Easy Text to Video with AnimateDiff

AnimateDiff lets you easily create videos using Stable Diffusion. Just write a prompt, select a model, and activate AnimateDiff!

How can animatediff be a video maker ?

  • Text-to-Video Generation

    With AnimateDiff, you can provide a text prompt describing a scene, character, or concept, and it will generate a short video clip animating that description. This allows creating conceptual animations or story visualizations directly from text.

  • Image-to-Video Generation

    AnimateDiff supports image-to-video generation where you provide a static image, and it animates that image by adding motion based on the learned motion priors. This can bring still images or artworks to life.

  • Looping Animations

    In addition to short clips, AnimateDiff can generate seamless looping animations from text or image inputs. These can be used as animated backgrounds, screensavers, or creative animated artwork.

  • Video Editing/Manipulation

    The video2video implementation of AnimateDiff utilizes ControlNet to enable editing of existing videos via text prompts. You could potentially remove, add or manipulate elements in a video guided by your text descriptions.

  • Personalized Animations

    When combined with techniques like DreamBooth or LoRA, AnimateDiff allows animating personalized subjects, characters or objects trained on specific images/datasets.

  • Creative Workflows

    Artists and creators can integrate AnimateDiff into their creative workflows, using it to quickly visualize animated concepts, storyboards or animatics from text and image inputs during the ideation phase.

While not a full-fledged video editing tool, AnimateDiff provides a unique way to generate new video content from text and image inputs by leveraging the power of diffusion models and learned motion priors. Its outputs can be used as a starting point for further video editing and post-processing.

AnimateDiff: A Text-to-Video Maker Bringing Motion to Diffusion Models

AnimateDiff enables text-to-video generation, allowing you to create short video clips or animations directly from text prompts. Here's how the process works:

Text Prompt

You provide a text description of the scene, characters, actions, or concepts you want to see animated.

Base Text-to-Image Model

AnimateDiff utilizes a pre-trained text-to-image diffusion model like Stable Diffusion as the backbone to generate the initial image frames based on your text prompt.

Motion Module

At the core of AnimateDiff is a motion module trained on real-world videos to learn general motion patterns and dynamics. This module is agnostic to the base diffusion model.

Animating Frames

AnimateDiff combines the base diffusion model and the motion module. It first generates key frames from your text prompt using the diffusion model. Then, the motion module interpolates intermediate frames between these keys, applying the learned motion priors to animate the scene.

Video Output

The resulting output is a short video clip depicting the concepts described in your text prompt, with the animated elements exhibiting natural motion learned from real videos.

Some key advantages of AnimateDiff for text-to-video generation are


It can animate any text-to-image model without extensive retraining or fine-tuning specifically for video.


You can guide the animation via the text prompt describing actions, camera movements etc.


Faster than training monolithic text-to-video models from scratch.

However, the animations are not always perfect and may exhibit artifacts, especially for complex motions. But AnimateDiff provides a powerful way to directly visualize text descriptions as animations leveraging pre-trained diffusion models.

AnimateDiff: An Image-to-Video Maker Breathing Life into Static Visuals

AnimateDiff can also be used for image-to-video generation, allowing you to animate existing static images by adding motion and dynamics. Here's how it works :

  • Input Image: You provide a static image that you want to animate. This could be a photograph, digital artwork, or a diffusion model output.

  • Base Image-to-Image Model: AnimateDiff utilizes a pre-trained image-to-image diffusion model like Stable Diffusion's img2img capability as the backbone.

  • Motion Module: The same motion module trained on real-world videos to learn general motion patterns is used.

  • Animating from Input: AnimateDiff takes the input image and uses the image-to-image diffusion model to generate slight variations that serve as key frames.

  • Applying Motion: The motion module then interpolates intermediate frames between these key frames, applying the learned motion dynamics to animate the elements in the input image.

  • Video Output: The end result is a video clip where the original static input image has been brought to life with natural motion and animation.

Some key advantages of AnimateDiff for text-to-video generation are :


It can animate any input image, including personalized models or artworks.


Motion is inferred automatically from the input without extra guidance.


The level of motion can be controlled by adjusting settings.


Simple instances work better than highly complex scenes.

While not as controllable as the text-to-video case, image-to-video with AnimateDiff provides an easy way to add dynamics to existing still images leveraging the power of diffusion models and learned motion priors.

what is AnimateDiff

AnimateDiff is an AI tool that can turn a static image or text prompt into an animated video by generating a sequence of images that transition smoothly. It works by utilizing Stable Diffusion models along with separate motion modules to predict the motion between frames. AnimateDiff allows users to easily create short animated clips without needing to manually create each frame.

Key Features of AnimateDiff

AnimateDiff can generate animations from text prompts alone.

Users can upload an image and AnimateDiff will predict motion to generate an animation.

Users don't need to manually create each frame, as AnimateDiff automatically generates the image sequence.

AnimateDiff can be seamlessly integrated with Stable Diffusion and leverage its powerful image generation capabilities.

How does AnimateDiff work

  • It utilizes a pretrained motion module along with a Stable Diffusion image generation model.

  • The motion module is trained on a diverse set of short video clips to learn common motions and transitions.

  • When generating a video, the motion module takes a text prompt and preceding frames as input.

  • It then predicts the motion and scene dynamics to transition between frames smoothly.

  • These motion predictions are passed to Stable Diffusion to generate the actual image content in each frame.

  • Stable Diffusion creates images that match the text prompt while conforming to the motion predicted by the module.

  • This coordinated process results in a sequence of images that form a smooth, high-quality animation from the text description.

  • By leveraging both motion prediction and image synthesis, AnimateDiff automates animated video generation.

What are some potential use cases and applications for AnimateDiff

Art and animation

Artists/animators can quickly prototype animations and animated sketches from text prompts. Saves significant manual effort.

Concept visualization

Helps visualize abstract concepts and ideas by turning them into animations. Useful for storyboarding.

Game development

Can rapidly generate character motions and animations for prototyping game mechanics and interactions.

Motion graphics

Create dynamic motion graphics for videos, ads, presentations etc. in a highly automated way.

Augmented reality

Animate AR characters and objects by generating smoother and more natural motions.


Preview complex scenes with animation before filming or rendering final production.


Create explanations and demonstrations of concepts as engaging animated videos.

Social media

Generate catchy animated posts and stories by simply describing them in text.

The capability to go directly from text/images to animation opens up many possibilities for easier and more rapid animation creation across several domains.

How to use AnimateDiff

You can use AnimateDiff for free on the animatediff.org website without needing your own computing resources or coding knowledge. On the site, you simply enter a text prompt describing the animation you want to create. AnimateDiff will then automatically generate a short animated GIF from your text prompt using state-of-the-art AI capabilities. The whole process happens online and you can download the resulting animation to use as you like. This provides an easy way to experience Animatediff's animation powers without setup. You can start creating AI-powered animations from your imagination in just a few clicks!

What are the system requirements for running AnimateDiff

An Nvidia GPU is required, ideally with at least 8GB VRAM for text-to-video generation. 10+ GB VRAM needed for video-to-video.

A sufficiently powerful GPU for inference is needed, like an RTX 3060 or better. The more powerful the GPU, the better the performance.

Windows or Linux. macOS can work through Docker. Google Colab is also an option.

16GB system RAM minimum recommended.

A decent amount of storage is required for saving image sequences, videos, and model files. At least 1 TB is recommended.

Works with AUTOMATIC1111 or Google Colab. Requires installing Python and other dependencies.

Currently only compatible with Stable Diffusion v1.5 models.

Overall, AnimateDiff works best with a powerful Nvidia GPU with abundant VRAM and compute capability, running on Windows or Linux. Lacking a strong enough GPU can result in slow generation speeds or issues with video quality.

How to Install the AnimateDiff extension

  • Start the AUTOMATIC1111 Web UI normally.

  • Go to the Extensions page and click on the "Install from URL" tab.

  • In the URL field, enter the Github URL for the AnimateDiff extension: https://github.com/continue-revolution/sd-webui-animatediff

  • Wait for the confirmation that the installation is complete.

  • Restart the AUTOMATIC1111 Web UI.

  • The AnimateDiff extension should now be installed and visible in the txt2img and img2img tabs.

  • Download the required motion modules and place them in the proper folders as explained in the documentation.

  • Restart AUTOMATIC1111 again after adding motion modules.

Now the AnimateDiff extension is installed and ready to use for generating animated videos in AUTOMATIC1111!

Advanced options about AnimateDiff

Close loop

Makes the first and last frames identical to create a seamless looping video.

Reverse frames

Doubles the video length by appending frames in reverse order. Creates more fluid transitions.

Frame interpolation

Increases frame rate to make motion look smoother.

Context batch size

Controls temporal consistency between frames. Higher values make changes more gradual.

Motion LoRA

Adds camera motion effects like panning, zooming, etc. Works similarly to regular LoRA.


Directs motion based on a reference video's motions using ControlNet capabilities.


Allows defining start and end frames to have more control over composition.


Frames per second control the speed of the animation.

Number of frames

Determines the total length of the generated video.

Motion modules

Different modules produce different motion effects.

By tweaking these settings, one can achieve more control over the style, smoothness, camera motion, speed, and length of the AnimateDiff videos.

What are some current limitations of AnimateDiff

Limited motion range

The motions are constrained by what's in the training data. It cannot animate very complex or unusual motions not seen in the training set.

Generic movements

The motion is not tailored specifically to the prompt, so it tends to produce generic movements loosely related to the prompt.


Can sometimes produce visual artifacts as motion increases.


Currently only works with Stable Diffusion v1.5 models. Not compatible with SD v2.0.

Training data dependence

Quality of motion relies heavily on diversity and relevance of training data.

Hyper parameter tuning

Getting smooth, high-quality motion requires tuning many settings like batch size, FPS, frames, etc.

Motion coherence

Maintaining logical motion coherence over long videos is still a challenge.

While-capable of generating short, basic animations from text, AnimateDiff has limitations around complex motions, motion quality, and seamless transitions. As the technology matures, we can expect many of these issues to be addressed.