What is AnimateDiff?

What is AnimateDiff?

AnimateDiffIs Stable DiffusionBy utilizing Create short animations (videos)This is an extension that can be used. Basically Create moving images while maintaining consistency between framesThe goal is to achieve this, and unlike existing static image creation, the core technology is to secure **temporal consistency**.

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1. Features of AnimateDiff

 Stable Diffusion based video generation

• Stable Diffusion의 Text-to-image conversion capabilitiesCreate **Text-to-Video (T2V)** using
• one imageGive movement by extending it to multiple frames

 Consistent Frame

• It's not just a simple frame interval adjustment. Real moving characters, backgrounds, and animation expressionsthis is possible
• Changes between frames are smooth and shake-free. Realize natural motion

 Supports LoRA (LoRA Motion Modules)

• Learning various movement patterns LoRA motion modelYou can add
• for example, Running motion, dancing motion, fluttering motion in the wind etc. can be applied

 Compatible with ControlNet

• Using ControlNet Pose, depth and edge detection Precise motion control is possible by combining

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2. How AnimateDiff works

Predict the flow between frames by adding a Time-aware Layer to Stable Diffusion's UNet network.This is the way to do it.

 Basic structure

1. Enter text prompt
    → "A cyberpunk city with neon lights, animated"
2. Create base image
    → Create first frame based on text prompt
3. Add time information (Time-step)
    → AnimateDiff Add time axis to existing UNetpredict the next frame
4. Create continuous frames
    → Create short animations consisting of 16 or 24 frames, etc.

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3. How to install and use AnimateDiff

 1) How to install AnimateDiff WebUI

AnimateDiff can be added as **AUTOMATIC1111's WebUI Extension**.

 Install Stable Diffusion WebUI

• AUTOMATIC1111's WebUI must be installed
• How to install WebUI Official GitHub reference

 Install the AnimateDiff extension

cd stable-diffusion-webui/extensions

git clone https://github.com/guoyww/AnimateDiff

 Download the required checkpoints

• Available in AnimateDiff Motion LoRA modelYou need to download
• Latest from Hugging Face AnimateDiff motion model download

• AnimateDiff Motion Model

 Running and setting up WebUI

cd stable-diffusion-webui

python launch.py --xformers

• After running WebUI Activate the “AnimateDiff” tab
• Motion Module(LoRA) Select and run

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4. How to use AnimateDiff

 Basic prompt example

masterpiece, best quality, anime style, a girl dancing in the forest, highly detailed, cinematic lighting

 Tips for Improving Animation Quality

• Adjust frame rate: Basically, 16 frames is the most stable
• Add LoRA motion model: Dancing, walking, and movement can be added
• Combined with ControlNet: Set to move more naturally by adjusting the pose

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5. Utilizing a combination of AnimateDiff and ControlNet

AnimateDiff Can be used with ControlNet to create even more sophisticated motions

 How to apply ControlNet

• Pose ControlNet: Set the basic pose of the character and set it to move with AnimateDiff
• Depth ControlNet: Create an animation with a sense of depth by adjusting the three-dimensional effect of the background and character

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6. Limitations and solutions of AnimateDiff

 1) Difficulty creating long animations

• AnimateDiff Stable creation of only short animations (16 to 24 frames)
• dissolvent: BelowUse with to extend the length, or Latest AnimateDiff extension update conjugation

 2) Motion is limited

• In addition to basic motions Difficulty expressing specific actions (fighting, jumping, etc.)
• dissolvent: LoRA Motion ModuleImplement additional motion by training

 3) Resolution issues

• The basic creation resolution is low, causing blurring when enlarged.
• dissolvent: Resolution correction using upscalers such as R-ESRGAN and CodeFormer

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7. Comparison of AnimateDiff with other video generation models

model	characteristic	frame duration	merit	disadvantage
AnimateDiff	Stable Diffusion-based moving image generation	16~24 frames	Text-based generation, LoRA applicable	long animation difficult
Runway Gen-2	Create video from text (AI Video Model)	4 to 16 seconds	Easy to use, commercially available	Free version limitations
Below	Apply motion by connecting frames	Possible for more than 10 seconds	Story production possible	High hardware requirements

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 AnimateDiff main parameter description

AnimateDiff lets you adjust several parameters to control the number of frames, the intensity and quality of movement, and more. Here we will look at the main parameters one by one.

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 Motion Module (LoRA motion model)

• AnimateDiff, unlike the typical Stable Diffusion model, uses the **"Motion Module" (LoRA-based movement model)** to generate motion.
• Download path: AnimateDiff Motion Modules
• example model:

• mm_sd_v15_v2.ckpt → Basic motion module (suitable for general animation)
• mm_sd_v15_run.ckpt → Add natural walking motion
• mm_sd_v15_dance.ckpt → Add dancing movements

 How to set up:
Motion module path → available .ckpt select file

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 Frame Settings

AnimateDiff is basically optimized for creating **short animations (around 16 frames)**.

• Frames
  

• Default: 16
• Description: Number of frames to produce in one animation
• Larger values ​​create longer, smoother animations, but are slower and use more memory.
• Example: 8~16 → Fast speed, 24~32 → Smoother motion possible

• FPS (Frames Per Second)
  

• Default: 8~16 FPS
• Description: Set how many frames per second to use
• Increasing FPS results in smoother animations, but increases file size

 Recommended settings

• Normal animation: Frames: 16, FPS: 12
• Smooth motion: Frames: 24, FPS: 16

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 Strength (adjusting movement intensity)

• Default: 0.5
• explanation: Control the intensity of movementoption to do
• If the value is low smooth motion, if the value is high exaggerated motion

 Recommended settings

• Natural movements: 0.3~0.5
• flight: 0.6~0.8

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 Seed (Seed value)

• Default: -1 (random)
• Description: When you want to repeatedly create a specific animation, Seed use value
• Similar animations can be created repeatedly by using the same seed value.

 How to use

• Random animation generation: -1
• Recreate the same animation: using a specific Seed value (12345 etc)

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 Sampler (sampling method)

Although it is the same sampling technique used in Stable Diffusion, AnimateDiff Create a frame considering the time axisThis is the way to do it.

• DDIM (default setting, fast)
• Euler a (fast, clear results)
• DPM++ 2M Karras (smooth quality, high computation required)

 Recommended settings

• Speed ​​is important → Euler has
• Quality first → DPM++ 2M Karras

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 Cfg Scale (Clip Scale)

• Default: 7
• explanation: How strongly to reflect the text prompt value to adjust
• If the value is too high Unnatural and distorted imagescan be

 Recommended settings

• Natural animation: 6~8
• Apply strong prompt: 9~10

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 Motion Mode

AnimateDiff allows you to choose from a variety of movement methods.

• default → General motion application
• loop → Create a smoothly repeating animation
• fast → Emphasize fast movement

 Recommended settings

• General video: default
• Loop animation: loop

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 Combination with ControlNet (Pose Control)

AnimateDiff ControlNetWhen used in conjunction with , you can create more precise movements.

• Pose ControlNet → Set to move based on a specific pose
• Depth ControlNet → Apply three-dimensional motion using depth detection

 Example of use
  dance animation

masterpiece, best quality, a girl dancing in the forest, highly detailed

• ControlNet: OpenPose → Create animation while the character maintains a specific dance pose

 Character walking animation

a man walking in the cyberpunk city, cinematic lighting, highly detailed

• ControlNet: Depth → Create natural motion while maintaining the depth of the background

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 AnimateDiff optimal settings summary

setting	Recommended value	explanation
Motion Module	mm_sd_v15_v2.ckpt	basic motion module
Frames	16	Basically, 16 frames are recommended.
FPS	12	Set to 12FPS for smooth animations
Strength	0.5	Setting 0.3~0.5 for natural motion
Seed	-1	Generate random animations
Sampler	Euler has	Fast and clear results
Cfg Scale	7	Maintain natural image quality
Motion Mode	default	Create common animations with default settings
ControlNet	Pose	Create animations that maintain specific poses

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Title: How to optimally set up AnimateDiff on a low-end PC – Frame, resolution, sampler optimization guide

Meta Description: Learn how to run AnimateDiff smoothly on low-end computers. We detail how to create smooth animations with optimal frame rate, resolution, and sampler settings.

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How to optimally set up AnimateDiff on a low-end PC

AnimateDiff is an animation creation model that utilizes Stable Diffusion and uses a lot of VRAM. Therefore, smooth animation production is possible only with optimized settings on low-end computers. In this article, we have summarized how to use AnimateDiff efficiently even in low-end environments.

1. Basic principles of low-end computer optimization

In low-end environments, you should adjust the following factors to reduce VRAM usage and optimize speed:

• Reduce frame count: Set to 8~12 frames
• Lower resolution: 512x512 or 640x360
• Sampler changes: Use Euler a (fast calculation)
• Avoid using ControlNet: Additional computational burden occurs
• Using the LoRA motion model: Effective movement can be realized

2. AnimateDiff low-end optimal settings

Setting items	Recommended value	explanation
Motion Module	mm_sd_v15_v2.ckpt	Use basic motion model
Frames	8~12	Save VRAM by reducing frame count
FPS	8	maintain appropriate softness
Resolution	512x512	Possible lack of VRAM when using high resolution
Sampler	Euler has	Fast speeds and decent quality
Cfg Scale	5~7	If the value is too high, VRAM usage increases
Strength	0.3~0.5	Maintain smooth motion
Seed	-1	Generate random animations
Batch Size	1	Set low to save VRAM
Motion Mode	default	General animation production
Use LoRA	Yes	Create effective motion while reducing VRAM usage

3. Low-spec optimization prompt example

① Basic animation (minimize movement)

masterpiece, best quality, anime style, a girl waving hand, highly detailed

• Apply settings: Frames: 8, FPS: 8, Euler a, 512x512

② Character walking animation (LoRA applied)

a young man walking in a cyberpunk city, cinematic lighting, highly detailed

• Apply settings: Frames: 12, FPS: 8, Euler a, 640x360

③ Low-resolution background animation

a beautiful night sky with moving clouds, ultra detailed, cinematic shot

• Apply settings: Frames: 10, FPS: 8, Euler a, 512x512

4. Additional optimization tips

1) When VRAM is insufficient --between me or --lowvram Use options

python launch.py --medvram

• --between me: Suitable for PCs with 6GB VRAM or less
• --lowvram: Can run in 4GB VRAM or less, but may be slower

2) Utilize upscaling (create at low resolution and then enlarge)

• After creating in 512x512 resolution R-ESRGAN upscalerIncreasing the resolution using allows for high-quality results without compromising performance.

3) Create long animations in combination with Deforum

• AnimateDiff specializes in short animations, so you can create longer animations by connecting multiple clips using Deforum.

In low-end environments, smooth animation creation is possible by appropriately adjusting AnimateDiff's frame rate, resolution, sampler, and LoRA motion model. especially 512x512 resolution, Frames 8~12, Euler a samplerYou can create animations without running out of VRAM.