AnimateDiff for ComfyUI

Improved AnimateDiff integration for ComfyUI, initially adapted from sd-webui-animatediff but changed greatly since then. Please read the AnimateDiff repo README for more information about how it works at its core.

Examples shown here will also often make use of these helpful sets of nodes:

• ComfyUI_FizzNodes for prompt-travel functionality with the BatchPromptSchedule node.
• ComfyUI-Advanced-ControlNet for loading files in batches and controlling which latents should be affected by the ControlNet inputs (work in progress, will include more advance workflows + features for AnimateDiff usage later).
• ComfyUI-VideoHelperSuite for loading videos, combining images into videos, and doing various image/latent operations like appending, splitting, duplicating, selecting, or counting.
• comfyui_controlnet_aux for ControlNet preprocessors not present in vanilla ComfyUI. NOTE: If you previously used comfy_controlnet_preprocessors, you will need to remove comfy_controlnet_preprocessors to avoid possible compatibility issues between the two. Actively maintained by Fannovel16.

Installation

If using Comfy Manager:

1. Look for AnimateDiff Evolved, and be sure the author is Kosinkadink. Install it.

If installing manually:

1. Clone this repo into custom_nodes folder.

How to Use:

1. Download motion modules. You will need at least 1. Different modules produce different results.
   • Original models mm_sd_v14, mm_sd_v15, mm_sd_v15_v2, v3_sd15_mm: HuggingFace | Google Drive | CivitAI
   • Stabilized finetunes of mm_sd_v14, mm-Stabilized_mid and mm-Stabilized_high, by manshoety: HuggingFace
   • Finetunes of mm_sd_v15_v2, mm-p_0.5.pth and mm-p_0.75.pth, by manshoety: HuggingFace
   • Higher resolution finetune,temporaldiff-v1-animatediff by CiaraRowles: HuggingFace
2. Place models in ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/models. They can be renamed if you want.
3. Optionally, you can use Motion LoRAs to influence movement of v2-based motion models like mm_sd_v15_v2.
   • Google Drive | HuggingFace | CivitAI
   • Place Motion LoRAs in ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora. They can be renamed if you want.
4. Get creative! If it works for normal image generation, it (probably) will work for AnimateDiff generations. Latent upscales? Go for it. ControlNets, one or more stacked? You betcha. Masking the conditioning of ControlNets to only affect part of the animation? Sure. Try stuff and you will be surprised by what you can do. Samples with workflows are included below.

Notable Updates

(December 6th, 2023) Massive rewrite of code

I just released a massive rework of the code that I've been working on the past week. Changes are almost all under the hood, and everything should still look the same generation-wise and performance-wise. ComfyUI design patterns and model management is used where possible now. If you experience any issues you did not have before, please report them so I can fix them quickly! Notable changes:

• Slightly lower VRAM usage (0.3-0.8GB) depending on workflow
• Motion model caching - speeds up consecutive sampling
• fp8 support (by casting in places that need to be casted)
• Model patches (like LCM) can be applied properly (no guarantees on improvements in generations though, might take some investigation to figure out why v2 models look weird with LCM)
• dtype and device mismatch edge cases should now be fixed
• Additional 'use existing' beta schedule to allow any ModelSampling nodes to take effect - will use beta schedule as the ModelSampling patch overwise

Features:

• Compatible with a variety of samplers, vanilla KSampler nodes and KSampler (Effiecient) nodes.
• ControlNet support - both per-frame, or "interpolating" between frames; can kind of use this as img2video (see workflows below)
• Infinite animation length support using sliding context windows (introduced 9/17/23)
• Mixable Motion LoRAs from original AnimateDiff repository implemented. Caveat: only really work on v2-based motion models like mm_sd_v15_v2, mm-p_0.5.pth, and mm-p_0.75.pth (introduced 9/25/23)
• Prompt travel using BatchPromptSchedule node from ComfyUI_FizzNodes (working since 9/27/23)
• HotshotXL support (an SDXL motion module arch), hsxl_temporal_layers.safetensors (working since 10/05/23) NOTE: You will need to use linear (HotshotXL/default) beta_schedule, the sweetspot for context_length or total frames (when not using context) is 8 frames, and you will need to use an SDXL checkpoint. Will add more documentation and example workflows soon when I have some time between working on features/other nodes.
• Motion scaling and other motion model settings to influence motion amount (introduced 10/30/23)
• Motion scaling masks in Motion Model Settings, allowing to choose how much motion to apply per frame or per area of each frame (introduced 11/08/23). Can be used alongside inpainting (gradient masks supported for AnimateDiff masking)
• AnimateDiff-SDXL support, with corresponding model. (introduced 11/10/23). Currently, a beta version is out, which you can find info about at AnimateDiff. NOTE: You will need to use linear (AnimateDiff-SDXL) beta_schedule. Other than that, same rules of thumb apply to AnimateDiff-SDXL as AnimateDiff.
• fp8 support: requires newest ComfyUI and torch >= 2.1 (introduced 12/06/23).
• AnimateDiff v3 motion model support (introduced 12/15/23).

Upcoming features:

• Alternate context schedulers and context types (in progress)

Core Nodes:

AnimateDiff Loader

The only required node to use AnimateDiff, the Loader outputs a model that will perform AnimateDiff functionality when passed into a sampling node.

Inputs:

• model: model to setup for AnimateDiff usage. Must be a SD1.5-derived model.
• context_options: optional context window to use while sampling; if passed in, total animation length has no limit. If not passed in, animation length will be limited to either 24 or 32 frames, depending on motion model.
• motion_lora: optional motion LoRA input; if passed in, can influence movement.
• motion_model_settings: optional settings to influence motion model.
• model_name: motion model to use with AnimateDiff.
• beta_schedule: noise scheduler for SD. sqrt_linear is the intended way to use AnimateDiff, with expected saturation. However, linear can give useful results as well, so feel free to experiment.
• motion_scale: change motion amount generated by motion model - if less than 1, less motion; if greater than 1, more motion.

Outputs:

• MODEL: model injected to perform AnimateDiff functions

Usage

To use, just plug in your model into the AnimateDiff Loader. When the output model (and any derivative of it in this pathway) is passed into a sampling node, AnimateDiff will do its thing.

The desired animation length is determined by the latents passed into the sampler. With context_options connected, there is no limit to the amount of latents you can pass in, AKA unlimited animation length. When no context_options are connected, the sweetspot is 16 latents passed in for best results, with a limit of 24 or 32 based on motion model loaded. These same rules apply to Uniform Context Option's context_length.

You can also connect AnimateDiff LoRA Loader nodes to influence the overall movement in the image - currently, only works well on motion v2-based models.

[Simplest Usage] [All Possible Connections Usage]

Uniform Context Options

TODO: fill this out

AnimateDiff LoRA Loader

Allows plugging in Motion LoRAs into motion models. Current Motion LoRAs only properly support v2-based motion models. Does not affect sampling speed, as the values are frozen after model load. If you experience slowdowns for using LoRAs, please open an issue so I can resolve it. Currently, the three models that I know are v2-based are mm_sd_v15_v2, mm-p_0.5.pth, and mm-p_0.75.pth.

Inputs:

• lora_name: name of Motion LoRAs placed in ComfyUI/custom_node/ComfyUI-AnimateDiff-Evolved/motion-lora directory.
• strength: how strong (or weak) effect of Motion LoRA should be. Too high a value can lead to artifacts in final render.
• prev_motion_lora: optional input allowing to stack LoRAs together.

Outputs:

• MOTION_LORA: motion_lora object storing the names of all the LoRAs that were chained behind it - can be plugged into the back of another AnimateDiff LoRA Loader, or into AniamateDiff Loader's motion_lora input.

[Simplest Usage] [Chaining Multiple Motion LoRAs]

Motion Model Settings

Additional tweaks to the internals of the motion models. The Advanced settings will take a whole guide to explain, and I currently do not have the time for that. Instead, I'll focus on the simple settings.

Inputs:

• motion_pe_stretch: used to decrease the amount of motion by stretching (and interpolating) between the positional encoders (PEs). TL;DR: number go up, animation slow down. Number up too much, animation begins to vibrate (vibration artifacts).

Outputs:

• MOTION_MODEL_SETTINGS: motion_model_settings object to be plugged into an AnimateDiff Loader.

Samples (download or drag images of the workflows into ComfyUI to instantly load the corresponding workflows!)

txt2img

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txt2img - (prompt travel)

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txt2img - 48 frame animation with 16 context_length (uniform)

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txt2img - (prompt travel) 48 frame animation with 16 context_length (uniform)

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txt2img - 32 frame animation with 16 context_length (uniform) - PanLeft and ZoomOut Motion LoRAs

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txt2img w/ latent upscale (partial denoise on upscale)

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txt2img w/ latent upscale (partial denoise on upscale) - PanLeft and ZoomOut Motion LoRAs

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txt2img w/ latent upscale (partial denoise on upscale) - 48 frame animation with 16 context_length (uniform)

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txt2img w/ latent upscale (full denoise on upscale)

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txt2img w/ latent upscale (full denoise on upscale) - 48 frame animation with 16 context_length (uniform)

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txt2img w/ ControlNet-stabilized latent-upscale (partial denoise on upscale, Scaled Soft ControlNet Weights)

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txt2img w/ ControlNet-stabilized latent-upscale (partial denoise on upscale, Scaled Soft ControlNet Weights) 48 frame animation with 16 context_length (uniform)

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txt2img w/ Initial ControlNet input (using Normal LineArt preprocessor on first txt2img as an example)

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txt2img w/ Initial ControlNet input (using Normal LineArt preprocessor on first txt2img 48 frame as an example) 48 frame animation with 16 context_length (uniform)

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txt2img w/ Initial ControlNet input (using OpenPose images) + latent upscale w/ full denoise

(open_pose images provided courtesy of toyxyz)

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txt2img w/ Initial ControlNet input (using OpenPose images) + latent upscale w/ full denoise, 48 frame animation with 16 context_length (uniform)

(open_pose images provided courtesy of toyxyz)

aaa_readme_cn_00024_.webm

img2img

TODO: fill this out with a few useful ways, some using control net tile. I'm sorry there is nothing here right now, I have a lot of code to write. I'll try to fill this section out + Advance ControlNet use piece by piece.

Known Issues

Some motion models have visible watermark on resulting images (especially when using mm_sd_v15)

Training data used by the authors of the AnimateDiff paper contained Shutterstock watermarks. Since mm_sd_v15 was finetuned on finer, less drastic movement, the motion module attempts to replicate the transparency of that watermark and does not get blurred away like mm_sd_v14. Using other motion modules, or combinations of them using Advanced KSamplers should alleviate watermark issues.