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

Higgsfield promised to open-source their entire platform. They didn’t, so we figured we’d help.

For the next two weeks, Comfy is putting up $25,000 in prizes for the best open-source builds on the Comfy Developer Platform. Rebuild Genjutsu or Cinema Studio, or make a creative app nobody’s thought of yet. The only rule that matters: publish the repo so anyone can run it, fork it, and improve it.

Prizes

• Grand Prize: $10,000. Every entry is automatically considered.
• Most Innovative: RTX 5090. A new kind of app, a novel custom node pack, or an unexpected use of ComfyUI. Your demo video needs to explain what makes it new.
• Most Viral: RTX 5090. Fun to use and made to be shared.
• Open Weights Only: RTX 5090. No partner nodes or APIs. Open-weights models only.
• 10 Runners-Up: $250 in Comfy credits each.

Partner bonuses

• LTX: $2,000 cash for the best VFX workflow with LTX as the core video model
• Black Forest Labs: $1,000 / $500 / $250 in FLUX API credits for builds where FLUX 3 is the core model
• MiniMax: $1,000 in API credits for builds where H3 is the core model

Backed by NVIDIA AI, Runpod, LTX, Black Forest Labs, and MiniMax.

Want to build IRL? We’re hosting build sessions with AI On The Lot:

• SF, 10/28: RSVP
• LA, 10/29: RSVP

Need ideas? Check out the example apps on Comfy Hub to see how they’re built.

Full rules and submission: Enter here
Announcement: blog.comfy.org

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... !

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.

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

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! ❤️

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

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

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.

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?

Hi! I'm the creator of AntiMatter Comfy. I put together an independent English handbook for people who can open a ComfyUI workflow but aren't sure what to change next. The whole learning bundle is free; no paid membership is required.

Inside: a 32-page PDF, the Markdown edition with copyable prompts, three original Comfy-Org starter JSONs (T2V, I2V and R2V), model links, setup checklists and troubleshooting notes. The templates retain their original credits and license.

A useful first exercise:

1. Start with one short shot and one clear action. Describe the subject, action, camera and sound separately.

2. For example: A courier waits beneath a shop awning in the rain. She checks the address on an envelope, then looks toward the street. A slow camera push-in. Rain taps on the awning; distant traffic; no dialogue.

3. Keep your seed and settings fixed, then change only the camera sentence. Compare the result before adding more actions or adapters.

4. For reference editing, write down what should change and what should stay. A reference is conditioning, not a locked editing layer.

Free PDF + complete ZIP:

https://www.patreon.com/AntiMatterComfy/posts/free-minimax-h3-170293702

This is an unofficial learning guide, not a claim of magic settings. This edition has not been GPU-benchmarked; model weights are not included. Check the current model license and compatibility for your hardware. Real interface screenshots inside the guide are from roadmaus's MIT-licensed MiniMax H3 Creator documentation, with full attribution; they are not my own UI or results. The cover is an AI-generated editorial illustration, not a software screenshot.

What part of H3 would you most like explained more clearly: setup, reference mapping, camera prompts or audio?

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.

I messed up my install so badly I had to create a second instance of ComfyUI and deleted the first. Do I still have 2 copies using memory or by deleting the first one I released that memory? I have been unable to documentation on this.

Recently, comfyui linux updated to require pytorch 2.7. This means that it does not work on linux 10 series Nvidia GPUs. The last version of pytorch compatable with 10 series gpus is 2.6.

I get the following error when running Comfy 0.39.0 on pytorch 2.6 on linux.

...

  File "/home/roger/miniconda3/envs/comfyui_2b/lib/python3.12/site-packages/comfy_kitchen/backends/eager/conv3d.py", line 37, in <module>

u/torch.library.custom_op("comfy_kitchen::fp16_conv3d_out", mutates_args=("out",))

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "/home/roger/miniconda3/envs/comfyui_2b/lib/python3.12/site-packages/torch/_library/custom_ops.py", line 121, in inner

schema_str = torch.library.infer_schema(fn, mutates_args=mutates_args)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "/home/roger/miniconda3/envs/comfyui_2b/lib/python3.12/site-packages/torch/_library/infer_schema.py", line 106, in infer_schema

error_fn(

  File "/home/roger/miniconda3/envs/comfyui_2b/lib/python3.12/site-packages/torch/_library/infer_schema.py", line 58, in error_fn

raise ValueError(

ValueError: infer_schema(func): Parameter stride has unsupported type list[int]. The valid types are: dict_keys([<class 'torch.Tensor'>, typing.Optional[torch.Tensor], typing.Sequence[torch.Tensor], typing.List[torch.Tensor], typing.Sequence[typing.Optional[torch.Tensor]], typing.List[typing.Optional[torch.Tensor]], <class 'int'>, typing.Optional[int], typing.Sequence[int], typing.List[int], typing.Optional[typing.Sequence[int]], typing.Optional[typing.List[int]], <class 'float'>, typing.Optional[float], typing.Sequence[float], typing.List[float], typing.Optional[typing.Sequence[float]], typing.Optional[typing.List[float]], <class 'bool'>, typing.Optional[bool], typi</error>

This seems to be caused by 3D mesh generation support.

My PC specs:

-32gb ddr3

-GTX 1080 ti

-Debian GNU/Linux 12 (bookworm)

I was able to work around this by commenting out 3D mesh generation in the following files:

- /home/roger/miniconda3/envs/comfyui_2b/lib/python3.12/site-packages/comfy_kitchen/backends/eager/__init__.py

/home/roger/miniconda3/envs/comfyui_2b/lib/python3.12/site-packages/comfy_kitchen/backends/eager/sol_attn.py

/home/roger/miniconda3/envs/comfyui_2b/lib/python3.12/site-packages/comfy_kitchen/backends/eager/group_norm_pad3d.py

Going forward, I kindly request the comfyui dev team provide a provide an option to disable functionality that requires pytorch 2.7. I'm running the 1080ti on a old laptop that can't upgrade the GPU. So us gpu peasants have to work with what we have.

I run Comfy using the EZi Desktop. It was simple enough to move the input/output folders onto my external drive where I save everything. Can someone help me out with how I can also move the 'temp' folder in Comfy to the same drive so it uses that during the creation process?

Hi everyone! I'm new to ComfyUI. My hardware specs are:

GPU: AMD Radeon RX 7600 (8GB VRAM)

CPU: AMD Ryzen 7 3700XT

RAM: 32GB

I'm trying to generate images using Krea 2 Turbo. Once the sampling iterations actually begin, it runs quite fast. However, reaching that first step takes anywhere between 6 to 7 minutes (or more), turning a quick generation into a very long wait.

Here are the models I'm using:

Diffusion Model: krea2_turbo_int8_convrot (I also have krea2_turbo_fp8_scaled)

VAE: qwen_image_vae or Wan2.1_vae

Text Encoder: qwen2vl_4b_fp8_scaled

My current startup parameters:DOSset PARAMS=--enable-dynamic-vram --disable-async-offload --preview-method none --disable-smart-memory --fast-disk --enable-manager --bf16-vae

if "!IS_LEGACY_GPU!"=="1" (

set "PARAMS=%PARAMS% --use-quad-cross-attention"

) else (

set "PARAMS=%PARAMS% --use-ck-attention"

)

Are these launch arguments correct for an 8GB AMD card? What is causing this huge delay before the steps start, and is there any way to optimize it? Thanks!

My setup is rx 6700xt 12 gb, cpu amd Ryzen 5 3600, 16gb ram, 1 tb ssd,2tb hdd.

I have flux schnell quantisized model that takes about a min to create an image, and juggernaut xl that does it in 20 seconds.

So im asking you fellow tech priests and wise men of Reddit..is there hope for me or should i give another kidney for a new pc build

Another short cinematic experiment, this time exploring an institutional archive that gradually becomes something impossible.

The main challenge was maintaining a consistent architectural language across AI-generated shots, using mostly frontal, symmetrical compositions, restrained camera movement, and environmental transitions rather than complex action.

I used ComfyUI with LTX-2.5 and MiniMax H3 for image-to-video generation, testing both models across different shots and refining the results through editing to maintain spatial and visual continuity.

I'm particularly interested in whether the environment feels like one coherent location as it changes, or whether the transitions reveal the limitations of AI-generated video.

Would appreciate feedback on the pacing, camera direction, and overall continuity.

When i start training the lora on ostris on runpod that error showed ,any solutions?

Please reply to the original post. Thx.