AnimateLCM
Maintainer: wangfuyun
222
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| 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 |
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Model overview
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.
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
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AnimateLCM-I2V
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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-I2V could 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.
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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.
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AnimateLCM-SVD-Comfy
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AnimateLCM-SVD-Comfy is a converted version of the AnimateLCM-SVD-xt model, which was developed by Kijai and is based on the AnimateLCM paper. The model is designed for image-to-image tasks and can generate high-quality animated videos in just 2-8 steps, significantly reducing the computational resources required compared to normal Stable Video Diffusion (SVD) models. Model inputs and outputs AnimateLCM-SVD-Comfy takes an input image and generates a sequence of 25 frames depicting an animated version of the input. The model can produce videos with 576x1024 resolution and good quality, without the need for classifier-free guidance that is typically required by SVD models. Inputs Input image Outputs Sequence of 25 frames depicting an animated version of the input image Capabilities AnimateLCM-SVD-Comfy can generate compelling animated videos from a single input image in just 2-8 steps, a significant improvement in efficiency compared to normal SVD models. The model was developed by Kijai, who has also created other related models like AnimateLCM and AnimateLCM-SVD-xt. What can I use it for? AnimateLCM-SVD-Comfy can be a powerful tool for creating animated content from a single image, such as short videos, GIFs, or animations. This could be useful for a variety of applications, such as social media content creation, video game development, or visualizing concepts and ideas. The model's efficiency in generating high-quality animated videos could also make it valuable for businesses or creators looking to produce content quickly and cost-effectively. Things to try Some ideas for what to try with AnimateLCM-SVD-Comfy include: Generating animated versions of your own photographs or digital artwork Experimenting with different input images to see the variety of animations the model can produce Incorporating the animated outputs into larger video or multimedia projects Exploring the model's capabilities by providing it with a diverse set of input images and observing the results The key advantage of AnimateLCM-SVD-Comfy is its ability to generate high-quality animated videos in just a few steps, making it an efficient and versatile tool for a range of creative and professional applications.
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AnimateDiff-Lightning
563
AnimateDiff-Lightning is a lightning-fast text-to-video generation model developed by ByteDance. It can generate videos more than ten times faster than the original AnimateDiff model. The model is distilled from the AnimateDiff SD1.5 v2 model, and the repository contains checkpoints for 1-step, 2-step, 4-step, and 8-step distilled models. The 2-step, 4-step, and 8-step models produce great generation quality, while the 1-step model is only provided for research purposes. Model inputs and outputs AnimateDiff-Lightning takes text prompts as input and generates corresponding videos as output. The model can be used with a variety of base models, including realistic and anime/cartoon styles, to produce high-quality animated videos. Inputs Text prompts for the desired video content Outputs Animated videos corresponding to the input text prompts Capabilities AnimateDiff-Lightning is capable of generating high-quality animated videos from text prompts. The model can produce realistic or stylized videos, depending on the base model used. It also supports additional features, such as using Motion LoRAs to enhance the motion in the generated videos. What can I use it for? AnimateDiff-Lightning can be used for a variety of applications, such as creating animated explainer videos, generating custom animated content for social media, or even producing animated short films. The model's ability to generate videos quickly and with high quality makes it a valuable tool for content creators and businesses looking to produce engaging visual content. Things to try When using AnimateDiff-Lightning, it's recommended to experiment with different base models and settings to find the best results for your specific use case. The model performs well with stylized base models, and using 3 inference steps on the 2-step model can produce great results. You can also explore the use of Motion LoRAs to enhance the motion in the generated videos.
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