siliconflow/ComfyUI-DynamiCrafterWrapper

siliconflow/ComfyUI-DynamiCrafterWrapper

Description: Wrapper to use DynamiCrafter models in ComfyUI

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Created: 2026-10-30T03:29:59Z

Pushed: 2026-10-30T06:50:56Z

Default branch: main

Fork: yes

Parent repository: kijai/ComfyUI-DynamiCrafterWrapper

Archived: no

README:

DynamiCrafter wrapper nodes for ComfyUI

Update2: Refactor

Changed lots of things to better integrate this to ComfyUI, you can (and have to) use clip_vision and clip models, but memory usage is much better and I was able to do 512x320 under 10GB VRAM. New example workflows are included, all old workflows will have to be updated.

Update: ToonCrafter

Initial ToonCrafter support with it's own node.

VRAM required for 512x320 seems to be slightly under 15GB for sampling, and peaks to ~17GB at decoding. Dropping the resolution a bit can reduce the memory use a lot.

Decoding is the most memory hungry operation in all this, and currently REQUIRES XFORMERS for full quality results, however it is possible to use the standard Comfy VAE decoder for bit less quality but far less memory used. The ToonCrafter model can also be used with the old Dynamicrafter I2V -node, quality then suffers a lot more, memory usage is halved, fitting under 8GB at best.

Fp8 option can also further reduce memory use by 1-2GB.

https://github.com/kijai/ComfyUI-DynamiCrafterWrapper/assets/40791699/96bf0902-40e6-42ad-beb9-a092f26c0458

Installing

Either manager and install from git, or clone this repo to custom_nodes and run:

pip install -r requirements.txt

or if you use portable (run this in ComfyUI_windows_portable -folder):

python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-DynamiCrafterWrapper\requirements.txt

Currently even if this can run without xformers, the memory usage is huge. Recommended to use xformers if possible:

pip install xformers --no-deps

or with portable:

python_embeded\python.exe -m pip install xformers --no-deps

UPDATE: Converted the models to bf16 and .safetensors format here: https://huggingface.co/Kijai/DynamiCrafter_pruned/tree/main

Models go to ComfyUI/models/checkpoints (can also be in subfolder, up to you)

If you want to use the original models, they are available here, they do need to be renamed to be used with the node: Name this: dynamicrafter_1024_v1.ckpt https://huggingface.co/Doubiiu/DynamiCrafter_1024

Interpolation model should be named: dynamicrafter_512_interp_v1.ckpt https://huggingface.co/Doubiiu/DynamiCrafter_512_Interp/

With fp16 1024x576 uses bit under 10GB VRAM, and interpolation at 512p can be done with 8GB

Looping example:

https://github.com/kijai/ComfyUI-DynamiCrafterWrapper/assets/40791699/d1a83fac-d654-487f-a02e-be00509d38d5

Interpolation example:

https://github.com/kijai/ComfyUI-DynamiCrafterWrapper/assets/40791699/96251573-4b15-4d51-becd-daf8a1e5eab5

https://github.com/kijai/ComfyUI-DynamiCrafterWrapper/assets/40791699/156aeb21-4936-4e9a-a9b4-1767a8f6bbee

ORIGINAL REPO:

https://github.com/Doubiiu/DynamiCrafter

___*DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors*___

🔆 Introduction

🔥🔥 Generative frame interpolation / looping video generation model weights (320x512) have been released!

🔥 New Update Rolls Out for DynamiCrafter! Better Dynamic, Higher Resolution, and Stronger Coherence!

🤗 DynamiCrafter can animate open-domain still images based on text prompt by leveraging the pre-trained video diffusion priors. Please check our project page and paper for more information.

😀 We will continue to improve the model's performance.

👀 Seeking comparisons with Stable Video Diffusion and PikaLabs? Click the image below. 

1.1. Showcases (576x1024)

1.2. Showcases (320x512)

1.3. Showcases (256x256)

"bear playing guitar happily, snowing" "boy walking on the street"

2. Applications

2.1 Storytelling video generation (see project page for more details)

2.2 Generative frame interpolation

Input starting frame Input ending frame Generated video

2.3 Looping video generation

📝 Changelog

• __[2024.03.14]__: 🔥🔥 Release generative frame interpolation and looping video models (320x512).
• __[2024.02.05]__: Release high-resolution models (320x512 & 576x1024).
• __[2023.12.02]__: Launch the local Gradio demo.
• __[2023.11.29]__: Release the main model at a resolution of 256x256.
• __[2023.11.27]__: Launch the project page and update the arXiv preprint.

🧰 Models

|Model|Resolution|GPU Mem. & Inference Time (A100, ddim 50steps)|Checkpoint| |:---------|:---------|:--------|:--------| |DynamiCrafter1024|576x1024|18.3GB & 75s (perframe_ae=True)|Hugging Face| |DynamiCrafter512|320x512|12.8GB & 20s (perframe_ae=True)|Hugging Face| |DynamiCrafter256|256x256|11.9GB & 10s (perframe_ae=False)|Hugging Face| |DynamiCrafter512_interp|320x512|12.8GB & 20s (perframe_ae=True)|Hugging Face|

Currently, our DynamiCrafter can support generating videos of up to 16 frames with a resolution of 576x1024. The inference time can be reduced by using fewer DDIM steps.

GPU memory consumed on RTX 4090 reported by @noguchis in Twitter: 18.3GB (576x1024), 12.8GB (320x512), 11.9GB (256x256).

⚙️ Setup

Install Environment via Anaconda (Recommended)

conda create -n dynamicrafter python=3.8.5
conda activate dynamicrafter
pip install -r requirements.txt

💫 Inference

1. Command line

Image-to-Video Generation

1) Download pretrained models via Hugging Face, and put the model.ckpt with the required resolution in checkpoints/dynamicrafter_[1024|512|256]_v1/model.ckpt. 2) Run the commands based on your devices and needs in terminal.

# Run on a single GPU:
# Select the model based on required resolutions: i.e., 1024|512|320:
sh scripts/run.sh 1024
# Run on multiple GPUs for parallel inference:
sh scripts/run_mp.sh 1024

Generative Frame...