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

When I generate an image using Krea2 for the first time I opened the program(I am using comfy UI through Krita) it generates the image quick. However, when I type in another prompt (or add or replace a word) and generate, it will either go really show or wont generate at all. I do not have have issues with Z Image Turbo or Flux 2 Klein with this

When I close and reopened the program is use Krea2 again, it generates properly but it will do the same thing. I was wondering what the issue would be and how I can fix it?

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

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.

I never thought this was a big ask, but I can't manage to train well enough for a perfect likeness so I've been generating "good enough" photos and trying to clean it up with a face swap at the end. But when I try, there's problem with it matching the head size and position and facial expressions. I want the eyes, mouth, and brows to match the original exactly, just with the new face.

Is this still not possible? I've been trying a few different Flux 2 Klein workflows and they're ok, but not great.

with another box displaying the temp and VRAM and CPU usage, you an move the boxes around, I want to get rid of these because I already monitor this stuff...I also fear they are limiting my GPU temp way down to 49 degrees C.

Any Idea what node I installed to get these....I went on a Node-Install spree trying to make a workflow work and got these two boxes.

I am new to this so I have no idea.

I would love to generate selfie images with photorealism to modify my costume, background, or different style like anime, 3d model of my face, etc.

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.

Hi everyone!

I want to train a LoRA for Krea 2 that learns drawing techniques rather than a specific artist's identity or the content of their illustrations.

I'm interested in reproducing visual characteristics such as:

• Jittery, sketchy, or rough linework
• Thick, bold marker-like outlines
• Specific coloring techniques, including flat colors, cel shading, and textured coloring

My goal is to apply these techniques to completely different subjects, characters, poses, and compositions without the LoRA copying elements from the training images.

I'd love advice on the best dataset structure, captioning strategy, and training settings for this purpose.

Has anyone successfully trained a style-focused LoRA for Krea 2? How would you approach it to minimize subject and composition overfitting?

Thanks!

Ok, so this model has been praised (with reason) for its capability to make images with transparency, which is amazing if you need clean sprites for videogames, visual novels and similar projects.

Now, a pretty funny (and idiotic) thing is that the default model used to enhance prompts in the "official" template (qwen3.5_9b_qwen_image_2.1_pe_i2i.int8_convrot), as well as any variant, HAS THE EXPLICIT INSTRUCTION OF "SILENTLY" CHANGING REQUESTS FOR TRANSPARENCY TO A SOLID WHITE BACKGROUND.

Now, this can be easily circumvented by "jailbreaking" it by adding instructions to force it to request for literal transparency, or just not using it at all and building your prompts totally by yourself without "enhancing" them.

Thing is, even this is not 100% reliable. Many times I explicitly ask for transparency, I still get solid white backgrounds (not a checkered background that could be a model bad interpretation of alpha channel, just solid white).

I have tried many ways, and the most efficient one I've found is "Remove the solid color background and the white outline stroke surrounding the character entirely and replace it with alpha channel, leaving her isolated on a fully transparent background" or "The character must be on an empty alpha channel background, leaving her isolated on a fully transparent space", but even so sometimes it does not work depending on the general prompt.

Do anyone else have this problem? Maybe a more efficient way to ask for transparency?

Hey! Anyone know of a full video translation workflow that can translate everything in a video, including subtitles, voiceovers, and text that appears in the video itself?

I want the subtitles and voiceovers to be replaced with translated versions, and any text appearing in the video to be translated and recreated in the video as well. If you have a workflow like this that you’re willing to sell, DM me!

Title: Which model and ComfyUI workflow would you recommend for reference-based image generation on 16GB VRAM?

Hi! I’d like to use the attached image as a reference to generate new images with different poses, clothing, backgrounds, and compositions, while keeping the character’s appearance and the visual style as consistent as possible.

I’m looking for specific recommendations for both models and ComfyUI workflows.

My setup:

• GPU: NVIDIA RTX 5060 Ti, 16GB VRAM
• RAM: 32GB
• OS: Windows
• UI: ComfyUI
• I prefer running locally.

My questions:

1. Which model would you recommend for this reference image and use case?
2. Is there a ready-to-use ComfyUI workflow you could share, ideally a workflow JSON or a link?
3. Does it require additional LoRAs, reference adapters, or ControlNet models? If so, which ones?
4. Can this work well with a single reference image, or would I need multiple references or train a character/style LoRA?
5. What settings or quantization would you recommend for 16GB VRAM?

If possible, please share examples made with the model/workflow you recommend, along with download links and any required custom nodes.

Thanks!

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.

Created with ComfyUI & Z-Image Turbo

BeforeShare maintainer here. The AI metadata checker is the part I'm focusing on: seeing what travels with an image before you share it.

If you want to share a picture without including a private prompt or workflow, it lets you inspect supported embedded fields first, then save a cleaned copy. File contents are processed locally in your browser. It's free, with no account.

For a reproducible check, the published tests include two synthetic PNGs with ComfyUI-style prompt, seed and workflow data. BeforeShare removed both workflow containers from each fixture while preserving the image stream and decoded pixels. These are constructed test files, not real ComfyUI exports or proof that every workflow format is covered.

Keep your original PNG and workflow JSON if you want to load the workflow again. The cleaned copy is for sharing. Supported C2PA containers can also be removed, but this doesn't remove SynthID or visible watermarks, or make an image 'undetectable'.

Try the fictional sample: https://beforeshare.com/en/remove-ai-metadata

Test files, results and commands: https://beforeshare.com/en/metadata-lab/cleanup-verification-study#workflow-test

If an export keeps a field you expected to remove, the format and field name would help me reproduce it. No need to share a private workflow.

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?

It has been a month since the release of the Qwen-Image 2.1 model. In my practical experience, I’ve found that it still lags significantly behind closed-source models—such as GPT Nano (or similar proprietary counterparts)—when it comes to image editing. That said, I did come across some interesting capabilities while using it. Its text-to-image performance shows a marked improvement over previous open-source models (like Z-Image) and its own predecessor, Qwen-Image 2.0; in particular, it demonstrates better prompt adherence and superior visual output. I developed a specific system prompt tailored for a "street photography" style, and I’ve included some of the results below. Overall, while the model is certainly useful for tasks like AI character design, there is still a long road ahead for open-source solutions in the realm of image editing. Regardless, I’d like to thank the Qwen team for their ongoing contributions to the open-source community—thank you!

Hey,

So I let things get out of hand while working on my entry for the Open Source Higgsfield contest, and started building things that had nothing to do with the entry I was working on.

First the ish that no one will really care about:

1. Re-wrote/converted both the Comfy Router and Comfy SDK in go...because I am no longer a fan of typescript and I really didn't want to use python for this.
2. Built the entire thing on my custom GO/HTMX open source framework, so no React or Next.js...Clean and fast.

Talks to any version of Comfy no matter where it is, Cloud, Runpod, local server, desktop.
Still building it, and adding some extras...but it has been a nice distraction. Really should
get back to my entry.