1 Star 0 Fork 1

ComfyUI CustomNodes Clone/ComfyUI-AnimateDiff-Evolved

加入 Gitee
与超过 1200万 开发者一起发现、参与优秀开源项目,私有仓库也完全免费 :)
免费加入
克隆/下载
贡献代码
同步代码
取消
提示: 由于 Git 不支持空文件夾,创建文件夹后会生成空的 .keep 文件
Loading...
README
Apache-2.0

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. image

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

image

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] image [All Possible Connections Usage] image

Uniform Context Options

TODO: fill this out image

AnimateDiff LoRA Loader

image

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] image [Chaining Multiple Motion LoRAs] image

Motion Model Settings

image

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

t2i_wf

aaa_readme_00001_

aaa_readme_00003_.webm

txt2img - (prompt travel)

t2i_prompttravel_wf

aaa_readme_00008_

aaa_readme_00010_.webm

txt2img - 48 frame animation with 16 context_length (uniform)

t2i_context_wf

aaa_readme_00004_

aaa_readme_00006_.webm

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

t2i_context_promptravel

aaa_readme_00001_

aaa_readme_00002_.webm

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

t2i_context_mlora_wf

aaa_readme_00094_

aaa_readme_00095_.webm

txt2img w/ latent upscale (partial denoise on upscale)

t2i_lat_ups_wf

aaa_readme_up_00001_

aaa_readme_up_00002_.webm

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

t2i_mlora_lat_ups_wf

aaa_readme_up_00023_

aaa_readme_up_00024_.webm

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

t2i_lat_ups_full_wf

aaa_readme_up_00009_.webm

txt2img w/ latent upscale (full denoise on upscale)

t2i_lat_ups_full_wf

aaa_readme_up_00010_

aaa_readme_up_00012_.webm

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

t2i_context_lat_ups_wf

aaa_readme_up_00014_.webm

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

t2i_lat_ups_softcontrol_wf

aaa_readme_up_00017_

aaa_readme_up_00019_.webm

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

t2i_context_lat_ups_softcontrol_wf

aaa_readme_up_00003_.webm

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

t2i_initcn_wf

aaa_readme_cn_00002_

aaa_readme_cn_00003_.webm

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)

t2i_context_initcn_wf

aaa_readme_cn_00005_

aaa_readme_cn_00006_.webm

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

t2i_openpose_upscale_wf

(open_pose images provided courtesy of toyxyz)

AA_openpose_cn_gif_00001_

aaa_readme_cn_00032_

aaa_readme_cn_00033_.webm

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

t2i_context_openpose_upscale_wf

(open_pose images provided courtesy of toyxyz)

aaa_readme_preview_00002_

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.

Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2023 Jedrzej Kosinski Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

简介

ComfyUI-AnimateDiff-Evolved 展开 收起
README
Apache-2.0
取消

发行版

暂无发行版

贡献者

全部

近期动态

不能加载更多了
马建仓 AI 助手
尝试更多
代码解读
代码找茬
代码优化
1
https://gitee.com/custom_nodes_clone/ComfyUI-AnimateDiff-Evolved.git
git@gitee.com:custom_nodes_clone/ComfyUI-AnimateDiff-Evolved.git
custom_nodes_clone
ComfyUI-AnimateDiff-Evolved
ComfyUI-AnimateDiff-Evolved
main

搜索帮助