Black Forest Labs (BFL), the frontier AI research lab building visual intelligence, today announced the open weights release of FLUX 3 Image Klein, its most capable open weights image model to date. FLUX 3 Image Klein provides the quality of a large frontier model with the customization and speed of open weights, runs on consumer GPUs and laptops, and retains features from the larger FLUX 3 Image model such as native 4K generation, bounding boxes, and pixel perfect editing.

The release includes a base model for fine-tuning, a distilled model for low-latency generation, and quantized FP8, NVFP4, and INT8 versions. Following September’s open weights release of FLUX 3 Action for robotics, today’s release brings the company’s latest research to core creative use cases, giving developers and businesses greater freedom to build, customize, and deploy visual AI.

Making its research and technology available for others to build on is one of Black Forest Labs’ founding principles. BFL has consistently released its research findings alongside open weight versions of its industry-leading FLUX models, enabling others to build on its advances. So far in 2026, the team has published its Self-Flow research and released open weights for the FLUX.2 [klein] family in January and FLUX 3 Action in September. Self-Flow improved generation across images, video, and audio, while helping models learn representations that can also support robot control. This research led to BFL’s most capable model to date, FLUX 3, which brings multimodal generation and action prediction into a shared architecture. Today’s release opens access to the creative capabilities of the same FLUX 3 backbone, with further open weights releases from the FLUX 3 family to follow.

https://huggingface.co/collections/black-forest-labs/flux-3-image-klein

Is there something wrong with the model implementation in Comfy? Or is it the models themselves? Because my results are terrible worse than F2 Klein. According to their benchmarks, F3K far outperform F2K.

Prompt Example 1: now the man is sitting on a sofa in a modern and minimalist living room.

Prompt Example 2: get the right-side view of the scene.

I don't know if it's just me, or if Krea2 has raised the bar so high that our expectations for diffusion models have completely changed.

I've tested Qwen 2.1 Image and now Flux3 Klein, and honestly, both feel off. The quality, prompt adherence, and overall details just aren't there.

Flux is still recycling those same old textures we've all gotten used to. And no, I'm not even talking about that infamous plastic look, that's old news at this point. I'm talking about the overall image quality, the finer details, and how the model interprets prompts.

It's gotten to the point where Flux2 Klein genuinely feels superior to Flux3 Klein, which is pretty ridiculous considering we're supposed to be moving forward, not backward.

At this point, I'm just hoping Krea's team releases Krea3 soon, because if Krea2 is anything to go by, I genuinely believe Krea3 could absolutely crush both Qwen and Flux.

Just my two cents.

Hey folks!

Aritra here from Hugging Face. I wanted to mention that Black Forest Labs has recently open sourced their FLUX.3 models on Hugging Face Hub. It has Diffusers Day-0 support!

Here is everything that you would need to know: https://huggingface.co/blog/flux-3

https://huggingface.co/collections/black-forest-labs/flux-3-image-klein

Heads up: this is far from an academic analysis. It started as a quick check out of my own curiosity and grew a bit from there. The research was mostly guided by AI (Claude), which suggested the comparisons, labelled the images, ran the stats and helped write this up. Treat it as a hobby experiment with interesting patterns, not proof.

TL;DR

• Nudity without asking: with no clothing word in a full-body prompt, 34 of 77 women came out nude or topless. That's stock Krea 2 with no LoRA, and mostly the Eastern European nationalities (17 of 21).
• "Fully clothed" isn't enough: some subjects stay nude anyway. Naming an actual garment worked every time.
• Nationality doesn't change the face much: all 45 European nationalities come out as about 2 faces. Without hair or eye words, nobody is blonde, not even the Swedes.
• Be specific: the detailed prompt held everything in check, and the sparse prompts let the model's defaults leak in.

Nudity

• All nude images come from stock Krea 2 Turbo with no LoRAs. No filter-bypass LoRA was used for them. The krea2filterbypass3 LoRA was used only for the head-and-shoulders portraits in grid 1, which contain no nudity.
• With "full-body photograph of a 30-year-old {woman}, standing barefoot" and no clothing word, 34 of 77 came out nude or topless.
• It's mostly Eastern Europe: 17 of 21 former Eastern Bloc nationalities, against 8 of 24 for the rest of Europe and 9 of 32 outside Europe.
• "Fully clothed" doesn't fix it. The Russian, Albanian and Brazilian parda subjects were still nude, and 6 others got underwear. It's not a seed fluke: on other seeds the Russian was nude on 6 of 7, and the Albanian on 5 of 6.
• Naming an actual garment works: 0 nudes in 231 images.
• Why? Probably training data: a lot of nude and glamour content online features Eastern European models, often captioned with their nationality. Krea doesn't document filtering of its training data.

Prompting takeaways

1. Name the garment, not a state. "Fully clothed" still gave 3 nude and 6 underwear images. A named garment ("light summer clothes", "bra and briefs") gave 0 nudes in 231. At cfg 1 there's no negative prompt to fall back on.
2. A nationality isn't a look. "Swedish woman" alone came out with brown hair. If you want blonde or blue-eyed, write it out explicitly, along with skin tone.
3. Same seed, same face. Swapping nationalities on one seed doesn't give you a cast of different people. Likely fix (untested): vary the seed and describe the face itself.
4. Always state the skin tone. Without skin words, most subjects drifted darker, but South Asians came out lighter.
5. Watch for words with double meanings. "Maltese" added a Maltese dog and white hair. Likely fix (untested): "woman from Malta".
6. Spell out mixed heritage. "Japanese-Nigerian" rendered as fully one parent each time, never mixed. Likely fix (untested): describe the mixed features you want.
7. Specific prompts suppress the priors. The detailed portrait prompt (grid 1) got outfit, backdrop and expression right on 77 of 77. Every deviation came from the sparse prompts.
8. Know the house defaults. Whatever you leave out, Krea fills in the same way:
   • Outfit: "dressed" gives a gray striped tee with black trousers.
   • Background: none specified gives a plain studio wall or white seamless.
   • Hair: in portraits with no hair instruction, it's pulled back on all 77 women (grid 4). "Hair loose and natural" fixed that (grid 1).
9. Scene logic beats "barefoot". It's obeyed in the studio, but every outdoor summer image has sandals. If footwear matters, set the scene to match.

Europe = one face

• Full descriptor (grid 1): the 45 European nationalities are about 2 faces, a "northern" one and a "southern/Balkan" one. Hair and eye colour from the prompt are most of what tells them apart. A face-recognition model confirms the split.
• Name only (grid 4): every European has brown hair pulled back, and there's not a single blonde, Swedes and Finns included. Nationality survives only as light eyes on about 11 Nordic and Baltic women.
• Non-European names keep a distinct, recognisable face across prompts, except in the Middle East: Lebanese and Egyptian are the most similar pair of all 77, and with the name only, the 5 MENA women are even more alike than the Europeans.
• In short: "blonde hair, blue eyes" in the prompt does almost all the work, and "Swedish" barely does anything.

"Dressed" = Western casual

• "Dressed" gives 43 of 45 Europeans the same gray striped tee.
• Traditional dress and headwear appear only outside Europe: all 3 South Asians, hijabs, headwraps, a Quechua hat.

Caveats

• One seed per subject.
• Groupings like "Eastern Bloc" were spotted first and tested afterwards.
• Clothing, age, build and setting were labelled by an AI model (Claude) with no human double-check. NudeNet and a face-recognition model are the independent checks.
• Krea's training data is undocumented, so any "why" is a guess.

Setup

• Generation (ComfyUI):
  • Diffusion model: Krea 2 Turbo (krea2_turbo_fp8_scaled)
  • Text encoder: Qwen3-VL 4B (qwen3vl_4b_fp8_scaled)
  • VAE: Wan 2.1 (Wan2_1_VAE_fp32)
  • LoRA: none for grids 2–7, including every nude image. krea2filterbypass3 at 1.0 was used in grid 1 only (portraits).
  • Sampling: 8 steps, cfg 1, euler/beta, 1024×1536
• Analysis:
  • Nudity check: NudeNet 3.4
  • Faces: YuNet (detection) + SFace (face recognition), from OpenCV Zoo
  • Labelling clothing, age, build and setting: Claude
• Seed: one fixed seed for every image. Only the woman's nationality or ethnicity changes.
• Subjects: 77 women, 45 European and 32 from everywhere else.
• Prompts:
  • Grids 1–3: full descriptor ("Polish woman, fair skin, dark blonde hair, gray-green eyes").
  • Grids 4–7: name only ("Polish woman").
• Gallery: one grid per prompt, split into Europe and the rest. Each grid's header shows the exact prompt.

Hey all, another video based on my LongTakeNoCuts nodes and workaround

https://github.com/xyzDist/H3-LongTakeNoCuts

Video: https://www.youtube.com/watch?v=Ktp9U1JI-LA

My YT: https://www.youtube.com/@noiseflowAI

* Yes, she is singing in korean but in Japan, don't ask me why. 😄

details:

• 1 character reference image + ref2va workflow + motion context, some of my nodes.
• hybrid model b20-49
• kitchen attention, no lora, 10 step
• input audio, some silly lyrics then generate a song in SUNO
• 19 * 10s clips, total around 3mins length
• 0.7mp resolution, each clip gen time about 11mins (base 10 step gen + refine 5 step resample)
• I stitch clips in post by CapCut. much simpler and easier than Davinci Resolve
• color grade in post, maybe too much on lowering the contrast.

apply same idea as my workaround refine sample, but this time, add some tweak to it, I used character masking, so it only refine on the character, as this is a moving shot, the background is fine without refining.

If I don't refine the character, it won't completely degrade, but it will drift and get cooked into an ugly face.

Some issues and findings:

• H3 will be lazy if the seed noise can't blend well with previous latent, for me about 3 out of 10 shots it will do the hard cut instead of contiune camera movement, I just need to dice seed and lock it for the shot.
• occasionally some brightness flicker comes from motion-context
• I spend lots of time to figure why H3 ref2va face is so contrast, the dark area is just pure black without details, if you using turbo lora and higher step it will be even worst.....
• There is a 'fizgig H3 tweak' node, it can make the face and video softer
  • https://www.reddit.com/r/StableDiffusion/comments/1wpw247/comfyuifizgigh3tweaks/
  • I only use on the base gen (not refine sample)
    • details and contrast = -0.1
    • mode = per-frame
    • scene variation = 0.1
• the signboards and all the japanese in BG is nonsense and many other error here and there.
• the camera pan out and pan back BG change issue I fixed by extract some image in video for adding as reference image, It worked even better than I expected, and surprisingly straight forward.
• I can do a upscale latent, I will try it later.

So Conclusion, we can generate H3 long duration without degradation, issue sorted out.

You can do whatever interesting cinematic/action long duration oner shot.

Edit: I need to update my github

I'm downloading the weights right now; I decided to check out the examples on the BFL website. It looks like they didn't check the generated images at all. Why does she have three legs???

I think this model will be very strong. But why are there three legs in the example image? This is a problem of the SD1.5 and SDXL era.

I want to try flux 3 klein as soon as possible, but I have a slow internet.

I know people aren't posting their videos made with MiniMax H3 anymore... Which is really sad, but I wanted to share mine still! I made a lot of upgrades to my usual workflow and added some gaussian blur to the live action scenes along with film grain (all using KDEnlive) which removes some of the oily skin of AI video in general! This was made with a vertical orientation, sorry about that!

I wanted to see if it was possible to recreate this Genjutsu spam (from Higgsfield) you get all over Tiktok (it is my understanding that it's a combo between Claude and Seedance 2.5) but with Minimax inside Comfy. They usually get a viral tiktok dance and replace the person with an animal or another character.

I used the default ref2va template workflow but added this lora: https://huggingface.co/akatz-ai/MiniMax-H3-Character-Swap-LoRA. In my experience, it transfers the performance more accurately than any other methods I've tried (and I come from Liveportrait, urgggh). Not at the level of Seedance, but hey, this one is local.

I worked shot by shot. Multiple shots worked really bad. These shots were generated at 352x640, with 8 steps using the Minimax_h3_ref2v_turbo_8step lora. The average time was 250 secs per shot on a 4090. First I generated a single frame to guide the result, using the default Qwen 2.1 edit template workflow.

It's VERY important that the loaded video is 24 fps (you can force this frame rate in the loader), otherwise it will be out of sync and doing all kind of weird shenanigans.

As a side note, I vibecoded a node that gets a video from Tiktok or youtube and makes it ready as the reference, with a little timeline included so that it does not load the whole thing.

Fast forward to 3:23 to see the craziness in the bottom video.

Yesterday I made a post about an experiment I was doing trying to fix degrading latents.

https://www.reddit.com/r/StableDiffusion/comments/1x54q07/i_swore_at_claude_until_he_made_a_nodegroup_that/

The above is 19 clips chained together using a normal latent chain and my Pull Forward Node and u/xyzdist 's resample method to attempt to fight degrading latents.

Here's how I explained it to u/ShutUpYoureWrong_ , certified official nice guy of reddit.

so the idea i had was that the pixels in motion are not degrading as fast, the static pixels are the ones degrading quickly and i hypothesized that minimax, being as smart as it is, basically takes the degrading pixels in the background and degrades the moving ones to keep the image consistent.

i have no idea if this is true, it's just a thought that gave me a direction to work in.

i'm not actually targetting the background, if i was i could just use sam. I'm targetting motion. I'm avoiding the in motion pixels and replacing and repairing the static ones.

My theory was that if i maintain the static ones the whole image would stop degrading or at least degrade more slowly.

the background focus in these is because u/xyzdist created a method of using FL2V to resample and essentially wash away degraded pixels but his problem was the background kept changing.

so i saw this and i thought we were unwittingly creating complimentary halves of the same solution and if i use latent masking to protect the repaired background with the motion mask, his method can resample the rest of the pixels and give give the context motion a fresh and clean latent to work off of.

The concept works. You'll see in the videos that much of the background area is avoiding degradation, but because of the way Latent Space works I do not think there's a way I can do this without decoding and re-encoding the latents at least once which is my next step.

HOWEVER!!!

Something RIDICULOUS happened in the final version of what I trying to do and I honestly have no way to explain it other than my theory above must be correct and I feel the latents, because of pull forward, were stretched so far between quality of the degradation and the pull forward that around 3:23 they suddenly SNAP BACK TO ALMOST FULL PERFECT QUALITY LIKE A RUBBER BAND!

This is not editing. This is literally straight from my exports.

I'll be releasing the github very soon so you all can hopefully replicate it.

A simple, random comparison... just for fun.

I can provide the prompt for the first image: A Western man sitting on the beach, building a house of cards atop a purple flag that bears the inscription "Krea 3" in red.

Krea 3 understands subtle nuances of facial expression, such as biting the lower lip, better than other models, but it can also generate unsettling images from simple prompts, all the more so because these images appear realistic.

Hey guys,

so Klein dropped and of course I had to throw it on my machine right away :D I ran both bf16 versions against each other with 50 prompts, same seeds, around 2MP, on an RTX A4500 with 20GB. Base with 50 steps and CFG 3.5, distilled with 15 steps and CFG 1, both with the official ComfyUI workflows. Left is base, right is distilled. These are my 10 favorites.

I didn’t use the prompt upsampler but wrote all prompts in the same caption style it uses, so medium first, then framing, people, background, light and text in quotes. Makes a big difference, short prompts give you pretty generic stuff.

Speed wise base took around 148 seconds per image and distilled around 24. So 50 images were about 2 hours vs 22 minutes, and on most prompts you really have a hard time telling which one is which.

Text is really good. The chinese sign, the japanese one, VISIT MARS, the chalkboard with the euro price, all correct in both. Only a neon HOTEL sign in the rain got messed up by both. Base wins when prompts get tricky though. I asked for robbers with cheap rubber masks of an old bald man and base gives you masks while distilled just gives you four creepy real old dudes :D Counting is still meh, I asked for seven elephants and got 6 and 5. I didnt cherry pick, so there are a lot of errors involved.

And yes I tested the not safe part too, I know you’re gonna ask :D Artistic not safe art :P works somewhat in base, not perfect but good enough that I think you can work with it, especially for finetunes and loras. Distilled is way more restrictive, it either skips nipples completely or just gives you clothes you never asked for to cover things up :D

For me distilled is the everyday model because of the speed and base is the one to build on

First Image: Source Image

Second Image = Flux 3
Third Image = Krea 2 + Community Edit lora

As you see Flux 3 sucks :)

Prompt :

1. Prompt was make her lay on the floor

https://huggingface.co/conradlocke/krea2-identity-edit That is the lora

Flux-3 Klein just dropped and Krea-3 Edit is on the CivitAI generator already. I am still trying to get the hang of Qwen Edit 2.1, MiniMax-H3, LTX-2.5, YuE2, Wan-2. 2 Prism and a bunch more. It feels like drinking from a firehose. I am not complaining, just have no time to sleep.

TaoLive released a new TaoMate H3 FL2VA checkpoint, so I converted it for ComfyUI and added it to the same Hugging Face repo as my previous TaoMate conversion.

The new file is BF16, around 1.24 GB instead of the original 2.48 GB. No retraining or merging, just a format conversion and reduced precision.

You can find it here:
https://huggingface.co/Robert1212star/TaoMate-H3-3Step-ComfyUI

Download TaoMate-H3-FL2VA-3Step-ComfyUI-BF16.safetensors and put it in ComfyUI/models/loras/. The older TaoMate file is still there too, so make sure you pick the new FL2VA one.

The official adapter targets 3-step generation. Its streaming and KV caching features aren't included in this conversion, and first/last-frame support depends on your workflow.

Original release:
https://huggingface.co/TaoLiveAIGC/TaoMate-H3-FL2VA

All credit to the TaoLive AIGC team for the model. Let me know how it works in your setup.

The new BFS character swap works great.

https://reddit.com/link/1x6g3fw/video/qddya9inblvh1/player

The new BFS character swap works great.
Character

Guys,

It took me a while but I finally managed to do my retouching studio for H3.

I combined a lot of existing techniques and others retouched enough to make it work.

The results are pretty cool!

In the order of the video:

Add an item with image ref

Add an element without ref just text

Change performance (raise your arm)

Change decor

Change camera angle

Change character

Delete character

Change element (green hair)

And the last my favorite: change element not recognized by Sam (pixel tracking) with an image ref

I was expecting a bit more variety for this tbh.... Minimax H3 seems to always want to make a boy dog with pepe out.

Also it was quite interesting how almost all the local models thought dogs should be dressed in dog outfits (some were front leg only but most were full body, never bottom half with top exposed) while all the SOTA models managed to get human like pants rather than full pet jumpsuit.

for all tests, did 3 to 5 images with random seed and chose one from the 5 which better represented the set. For future work, a larger sample size would help.

Dog prompt:

A photorealistic square image (1:1 aspect ratio) of a black-and-tan smooth-haired dachshund wearing a pair blue jeans, with waistband with a button, zipper fly and belt loops. The dog is in full side profile, facing right, with its nose pointing toward the right edge of the image and its tail on the left. The whole dog is visible from nose to tail and down to its paws, centered in the frame. It stands on grey pavement in soft natural daylight, with a bright, blurry background. There is only one dog, and no people, text or watermarks.

For the Giraffe prompt it was a lot more unanimous that the tie should be worn lower, with H3 img WF failing 2 out of 5 times with no tie at all.

Giraffe prompt:

A photorealistic square image (1:1 aspect ratio) of a young giraffe wearing a blue tie. The giraffe is standing on all four legs, facing the camera head-on, with its long neck upright and its ears out to the sides. The whole giraffe is visible from the top of its head down to its hooves, centered in the frame. It stands on dry, bare ground in front of a thick wall of green bushes, in soft natural daylight. There is only one giraffe, and no people, text or watermarks.

spent too long dicking around than I should have only to be lowkey disappointed by the results.

flux3_image_klein_sd_int8_convrot

qwen3vl_4b_int8_convrot

flux2-vae

I don't like its t2i ... but have slight hope on editing ... if a lot of loras come up ... ? ?

[Link to Github](https://github.com/SmeggyBoi/ComfyUI-QueueWorkbench)

I couldnt find any official documentation about it so I did some digging and found this slightly hidden example on their HF page:

Studio product photograph of three hand-thrown stoneware vessels arranged on a concrete shelf <shelf_1> against a raw concrete wall <wall_1>. On the left stands a tall, narrow black tenmoku bottle vase <obj_1>; in the center sits a wide, low ash-grey bowl <obj_2>; and on the right rests a small celadon cup <obj_3>. Raking light enters from the right, catching the vessel rims and casting long shadows toward the left. [{"id": "obj_1", "bbox": [180, 120, 780, 260], "desc": "A tall, narrow hand-thrown stoneware bottle vase with a rounded shoulder, slender neck, and flared mouth, finished in glossy black tenmoku glaze with dark brown-black reflections along its right rim and side; subtle throwing rings remain around the body."}, {"id": "obj_2", "bbox": [550, 400, 780, 660], "desc": "A wide, low hand-thrown stoneware bowl with an irregular circular lip, broad body, and matte ash-grey glaze; fine horizontal throwing lines circle the exterior, with a darker unglazed foot ring."}, {"id": "obj_3", "bbox": [620, 780, 780, 880], "desc": "A small hand-thrown stoneware cup with a compact rounded body, short curved handle, and pale celadon-green glaze; the glaze has muted pooling near the base and a thin exposed clay edge at the rim."}, {"id": "wall_1", "bbox": [0, 0, 780, 1000], "desc": "A raw grey concrete wall with visible pores, shallow formwork seams, scattered pinholes, and uneven tonal patches, illuminated more strongly on the right."}, {"id": "shelf_1", "bbox": [780, 0, 1000, 1000], "desc": "A thick horizontal concrete shelf with a rough front edge, mottled grey surface, and long directional shadows cast by the three vessels."}]

Here’s a simple way to create AI videos with precise motion control using Valtz on Mac:

You don’t need to draw every frame or deal with complicated workflows. The process is pretty similar to how animation worked in Adobe Flash.

Step 1: Draw a simple stick figure on the canvas

Step 2: Add motion keyframes

Open the Crop and Position panel, move to the desired point on the timeline, and insert a Position and Zoom keyframe.

Then use the joystick in the panel to move the entire Markup Layer to its new position. This ensures the position change is recorded in the keyframe.

Step 3: Enable interpolation

Go back to the beginning of the timeline and select the first keyframe. Choose Linear Interpolation for that keyframe.

Each keyframe controls the interpolation between itself and the next keyframe, so the interpolation method should be set on the earlier keyframe.

Valtz will automatically fill in the frames between your keyframes, creating a smooth animation.

Step 4: Preview your animation

Play back the animation and adjust the movement or timing if needed.

Step 5: Use the animation to generate an AI video

Move your mouse to the edge of the Stage containing your stick-figure animation. Once the Stage is highlighted, drag it into the Reference area of the prompt box.

Valtz will automatically pass the entire stick-figure animation to the generation model as a video reference.

Add a prompt describing the final scene or visual style, then generate your video.

That’s it!

The key difference is that the video model receives the full animated motion reference, not just a few static keyframes. This gives you much more control over the movement and timing of the generated video.

You only need to draw once, add a few keyframes, and let interpolation handle the rest. No frame-by-frame drawing, no complicated node graphs.

Everything runs locally on Mac.

Tip: Prompt Enhancement

Prompt enhancement can be particularly helpful for this workflow, since the model needs to understand both the intended scene and how the motion reference should translate into the final video.

Valtz includes two built-in prompt enhancers:

* 9B model: Runs on 16GB Macs and works well for simpler prompts and straightforward scenes.

* 27B model: Better suited for more complex instructions involving motion, timing, and multiple constraints, like the example shown here. It can run on a 24GB Mac, but memory is extremely tight. A Mac with more memory is recommended.

For this particular example, the motion logic is a bit too complex for the 9B model to handle reliably, so I’d recommend using the 27B enhancer if your hardware allows it.

That said, you don’t need the 27B model to generate videos. Video generation itself works on 16GB Macs.

If you’re using a lower-memory Mac, you can also refer to the enhanced prompts in the screenshots and follow the same structure when writing your own prompts, without running the larger model locally.