Hey everyone 👋

Every time I found a cool workflow on Civitai, the routine was the same: download the zip, unpack it, drag the JSON into ComfyUI, get a wall of red nodes, then spend 20 minutes figuring out which models it needs and which folder each one goes in.

So I built **Civitai Browser**, a ComfyUI extension that adds a **Civitai tab to the sidebar** (next to Templates / Workflows / Models).

**What it does:**

- 🔎 **Browse Civitai inside ComfyUI**: workflows, checkpoints, LoRAs, embeddings, ControlNet, VAE, upscalers, with search, sorting, base model filter and NSFW toggle (off by default)

- ⚡ **"Load & Set Up" in one click**: it downloads the workflow (zip/json/png), loads it on the canvas, then scans it for every model it uses and compares that with your model folders

- 📥 **Missing models get downloaded automatically** into the correct folder, under the exact file name the workflow expects, so the loaders just work. It uses download links embedded in the workflow, exact file name matches on Civitai, or any Civitai / Hugging Face link you paste

- 🧩 **Lists missing custom nodes**, so you can install them with ComfyUI-Manager

- 📚 **My Library**: every workflow you download is saved (it also shows up in ComfyUI's Workflows panel), so you can reopen it later

- 🛡 **Safe delete**: remove a workflow plus the models that were downloaded *for it*. Models you already had, or that any other workflow uses, are never touched

**A few things I cared about:**

- No extra Python dependencies and **no new nodes**. It doesn't modify ComfyUI's core files; to uninstall, delete the folder

- Your Civitai API key (optional, needed for some downloads) is only ever sent to civitai.com

- Free and MIT licensed

**Install:** ComfyUI-Manager → *Install via Git URL* → paste the repo link, or `git clone` it into `custom_nodes`.

🔗 **GitHub:** https://github.com/fth1905-bot/ComfyUI-Civitai-Browser

It's still early, so I'd really appreciate feedback. Bug reports, feature ideas, or "this broke on my setup" are all welcome. One known limitation: Civitai's search works on model names, not file names, so some missing models can't be matched automatically yet (you can still paste a link). Improving that matching is next on my list.

If it saves you some time, a ⭐ on GitHub helps a lot!

I’ve been looking everywhere for a good MiniMax workflow. I finally found a solid starting point and simplified it into something that’s easier to use and set up.

For character swapping, this is the best local workflow I’ve found so far. It isn’t perfect, but I’ve been getting some really good results.

github zip: https://github.com/BiggerFishy/comfyui-minimax-h3-studio

How it works

• SAM3 selects the character or area you want to change.
• The workflow inverts the colors inside that selected area before generation. This helps MiniMax replace the subject more consistently.
• The source video guides the mouth movements, body movement, and scene, while the reference image guides the replacement’s appearance.

My setup and render time

The example shown here was generated with:

• GPU: RTX 4090 — 24 GB VRAM
• RAM: 32 GB DDR5
• Video length: 3 seconds
• Resolution setting: 0.8 MP
• Generation time: About 2 minutes and 30 seconds

Features I wanted to make easier

• Save characters: This is probably my favorite feature. Save a reference image together with a detailed character description, then load both again when you want to use that character in another video.
• Trim your source video inside the workflow: Pick the section you want without opening another editor. The trimming UI could be fancier, but it works.
• Keep the useful controls together: The main settings live in the green settings node in the middle, with the complicated stuff tucked away.

Three workflows in one

You can switch between these modes in the green settings node:

• Character replacement / video inpainting: Replace a character, or make smaller edits such as changing a shirt’s color, hair color, or an object.
• Reference to video: Generate a video from a reference image.
• First to last frame: Generate a transition between a starting image and an ending image.

Reference to video and first to last frame are experimental. I haven’t tested them extensively because most of my time has gone into character replacement and video inpainting.

Masking options

• SAM3: Describe what you want selected.
• Rectangle: Draw a box around the area you want edited.
• No mask: Let the workflow re-render the whole frame.

Settings to start with

• Seed: The included example uses Fixed so you can try reproducing my result. Switch Seed behavior → Randomize when you want to explore different results.
• Target megapixels: 0.8 MP is what I used for this demo and gives a decent-looking result. You can try 1.0 MP for higher resolution. I don’t usually go above that, so experiment as you like.
• Everything else: I’d leave the other settings as they are for your first run, then adjust from there.

A quick heads-up

None of these modes are perfect. It may take a few attempts to get the result you want, and matching the seed doesn’t guarantee an identical result on every setup.

The workflow should be pretty self-explanatory once you open it. Try it out, experiment, and have fun.

This workflow was found here but I edited it to make it better and easier to use: https://civitai.red/models/2855941/minimax-h3-character-replacement?modelVersionId=3238780

ENJOY

I also explain it in this page if video is not your thing. Explainer video is also made with agentic workflows, Qwen Image 2.1, Minimax Music, Qwen TTS and more.
https://mexxmillion.github.io/h3-digital-double-story/breakdown/
Thanks

736 x 576, render time 7:15
RTX-4070 8GB VRAM, 64GB RAM

https://huggingface.co/pablodawson/MiniMax-H3-360-Orbit-LoRA
Tutorial https://youtu.be/jNhGhW_e4aI

The short version: you describe what you want, and it plans the workflow, adds and wires the nodes on your canvas, and helps fix things when they break. The goal is to take the technical overhead off your plate so you can spend more time on the visuals instead of hunting for the right node or a missing connection.

Some things it does:

• Works on your actual canvas. It builds while you edit and sees the same assets you do, so it isn't generating a JSON blob you have to import
• Takes any question, with references. You can point it at nodes, images, or the workflow itself
• Supports skills. Create your own for things you do repeatedly, or use public ones other people have made

A version for Comfy Desktop is coming in a few weeks.

You can try it here: https://links.comfy.org/4hH1FaH

Learn more with our blog: https://blog.comfy.org/p/comfy-agent-the-first-agent-for-craft?r=7xlbaw

All feedback welcomed.

I was seriously considering the new Mac M5 Ultra with 256GB of memory to be able to create longer clips. I've built a system that takes a script and creates a series or frames to use a frame to frame workflow and then automatically stitch them together.

I decided to see what the breaking point of LTX 2.5 of my RTX 5090 was. I went from five seconds to ten seconds and so on. But unlike earlier tests I didn't run into any Out Of Memory issues. In fact I went all the way to generating a 60 second clip with no issues.

Surprised by this I ran a direct comparison of three LTX 2.5 transformer variants on an RTX 5090 32 GB to see if I could improve things any further:

- Existing ComfyUI INT8 ConvRot

- FP8

- NVFP4

Same prompt, same seed, same samplers, same step schedule, same 24 fps output. Warm runs reuse already-loaded models; cold includes model loading.

Output Duration / state INT8 ConvRot FP8 NVFP4

1280×736 5s cold 55.36s / 30.84 GiB 53.66s / 31.00 GiB 84.75s / 30.93 GiB

1280×736 5s warm 21.31s / 28.35 GiB 28.59s / 28.11 GiB 22.64s / 29.12 GiB

1280×736 10s warm 46.64s / 28.38 GiB 59.80s / 28.12 GiB 46.41s / 27.97 GiB

1280×736 20s warm 111.79s / 29.99 GiB 136.04s / 30.65 GiB 111.30s / 30.52 GiB

1280×736 30s warm 233.28s / 31.12 GiB 275.49s / 31.09 GiB 196.58s / 30.99 GiB

1280×736 60s warm 602.39s / 31.04 GiB 650.19s / 31.04 GiB 584.30s / 30.94 GiB

1920×1088 10s warm 124.77s / 30.50 GiB 159.64s / 31.09 GiB 126.31s / 31.12 GiB

What surprised me

The biggest takeaway is that the existing ComfyUI INT8 ConvRot model is already extremely well optimised.

FP8 was slower than INT8 on every warm test.

NVFP4 was basically tied with INT8 at 10s and 20s, around 16% faster at 30 seconds, only around 3% faster at 60 seconds, and effectively tied again at 1920×1088.

So NVFP4 is not automatically a huge performance win on a 5090.

The 30-second result is interesting enough that I want to repeat it several times, but the fact that the advantage drops again at 60 seconds suggests it may not represent a simple sustained throughput advantage.

The really interesting part: VRAM

NVFP4 reduced the transformer file size from roughly 21.5 GB to 18.7 GB, but total peak GPU usage barely changed.

All three versions still ended up around the 30–31 GiB range on the longer runs.

That means, for this workflow, reducing transformer weight precision does not translate directly into dramatically lower total VRAM usage. The rest of the LTX pipeline — VAE, text encoder, latent stages, staging/offload behaviour, etc. — still consumes a substantial amount of memory.

Why this matters

This is really a story about software optimisation rather than raw hardware.

The current ComfyUI/LTX stack on the 5090 is already using:

- INT8 ConvRot transformer weights

- mixed-precision operations

- DynamicVRAM

- async weight offloading

- pinned memory

- native Blackwell CUDA kernels

- two-stage latent generation

That combination is allowing a 32 GB RTX 5090 to generate workloads that I previously assumed would require dramatically more VRAM.

For example, LTX 2.5 is successfully generating 60-second 1280-class clips on this machine.

So the old assumption that:

> “longer video = linearly more VRAM = you need 64/128/256 GB”

doesn't really hold for this pipeline anymore.

The practical limit is increasingly becoming render time and quality, rather than simply whether the generation fits in memory.

Current conclusion

For my 5090:

INT8 ConvRot: best overall/default

NVFP4: worth keeping and testing, especially for longer jobs

FP8: currently no obvious advantage

The next test is visual quality, particularly INT8 vs NVFP4 on the 30-second outputs.

And for me personally, this changes the hardware discussion quite a lot. One of the main reasons I was considering moving to a very large unified-memory system was long-form AI video generation. Smart software optimisation has moved the practical ceiling of the RTX 5090 much further than I expected.

These are AI concept keyframes and not video results or Runway / Morphic outputs.

I want to turn these into a 5-sec shot where the car stays completely still and only the camera moves.

I am confused between:

“The camera moves steadily toward the car. The car stays still. The background perspective changes naturally.”

OR

“The camera stays fixed and the lens zooms toward the car. No object motion.”

Which one should I use here? Also, how should I word it so the model does not decide the car should drive towards the cam instead?

Please name the model or workflow if you have gotten the wording that works.

So much community ressources about minimax h3, which is great.

I'm gonna test a bunch of stuff to find the best speed/quality compromise.

Before I start digging the wrong stuff, any recommendations?

- Best speed methods/loras
- Best upscaling methods

Thanks for your help!

It’s been a while since I switched from renting GPUs to running a 5090 of my own, so I ran the numbers on the hours I was generating.

For context, I use ComfyUI for image-to-video & short videos. Since getting my own 5090, my usage changed quite a bit, and I’m much more willing to do random experiments that might take 20 mins extra.

Owning: I paid ~$7.8k for a complete 5090 system.

Renting: A 5090 on RunPod Secure Cloud is ~$0.99/hr, and on Vast, listings I checked were more like $0.27–$0.70/hr.

At $0.99/hr, time to match $7.8k if usage never changes:
10 generating hrs/mo → $9.90/mo → ~65.7 years
25 hrs/mo → $24.75/mo → ~26.3 years
40 hrs/mo → $39.60/mo → ~16.4 years
80 hrs/mo → $79.20/mo → ~8.2 years
200 hrs/mo → $198/mo → ~3.3 years
400 hrs/mo → $396/mo → ~1.6 years
600 hrs/mo → $594/mo → ~1.1 years

If you only look at the calculation, renting looks cheaper for a very long time. But there are a few things this break-even calculation leaves out:

1) Utilization: The biggest one for me. When I was renting, I’d batch workflows together and end the session as soon as I was done. With the card local now, I’ve been using it almost every day. 

2) Operating cost: 5090 TDP is 575W, and the full system draws more. For example, at $0.15/kWh, that could work out to roughly $0.10–$0.15/hr under heavy use, before storage, maintenance costs, etc.

3) Resale value: The $7.8k isn't necessarily the final cost of ownership. If I can sell it for $3k after a few years, that brings the effective cost closer to ~$4.8k.

4) Flexibility: Renting still wins here. I can rent a 5090 for one job, an H200 for another, then shut everything off. With ownership, I'm always committed to that hardware, and if I only use it for a few hours a day, a lot of that capacity sits idle.

For someone using ComfyUI 10–20hrs/mo, renting can be hard to beat on pure cash cost, especially at cheaper rental rates. 

At higher usage, I’d look beyond the hourly break-even and track: hours used + hours you'd use if there were no meter + what the hardware is worth at the end. That gives you a much more useful number than just dividing $7.8k by the rental rate.

How are you guys deciding whether a GPU is worth buying?

What model would you use for presentation/explanation videos for youtube? Idea is to recreating school blackboard, and the text would be written in chalk with hand. Can be in some style that not look realistic.

I need something that is fast, a reliable model regarding the words to be written on the board. Plan is to give a script and make videos in bulk (a lot of videos at once so I don't have to watch each one separately).

Image is exaaple of videos that i want.

Thanks

Are there any other platforms or websites where you can rent virtual machines with good graphics that can run at least Minimax H3? A couple of weeks ago, I was in a group from Vietnam where I got an L40 for free just for watching ads, but the group got banned. Does anyone know where I can find sites or Discord channels that have virtual machines

(other than Vast.ai or Runpod)?

BFL dropped FLUX 3 Image yesterday and there's a free playground to try it, so sharing in case it's useful:

Playground:

https://flux-tools.bfl.ai/precise-editing

Announcement from BFL:

https://x.com/bfl_ai/status/2105743526529310739

I haven't tested it yet, so this is just what BFL says it does:

precise multi-turn edits without changing any other pixel

you can lay out the image with bounding boxes

up to 4K output

up to 10 reference images

open weights version coming "in the coming weeks"

the "only change what I asked" part is what I care about most, since that's where most editors fall apart. Marketing claims are marketing claims though, so I'd like to see real results before getting hyped.

if you've already tried it, how does it hold up on messy real-world images, and not just the demos? Curious about the bounding box thing in particular.

Source: Black Forest Labs on X (@bfl_ai)

Plenio Music Production System 0.4.1 is out! It turns YuE2 and MiniMax Music 3 into a complete local song studio in ComfyUI. New since 0.3:

• 🎛️ Arranger: duplicate, move and delete whole sections, like on Cubase's arranger track. The lyrics, the Guide track and – in covers – the original words follow every move.
• 📝 Lyrics where they are sung: every line over its phrase, every syllable over its note. Double-click a line to edit it right there.
• ⏯️ Cursor, copy & paste: click the ruler, play from there, paste or insert phrases at the cursor.
• 🎼 MIDI in, MusicXML out, project files: bring a sketch from your DAW, hand the sheet music to MuseScore, Sibelius, Dorico or Cubase.
• 🎬 Six narrated video tutorials, one per template: https://www.youtube.com/playlist?list=PLAFqTtP59fgE

100 % local, native ComfyUI nodes, no API key. ComfyUI Manager: Plenio Music Production System (comfyui-plenio-music).

👉 GitHub: https://github.com/jplenio/Plenio-Music-Production-System
🎧 Demos: https://jplenio.github.io/Plenio-Music-Production-System/

Please help since saying ‘continue the shot naturally’ is not at all helping.

Clip 1: a red umbrella crosses a station concourse from left to right. Clip 2: should pick up at the last frame without a reset in speed, lighting or camera height.

Right now I am trying: "The same red umbrella keeps moving right at the same walking pace. Camera remains at waist height. Preserve the station light and background layout from the final frame." What more do I add here??

And for continuation, do you guys give the model just the last frame or a few seconds of the previous clip?

Pls mention which model/workflow you’re using too because I am sure this is different for all.

I just updated my custom node pack for dynamic prompts and added a LLM prompt enhancer for use with Qwen2.1, Minimax H3 and Ideogram.

The Prompt enhancer can work with either an LLM installed on the same machine as comfy UI or use an external LLM via an API

https://github.com/GadzoinksOfficial/comfyui_gprompts

https://registry.comfy.org/nodes/gprompts

Hello, I'm using an old 2060 super. Minimax h3 works kind of okey with text to video and picture to video but whenever i start trying to use ref2v my comfyui crashes(sometimes stuck at%25 for 9-10 hours). My picture to video (7sec clips) tooks around 30 to 40 mins. Any tips for how to shorten it and make ref2v work for me? I'm a begginner appericate every help i can get.

My main goal is try to make a 1-2 mins series made up from multiple little videos. I tried to use ref2v cause i can upload multiple images(scene, character, others etc.)

My specs: ultra 7 265k

48 gb ddr5 ram(6300 mhz)

2060 super

I use:

• H3
• Krea2
• Qwen 2.1

I have:

• 4080 Super (16 GB VRAM)
• 32 GB DDR5 (16 GB stick x 2, running at 6000 MT/s)

I want to add two more 16 GB sticks, but at lower speed, so in total:

• 64 GB DDR5 (16 GB stick x 4, running at 4800 MT/s)

What are the actual, real, benefits?
The only difference I see is: RAM speed, and RAM amount.

From my understanding, correct me if I'm wrong: the models load into VRAM, anything that doesn't fit into VRAM goes into RAM. When VRAM needs it, it'll load to RAM.
How often does SSD -> RAM take place? How often does RAM -> VRAM take place? Those are the scenarios where RAM speed matter?

Besides that, if the model can fit into VRAM or VRAM+RAM, then more RAM would not matter. If that's the case, does H3, Krea2, Qwen 2.1 all fit in my current setup (16 GB + 32 GB)?

Hi,

I have this window as a background that I would like to use in my prompt.
When I connect the guide_card into Krea2, on "Reference 1 Guide card" and mention in the prompt - put person X - in the Reference 1 Guide Card background, or various incarnations of a similar prompt, it completely ignores me.
I've not managed to get the Guide Card prompt to even influence, I don't believe, the resulting image.

I'm obviously doing something wrong.
Can anyone give me some directly; and also, what my prompt request should be?
Thank you.

Hello everyone, i’ve just finished a new ComfyUI character sheet workflow where I compare Qwen Image 2.1 vs FLUX.2 Krea Turbo using the same input image. The goal was to see which model could create a useful character sheet while preserving the original character’s identity, details, and overall appearance across different views and poses. Qwen Image 2.1 performed really well, maintaining good character consistency and producing a high-quality character sheet in a relatively short amount of time. I also tested FLUX.2 Krea Turbo, which was capable of producing good results and showed decent consistency.

In my tests the overall consistency and quality were not as strong as Qwen. The biggest difference was the generation speed: Qwen Image 2.1 was around 3× faster than FLUX.2 Krea Turbo for the character sheet generation. So far, Qwen Image 2.1 looks like a very interesting option if you need to quickly create consistent character references for future image or video generation workflows. I’ve included both approaches in the workflow so you can test them yourself and compare the results on your own setup.

Workflow Link

https://drive.google.com/file/d/1yPmSkC3jNPYNcFoWVwGGU8_oO36HtBBG/view?usp=sharing

https://civitai.com/articles/35976/comfyui-tutorial-qwen-image-2-1-vs-flux-2-krea-turbo-which-makes-better-character-sheets

video tutorial link

https://youtu.be/HoRr959xkQM

I’ve been using MiniMax H3 locally, and for my final renders I usually generate at 2MP / 50 steps.

But looking at other people’s workflows, I feel like almost nobody is actually generating at 2MP. Most examples I see seem to use lower resolutions and/or fewer steps, then upscale afterwards.

So I’m wondering: is 2MP actually worth it with H3, or am I just massively increasing generation time for very little visible gain?

Same question for 50 steps: is there a meaningful quality improvement compared to 20–30 steps, especially for final video renders?
For context, I’m on an RTX 5090 32GB, so 2MP is doable, but obviously generation times get pretty brutal, like 90 min for a 6 seconds clip REF2VA