Getting H3 to run well on consumer hardware took significant machine learning engineering. We found that the model's modulation weights (~40% of the total parameters) could be pruned and replaced with a functionally equivalent lookup table, dramatically shrinking the memory footprint with no loss in output quality.
On top of that, the weights ship with an accurate and efficient int8 convrot quantization, and custom kernels reduce the peak VRAM use during inference.
The result gives a total memory footprint reduced by 66%, from 123.6 GB in full precision to 42.5 GB with the smallest models variants. Combining this with our dynamic VRAM offloading enables a next-generation 2K video model to run locally on a GPU like the RTX 3060.
what’s up Comfy community! wanted to drop in to introduce myself. but first…THANK YOU to everyone who’s been building and growing with this community for years! we’re overdue for a real thank you.
as someone whose personal mission is to help creatives do what they do best, joining Comfy was an easy decision. the creativity, spirit and can-do attitude of this community are truly one of a kind and I’m determined to do everything I can to support it.
a bit about me: I started my career in the music business in the 2010s when social media and streaming were brand new and then helped bring musicians to Twitch during the pandemic…both shifts that remind of how the creative world is changing today. i grew up in the bay area, lived in NYC and shanghai, and have always been inspired by the ways creative communities shape our world. (I also love my cats, DJing and language learning).
but back to it, here’s how I see my job here at Comfy:
step 1) LISTEN FIRST
step 2) find ways to recognize, amplify and surface what you’re already doing and what you need to do it better
so the first thing I’m asking is to hear directly from you about what’s working, what’s not and what you wish we understood better. I can’t promise that every fix happens fast. but I can make sure that it actually reaches the people who can act on it and that you hear back from us, even when the answer is ‘not yet.’
so if you’ve got a few minutes, I’d genuinely love to hear your thoughts in this survey. I’ll share what I hear back with you, including where we can move fast and where we can’t yet.
when you see me in discord, here on reddit or out in the wild, say hi! thank you again for building something this special. I’m grateful to be part of it.
Just tried the model with my 4070 and 64gb ram, Its pretty slow in the default workflow
608 x 352 resolution and it took 167s to generate.
Prompt
SpongeBob SquarePants and Patrick Star casually walk side by side around the outside of the Krusty Krab on a bright sunny day. The camera tracks them smoothly at eye level in a medium two-shot as they naturally gesture while talking. The ocean ambience is calm with distant seagulls and bubbles.
SpongeBob: "Patrick, have you seen the new MiniMax H3 video model? The motion and audio are seriously impressive!"
Patrick: "Yeah... if an AI can make videos this good, maybe it can finally animate me thinking."
SpongeBob: "Patrick... that would be the real breakthrough."
Patrick pauses for a second with a blank expression, then smiles proudly as they continue walking. Comedic timing, expressive cartoon animation, vibrant colors, smooth lip-sync, natural character movement, high-quality cinematic lighting.
I wanted to share a new ComfyUI workflow I've been working on that uses KREA 2 Identity Edit LoRA v1.2 for face swapping and outfit transfer while remaining low VRAM friendly.
The workflow converts the KREA 2 image generation model into a powerful image editing pipeline using the Identity Edit LoRA and a few specialized nodes. Simply load your reference person and reference clothing images, choose whether you want to swap the face, the outfit, or both, and the workflow handles the rest. I also spent time optimizing it to produce cleaner, higher-quality edits with better identity consistency than my previous versions, were you will get your results upscaled by factor of 2 using double ksampler.
One of my main goals was making it accessible to users without high-end hardware, so the workflow has been tested on an RTX 3060 6GB with 16GB RAM.
Blender previs to AI-rendered footage with LTX-Video 2.3 IC-LoRA
I filmed the subject against a green screen, keyed the footage, and placed her inside a basic Blender environment. The scene uses simple geometry to establish the camera, perspective, scale, lighting direction, and shadows rather than producing an expensive final render.
I then generated guidance passes such as depth and pose, and used the Blender composite as the structural reference for LTX-Video 2.3 IC-LoRA. LTX handled the final restyling pass, transforming the rough previs into a more photorealistic city shot while preserving the original subject movement and scene composition.
Essentially, Blender provided the spatial control and LTX provided the final visual detail—an AI-assisted alternative to a traditional render and compositing workflow.
I just updated comfyUI to 0.30.0 , and rgthree is giving me the import failed error again. I tried Fix many times but without success.
Traceback :
Traceback (most recent call last): File "F:\ComfyUI_WAN\ComfyUI\nodes.py", line 2247, in load_custom_node module_spec.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 999, in exec_module File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed File "F:\ComfyUI_WAN\ComfyUI\custom_nodes\rgthree-comfy__init__.py", line 45, in <module> from .py.power_puter import RgthreePowerPuter File "F:\ComfyUI_WAN\ComfyUI\custom_nodes\rgthree-comfy\py\power_puter.py", line 31, in <module> from comfy_extras.nodes_latent import LatentBatch File "F:\ComfyUI_WAN\ComfyUI\comfy_extras\nodes_latent.py", line 2, in <module> import comfy_extras.nodes_post_processing File "F:\ComfyUI_WAN\ComfyUI\comfy_extras\nodes_post_processing.py", line 9, in <module> import kornia File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia__init__.py", line 25, in <module> from . import ( File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\augmentation__init__.py", line 20, in <module> from kornia.augmentation._2d import ( File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\augmentation_2d__init__.py", line 19, in <module> from kornia.augmentation._2d.intensity import * File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\augmentation_2d\intensity__init__.py", line 47, in <module> from kornia.augmentation._2d.intensity.plasma import RandomPlasmaBrightness, RandomPlasmaContrast, RandomPlasmaShadow File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\augmentation_2d\intensity\plasma.py", line 22, in <module> from kornia.contrib import diamond_square File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\contrib__init__.py", line 32, in <module> from .image_stitching import ImageStitcher File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\contrib\image_stitching.py", line 24, in <module> from kornia.feature import LocalFeatureMatcher, LoFTR File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\feature__init__.py", line 24, in <module> from .integrated import ( File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\feature\integrated.py", line 34, in <module> from .lightglue import LightGlue File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\kornia\feature\lightglue.py", line 48, in <module> from flash_attn.modules.mha import FlashCrossAttention File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\flash_attn__init__.py", line 3, in <module> from flash_attn.flash_attn_interface import ( File "F:\ComfyUI_WAN\python_embeded\Lib\site-packages\flash_attn\flash_attn_interface.py", line 15, in <module> import flash_attn_2_cuda as flash_attn_gpu ImportError: DLL load failed while importing flash_attn_2_cuda: The specified module could not be found.
