Text-to-Video vs. Text-to-Image With so many recent developments, it can be difficult to keep up with the current state of text-to-image generative models. Let's do a quick recap first. Just two years ago, the first open-vocabulary, high-quality text-to-image generative models...
Image-to-image For image-to-image generation, make sure thatnum_inference_steps * strengthis larger or equal to 1. The image-to-image pipeline will run forint(num_inference_steps * strength)steps, e.g.0.5 * 2.0 = 1step in our example below. from diffusers import AutoPipelineForImage2Ima...
v0.30.3: CogVideoX Image-to-Video and Video-to-Video This patch release adds Diffusers support for the upcoming CogVideoX-5B-I2V release (an Image-to-Video generation model)! The model weights will be available by end of the week on the HF Hub at THUDM/CogVideoX-5b-I2V (Link)...
Decoupled Video Segmentation Approach(解耦的视频分割方法) Image-level segmentation(图像级分割) Bi-directional temporal propagation(双向时间传播) Data-scarce tasks(数据稀缺任务) Online fusion(在线融合) 从实用性、创新性和推荐度进行打分 实用性:4分 创新性:5分 推荐度:4分 注:分数基于该方法在处理数据稀缺...
Transformer 4.25 引入了 ImageProcessor,让用户能够利用更为强大的图像处理能力。同时,部分 API 也更加统一,参数配置项也改为使用dict,更直观也更方便。 示例地址:https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification ...
3.3 自定义接口实现text2Video的API Wrapper 继承类 BaseAPI 入参 出参 output_dict| 字典| API输出的结果的dict,包含4个key text,image,audio,video字段 核心逻辑 model继承自 Huggingface的 text_to_video的 pipeline (https://huggingface.co/docs/diffusers/api/pipelines/text_to_video) ...
[Feat] add I2VGenXL for image-to-video generation by @sayakpaul in #6665 Release: v0.26.0 by @<NOT FOUND> (direct commit on v0.26.0-release) fix torchvision import by @patrickvonplaten in #6796 Significant community contributions ...
video, image_id, nvid: Video file name. id: Unique video ID. whole_caption: Video summary. whole_ASR: Full-video ASR from Whisper Large-v2. video_names: Array of video shot names. audio_captions: Array of narration captions per shot. captions: Array of video captions per shot. ASR:...
Panda-70M 由 Snap 提出,通过 ImageBind 提取视频特征,并采用多模态模型生成标题,使用 UMT 模型优化标题,同时通过 student captioning model 降低计算成本。MiraData 由腾讯推出,其构建过程与 Panda-70M 类似。MMtrail 数据集则特别地包含了视频的音乐描述。
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/140_text-to-video/make-a-video.png" alt="samples"><br> <em>Examples of videos generated from various text description inputs, image taken from <a href=https://arxiv.org/abs/2209.14792>Make-a-Vide...