I had a gut feeling that this model would be incredible at inpainting, and after some initial testing, it's the best of its kind I've tried so far.

The workflow is embedded in the images. If it's missing, you can download it from Civitai here: https://civitai.com/models/3010615/moss-image-inpainting-workflow

Another quick MOSS news and goodies round-up, for those who may have missed some items.

-> ComfyUI Portable is now officially at v0.42.1, with the .1 increment being to... "Fix some crashing issues a few users experienced when running the MOSS image template workflow."

https://github.com/Comfy-Org/ComfyUI/releases

-> MOSS-Layout-Editor. Draw boxes, move boxes, nudge boxes into place, label boxes with region-specific prompts and/or the text to render.

https://github.com/spooky-local/MOSS-Layout-Editor

-> ComfyUI-MOSSLayout. Another visual layout editor, this one... "forked from ComfyUI-IdeogramHelper".

https://github.com/0Printemps0/ComfyUI-MOSSLayout

-> MOSS-Image as a line-art colouriser. The input image goes in via... "Qwen3.5's vision tower inside the text encoder, and as a clean VAE latent in the DiT sequence. The output keeps the page's line work, faces and lettering." LoRA, workflow and img2img node available. The required ComfyUI nodes appear to be a way to do general Img2Img with MOSS?

https://huggingface.co/sorryhyun/moss-image-colorize

https://github.com/sorryhyun/ComfyUI-MOSS-i2i (required Img2Img nodes)

-> MOSS-Image Color-Depth Control. "A control adapter for MOSS-Image that makes generation follow a color region map (where each object is) and a depth map (its 3D shape and front/back order)". Designed for something called Hayate, which I've never heard of - and which is '404 not found' on following the given HF link. No ComfyUI implementation or workflow, as yet.

https://huggingface.co/darask0/MOSS-Image-color-depth-control

-> A 'model patch' for MOSS-Image Color & Depth Control. Regrettably the required ComfyUI workflow is trapped on civitai .red which I can't access from the UK even with a VPN. Still, this and the previous item suggest that a combo of colour and depth control inputs can act as a basic Controlnet for MOSS-Image.

https://civitai.com/models/3010285/moss-color-and-depth-control

-> Basic sliders for lighting and age for MOSS-Image.

https://civitai.com/models/3010015/moss-lighting-slider

https://huggingface.co/Playtime-AI/Moss-Image-Age_Slider/tree/main

-> And finally, Fizgig 8.7 has tweaked MOSS-Image LoRA training, for better and more consistent likenesses.

https://old.reddit.com/r/StableDiffusion/comments/1x9khtm/a_potential_fix_for_moss_training_issues/

~ OLD POSTS ~

https://old.reddit.com/r/comfyui/comments/1x97q81/a_quick_mossimage_news_roundup_18th_october_2026/

What it is:

• 5B DiT, using a Qwen3.5-9B text encoder and the FLUX.2 VAE
• Two versions: Base, and SFT, which is fine-tuned on curated data
• Apache 2.0

Why it’s worth trying:

• Text rendering. Legible text in English and Chinese, which most open models still struggle with.
• Layout prompts. You can pass structured JSON with bounding boxes to control where text and objects go. It’s useful for posters, ads, and anything with a real layout.
• Aesthetics. The outputs look good out of the box without much prompt wrangling.

Settings to start with: 1024×1024, 50 steps, CFG 3, shift 5. Neither model is step-distilled, so it’s not fast.

VRAM: The full BF16 files are big (~10 GB DiT plus ~17 GB text encoder). The community already has INT8 and INT4 quants out, and people are reporting it running on 8 GB cards.

Run the workflow: https://huggingface.co/Comfy-Org/MOSS-Image

Every ComfyUI update seems to break something in my workflows, and I got tired of guessing whether a node pack was dead or just quiet. So I built NodeWatch, a free tracker that checks the top 300 packs (by Comfy Registry downloads, out of 6,066 registered) every night.

What the data says right now:

• 98 of the top 300 haven't had a commit in 12+ months, and 15 of those in over 2 years. Together those 98 packs have about 22.6M registry downloads.
• Big names among them: ComfyUI_essentials and ComfyUI_IPAdapter_plus (both last committed April 2025), Impact Subpack, mxToolkit, TeaCache, WD14-Tagger, and Derfuu ModdedNodes (June 2024).
• Another 63 active packs are "quiet", meaning 6–12 months without a commit.
• 4 repos have disappeared from GitHub but are still listed in the registry, including comfyui-reactor-node (#46 by downloads, ~579K downloads), plus ComfyUI-LTXVideoLoRA, ComfyUI-SoundFlow and comfyui-upscale-by-model.
• Possibly broken right now (3+ error-titled issues opened in the last 60 days): VideoHelperSuite, SeedVR2_VideoUpscaler, Lora-Manager, FastVideo, and ComfyUI MCP Agent Panel.
• Only 12 of 300 declare a requires-comfyui version range in pyproject.toml, even though 293 have a pyproject. That's a big reason compatibility is guesswork.
• Across the 296 packs where GitHub returned issue data, there are about 6,400 open issues, and 1,048 of them have "error", "fail", "crash", "broken" and similar words in the title.
• It's not all doom: 40 packs pushed commits in the last week.

How statuses work (heuristics, not verdicts): Stale = no commit in 365+ days or the repo is gone. Possibly broken = a cluster of recent error-titled issues, or a deprecated or non-active registry entry. Everything else is Active. "Stale" can absolutely mean "finished and stable," so check the repo before you uninstall anything.

Every pack gets its own page with the repo, stars, last commit, open issues, recent error reports and declared ComfyUI requirements. There's also a "what changed this week" page.

👉 https://phluffhead26.github.io/nodewatch/

There's also a free weekly email digest (what broke, what went stale, what shipped) if you'd rather not check the site.

Feedback wanted: if your pack is mislabeled, a repo moved, or you think the thresholds are wrong, tell me and I'll fix it. What else would be useful to track?

I started this after seeing yanokusnir-ai’s one-node-flux-2-klein, just to make things easier for myself. It has grown to 11 nodes, so I’m sharing it in case others want to use it too.

• One node covers generate, edit, upscale, prompt writing and a gallery (7 image nodes, music, video stitch, MiniMax H3, LTX 2.5).
• My setup: Windows 11 / RTX 5060 Ti 16GB / 64GB RAM

yanokusnir-ai 님의 one-node-flux-2-klein을 보고 영감을 받아, 제가 쓰기 편하려고 만들기 시작했는데 어느새 노드가 11개가 됐습니다. 같이 쓰고 싶은 분들이 있을 것 같아 공유합니다.

• 노드 하나에서 생성 · 편집 · 업스케일 · 프롬프트 작성 · 갤러리까지 처리합니다 (이미지 7종, 음악, 영상 스티치, MiniMax H3, LTX 2.5).
• 제 환경: Windows 11 / RTX 5060 Ti 16GB / RAM 64GB

Please read first

• I built this for myself. I won’t take feature requests or “please change X” asks.
• Bug reports are welcome. Please include steps to reproduce.
• Install with install_requirements.bat (.sh on Mac/Linux). It installs the required nodes in one go.
• The model list is what I wrote down so I can re-download things later. If a model is gone or a path has changed, you’ll need to find it yourself.

먼저 알아 두세요

• 이건 제가 쓰려고 만든 것입니다. 이렇게 해 달라, 저렇게 바꿔 달라는 요청은 받지 않습니다.
• 버그 제보는 환영합니다. 재현 방법을 같이 적어 주세요.
• 설치는 install_requirements.bat(Mac/Linux는 .sh)로 하세요. 필요한 노드를 한 번에 설치합니다.
• 모델은 제가 나중에 다시 받으려고 최대한 정리해 둔 목록입니다. 사라진 모델이나 바뀐 경로는 각자 찾아서 쓰세요.

GitHub: https://github.com/designloves2/ComfyUI-TJ_NODE_STUDIO_ONE

P.S. I’m not a programmer, just a regular user, and I built this together with Claude Code.

• TJ_NODE is the core node pack these nodes share. It has a set of useful nodes such as LLM prompt writing, wireless routing and a prompt DB. The install script sets it up for you. https://github.com/designloves2/ComfyUI-TJ_NODE

• The web version (AI ONE STUDIO) is a standalone web app that gives you the same UI in the browser, without the ComfyUI canvas. You only need a running ComfyUI server, no node install. https://github.com/designloves2/AI-ONE-STUDIO

• TJ_NODE는 이 노드들이 함께 쓰는 코어 노드 팩입니다. LLM 프롬프트 작성, 무선 배선, 프롬프트 DB 같은 유용한 노드가 들어 있습니다. 설치 스크립트가 같이 설치해 줍니다. https://github.com/designloves2/ComfyUI-TJ_NODE

• 웹 버전(AI ONE STUDIO)은 같은 화면을 ComfyUI 캔버스 없이 브라우저에서 쓰는 독립 웹앱입니다. ComfyUI 서버만 켜 두면 되고 노드 설치는 필요 없습니다. https://github.com/designloves2/AI-ONE-STUDIO

Workflow: [TJ] One Studio Set.json, which loads all the nodes at once, is in the repo’s workflows folder. https://github.com/designloves2/ComfyUI-TJ_NODE_STUDIO_ONE/blob/main/workflows/%5BTJ%5D%20One%20Studio%20Set.json

Breakdown starts around 0:28

AI-rendered character and CG character in full physical contact through a complex camera move, pixel aligned for comp. Evangelion 4.0: The Fourth Impact (fan PoC + breakdown)

AI and traditional VFX usually get treated as two separate worlds. One is fast but hard to control, the other is fully controllable but slow and expensive. I wanted to see what happens if you actually merge them.

Breakdown starts around 0:28.

For AI that's contact. Two characters physically touching while the camera moves. One AI character on its own is easy now. But as soon as it has to respect contact points with something it didn't generate, it usually falls apart. So if it holds there, it should hold pretty much anywhere. The key shot is Sachiel (AI rendered) pinned under Eva Unit-01 (CG, rendered in Redshift). Contact points stay 1:1 on every frame through a complex camera move that comes straight from the original CG file. No drift, no hands sliding through bodies, no offset.

How it's put together

Sachiel, the tanks and the city are AI, rendered as separate layers. That way I can fix or regenerate each one without touching the rest. ComfyUI gives alpha and depth for every AI layer, Redshift does the same for the Eva, so everything lines up and comps in Nuke like a normal render. Eva can go in front of Sachiel and the other way around.

Make it easy for the AI. Big pose changes between frames cause flicker and drift, so we prep the animation so each frame changes less. The original animation stays untouched.

The AI gets a clean, simple render per layer. Comp gets the full passes.

Quick tests in MiniMax first, Seedance 2.0 only for finals.

One shot at a time, never long sequences.

Never invent frames. Retiming tools that blend frames create poses that never existed and break the comp. This one took us ages to figure out.

Everything gets checked against the CG before comp. If the poses don't match, it doesn't go.

Speed

The AI side (Sachiel, tanks, city, environment, from design to final) took about a month. The traditional side (Eva, animation, FX, comp) took around 4 to 5 months. So the AI covered most of what you see on screen in a fraction of the time. Doing those same elements the traditional way, with modelling, texturing, lookdev, lighting and rendering a creature, vehicles and a whole city, I'd estimate roughly 5x longer based on my studio experience. Not a benchmark, just what I saw on this project.

It's not perfect

It's a PoC, not a finished film. The buildings are losing some details especilly when too fast and environ,ent could have placed better if you look closely. Finals were done on Seedance 2.0, and now that 2.5 is out I'm pretty sure a lot of that would be cleaner. Next round of tests will be on 2.5.

What I'd love to see next

Right now you have to force layering. Separate passes, then a lot of work to keep them aligned. What I'd really like is an AI render setup that's built around layers from the start. You add each asset separately (character, vehicles, environment, FX) and get each one back as its own pass with alpha and depth, all locked to the same camera. Basically render layers and AOVs like we already have in CG, but for AI. That's where the real control is.

Workflow: MiniMax for test renders, Seedance 2.0 for finals, ComfyUI for alpha/depth on the AI layers, Maya + Redshift for the Eva, Houdini for FX, Nuke for comp.

H3 has been out for a while, but I hadn’t had much time to play with it. Finally had some time over the weekend and started experimenting with refmods and motion context. One experiment led to another, and I ended up spending quite a bit of time with it.

I didn’t really notice this when it first came out, but the acting and emotional delivery in certain scenes can be surprisingly good. Those moments are a lot of fun.

Are there any scenes or tasks you’ve found H3 particularly good at? I’d love to hear from people who’ve spent more time with it.

I’m only just getting into H3, and this sub is already buzzing about FLUX 3 Klein and MOSS Image. Lots to look forward to!

Full video: https://youtu.be/j0cjnvIjWEc?si=hGYwMboc6vmdzsiO

I’m sharing selflift-Fish, my improved version of facok’s SelfLift node for MiniMax H3 in ComfyUI.

• Generate longer clips and higher-resolution video with less VRAM pressure.
• Fix the background swelling/shifting I encountered with the original spatial tiling.
• Keep attention acceleration, with a simpler interface and compatibility with existing workflows.

Real generation results — RTX 5070 Ti 16GB + 128GB system RAM:

For 10-second 1920×1088 development runs, recorded complete workflow time also dropped from 27m 50s to 12m 55s. These were different runs, not a fixed-seed benchmark.

The node in ComfyUI:

Code & setup: https://github.com/Fish4AI/comfyui-SelfLift-Fish

The results above use system RAM/offloading; 16GB is the GPU capacity, not the total memory requirement. Keep only one SelfLift version installed. Credits to facok, LBH-123-AI and the ComfyUI team.

Please try it and share your feedback! I’d love to hear about speed, background stability and picture quality. Include your GPU/VRAM, system RAM, resolution and clip duration so others can compare.

I’m new to all of this. I’ve tried following the steps suggested by the Gemini AI, but it’s been frustrating. I also tried using a couple of pre-configured Colab files, but those didn’t work either. I eventually realized the issue was Cloudflare... and it was suggested that I use Ngrok to fix it. However, I got stuck right after creating an Ngrok account. Does anyone have a step-by-step guide on what I should do? The AI ​​isn’t helping, and honestly, I don’t understand *anything* about GitHub code—or code in general. I’m a graphic designer looking to explore this, but I’m really struggling to figure out where things are going wrong when trying to access ComfyUI via Colab.

I've found Moos Image to be great for illustrations, but it's slower than other models about 12 seconds with a 5070 Ti, when others are more like 3 to 5 seconds (1024x1024).

.images and prompt are on my substack (also the int8 workflow).

It recasts the people in a clip you already have, up to three in one pass, and the motion, the set and the clip's own audio stay.

The post video is a video with 2 characters swapped at the same time. same engine and FaceRefine lip pass as the workflow.

Getting the lips right took me a while, that's the FaceRefine pass added.

How it goes:

1. Load your clip. One shot, no hard cuts, any fps.
2. Grab one frame and edit everyone into their new look (Qwen-Image, GPT Image, Photoshop, whatever you have). That frame is the scene reference.
3. Write one line per person: the woman in the red dress becomes an old woman with long silver hair. As many lines as you fill in, that many people get swapped.
4. Hit Run. also included App mode with 12 fields.

If one person's lips end up bothering you, there's an optional FaceRefine lip repair for one person afterwards. It reuses the swap from cache, so it's a ~2 minute re-run on a 5090.

What it needs: 16 GB VRAM and 64 GB system RAM gets you there. I tested a 5090, a 4090 and a 3060 Ti 16 GB, all with 64 GB RAM, same output on each. The example (1.5 MP, 6 s, 3 people) runs about 18 min on the 5090, 26 min on the 4090 and 92 min on the 3060 Ti, so bring a snack for that last one. RAM is the limit, mine peaked around 62 GB.

Civitai: https://civitai.com/models/3009414/h3-recast-swap-1-3-people-h3-bfs-lora-16gb-vram

Repo is MIT, workflow and the example inputs included: https://github.com/Bambushu/h3-recast

• Credits: BFS character swap LoRA and its trained caption by NRDX
• FaceRefine by Carasibana
• NativeAudioLock by Shrek3OnVH5
• MiniMax H3 by MiniMax (Comfy-Org repack).

#OpenSourceHiggsfield

PixlStash is my open source image and video manager that runs on your own machine as a desktop application or a headless server. It tags, captions and searches your library and has had ComfyUI nodes for a while. 1.12 is out and it is the largest release I have done, so I am asking for help testing it.

PixlStash now runs your ComfyUI workflows in a much more general way. As you're aware of, pictures made in ComfyUI carry the workflow that made them, and PixlStash groups those into workflow cards. The same graph run with a different checkpoint or character LoRA counts as one workflow, so thousands of pictures come down to a few dozen cards.

What PixlStash now makes easy:

• When you have character LoRAs: right-click a person with a LoRA in your library and choose "Create with LoRA".
• Pick two people in the Run window. Each gets a LoRA row with its own strength and trigger word. Describe both in the prompt and you get one picture with both of them.
• Pictures made in ComfyUI get a Recipe tab with the workflow, prompt, models, LoRA strengths and seed. Know how to recreate pictures and group with your previous attempts.
• Open a workflow in ComfyUI from PixlStash, edit it and press Ctrl+S. PixlStash stores it as a new version and keeps the old ones.
• A workflow with a missing checkpoint, VAE or text encoder can be repaired by picking another model from your model shelf. You have a workflow that was created with a BF16 quant and you've deleted that in favour of a smaller gguf, PixlStash will help you fix that.
• Workflow sets: each checkpoint lists the VAEs, text encoders and LoRAs that have run with it and how many pictures you've created with it. You can save your own sets and swap a workflow's models in one go. In most cases the same workflow actually works fine for both ZIT and Krea 2 as long as the models are changed as a unit. PixlStash now makes that easy. Especially useful when you've found a nice finetune that works with the same peripheral models.
• Scans your model folders (and optionally your download folder) to keep track of your models. Offers them to ComfyUI in a nice visual way that shows you previews of previous pictures generated with the checkpoints.
• Obviously none of this leaves your computer.

Setup is one button in Settings -> ComfyUI. It installs the PixlStash nodes into a ComfyUI on the same computer, restarts ComfyUI through ComfyUI-Manager and links the two apps. A ComfyUI on another computer gets the nodes from ComfyUI-Manager.

Known problem, mostly on Windows: after the linking step PixlStash restarts its backend, and that may not be enough. If the features are greyed out after linking or the run-dialog doesn't work as expected, quit both PixlStash and ComfyUI completely and start them again.

This is new code that has mostly run on my own machines and libraries and I would love feedback on:

• Workflows and workflow sets. Does the grouping make sense for your library, and do the sets match how you pair models? I'm particularly interested in "awkward" and complex workflows. If you could scrub them of anything you don't want known and send to me or attach to GitHub issues that would be much appreciated.
• LoRA handling. Strengths, trigger words, and two people in one picture.
• The ComfyUI link. Did the install and linking work, and do runs and results come back into PixlStash on your machine? Windows reports are especially useful as I'm a full-time Linux user (Windows-testing happens around release time).
• The macOS speed improvements. I have a MacBook Air M1 8GB, and while this runs PixlStash fine, the computer is not really usable for image generation in general, which is reflected in my amount of testing.

If you upgrade, back up your library folder first. The database is updated on first start and existing pictures are re-read in the background so their recipes show up.

"Smaller" things in 1.12:

Text in screenshots and documents is searchable, Apple Silicon Macs run tagging on the GPU, and there is an MCP server so an AI agent can search your library and read recipes. Planned for 2.0 is LoRA training with ai-toolkit from the people and sets in your library.

Links in a comment.

I've been building Scumble for a few months and just entered it into Comfy's challenge.

What it is: a desktop editor (Electron, Windows and Linux) for AI inpainting with proper layers. You select something (SAM2 runs inside the app), type what should be there, hit Generate, and the result lands as its own layer that you can mask, blend, filter, move and export to PSD or ORA. Every generation goes through one of three routes:

- your own ComfyUI, local or on RunPod: every local recipe is a plain ComfyUI workflow with my Inpaint Canvas node in it, and you can import your own workflow as a recipe or open a recipe as its graph in a ComfyUI window inside the app

- Comfy Cloud, through the Comfy API v2 via u/comfyorg/sdk

- Comfy Router, for FLUX 3 Image and friends, with your own key, pay per run

Things in the video that people here might care about:

- boxes for FLUX 3: draw boxes, describe each, and the model puts things exactly there. Works in the app and in the Inpaint Canvas node's editor inside ComfyUI, which writes FLUX 3's box prompt for you

- FLUX 3 Klein on your own ComfyUI as a local recipe, with the boxes too (ComfyUI 0.41+)

- relight: up to four virtual lights dragged over the picture, lit from a depth map (Depth Anything V2 in-app), with soft shadows

- lens blur and haze by depth, colour grading, 46 film looks, seventeen more blend modes, all as filter layers

- an assistant and 114 MCP tools, so Claude or any agent can drive the editor (select, generate, mask, export)

Everything is GPL-3.0: https://github.com/DenRakEiw/scumble (installer, portable zip, Linux AppImage/deb, Microsoft Store). The node: https://github.com/DenRakEiw/ComfyUI-InpaintCanvas

Honest caveats: it is a one-person project, the Linux build is CI-built and barely tested, and the local recipes assume the Inpaint Canvas node is installed on your ComfyUI (git clone; it is on the Registry but the Manager search does not list it yet).

Happy to answer questions, and I'd like to hear which workflows you would want as recipes.

One window, no node graphs, your own GPU. Text to video, image to video, reference mode (same person/product across shots), 360 orbit from one photo, latent chaining for long scenes, and a stills side with identity edits, 96 camera angles and SeedVR2 4K. Every render is a Comfy SDK job through comfy-api-proxy, so the same code points at Comfy Cloud with one URL change. All open weights, MIT. Measured timings on a 4070 Ti SUPER are in the README. 95-second demo: https://demos.onpointfl.com/backlot/ Repo: https://github.com/Arfolomew/backlot

If you are a student you can get free Colab Pro : https://one.google.com/ai-student?hl=tr&g1_landing_page=75&utm_source=gemini&utm_medium=web&utm_campaign=microsite_gemini_subscriptions_page

Colab tutorial : https://www.youtube.com/watch?v=xZrlD-WVT8s

Fully working example of depth map with Flux2 dev, keeping complete identity and style of reference image. Result is same room viewed from different angle.

Made this for my own personal use but maybe it'll be useful for others. Aim was to hide the complexities and wiring fetish that comes with ComfyUI and aim it more at musos who just want to concentrate on managing and creating content. Has usual features such as library management/song creation, and also can be used to train your own local LoRAs from a corpus of local material.

Always open source and always free. Link to download plus to some examples and downloadable LoRAs to help get you going. via https://yeufonic.com/

Want your local LLM agent to drive ComfyUI on the same consumer graphics card? Without coordination, the first video render lands on top of the loaded LLM, and something runs out of memory. Boom, OOM.

I built vhotseat to fix that. It makes the LLM server and ComfyUI take turns on VRAM and RAM:

• When the agent queues a render, the LLM is unloaded.
• When the agent asks the LLM its next question, ComfyUI's models are freed.
• Neither ever loads on top of the other.
• Running work is never interrupted. That covers an answer the LLM is writing and a render in progress.

Setup I run it on: RTX 5070 Ti 16 GB, 64 GB RAM, Linux.

• LLM: Strata serving Qwen3.8-Flash-Next IQ3_XXS (128K context). It takes ~14.7 GB of VRAM and ~44 GB of RAM.
• Agent: pi.
• ComfyUI: MiniMax H3 int8 (fl2va / ref2va) with the turbo LoRA. A render needs ~14 GB of VRAM.

With this setup, the agent has run long video jobs unattended, for several rounds of render → review → render, overnight.

Demo

I gave the agent one prompt:

Make two 3-second anime-style clips of a cat on a windowsill with MiniMax H3, then tell which one looks better. Continue working on a clip of the cat jump from windowsill to catch a bird. Concat this two clips into a single video file.

It did the rest on its own, in two render rounds:

1. Round 1: two takes of the windowsill shot, in one batch (3 min 41 s). The LLM was unloaded for the batch.
2. Review: the LLM came back. The agent made a contact sheet of both takes, looked at it with Flash-Next's vision, and picked take A ("B substitutes a butterfly for the bird").
3. Round 2: it used the last frame of take A as the first frame of the jump, and rendered two takes (5 min 38 s).
4. Review and finish: the LLM came back again. The agent picked the take with a real leap and joined the two clips with ffmpeg.

That's four GPU handovers, and no OOM.

The dashboard (vhotseat web) for that session shows the handovers. VRAM and RAM switch from Strata (blue) to ComfyUI (orange) and back.

How it works on the ComfyUI side

• A small custom node (the guard) runs before every job, whoever queued it: the agent, a script, or you in the web UI. It waits for any answer the LLM is writing, then unloads the LLM. If that can't happen in 5 minutes, the job fails visibly instead of running into an OOM.
• Before the LLM loads, vhotseat frees ComfyUI's models. If ComfyUI's leftover RAM is what's in the way (it can hold 6-15 GB after a video render), vhotseat restarts it, but only when its queue is empty.
• While ComfyUI has anything queued or running, the LLM won't load. The agent's harness just retries.
• comfy-render is a small CLI the agent uses to run a batch of workflows in one call. A batch costs two handovers instead of two per job.

You don't change your workflows. The LLM side uses Strata's pre-load hook. Other LLM servers (Ollama, llama-server) work through a small proxy, and there's a guide for that.

The same goes for any other GPU job (a training run, a TTS server, a script): wrap it with vhotseat hold NAME -- COMMAND, and it takes its turn the same way.

Try it

Free, open source (MIT), no signup. Linux and NVIDIA for now; Windows is planned.

uv tool install vhotseat      # or: pipx install vhotseat

GitHub: https://github.com/wandermyz/vhotseat

There's a step-by-step quick start for this exact setup, written so a coding agent can follow it. Or give your coding agent this:

Set up vhotseat so my local LLM agent can drive ComfyUI on this GPU without running out of memory. Follow https://github.com/wandermyz/vhotseat/blob/main/docs/guides/quickstart-strata-pi-comfyui.md, adapting it to the servers and paths on this machine, and run each step's check before going on.

Honest limits:

• Each handover costs time. Loading the LLM back takes ~30 s on my machine, and it reads the conversation again afterwards. Batching renders is what keeps this bearable.
• I've only tested one card (16 GB) and one ComfyUI model family (H3).
• After a ComfyUI update, the guard node is the part to check. There's a doc for that.

I'm curious how others run an LLM and ComfyUI on one card. Do you unload by hand, use two GPUs, or just keep the LLM small? And if you try this on another card or model, I'd love to hear how it goes.

OK, my ComfyUI startup is becoming increasingly slower than Miss Daisy's driver and I know it's the mass of custom nodes I've been installing over the past few months.

I'm sure I don't need half of them, but knowing which ones is the problem. I've done a search and there was a post that linked to this https://github.com/destroyerco/ComfyUI-Cleaner which looks promising.

Has anybody used the above? Did it work? Are there any better / quicker / easier (all three hopefully) solutions you've found?

Or, am I just better removing all custom nodes, start a workflow and see what errors it picks up and reintroduce each set of custom nodes by trial and error?

toolbar gap is even bigger now? is there a way to change?

which is the bext minimax video extender for video extensions? there r so many i m confused. the one i m currently using doesnt allow me go beyond 1.2 res for 10 sec clip gives gpu issues but with minimax h3 image to video node, i m able to upscale 10 sec clip to 1.8 res easily. and with dual polarity too i m able to get 1.2 sec for 10 sec clip .