choice(len(c_ref),  n_sc, replace=False)]
+        b_sc   = c_vert[rng.choice(len(c_vert), n_sc, replace=False)]
+        rms_a  = float(np.sqrt(np.mean(np.sum(c_ref ** 2,  axis=1))))
+        rms_b  = float(np.sqrt(np.mean(np.sum(c_vert ** 2, axis=1))))
+        scale  = float(np.clip(rms_a / rms_b, 0.1, 10.0)) if rms_b > 1e-10 else 1.0
+        best_cd, best_R = np.inf, np.eye(3, dtype=np.float32)
+        for R in _ROTS:
+            bt     = scale * (b_sc @ R.T)
+            d1, _  = cKDTree(a_sc).query(bt, workers=-1)
+            d2, _  = cKDTree(bt).query(a_sc, workers=-1)
+            cd     = float(np.mean(d1 ** 2) + np.mean(d2 ** 2))
+            if cd < best_cd:
+                best_cd, best_R = cd, R
+        aligned = (scale * c_vert @ best_R.T + ref.mean(axis=0)).astype(np.float32)
+        return _chamfer(ref, aligned)
+
+    # Occlusion fractions: (N_obj, T), a frame is valid when ALL objects are visible enough
+    occ_fracs = None
+    if masks_path is not None and os.path.isfile(masks_path):
+        masks  = load_masks_npz(masks_path)
+        counts = masks.reshape(masks.shape[0], masks.shape[1], -1).sum(axis=2).astype(np.float32)
+        first  = counts[:, 0:1]
+        with np.errstate(invalid="ignore", divide="ignore"):
+            occ_fracs = np.where(first > 0, counts / first, 0.0).clip(0.0, 1.0)  # (N_obj, T)
+
+    def _frame_visible(stem):
+        """Return True if all objects are sufficiently visible at this video frame."""
+        if occ_fracs is None:
+            return True
+        try:
+            frame_idx = int(stem)
+        except ValueError:
+            return True
+        if frame_idx >= occ_fracs.shape[1]:
+            return True
+        return bool(np.all(occ_fracs[:, frame_idx] >= MIN_OCCLUSION_FRACTION))
+
+    all_distances = []
+    obj_idx = 0
+    while True:
+        mesh_dir = Path(output_dir) / "sam3D" / f"obj{obj_idx}" / "sam3D" / "mesh"
+        if not mesh_dir.is_dir():
+            break
+        ply_files  = sorted(f for f in os.listdir(mesh_dir) if f.endswith(".ply"))
+        ply_files  = [f for f in ply_files if _frame_visible(Path(f).stem)]
+        verts_list = [_load_verts(mesh_dir / f) for f in ply_files]
+        ref_verts  = next((v for v in verts_list if v is not None), None)
+        if ref_verts is not None:
+            for verts in verts_list:
+                if verts is not None and verts is not ref_verts:
+                    all_distances.append(_align_and_chamfer(ref_verts, verts))
+        obj_idx += 1
+
+    return robust_max(all_distances) if all_distances else float("nan")
+
+
+def background_mse(video_path, masks_path):
+    """Robust-max per-frame background MSE vs frame 0, normalized to [0, 1].
+
+    Background region: pixels belonging to no object in frame 0.
+    """
+    frames = load_video_frames(video_path)
+    masks  = load_masks_npz(masks_path)       # (N_obj, T, H, W) bool
+    T, H, W = frames.shape[:3]
+
+    if masks.shape[2] != H or masks.shape[3] != W:
+        from skimage.transform import resize as sk_resize
+        resized = np.zeros((masks.shape[0], T, H, W), dtype=bool)
+        for i in range(masks.shape[0]):
+            for t in range(T):
+                resized[i, t] = sk_resize(masks[i, t].astype(float), (H, W), order=0) > 0.5
+        masks = resized
+
+    obj_union = masks.any(axis=0)         # (T, H, W)
+    bg0       = ~obj_union[0]             # background pixels in frame 0
+    ref       = frames[0].astype(np.float32) / 255.0
+
+    mse_per_frame = []
+    for t in range(1, T):
+        region = bg0 & ~obj_union[t]
+        if region.sum() == 0:
+            continue
+        ft  = frames[t].astype(np.float32) / 255.0
+        mse_per_frame.append(float(np.mean((ft - ref)[region] ** 2)))
+
+    return robust_max(mse_per_frame) if mse_per_frame else float("nan")
+
+
+def vlm_task_performance(vlm_path):
+    """Return the VLM positive fraction from vlm_predictions.json."""
+    data = load_vlm_predictions(vlm_path)
+    val  = data.get("positive_fraction")
+    return float(val) if val is not None and np.isfinite(val) else float("nan")
+
+
+def disappearance_fraction(masks_path, tracks_path, edge_margin_frac=0.05):
+    """Fraction of tracked objects that permanently vanish inside the frame.
+
+    Occlusion is detected from segmentation masks: for each object, finds the
+    earliest frame where it becomes permanently invisible (zero mask pixels for
+    all remaining frames).  The centroid at that frame is taken from CoTracker
+    per-object tracks (tracks_obj{i} in tracks.npz).  If the centroid is not
+    near any border, the object is counted as a mid-frame disappearance.
+
+    Returns 0.0 when no objects permanently vanish inside the frame, or nan if
+    required files are missing.
+
+    edge_margin_frac : fraction of each frame dimension used as the
+        edge-proximity threshold (default 0.05, matching causal-bench).
+    """
+    masks = load_masks_npz(masks_path)  # (N_obj, T, H, W) bool
+    N, T, H, W = masks.shape
+    margin_x = edge_margin_frac * W
+    margin_y = edge_margin_frac * H
+
+    tracks_list, vis_list = load_tracks_npz(tracks_path)
+    if tracks_list is None:
+        return float("nan")
+
+    n_evaluated  = 0
+    n_disappeared = 0
+
+    for obj_idx in range(min(N, len(tracks_list))):
+        occ = masks[obj_idx].any(axis=(1, 2))  # (T,) — True when object has any visible pixel
+
+        # Skip objects already invisible at frame 0 or visible throughout
+        if not occ[0] or occ.all():
+            continue
+
+        # Earliest frame where the object is permanently invisible
+        t_gone = None
+        for t in range(T):
+            if not occ[t] and not occ[t:].any():
+                t_gone = t
+                break
+        if t_gone is None:
+            continue
+
+        n_evaluated += 1
+
+        tr = tracks_list[obj_idx]  # (T_tr, Ni, 2) — columns: x, y
+        vi = vis_list[obj_idx]     # (T_tr, Ni)
+        T_tr = tr.shape[0]
+
+        # Find the last frame with any visible track point up to t_gone
+        last_vis_t = None
+        for t in range(min(t_gone, T_tr - 1), -1, -1):
+            if vi[t].any():
+                last_vis_t = t
+                break
+
+        if last_vis_t is None:
+            # Tracker never saw this object — treat as mid-frame disappearance
+            n_disappeared += 1
+            continue
+
+        # Centroid from track positions at t_gone (matches causal-bench)
+        pts = tr[min(t_gone, T_tr - 1)]  # (Ni, 2)
+        cx  = float(np.mean(pts[:, 0]))
+        cy  = float(np.mean(pts[:, 1]))
+
+        at_edge = (
+            cx < margin_x
+            or cx > W - margin_x
+            or cy < margin_y
+            or cy > H - margin_y
+        )
+        if not at_edge:
+            n_disappeared += 1
+
+    return float(n_disappeared / n_evaluated) if n_evaluated > 0 else 0.0
+
+
+def compute_success(metrics, thresholds=None):
+    """Return True if every threshold criterion is satisfied; NaN values fail."""
+    if thresholds is None:
+        thresholds = FAILURE_THRESHOLDS
+    for name, (direction, bound) in thresholds.items():
+        val = metrics.get(name)
+        if val is None or not np.isfinite(val):
+            return False
+        if direction == "min" and val < bound:
+            return False
+        if direction == "max" and val > bound:
+            return False
+    disp = metrics.get("disappearance_fraction")
+    if disp is None or not np.isfinite(disp) or disp > 0.1:
+        return False
+    return True
+
+
+def compute_all(video_path, ref_dir, output_dir):
+    """Compute all metrics for one evaluated video.
+
+    Returns a dict with raw metric values, scaled values, and a success flag.
+    """
+    dino_path   = os.path.join(output_dir, "dino_embeddings.npz")
+    dismo_path  = os.path.join(output_dir, "dismo_embeddings.npz")
+    gt_dismo    = os.path.join(output_dir, "gt_dismo_embeddings.npz")
+    masks_path  = os.path.join(output_dir, "segmentation_masks.npz")
+    vlm_path    = os.path.join(output_dir, "vlm_predictions.json")
+    tracks_path = os.path.join(output_dir, "tracks.npz")
+
+    raw = {}
+    raw["object_consistency"]    = (object_consistency(dino_path, masks_path)
+                                    if os.path.isfile(dino_path) else float("nan"))
+    raw["motion_similarity"]     = (dismo_similarity(dismo_path, gt_dismo)
+                                    if os.path.isfile(dismo_path) and os.path.isfile(gt_dismo)
+                                    else float("nan"))
+    raw["mean_chamfer_distance"] = chamfer_distance(output_dir, masks_path)
+    raw["bg_mse"]                = (background_mse(video_path, masks_path)
+                                    if os.path.isfile(masks_path) else float("nan"))
+    raw["vlm_positive_fraction"] = (vlm_task_performance(vlm_path)
+                                    if os.path.isfile(vlm_path) else float("nan"))
+    raw["disappearance_fraction"] = (disappearance_fraction(masks_path, tracks_path)
+                                     if os.path.isfile(masks_path) and os.path.isfile(tracks_path)
+                                     else float("nan"))
+    raw["success"]               = compute_success(raw)
+
+    _no_scale = {"success", "disappearance_fraction"}
+    scaled = {f"{k}_scaled": scale_metric(k, v)
+              for k, v in raw.items() if k not in _no_scale}
+    return {**raw, **scaled}
+
+
+def save_metrics(metrics, out_path):
+    os.makedirs(os.path.dirname(os.path.abspath(out_path)), exist_ok=True)
+    with open(out_path, "w") as f:
+        json.dump(metrics, f, indent=2)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Compute evaluation metrics from predictions.")
+    parser.add_argument("--video",   required=True, help="Generated .mp4 file")
+    parser.add_argument("--ref",     required=True, help="Reference dataset directory")
+    parser.add_argument("--output",  required=True, help="Predictions output directory")
+    parser.add_argument("--metrics", default=None,  help="Output .json path (default: output/metrics.json)")
+    args = parser.parse_args()
+
+    out_path = args.metrics or os.path.join(args.output, "metrics.json")
+    metrics  = compute_all(args.video, args.ref, args.output)
+    save_metrics(metrics, out_path)
+
+    for name in ("object_consistency", "motion_similarity",
+                 "mean_chamfer_distance", "bg_mse", "vlm_positive_fraction",
+                 "disappearance_fraction"):
+        val = metrics[name]
+        print(f"  {name:<25} {val:.4f}" if np.isfinite(val) else f"  {name:<25} nan")
+    print(f"  {'success':<25} {metrics['success']}")
+    print(f"Metrics saved → {out_path}")
+
+
+if __name__ == "__main__":
+    main()

video_gen_14d/third_party/CRONOS-benchmark/metrics/utils.py

@@ -0,0 +1,120 @@
+import json
+import os
+import sys
+
+import numpy as np
+import imageio.v3 as iio
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from config import METRIC_RANGES, METRIC_DECAY
+
+
+# ── Math helpers ──────────────────────────────────────────────────────────────
+
+def cosine_similarity(a, b):
+    """Per-row cosine similarity between (T, D) arrays. Returns (T,) float."""
+    a_n = a / (np.linalg.norm(a, axis=1, keepdims=True) + 1e-8)
+    b_n = b / (np.linalg.norm(b, axis=1, keepdims=True) + 1e-8)
+    return np.sum(a_n * b_n, axis=1)
+
+
+def robust_min(values, fraction=0.05):
+    """Mean of the lowest `fraction` of finite values. Returns NaN if empty."""
+    arr = np.asarray(values, dtype=float)
+    arr = arr[np.isfinite(arr)]
+    if arr.size == 0:
+        return float("nan")
+    k = max(1, int(np.ceil(arr.size * fraction)))
+    return float(np.mean(np.partition(arr, k - 1)[:k]))
+
+
+def robust_max(values, fraction=0.05):
+    """Mean of the highest `fraction` of finite values. Returns NaN if empty."""
+    arr = np.asarray(values, dtype=float)
+    arr = arr[np.isfinite(arr)]
+    if arr.size == 0:
+        return float("nan")
+    k = max(1, int(np.ceil(arr.size * fraction)))
+    return float(np.mean(np.partition(arr, -k)[-k:]))
+
+
+# ── Loaders ───────────────────────────────────────────────────────────────────
+
+def load_video_frames(path):
+    """Load video as (T, H, W, 3) uint8 array."""
+    frames = iio.imread(path)
+    if frames.ndim == 4 and frames.shape[-1] == 4:
+        frames = frames[..., :3]
+    return frames
+
+
+def load_masks_npz(path):
+    """Load segmentation_masks.npz. Returns (N_obj, T, H, W) bool array."""
+    return np.load(path)["masks"].astype(bool)
+
+
+def load_dino_embeddings(path):
+    """Load dino_embeddings.npz. Returns (embeddings_list, validities_list)."""
+    if not os.path.isfile(path):
+        return None, None
+    npz = np.load(path)
+    embeddings, validities = [], []
+    i = 0
+    while f"emb_obj{i}" in npz:
+        embeddings.append(npz[f"emb_obj{i}"])
+        vkey = f"valid_obj{i}"
+        validities.append(npz[vkey] if vkey in npz else None)
+        i += 1
+    return (embeddings, validities) if embeddings else (None, None)
+
+
+def load_dismo_embeddings(path):
+    """Load dismo_embeddings.npz. Returns (T, D) float array."""
+    return np.load(path)["embeddings"].astype(float)
+
+
+def load_vlm_predictions(path):
+    """Load vlm_predictions.json. Returns dict."""
+    with open(path) as f:
+        return json.load(f)
+
+
+def load_tracks_npz(path):
+    """Load tracks.npz written by run_cotracker.py.
+
+    Returns (tracks_list, vis_list) where each element is a per-object array:
+        tracks_list[i] : (T, Ni, 2)  float  — x, y coordinates
+        vis_list[i]    : (T, Ni)     bool   — CoTracker visibility flag
+    Returns (None, None) if the file is absent or contains no per-object keys.
+    """
+    if not os.path.isfile(path):
+        return None, None
+    npz = np.load(path)
+    tracks_list, vis_list = [], []
+    i = 0
+    while f"tracks_obj{i}" in npz:
+        tracks_list.append(npz[f"tracks_obj{i}"])
+        vis_list.append(npz[f"visibility_obj{i}"])
+        i += 1
+    return (tracks_list, vis_list) if tracks_list else (None, None)
+
+
+# ── Normalization ─────────────────────────────────────────────────────────────
+
+def scale_metric(name, value):
+    """Normalize a raw metric to [0, 1].
+
+    Linear range from METRIC_RANGES; exponential decay exp(-λ·v) from
+    METRIC_DECAY for metrics where lower raw value is better.
+    Returns NaN for unknown or non-finite inputs.
+    """
+    if not np.isfinite(value):
+        return float("nan")
+    if name not in METRIC_RANGES:
+        return float("nan")
+    rng = METRIC_RANGES[name]
+    if rng is not None:
+        lo, hi = rng
+        return float(np.clip((value - lo) / (hi - lo + 1e-8), 0.0, 1.0))
+    lam = METRIC_DECAY.get(name, 1.0)
+    return float(np.exp(-lam * value))

video_gen_14d/third_party/CRONOS-benchmark/requirements-cronos.txt

@@ -0,0 +1,47 @@
+# CRONOS evaluation environment — all scripts except run_sam3d.py
+# Requires PyTorch 2.5.1+cu124 pre-installed (via conda or setup_cronos.sh).
+# Install after PyTorch; do NOT include torch/torchvision here.
+
+# ── Video / image I/O ──────────────────────────────────────────────────────────
+imageio>=2.37.0
+imageio-ffmpeg
+
+# ── Array / vision ─────────────────────────────────────────────────────────────
+numpy>=1.26,<2
+scikit-image>=0.23
+scikit-learn
+pillow
+opencv-python-headless
+trimesh
+pyyaml
+
+# ── SAM3 core deps  (SAM3 package itself: pip install -e <SAM3_MODEL_PATH>) ───
+timm>=1.0.17
+ftfy==6.1.1
+regex
+iopath>=0.1.10
+tqdm
+huggingface_hub
+decord
+pycocotools
+
+# ── CoTracker3 / DINOv2 (loaded via torch.hub) ────────────────────────────────
+einops
+omegaconf
+matplotlib
+
+# ── DisMo (loaded via torch.hub) ──────────────────────────────────────────────
+jaxtyping
+transformers>=4.45.0,<5.0
+diffusers>=0.30.0
+tokenizers
+peft>=0.10.0
+sentencepiece
+bitsandbytes>=0.46.0
+protobuf
+fire
+
+# ── Qwen3-VL (via Transformers) ───────────────────────────────────────────────
+# qwen-vl-utils: pip install -e <models/Qwen3-VL/qwen-vl-utils>
+accelerate
+av

video_gen_14d/third_party/CRONOS-benchmark/scripts/generate_prompt.py

@@ -0,0 +1,106 @@
+import argparse
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from config import OBJECT_NAMES, OBJECT_MATERIALS, SURFACE_DICT, EXTRA_ELEMENTS
+
+
+def get_exp_description(event, scene, object_name, material_desc, surface, view):
+    """Build the event-specific part of the generation prompt."""
+    movement = "rolling"
+
+    if event == "fall":
+        return (f"The video shows a {view} view of a {material_desc} {object_name} "
+                f"smoothly {movement} across a {surface} in a {scene}. "
+                f"When the {object_name} reaches the edge, it falls off the {surface} "
+                f"and vertically descends until it hits the ground. "
+                f"The object maintains its shape and does not break during the fall.")
+
+    elif event == "collision":
+        extra = EXTRA_ELEMENTS["collision"][scene]
+        return (f"The video shows a {view} view of a {material_desc} {object_name} "
+                f"smoothly {movement} across a {surface}, colliding with a {extra} "
+                f"on a {scene}. "
+                f"The objects react to the collision but preserve their rigidity "
+                f"and do not break.")
+
+    elif event == "occlusion":
+        extra = EXTRA_ELEMENTS["occlusion"][scene]
+        return (f"The video shows a {view} view of a {material_desc} {object_name} "
+                f"smoothly {movement} and passing behind a {extra} on a {surface} "
+                f"in a {scene}. "
+                f"The {object_name} becomes temporarily occluded by the {extra} "
+                f"before reappearing. "
+                f"The {extra} remains stationary throughout the sequence.")
+
+    raise ValueError(f"Unknown event: {event!r}")
+
+
+_SUFFIX = (
+    "Everything on the video follows the natural behaviour of solid objects "
+    "in a physical environment. Objects do not fly, morph or disappear. "
+    "No new elements appear on the scene. "
+    "The background is static. Fixed camera view, no camera movement."
+)
+
+
+def get_prompt(event, scene, obj, appearance, view, generic_object=False):
+    """Build the generation prompt for one CRONOS experiment.
+
+    Parameters
+    ----------
+    event      : "fall" | "collision" | "occlusion"
+    scene      : "kitchen" | "house" | "apartment" | "garden" | "pool"
+    obj        : object key from OBJECT_NAMES (e.g. "TennisBall")
+    appearance : material variant, e.g. "material0", "material1", "material2"
+    view       : camera view string, e.g. "front", "side", "top", "close"
+    generic_object : if True, replace the specific object name with "object"
+    """
+    object_name   = "object" if generic_object else OBJECT_NAMES[obj]
+    material_desc = OBJECT_MATERIALS[obj][int(appearance[-1])]
+    surface       = SURFACE_DICT[event][scene]
+
+    description = get_exp_description(event, scene, object_name, material_desc, surface, view)
+    return description + " " + _SUFFIX
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate a text prompt for a CRONOS experiment.",
+    )
+    parser.add_argument("--event",      required=True,
+                        choices=["fall", "collision", "occlusion"])
+    parser.add_argument("--scene",      required=True,
+                        choices=["kitchen", "house", "apartment", "garden", "pool"])
+    parser.add_argument("--object",     required=True, choices=list(OBJECT_NAMES.keys()))
+    parser.add_argument("--appearance", required=True,
+                        help="Material variant, e.g. material0, material1, material2")
+    parser.add_argument("--view",       required=True,
+                        help="Camera view, e.g. front, side, top, close")
+    parser.add_argument("--generic-object", action="store_true",
+                        help="Use 'object' instead of the specific object name")
+    parser.add_argument("--output", default=None,
+                        help="Write prompt to this file instead of stdout")
+    args = parser.parse_args()
+
+    prompt = get_prompt(
+        event=args.event,
+        scene=args.scene,
+        obj=args.object,
+        appearance=args.appearance,
+        view=args.view,
+        generic_object=args.generic_object,
+    )
+
+    if args.output:
+        os.makedirs(os.path.dirname(os.path.abspath(args.output)), exist_ok=True)
+        with open(args.output, "w") as f:
+            f.write(prompt + "\n")
+        print(f"Prompt saved → {args.output}")
+    else:
+        print(prompt)
+
+
+if __name__ == "__main__":
+    main()

video_gen_14d/third_party/CRONOS-benchmark/scripts/run_cotracker.py

@@ -0,0 +1,109 @@
+import argparse
+import os
+import sys
+
+
+import imageio.v3 as iio
+
+import numpy as np
+import torch
+from skimage.transform import resize
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from config import COTRACKER_HUB, COTRACKER_GRID_SIZE
+
+
+def load_model(device):
+    model = torch.hub.load(*COTRACKER_HUB).to(device)
+    return model.eval()
+
+
+def _split_tracks_by_object(tracks, visibility, mask):
+    """Split (T, N, 2) tracks into per-object lists using first-frame mask positions."""
+    n_objects = mask.any(axis=(0, 1)).sum()
+    ix = np.clip(np.round(tracks[0, :, 0]).astype(int), 0, mask.shape[1] - 1)
+    iy = np.clip(np.round(tracks[0, :, 1]).astype(int), 0, mask.shape[0] - 1)
+    per_obj_tracks, per_obj_vis = [], []
+    for i in range(n_objects):
+        sel = mask[:, :, i][iy, ix]
+        per_obj_tracks.append(tracks[:, sel])
+        per_obj_vis.append(visibility[:, sel])
+    return per_obj_tracks, per_obj_vis
+
+
+def compute_predictions(model, video_frames, mask, device):
+    """
+    Args:
+        video_frames: (T, H, W, 3) uint8 ndarray
+        mask: (H, W, N_obj) bool ndarray — first-frame mask
+    Returns:
+        tracks (T, N, 2), visibility (T, N),
+        per_obj_tracks list[(T, Ni, 2)], per_obj_vis list[(T, Ni)]
+    """
+    H, W = video_frames.shape[1:3]
+    if mask.shape[:2] != (H, W):
+        mask = resize(mask.astype(float), (H, W), order=0,
+                      preserve_range=True, anti_aliasing=False) > 0.5
+
+    combined = torch.from_numpy(mask.any(axis=-1)).float().to(device)[None, None]
+    video = torch.from_numpy(video_frames).permute(0, 3, 1, 2).unsqueeze(0).float().to(device)
+
+    with torch.no_grad():
+        raw_tracks, raw_vis = model(video, grid_size=COTRACKER_GRID_SIZE, segm_mask=combined)
+
+    tracks = raw_tracks.squeeze().cpu().numpy()
+    visibility = raw_vis.squeeze().cpu().numpy()
+    per_obj_tracks, per_obj_vis = _split_tracks_by_object(tracks, visibility, mask)
+    return tracks, visibility, per_obj_tracks, per_obj_vis
+
+
+def save_predictions(out_dir, tracks, visibility, per_obj_tracks, per_obj_vis):
+    os.makedirs(out_dir, exist_ok=True)
+    save_dict = {"tracks": tracks, "visibility": visibility}
+    for i, (t, v) in enumerate(zip(per_obj_tracks, per_obj_vis)):
+        save_dict[f"tracks_obj{i}"] = t
+        save_dict[f"visibility_obj{i}"] = v
+    np.savez_compressed(os.path.join(out_dir, "tracks.npz"), **save_dict)
+
+
+def _load_mask(path):
+    """Load mask file and return (H, W, N_obj) bool array."""
+    suffix = os.path.splitext(path)[1].lower()
+    if suffix in (".jpg", ".jpeg", ".png"):
+        img = iio.imread(path)
+        if img.ndim == 2:
+            return img[:, :, None] > 0
+        # Multi-channel: each channel is one object
+        return img > 0
+    data = np.load(path)
+    mask = data[data.files[0]] if hasattr(data, "files") else data
+    if mask.ndim == 2:
+        mask = mask[:, :, None]
+    elif mask.ndim == 4 and mask.shape[-1] <= 10:   # (T, H, W, N_obj) → take frame 0
+        mask = mask[0]
+    elif mask.ndim == 4:                              # (N_obj, T, H, W) → take t=0, transpose
+        mask = mask[:, 0].transpose(1, 2, 0)
+    return mask > 0.5
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run CoTracker on a video.")
+    parser.add_argument("--video",  required=True, help="Input .mp4 file")
+    parser.add_argument("--mask",   required=True, help="First-frame mask (.npy/.npz, H×W×N_obj)")
+    parser.add_argument("--output", required=True, help="Output directory for tracks.npz")
+    args = parser.parse_args()
+
+    frames = iio.imread(args.video)
+    if frames.ndim == 4 and frames.shape[-1] == 4:
+        frames = frames[..., :3]
+
+    mask = _load_mask(args.mask)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = load_model(device)
+    results = compute_predictions(model, frames, mask, device)
+    save_predictions(args.output, *results)
+    print(f"Saved tracks → {args.output}/tracks.npz")
+
+
+if __name__ == "__main__":
+    main()

video_gen_14d/third_party/CRONOS-benchmark/scripts/run_dino.py

@@ -0,0 +1,117 @@
+import argparse
+import os
+import sys
+
+import numpy as np
+import torch
+from torchvision import transforms
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from config import DINO_HUB, DINO_MIN_MASK_SIZE
+
+
+def load_model(device):
+    model = torch.hub.load(*DINO_HUB).to(device).eval()
+    preprocess = transforms.Compose([
+        transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
+        transforms.CenterCrop(224),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    return model, preprocess
+
+
+def _embed_frames(frames, model, preprocess, device):
+    """Embed a list of H×W×3 uint8 arrays with DINOv2."""
+    try:
+        t = preprocess(
+            torch.from_numpy(np.stack(frames)).permute(0, 3, 1, 2).float() / 255.0
+        ).to(device)
+        with torch.no_grad():
+            return model(t).cpu().numpy()
+    except Exception:
+        embs = []
+        for f in frames:
+            t = preprocess(
+                torch.from_numpy(f).permute(2, 0, 1).unsqueeze(0).float().to(device) / 255.0
+            )
+            with torch.no_grad():
+                embs.append(model(t))
+        return torch.cat(embs).cpu().numpy()
+
+
+def _mask_validity(mask):
+    """Return (T,) bool: True where mask bounding box ≥ DINO_MIN_MASK_SIZE in both dims."""
+    valid = np.zeros(len(mask), dtype=bool)
+    for t, m in enumerate(mask):
+        ys, xs = np.where(m)
+        if (len(ys) and ys.max() - ys.min() >= DINO_MIN_MASK_SIZE
+                and xs.max() - xs.min() >= DINO_MIN_MASK_SIZE):
+            valid[t] = True
+    return valid
+
+
+def _crop_and_mask(frames, mask):
+    """Zero background and crop to bounding box. Returns list of H'×W'×3 arrays."""
+    out = []
+    for f, m in zip(frames, mask):
+        ys, xs = np.where(m)
+        if len(ys) == 0:
+            out.append(f)
+            continue
+        f = np.where(m[:, :, None], f, 0)
+        out.append(f[ys.min():ys.max() + 1, xs.min():xs.max() + 1])
+    return out
+
+
+def compute_predictions(model, preprocess, video_frames, masks_list, device):
+    """
+    Args:
+        video_frames: (T, H, W, 3) uint8 ndarray
+        masks_list: list of (T, H, W) bool ndarrays — one per object
+    Returns:
+        dict with keys emb_obj{i} (T, D) and valid_obj{i} (T,) per object
+    """
+    T = min(video_frames.shape[0], *(m.shape[0] for m in masks_list))
+    frames = video_frames[:T]
+    results = {}
+    for i, mask in enumerate(masks_list):
+        masked = _crop_and_mask(frames, mask[:T])
+        results[f"emb_obj{i}"]   = _embed_frames(masked, model, preprocess, device)
+        results[f"valid_obj{i}"] = _mask_validity(mask[:T])
+    return results
+
+
+def save_predictions(predictions, out_path):
+    os.makedirs(os.path.dirname(os.path.abspath(out_path)), exist_ok=True)
+    np.savez(out_path, **predictions)
+
+
+def _load_masks(path):
+    """Load segmentation_masks.npz → list of (T, H, W) bool arrays, one per object."""
+    masks = np.load(path)["masks"]   # (N_obj, T, H, W)
+    return [masks[i] > 0.5 for i in range(masks.shape[0])]
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Compute DINOv2 embeddings per object.")
+    parser.add_argument("--video",  required=True, help="Input .mp4 file")
+    parser.add_argument("--mask",   required=True,
+                        help="Segmentation masks (.npz with key 'masks', shape N_obj×T×H×W)")
+    parser.add_argument("--output", required=True, help="Output .npz path (e.g. dino_embeddings.npz)")
+    args = parser.parse_args()
+
+    import imageio.v3 as iio
+    frames = iio.imread(args.video)
+    if frames.ndim == 4 and frames.shape[-1] == 4:
+        frames = frames[..., :3]
+
+    masks_list = _load_masks(args.mask)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model, preprocess = load_model(device)
+    predictions = compute_predictions(model, preprocess, frames, masks_list, device)
+    save_predictions(predictions, args.output)
+    print(f"Saved embeddings ({len(masks_list)} object(s)) → {args.output}")
+
+
+if __name__ == "__main__":
+    main()