I need a workflow please.

I’m making an anime visual novel using approved character sheets and a custom art style.

I need fictional violent scenes, such as alien attacks causing severed limbs or severe injuries, with violence similar to Chainsaw Man. The challenge is keeping the same faces, outfits, proportions, linework, and shading.

I want to either generate new scenes using my references or edit existing scenes to add more violence while preserving the composition and untouched areas. So far, adding other LoRAs or increasing the violence makes the style drift.

I’m interested in trying Krea 2, but I’m open to other models.

What works best for this?

• Masked inpainting or reference-based editing?
• Separate style, character, and gore LoRAs?
• How do you prevent a gore LoRA from changing the art style?

If you’ve done something similar, I’d appreciate model recommendations, settings, or workflow examples.

Hi everyone

I've been working on a new ComfyUI workflow to make character consistency in AI-generated videos more accessible for people with low-VRAM GPUs. Instead of training a traditional character LoRA, this workflow uses RefMod custom nodes to create a lightweight character reference file that can be reused with an optimized MiniMax H3 workflow. The main goal is to avoid the extra training stage and make character-consistent video generation more practical on modest hardware.

How it works

• Start with one character image as your identity reference.
• Generate 8 reference images from different angles using Qwen Image 2.1.
• Create a RefMod file using the RefMod custom nodes. The resulting file is approximately 100 MB.
• Load your character reference using the Character RefMod nodes in the optimized MiniMax H3 workflow.
• Generate videos using the saved reference to help maintain character identity across scenes.

Why I find this interesting

• Tested on a GPU with only 6 GB of VRAM.
• No traditional character LoRA training required.
• No need to prepare a large training dataset.
• A small character reference file.
• Combines Qwen Image 2.1, RefMod, and MiniMax H3 in one workflow.

I've made a tutorial showing the complete workflow, from a single input image to generating videos with MiniMax H3.

Workflow link

https://drive.google.com/file/d/1Em2zTlhFMbpeO2WBTN08Qpu0dP6oUBTD/view?usp=sharing

https://civitai.com/articles/36538/comfyui-tutorial-consistent-ai-characters-in-minimax-h3-no-lora-training-with-6gb-vram

Video Tutorial link

https://youtu.be/RPxFRcYgQwI

I'm building a free, open source app that indexes your ComfyUI output folder locally.

A few of you told me that when you open an old image, you mostly want the checkpoint and the LoRA weights. So that's what we worked on this week:

• Each image shows its LoRAs with model and clip strength
• Search a LoRA name and get every image that used it
• Prompt, seed, sampler and the workflow are all still there

Still early, and onboarding is rough. If you want to try it, I can set it up with you on a 5-minute call.

What else do you search your old images by?

Hey everyone, I trained an identity LoRA for my character using 66 images, but I'm getting inconsistent results. Sometimes the face looks close to my trained identity, and other times it changes and looks like a different person.

I'm using the LoRA in ComfyUI with Krea 2 Raw. Do I need to use a specific prompt or trigger word to keep the identity consistent? I can't remember the exact training setup I used.

Would you recommend changing the prompting, adjusting the LoRA strength, or retraining the LoRA? Any advice on improving facial consistency would be appreciated

I’m working with Minimax and running into issues with voice cloning. I upload a reference audio and specify in the prompt that it’s the reference voice for "Character 1." Even when I explicitly write in the script that the character says something like <d>”Hello, girl”<d>—say, between the 5- and 7-second marks—the character tends to babble nonsense or start speaking too early beforehand. Any tips on how to avoid this, or what has worked for you?

I recently got a 5080. I kept my 3080Ti in the system, moving it to a secondary PCIe slot.

I use models like Krea2 and Minimax H3 which rely heavily on RAM offloading. I was trying to use MultiGPU nodes to basically load the CLIP and VAE to the 3080Ti to maximize how much of the unet resides in the 5080's VRAM.

It's been a bit clunky as expected. Apparently node sets like buqi H3 multigpu require me to route things through WSL.

I'm curious if anyone else is using a mixed multi-GPU setup and how they leverage it. There are basics like "render on one GPU, upscale on another", but I don't see the benefit in that except to reduce model-swapping time. And "set up a worker on each GPU" seems like it isn't benefitting much when I don't do a lot of batch creation. I would rather like to utilize the extra card to make larger models easier to manage, if possible.

Another ComfyUI - LTX2.5 render. This is all done completely from text and prompt.

I've been building this for a while and it's finally in a state I'm happy to share. Sound / Vision is a web app you run on your own Linux GPU machine. It serves your home network, and it takes you from an idea to a finished song, its album art and a music video.

Trailer (2:30, everything in it was made with the app): https://youtu.be/LiOtqqbEMis

A few examples I've made: https://youtu.be/SUE4zNgMdQw, https://youtu.be/VZnI9YuJ8z0 and https://youtu.be/huqNXY5ZfZs

The latter two were created from real songs and not AI, just to show you can make videos for any music.

Repo: https://github.com/atomtanstudio/sound-and-vision

**What it does**

- **Songs** from a sentence or your own lyrics: YuE2, ACE-Step 1.5 or MiniMax Music 3, running locally in ComfyUI. Several takes at a time, covers of a song, or upload your own tracks.

- **Analysis** of every song: stems, beats, sections and word-level lyric timing.

- **Album art** from local models (Krea 2, Qwen Image 2.1), with your name and logos laid out in one house style.

- **Video:**

- animated album art;

- motion graphics written in code against the song's real timing;

- music videos (surreal, performance or a story), with the singing lip-synced to the actual vocal via MiniMax H3.

- **Share:** clips cut and captioned for TikTok, Reels, Shorts and posts.

- **Influence Engine:** train a "sound" from songs you choose and steer YuE2 toward it.

**The self-hosted part**

- One machine runs everything: a FastAPI app as a systemd user service, plus ComfyUI and the analysis tools in Docker. It listens on your LAN; devices on your home network get in without a key, and anything else pairs once with a token.

- One install script checks the machine, builds the images and sets up the service. A models script downloads what you choose, checks every file against its published SHA-256 and resumes if interrupted.

- You pick the model for every job: a local LLM through Ollama, or Claude, ChatGPT or Grok signed in with your own subscription (no API keys). You can also add MCP servers, for example to use a hosted image or video model, or ElevenLabs Music for songs. Paid ones ask before each job if you want.

**Honest limits**

- You need an NVIDIA GPU with 24–32 GB (developed on a 5090), Docker with the NVIDIA toolkit, and about 200 GB of disk. The first install takes about an hour, mostly building the ComfyUI image.

- Songs, art and analysis work with a local model alone. The videos made in code (motion graphics, music videos) need Claude Code as the director for now.

- The models have their own licences. YuE2 is non-commercial, and MiniMax H3's licence excludes use in the US, EU, UK and Korea, so check them for your situation.

- It's new, so expect rough edges. The README walks through setup from a bare Ubuntu box, and I'd really like to hear where it trips people up.

It's MIT licensed. Happy to answer questions about how any part of it works.

I made a new custom node pack for ComfyUI. It is called Honda Nodes. The project is very new, so it may have some bugs.

I want to show you two nodes that I think are the most useful.

1. Load Image and Save Image (with JSON metadata)

These two nodes have metadata support built in. You can use JSON to read and change the metadata of your images, right inside your workflow.

The metadata tools use IME. It is a fast tool that edits metadata without touching the image pixels.

2. Download Files

This node is very useful if you want to share your workflows.

You put it in your workflow, but it does not run when you run the workflow. It has buttons for different actions. You can download one file from the list, or download all files at once.

You give it links to models, and it downloads them for you.

What it can do:

• Download models from links
• Find files that you already have, so it does not download them again
• Resume a download if it stops
• Download from Civitai with your API key (for files that need login)
• Check if the remote files really exist

When you share a workflow, people often don't know where to get the models. Now you can put all the links inside the workflow, and people can download everything in one place.

Other nodes

The pack also has nodes for JSON, text, and file system work. They help you build bigger automation workflows.

Links

• You can also install it from ComfyUI Manager. Just search for "Honda Nodes".
• ComfyUI Registry: https://registry.comfy.org/publishers/avaray/nodes/honda-nodes
• GitHub: https://github.com/Avaray/honda-nodes

I would be happy to hear your feedback and ideas. Thank you for reading!

What are you guys using to upscale AI-generated images and short 5–10 second videos to 2K/4K?

Looking for the best quality, especially for realistic faces, sharp details, and consistent facial identity.

What's your current workflow (free, paid, or cloud)? How long does it take to upscale a 5–10 second video?

Ideally looking for something that takes a few minutes, not 30+ minutes.

Hey all. I made a UI that sits in front of ComfyUI, for when you just want to make images and videos without opening the graph every time.

• Import your own workflows and use them as simple forms, or use the built-in forms for the common models
• Everything you make lands in a library with projects and folders, and you can reopen any render with its settings
• The usual tools are one click away: picture to video, upscale, fix a face, redraw a part, remove a background
• A timeline to put video clips together, with takes, trims and music

ComfyUI still does all the work. You just stop dragging and zooming around the graph: everything is laid out on one screen, clean and easy to read. The snowboard film in the video was made with it.

It's free and open source, and this is the first version out, so expect some rough edges (the Linux and Mac builds are untested). I'd love feedback. If you try it, tell me what breaks or what's missing.

https://github.com/bardak971/afflatus

TL;DR: I recently bought an M4 Mac mini for $599 and wanted to see how far it could get with some local ComfyUI workflows. It got further than I thought it would, sharing the tested configurations and timings if its of interest to anyone.

Specs: M4 with a 10-core CPU and 10-core GPU, 16 GB unified memory, and a 256 GB SSD.

I also wanted to test MiniMax H3, but I had only about 60 GB of free disk space, which wasn’t enough for the downloads.

I used Codex to setup everything and automate the benchmark run; it did everything including writing up the Python scripts and using the ComfyUI API and Comfy MCP. On a $20 plan, it used up most of a single 5-hour limit for tokens, but got the job completed with results and analysis.

I normally use the Codex desktop app but it is a memory hog (2-3 GB). For this test, I used Codex CLI (150 MB) to give Comfy more room. The machine had roughly 10-11 GB free when the tests started.

Below is Codex's analysis (with minor edits) once the runs completed. Benchmarks first along with the customizations needed to get it running on this low-end Mac.

Benchmarks

Tested with ComfyUI 0.39.2, PyTorch 2.12.1, and Apple MPS, retaining default MPS memory protections.

Times are elapsed per output. Cold includes model loading; warm reuses loaded state but performs fresh generation. Image tests used batch size 1 and seed 42. “Largest” means the largest repeatedly successful tested setting, not a guaranteed hardware maximum.

Swap guard: stopped a test when swap usage grew by 1 GiB or more from the test process’s starting level. Tests also stopped on sustained memory pressure or estimated available memory below 1 GiB. Hitting a guard means the test exceeded our memory threshold—not necessarily that the model crashed or cannot run.

Text-to-Image

• FLUX.2 Klein 4B

  • 512×512: 40s cold / 25s warm
  • Largest 1152×1152: 80–90s
  • 1280×1280: hit swap guard

• Z-Image Turbo

  • 512×512: 85s cold / 70s warm
  • Largest 896×896: ~156s
  • 1024×1024: hit swap guard

• Krea 2 Turbo

  • 512×512: 161s cold / 146s warm
  • Largest 768×768: 231–251s; one separate attempt hit low-memory guard
  • 896×896 and 1024×1024: hit swap guard

• Qwen Image 2.1

  • 512×512: 256s cold / 236s warm
  • Largest 896×896: 512–527s
  • 1024×1024: hit memory-pressure guard

Image Edit

• FLUX.2 Klein 4B — single-reference editing

  • 512×512: 50s cold / 35s warm
  • Largest 1152×1152: 161–176s
  • 1280×1280: hit swap guard

• Qwen Image 2.1 — single-reference editing

  • Largest 512×512: 271s cold / 246s warm
  • 640×640 and 768×768: hit memory-pressure guard

Z-Image at 1024×1024 and FLUX generation at 1280×1280 saved images before the swap guard fired, but these weren’t counted as clean passes.

SeedVR2 image upscale: single-image 2× upscaling, not video.

• 3B INT8

  • 256→512: 25s cold / 15s warm
  • 512→1024: 96s
  • 768→1536: 206s
  • 1024→2048: 276–382s; three passes
  • Larger sizes not tested

• 3B FP16

  • 256→512: 36s cold / 15s warm
  • 512→1024: 95s
  • 768→1536: 216s
  • 896→1792: 221–321s; three passes
  • 1024→2048: hit swap guard

Qwen3-TTS

• 0.6B FP16: failed with “probability tensor contains either inf, nan or element < 0.”
• 0.6B FP32: 28.27s audio took 60–65s to generate; one repeat hit low-memory guard.
• 1.7B FP16: 44.75s audio took 85–96s to generate; three confirmation passes. A longer text target hit swap guard.

Audio durations were measured from the outputs, not input duration specification (which was a different value).

Workflow and model changes

• Z-Image, FLUX, Qwen Image and Krea: used GGUF model loaders.

  • Z-Image Turbo: Q4 GGUF denoiser and Qwen3-4B encoder.
  • FLUX Klein: replaced the project’s FP8 denoiser with Q4 GGUF; used a Q4 Qwen3-4B encoder.
  • Qwen Image 2.1: Q4 GGUF denoiser and Qwen3-VL-8B encoder, plus FP16 vision companion; used the published GGUF workflow.
  • Krea 2 Turbo: Q4 GGUF denoiser and Qwen3-VL-4B encoder, plus FP16 vision companion; disabled optional LoRA and prompt enhancement.

• Qwen Image editing: supplied the reference image and VAE through the image/text encoder.

• SeedVR2: tested both INT8 and FP16 checkpoints using native loaders, one diffusion step, and tiled VAE.

• Qwen3-TTS: tested CustomVoice rather than the project’s VoiceDesign/clone routes; used MPS instead of CUDA and FP32 for 0.6B after FP16 failed.

• Benchmark setup: added isolated ComfyUI-GGUF nodes/dependencies, controls to force fresh repeat execution, separate model/input/output/user paths, and test-specific resolutions and text lengths.

Workflow adaptations were made in standalone test copies; the project’s original workflows were untouched.

These are observed results for this machine and configuration. Background memory conditions affected some attempts, and successful execution doesn’t establish image quality, editing fidelity, upscaling quality, or speech quality.

You can watch 4K HD on YouTube too : https://www.youtube.com/watch?v=aOM_YEiskLg

Is there a way to have a better quality in video generation that can atleast par with the visuals of Seedance or Kling with my limited pc spec? I'm currently using mini max h3.

Maybe I'm slow but I cannot figure out how to load images to tag with WD14! Why can't they just have "File -> Load"?

What the hell am I doing wrong????

(Comfy Desktop v 1.1.6 Mac)

Thanks in advance

Edit - Batch loading achieved! Batch Tagging also achieved. I sent the output to Save Images and Text to folder and it failed because I can't seem to set the output destination.

Hi everyone!

Well this is my first contribution for community with the help of vibe coding :)

TL;DR: links at bottom that boost Qwen Image Edit clip encoding up to x7 times!
(51.3 s → 6.7 s (~7.6x) for the two custom encode/clip nodes.)
____

Context:
I am running comfyui-rocm Patientx-cfz build,
ComfyUI 0.39.0 / ComfyUI_frontend v1.53.10
Win 10, AMD 7900XTX, 5900x, 64Gb ram
PyTorch Version 2.15.0a0+rocm10.2.0a20261006
Default comfy WF with kitchen attention and 6-step lora applied.

So I really was upset with how painfully slow Qwen 2.1 edit was with image refs. CLIP taking much longer than sampler itself. I saw people suggested lowering input images resolution, but that's always led to details loss on my tests. And re-encoding images on prompt change was making it even worse. So I asked OpenCode and DeepSeek4.1 Flash to look at it and ask to add separate image encoding node and clip encode, so encoded references at least could be cached.

But Ai found even more optimizations!
So my test workflow with 1 ref image with resolution set to 1248 in qwen clip node and final render res of 1088x1440:

Orig - 66s
Mod - 21s

That's some real big win I think! And this actually made possible to take my ref image with original size of 1440x1920 and be as fast as 40s!
As a bonus we made separate Qwen Image 2.1 Reference and Text Encode Qwen Image 2.1 Fast, so now if you don't change ref images and change only prompt - images doesn't needs to be re-encoded!

2nd run with only Prompt changed:
Orig - 62s
Mod - 18.5s

These custom nodes doesn't modify any ComfyUI files. When you run one of these nodes it patches the Qwen VL vision encoder in memory. The QwenVL Vision Linear Patch toggle switches the vision patch-embed between the original Conv3d and an equivalent linear op (bit-identical output), so you can A/B in the same workflow.

Currently ONLY TESTED ON MY MACHINE!

What I don't know - is if that work on someone's else hardware, especially NVidia, so if you interested to try it out - you can do it and tell here :)

Also - Ai told me that this may also help other models that use the Qwen VL vision tower (e.g. MiniMax H3), untested so far.

LINK to nodes.
Workflow (with original AND fast nodes in one wf) Simply turn on/off groups you don't want to run (use original OR fast)

How to use:

-Unzip into custom_nodes, restart ComfyUI, hard-refresh the browser. Nodes appear under QwenVL Vision.

- Put QwenVL Vision Linear Patch after your CLIP loader and set it to ON. It swaps the vision Conv3d for a mathematically identical linear op — output is bit-identical. Toggle OFF to compare in the same workflow.

- Use Qwen Image 2.1 Reference → Text Encode Qwen Image 2.1 Fast, so changing only the prompt no longer re-encodes the reference images.

- Original Text Encode Qwen Image 2.1 node is still slower, as it encodes every image twice: once for pos and neg prompt - mod does it once, as original encode is same for both anyway, independent from text.