Black Forest Labs just released FLUX 3 Image Klein, the compact open-weights model in the FLUX 3 family. It runs natively in ComfyUI.

There are two checkpoints:

• Base: full undistilled weights, for fine-tuning, LoRA training, and research
• Distilled: a low-latency “turbo” variant for fast generation

Features:

• Native 4K generation, not upscaled
• Bounding boxes to set layout and composition before generation
• Multi-reference generation and editing, with subjects staying consistent across inputs
• Text rendering, plus editing of existing text in an image
• Reference-based style transfer
• A separate prompt upsampler model that expands short prompts

Speed on consumer GPUs: INT8 ConvRot quantization plus the step-distilled model runs 1024×1024 text-to-image 4.3× to 9.3× faster than the BF16, 50-step baseline.

• RTX 5070: 71s → under 8s
• RTX 4070: 78s → under 9s

Download the weights and learn more with our blog: https://open.substack.com/pub/comfyui/p/flux-3-image-klein-in-comfyui-56b?r=7xlbaw&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Hey guys ! i posted a message few weeks back about qwen quality ! But i'm starting to find a good way to push details.. its not perfect when you zoom alot, but, for a T2I workflow for now, it's quite cool. Now i'll go on photoshop, then try to enhance back with I2I... !

which model is best for refrence based image generation (bullk) any refrence or prompt given it should make it like chat gpt does it

But the README.md currently has a note still "[TO CONFIRM: Stephen: the ComfyUI version that supports FLUX 3 Image Klein and the workflow template links (text-to-image, editing, prompt upsampler); the public ComfyUI tree has no FLUX 3 Image loader yet.]"

Kijai has pushed commit to run it if you use the git version. I don't know about the managed release versions.

BF16, int8_convrot, and NVFP4 are there. *I'm not affiliated in anyway, just excited.

There are already some threads recommending removing Mixlab, but in my case another node pack, ComfyUI-JDCN, was also blocking file drops.

You can give Claude Code, Codex, Qwen, or another coding assistant access to your custom_nodes folder and ask:

Check frontend JavaScript for global drop handlers that call preventDefault() or stopPropagation() and block ComfyUI’s native file handling. ComfyUI’s native drop handler skips events marked defaultPrevented. Ensure files the extension does not handle, including ordinary workflow JSON files, reach ComfyUI’s native handler while preserving extension-specific import behavior.

You can also check manually: search the extensions’ .js files for addEventListener('drop' and addEventListener("drop". Look for handlers attached to document or document.body that cancel every drop before checking whether they actually handle the file.

My problem is that, after a fresh installation of ComfyUI, metadata - specifically the prompt -is no longer being saved along with the images. Is this intentional, or am I missing something? It’s not a huge deal, but sometimes I do want to regenerate the images with some modifications.

Hey everyone!

I've built a new ComfyUI workflow that combines RefMod and MiniMax H3 to generate images of your own character without needing to train a traditional character LoRA.

How does it work?

1. Prepare 8 photos of your character.
2. Use the RefMod custom nodes to create a RefMod file.
3. Load the file into the MiniMax H3 workflow.
4. Enter your prompt describing the scene, outfit, pose, or environment.
5. Generate new images featuring your character!

You can explore different scenarios, clothing styles, locations, and compositions while aiming to maintain your character's identity. The main advantage is No traditional LoRA training required. Create your RefMod file, load it, write your prompt, and start generating!

Generation time at 8 Steps 6 Min for 1.8 Megapixel.

Generation time at 25 Steps 14 Min for 1.8 Megapixel.

Workflow Links

https://drive.google.com/file/d/10Y95Kzs4XtogM8ZEGjucJxSz6QVtFfb5/view?usp=sharing

https://civitai.com/articles/36763/comfyui-tutorial-refmod-minimax-h3-image-character-consistency-without-lora-training

Video Tutorial Link

https://youtu.be/-CfyYOcsnVw

I'm currently playing around with pm Lora merger in comfyui. But what is your preferred method of combining Loras to save vram and to merge similar Loras to prevent over saturated images?

I have a setup where 3 dropdowns control my prompts using the Pythonsssss preset text node.

Basically Box1 - Character, Box2 - style, Box3 - scene and then i can mix and match.

My issue is that with pythonsssss preset text, all the nodes share one pool of options so styles and scenes are showing in the character dropdown and vice versa.

I want an easily switchable drop-down that will hold value:string pairs and output the string when I select a value from the drop-down.

This first video is the starting point: what a node is, how the wires connect, how to change the settings, and what to do when a node turns red. Then we build a Flux 2 Klein workflow together from an empty canvas, and a little painter analogy shows why every node is connected the way it is.

There is no workflow to download this time: we build it step by step on screen, so you can make it yourself.

If you know someone who wants to learn ComfyUI from zero, send it their way. Leave a like and a comment on the video, it really helps!

Thanks! ❤️

Hi.

Those images were made with chatgpt. I write harem novels and also litrog web novels and I need to be able to make a lora of style and characters to able to make consistent pieces to marketing, and so on.

I have played with comfy with my rtx 2060 6gb and 32gb ram using illustrious for a few days. But I have been failing miserable and only producing garbage.

Anyone has any tips?

Because of the harem side, I also need to make NSFW art of the characters.

Hi all, I’ve been getting pretty familiar with comfyui the last few months. The other week I put in a second gpu to offload encoder and vae to get access to Krea2 4bit quant (I use an rtx 3060 and an rtx 2060 super). It worked straight away I was stoked! Then I switched my cards around so i could also run desktop off the smaller card and leave krea on the 12gb without offload to ram. It didn’t work for a range of Linux related and care factor reasons. Anyway, put my cards back in their original configuration.

Since then I’ve been unable to use the second card via multigpu nodes and get the: “CUDA error: an illegal memory access was encountered (cudaErrorIllegalAddress)” error which kills the generation.

I’m able to run either card alone and layer split LLMs via ollama so it’s not a hardware issue. I updated to v0.40 then downgraded to 0.39 as that was the last release that I had the multi gpu set up working.

I know this is a known issue and reported at the git but wondered if anyone had found a solution? I know it can work, the problem only came up after I stuffed around with gpu placement and device order so figured people smarter than me might have some ideas?

I converted the new TaoMate-H3-FL2VA 3-step LoRA to ComfyUI format and tested it successfully.

Two versions are available:

- Full rank-128 BF16: 1.24 GB

- Dynamic average-rank-19 BF16: 169 MB

The smaller version is much easier on storage/VRAM and worked normally in my tests. Recommended starting settings: 3 steps, CFG 1.0, LoRA strength 1.0, and Euler.

Download and details:

https://huggingface.co/austinit/TaoMate-H3-FL2VA-ComfyUI

Simple trick.

So, I set up this complex video upscale, along with 7 alterations to be made to the video, which is a 2 second clip. While H3 followed one or two points of the prompt and color graded properly to match another reference image, it ignored all the other parts of the prompt - removing a chair in the foreground, removing two doors, removing some AI gobbledygook at the end of the corridor, etc.

After I made a simple change to the prompt, it did all that I had requested. Eventually my list of requests reached 13 items, and it completed every last one of them.

HERE is the trick. In my updated prompt, I merely added phrases in front of all the alterations. So, instead of the alteration saying:

"Remove the chair in the foreground",

I changed it to read

"There is a chair in the foreground, next to the kitchen island. Remove the chair."

See the simple change? Point out the item succinctly FIRST.

Thought it would help someone(s)!

I’ve been upscaling my MiniMax H3 clips with both and I honestly can’t decide which one I prefer, so I’d like to hear how others are handling this.

My test: same 16-second clip, [768p] → [2K], on an RTX 5090 laptop (24 GB VRAM).

SeedVR2

• Time: 34 min
• Adds real detail: skin pores, fabric, hair strands
• Heavy on VRAM, and long clips need chunking

RTX Video Super Resolution

• Time: 20 sec
• Very fast, clean, and stays faithful to the original. It doesn’t hallucinate anything
• But it mostly sharpens and cleans artifacts rather than adding new detail, so close-ups look more “upscaled”

So far my feeling is: SeedVR2 for hero shots and close-ups, RTX VSR for everything else when speed matters. But I’m not sure that’s the right split.

Questions for you:

1. Which one do you use for your final renders, and why?
2. Has anyone found SeedVR2 settings that reduce the hallucinated textures or flicker?
3. Is anyone combining them, e.g. RTX VSR first and then a light SeedVR2 pass, or the other way round?

Test set made with FLUX.3 Image Klein (Base, NVFP4) in ComfyUI.

Same model, same workflow, three anime scenes and one natural-light still life, to see how it handles both styles.

Models used:

- Diffusion model: flux3_image_klein_nvfp4.safetensors (Klein Base, NVFP4, ~5.3 GB)

- Text encoder: qwen3vl_4b_int8_convrot.safetensors (CLIPLoader type: flux3)

- VAE: flux2-vae.safetensors

- Source: black-forest-labs/flux-3-image-klein-comfy on Hugging Face (accept the license to download)

Settings:

- Canvas: 1024x1344 (portrait) / 1344x768 (wide), multiples of 16

- Sampler: euler, scheduler: simple (er_sde worth trying)

- Steps: 40 (official default is 25; 50 gives slightly more detail)

- CFG: 3.5 for anime, 2.5-3 for photo-style images

- Negative prompt: empty (default), batch size 1

- Nodes: UNETLoader > CLIPLoader (flux3) > CLIPTextEncode > EmptyFlux2LatentImage > KSampler > VAEDecode

- No LoRAs, no upscaler, no inpainting

https://civitai.com/posts/31670200

Every fisherman in the first image comes from the same prompt, seed and model. The only difference is that one layer inside the model got bent/manipulated while it was generating.

Model bending means reaching into a diffusion model mid-sampling and messing with its activations: rotate a feature map, multiply a block by zero, pour noise into an attention layer. It isn't a better prompt or a style LoRA. It's closer to circuit bending: you break the model on purpose and see what falls out. Sometimes that's a visual variation (the fisherman ends up on a dock next to his boat). Sometimes it's uncanny (a burlap-and-rope twin). And sometimes the image comes apart into paint smears, collage, an etching or leopard print. Each bend needs a recipe: model, layer address, bending operation/amount.

Everything is free and open source:

Model Bending Navigator (17,000+ more bending examples)

https://huggingface.co/spaces/abuzreq/model-bending-navigator

Each render is a single bend next to its unbent original. The fisherman is one of the test prompts, with hundreds of bends of him alone. Filter by effect (glitch, abstract, painterly, new composition…), by model part or by operation, then hit Run this bend to get a ready-to-run ComfyUI workflow.

ComfyUI Model Bending (the node pack to run them or make your own; MIT, in ComfyUI Manager)

https://github.com/abuzreq/ComfyUI-Model-Bending

Features:

• Bend any layer with add, multiply, noise, rotate, scale, translate, flip, blur, sharpen, erosion, dilation, Sobel, Fourier amplify and more
• Interactive web UI: see the model's structure, click a layer, drag a slider, watch it break
• Limit a bend to early, middle or late steps. Early bends tend to rebuild the composition; late ones mess with the surface.
• SD 1.5 / SDXL U-Nets, plus Flux, SD3 and other DiTs. You can bend LoRAs and VAEs too.
• Experimental: WAN 2.1 / 2.2 video bending. It's only been tested on tiny random-weight models so far, so if you run WAN locally, I'd love to see what you get.

This came out of my PhD research on treating AI models as a material for art. The hope is to reintroduce more surprise into generative AI, as was commonplace with creative coding or earlier generative systems. If you break something interesting, please post it here! Open to feature requests.

There are lots of posts about H3 these days. What is the best currently available workflow for extending clips and minimizing degradation? I see the clown girl on here a lot but I'm not following what is needed to match what they have going on with their minutes long clips.