AnimateLCM-I2V
Maintainer: wangfuyun
58
🐍
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
AnimateLCM-I2V is a latent image-to-video consistency model finetuned with AnimateLCM following the strategy proposed in the AnimateLCM-paper without requiring teacher models. It can generate high-quality image-conditioned videos efficiently in just a few steps.
Model inputs and outputs
AnimateLCM-I2V takes an input image and generates a corresponding video sequence. The model is designed to maintain semantic consistency between the input image and the generated video, while also producing smooth, high-quality animation.
Inputs
- Input Image: A single image that serves as the starting point for the video generation.
Outputs
- Video Frames: The model outputs a sequence of video frames that depict an animation consistent with the input image.
Capabilities
AnimateLCM-I2V is capable of generating high-quality, image-conditioned videos in a fast and efficient manner. By leveraging the consistency learning approach proposed in the AnimateLCM-paper, the model is able to produce smooth, semantically consistent animations from a single input image, without the need for complex teacher models.
What can I use it for?
AnimateLCM-I2V can be a powerful tool for a variety of applications, such as:
- Animation Generation: The model can be used to quickly generate animated content from still images, which could be useful for creating short animated videos, video game assets, or other multimedia content.
- Visualization and Prototyping: The model could be used to create dynamic visualizations or prototypes of product designs, architectural plans, or other conceptual ideas.
- Educational and Explainer Videos:
AnimateLCM-I2Vcould be used to generate animated videos that explain complex concepts or processes, making them more engaging and accessible to viewers.
Things to try
One interesting thing to try with AnimateLCM-I2V is experimenting with different input images and observing how the model translates the visual information into a coherent video sequence. You could try providing the model with a wide variety of image types, from realistic scenes to abstract or stylized artwork, and see how the generated videos capture the essence of the input.
Another idea is to explore the model's ability to maintain semantic consistency by providing it with input images that contain specific objects, characters, or environments, and seeing how the model represents those elements in the output video. This could be a useful way to assess the model's understanding of visual semantics and its ability to preserve important contextual information.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
📉
AnimateLCM
222
AnimateLCM is a fast video generation model developed by Fu-Yun Wang et al. It uses a Latent Consistency Model (LCM) to accelerate the animation of personalized diffusion models and adapters. The model is able to generate high-quality videos in just 4 steps, making it significantly faster than traditional video generation approaches. The AnimateLCM model builds on previous work, including AnimateDiff-Lightning, which is a lightning-fast text-to-video generation model that can generate videos more than ten times faster than the original AnimateDiff. The animate-lcm model from camenduru and the lcm-animation model from fofr are also related models that utilize Latent Consistency Models for fast animation. Model inputs and outputs Inputs Prompt**: A text description of the desired video content. Negative prompt**: A text description of content to avoid in the generated video. Number of frames**: The desired number of frames in the output video. Guidance scale**: A value controlling the strength of the text prompt in the generation process. Number of inference steps**: The number of diffusion steps to use during generation. Seed**: A random seed value to use for reproducible generation. Outputs Frames**: A list of images representing the generated video frames. Capabilities The AnimateLCM model is able to generate high-quality, fast-paced videos from text prompts. It can create a wide range of video content, from realistic scenes to more stylized or animated styles. The model's ability to generate videos in just 4 steps makes it a highly efficient tool for tasks like creating video content for social media, advertisements, or other applications where speed is important. What can I use it for? The AnimateLCM model can be used for a variety of video generation tasks, such as: Creating short, eye-catching video content for social media platforms Generating video previews or teasers for products, services, or events Producing animated explainer videos or educational content Developing video assets for digital advertising campaigns The model's speed and flexibility make it a valuable tool for businesses, content creators, and others who need to generate high-quality video content quickly and efficiently. Things to try One interesting aspect of the AnimateLCM model is its ability to generate video content from a single image using the AnimateLCM-I2V and AnimateLCM-SVD-xt variants. This could be useful for creating animated versions of existing images or for generating video content from a single visual starting point. Additionally, the model's integration with ControlNet and its ability to be combined with other LoRA models opens up possibilities for more advanced video generation techniques, such as using motion cues or stylistic adaptations to create unique and compelling video content.
Updated Invalid Date
📉
AnimateLCM-SVD-xt
161
The AnimateLCM-SVD-xt model, developed by maintainer wangfuyun, is a consistency-distilled version of the Stable Video Diffusion Image2Video-XT (SVD-xt) model. It follows the strategy proposed in the AnimateLCM paper to generate good quality image-conditioned videos with 25 frames in 2-8 steps at 576x1024 resolution. Compared to normal SVD models, AnimateLCM-SVD-xt can generally produce videos of similar quality in 4 steps without requiring classifier-free guidance, saving 12.5 times computation resources. Model inputs and outputs Inputs An image to condition the video generation on Outputs A 25-frame video at 576x1024 resolution, generated from the input image Capabilities The AnimateLCM-SVD-xt model can generate high-quality image-conditioned videos in just 4 inference steps, significantly reducing the computational cost compared to normal SVD models. The generated videos demonstrate good semantic consistency and temporal continuity, with examples ranging from landscapes to science fiction scenes. What can I use it for? The AnimateLCM-SVD-xt model is intended for both non-commercial and commercial usage. It can be used for research on generative models, safe deployment of models with the potential to generate harmful content, probing and understanding model limitations and biases, generation of artworks and creative applications, and educational tools. For commercial use, users should refer to the Stability AI membership information. Things to try One interesting aspect of the AnimateLCM-SVD-xt model is its ability to generate high-quality videos in just 4 inference steps, while normal SVD models require more steps and guidance to achieve similar results. This makes the AnimateLCM-SVD-xt model particularly well-suited for applications where computational resources are limited, or where fast video generation is required.
Updated Invalid Date
✅
MS-Image2Video
110
The MS-Image2Video (I2VGen-XL) project aims to address the task of generating high-definition video from input images. This model, developed by DAMO Academy, consists of two stages. The first stage ensures semantic consistency at low resolutions, while the second stage uses a Video Latent Diffusion Model (VLDM) to denoise, improve resolution, and enhance temporal and spatial consistency. The model is based on the publicly available VideoComposer work, inheriting design concepts such as the core UNet architecture. With a total of around 3.7 billion parameters, I2VGen-XL demonstrates significant advantages over existing video generation models in terms of quality, texture, semantics, and temporal continuity. Similar models include the i2vgen-xl and text-to-video-ms-1.7b projects, also developed by the ali-vilab team. Model inputs and outputs Inputs Single input image: The model takes a single image as the conditioning frame for video generation. Outputs Video frames: The model outputs a sequence of video frames, typically at 720P (1280x720) resolution, that are visually consistent with the input image and exhibit temporal continuity. Capabilities The I2VGen-XL model is capable of generating high-quality, widescreen videos directly from input images. The model ensures semantic consistency and significantly improves upon the resolution, texture, and temporal continuity of the output compared to existing video generation models. What can I use it for? The I2VGen-XL model can be used for a variety of applications, such as: Content Creation**: Generating visually appealing video content for entertainment, marketing, or educational purposes based on input images. Visual Effects**: Extending static images into dynamic video sequences for use in film, television, or other multimedia productions. Automated Video Generation**: Developing tools or services that can automatically create videos from user-provided images. Things to try One interesting aspect of the I2VGen-XL model is its two-stage architecture, where the first stage focuses on semantic consistency and the second stage enhances the video quality. You could experiment with the model by generating videos with different input images, observing how the model handles different types of content and scene compositions. Additionally, you could explore the model's ability to maintain temporal continuity and coherence, as this is a key advantage highlighted by the maintainers. Try generating videos with varied camera movements, object interactions, or lighting conditions to assess the model's robustness.
Updated Invalid Date
sdxl-lightning-4step
412.2K
sdxl-lightning-4step is a fast text-to-image model developed by ByteDance that can generate high-quality images in just 4 steps. It is similar to other fast diffusion models like AnimateDiff-Lightning and Instant-ID MultiControlNet, which also aim to speed up the image generation process. Unlike the original Stable Diffusion model, these fast models sacrifice some flexibility and control to achieve faster generation times. Model inputs and outputs The sdxl-lightning-4step model takes in a text prompt and various parameters to control the output image, such as the width, height, number of images, and guidance scale. The model can output up to 4 images at a time, with a recommended image size of 1024x1024 or 1280x1280 pixels. Inputs Prompt**: The text prompt describing the desired image Negative prompt**: A prompt that describes what the model should not generate Width**: The width of the output image Height**: The height of the output image Num outputs**: The number of images to generate (up to 4) Scheduler**: The algorithm used to sample the latent space Guidance scale**: The scale for classifier-free guidance, which controls the trade-off between fidelity to the prompt and sample diversity Num inference steps**: The number of denoising steps, with 4 recommended for best results Seed**: A random seed to control the output image Outputs Image(s)**: One or more images generated based on the input prompt and parameters Capabilities The sdxl-lightning-4step model is capable of generating a wide variety of images based on text prompts, from realistic scenes to imaginative and creative compositions. The model's 4-step generation process allows it to produce high-quality results quickly, making it suitable for applications that require fast image generation. What can I use it for? The sdxl-lightning-4step model could be useful for applications that need to generate images in real-time, such as video game asset generation, interactive storytelling, or augmented reality experiences. Businesses could also use the model to quickly generate product visualization, marketing imagery, or custom artwork based on client prompts. Creatives may find the model helpful for ideation, concept development, or rapid prototyping. Things to try One interesting thing to try with the sdxl-lightning-4step model is to experiment with the guidance scale parameter. By adjusting the guidance scale, you can control the balance between fidelity to the prompt and diversity of the output. Lower guidance scales may result in more unexpected and imaginative images, while higher scales will produce outputs that are closer to the specified prompt.
Updated Invalid Date