Wangfuyun

Models by this creator

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AnimateLCM

wangfuyun

Total Score

214

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.

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Updated 5/19/2024

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AnimateLCM-SVD-xt

wangfuyun

Total Score

158

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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Updated 5/19/2024