Train and block edit and save LoRAs directly inside ComfyUI for Z-image, Flux Klein, SDXL, Flux, WAN 2.2, SD 1.5
A Collection of Google Colab Notebooks for various projects
A ComfyUI node pack that implements FreeLong (NeurIPS 2024) spectral blending for Wan 2.2 video generation
LoRA Pilot is an ultimate docker image for all Stable Diffusion LoRA trainers. Includes kohya_ss, diffusion pipes and TensorBoard for trainings and ComfyUI and InvokeAI for validation. Features shared models, modules, custom integrations and automatization scripts.
A ComfyUI Workflow for low vram users
This allows you to utilize Google Colab (or other notebooks) to run wan2.2 image to video
ComfyUI node for WAN I2V control with built-in person/face mask generation using MediaPipe
A professional node pack by **Magos Digital Studio** for DWPose & NLF skeleton editor, retargeter, and renderer for ComfyUI.
Non-linear video editing and generation based on Comfy backend.
Timeline-based video editor for ComfyUI with scenes, project assets, gallery, images as keyframes, render staging, and takes for iterative long-form generation.
Wan2.2-Fast is an optimized, high-performance image-to-video (I2V) generation suite powered by the Wan-AI/Wan2.2-I2V-A14B-Diffusers model.
RTX 5090 Laptop (24GB) + ComfyUI: 12 model lines / 21 measured configs — MiniMax H3, Wan 2.2, Qwen-Image 2512, HunyuanVideo 1.5, FLUX.2 Klein, Z-Image-Turbo, LTX-Video, ACE-Step 1.5, YuE2, Stable Audio 3, Hunyuan3D 2.1, ERNIE-Image. A controlled A/B proves NVFP4 beats INT8. Bilingual 中文/English, licence-verified.
Training-free sparse attention for video generation and world models.
Docker service stack for a comprehensive local ai experience, running on a normal home hardware setup with single GPU. Project-Status: Working, Backlog.
Wan 2.2 Image-to-Video inference pipeline deployed on RunPod Serverless using a Docker-based GPU setup.
Contains the notebooks and workflows configured to run inference from Wan 2.2 Animate with ComfyUI on Kaggle T4 GPUs smoothly
ComfyUI_Wan22Blockswap tries to uzse advanced VRAM optimization techniques for WAN 2.1/2.2 models.
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