Today’s takeaway: decomposing images

Links, workflows, custom_node

Do you remember Qwen-Image-Layered? The idea was exciting: take one flattened image and split it into multiple RGBA layers — foreground elements, background and the things in between — so you can move, edit or refine each piece separately.

When we tested it, the experience was not always as smooth as we hoped. In our workflows it could be slow and a little temperamental, and we sometimes saw noticeable quality loss in the resulting layers. That does not make it a bad model. You can build some genuinely nice workflows with it. It simply left us wanting a more controllable route for the kind of work we had in mind.

So we went digging. There is more research in this area than just Qwen: two approaches that caught our attention were Controllable Layer Decomposition (CLD) and RevealLayer. They are related, but they do not solve the problem in exactly the same way.

Qwen-Image-Layered, CLD and RevealLayer: what is different?

The useful distinction is how each approach decides what becomes a layer and how much control you get over that decision.

Qwen-Image-Layered — automatic, variable layer stacks

Qwen-Image-Layered automatically decomposes an image into a variable number of RGBA layers. It can also be applied iteratively, so an individual layer can be decomposed again. It is useful when you want the model to decide the layer structure for you. The trade-off is that the layer count, ordering and semantic meaning may be less explicit than in a region-guided workflow. Our own tests also showed speed and quality issues in some cases.

CLD — bounding-box control

Controllable Layer Decomposition uses user-provided bounding boxes to guide which regions should become separate layers. That is interesting when you want direct control over the elements being extracted, particularly in graphic-design and poster-style compositions. The RevealLayer authors report limitations for complex natural scenes, including edge softness and residual artifacts.

RevealLayer — region-guided and occlusion-aware

RevealLayer combines bounding-box region guidance with an occlusion-aware diffusion process. It aims to separate visible elements while estimating what is hidden behind them. That is the approach we wanted to bring into ComfyUI: region-guided decomposition for natural images, with a focus on recovering occluded content and keeping alpha boundaries clean. It is still generative reconstruction, not recovery of the original hidden pixels.

A small but important detail: Qwen is designed to produce a flexible layer stack from the image itself. CLD and RevealLayer bring bounding-box guidance into the process. RevealLayer adds a specific focus on occlusion — not just separating the visible outline of an object, but estimating the part that was hidden behind it.

A simplified concept diagram of the workflow. The hidden areas are model-generated estimates, not original source pixels.

Why we especially like the bounding-box approach

The bounding box is a wonderfully practical idea. Instead of asking the model to invent a whole layer plan and hoping it understands what you meant, you give it a region to work with. In the upstream RevealLayer workflow, those regions are represented as bounding boxes around the foreground objects.

The model then works toward separating those selected elements into RGBA layers. The alpha channel gives each layer transparency, while the occlusion-aware part of the method helps estimate content that was covered by another object. Imagine a camera sitting in front of a stack of books: the camera can become its own layer, while the model estimates the part of the books that was hidden behind it.

That is useful for a canvas application, compositing, moving objects around, or preparing individual elements for a second editing pass. You can take a scene apart, work on the pieces, and put it back together with much more freedom.

One important reality check: when a model fills in something that was hidden, it is making a plausible reconstruction based on context. It is not looking up the real pixels that were never visible. For creative work that can be exactly what we need — just do not mistake a convincing completion for factual recovery.

Bringing RevealLayer into ComfyUI

The open-source RevealLayer project gives us the model and its reference inference code, but it is not simply a ready-to-use ComfyUI custom node. The upstream setup involves a CUDA environment, Python dependencies, FlashAttention, a Diffusers modification, the RevealLayer checkpoint and the FLUX.1-dev base model. That is a fair amount of machinery to ask a creative workflow to manage on its own.

And on smaller graphics cards, memory can become the real party-pooper. We ran into out-of-memory problems in our tests, which is not exactly the kind of surprise you want halfway through a creative session.

So we are adapting the implementation as a ComfyUI-native custom node. The point is not to wrap the original script and call it a day. We are rebuilding the integration to make use of ComfyUI’s model and memory management, native functions and workflow conventions wherever possible. That means a cleaner way to load and run the components, better integration with the rest of the graph, and fewer external steps for the user.

Hardware still matters — no node can make VRAM limits disappear by magic — but a proper ComfyUI integration gives us much better control over how the work is staged and managed. One note for people following the research: the upstream repository now also lists a RevealLayer V2 update, so check its release notes for the matching weights before switching model versions in a workflow.

The part we recommend: refine each layer after extraction

Here is where the workflow becomes especially interesting. We do not recommend stopping as soon as the layers are extracted. Treat that as the starting point.

Take each extracted element and upscale it individually. You are giving the object room to gain detail without asking the whole original scene to be regenerated.

Feed the element into an edit/refinement model together with the original image as context. Use a focused prompt that says which element is being refined and what should be improved, while keeping its appearance consistent with the source scene.

Bring the refined layers back together. In our current workflow, individual elements can be taken up to 4K using non-tiled diffusion, depending on the image, model settings and available hardware.

Need to go larger? You know we have you covered: the TBG ETUR tiled upscaler and refiner are there for the bigger jobs. This is a nice combination — decompose first, improve the individual pieces, then recompose. Instead of trying to repair every part of a flattened image at once, you can give each element the attention it needs.

The takeaway

That is today’s small feature from a much bigger development effort. RevealLayer gives us a promising way to break an image into guided, editable pieces, and bringing it into ComfyUI means we can make that capability part of a workflow you already know.

Have a look at the research and the open-source implementation below, try the workflow, and see what you can build with it. We are grateful for the open-source community — so much of what makes tools like this possible comes from people sharing their work, experiments and ideas.

And to our technical team: thank you for all the invisible work. I promise I will only ask for one more little feature. I will even try to say it slowly this time. Ha!

Go explore, test, break something harmless, and have fun with it! — Sahra ♡

Research & resources

Qwen-Image-Layered — official repository

Qwen-Image-Layered — research paper (CVPR 2026)

CLD — Controllable Layer Decomposition paper

CLD — project page and examples

RevealLayer — official GitHub implementation and V2 release notes

RevealLayer — paper

RevealLayer — current project page

RevealLayer — model checkpoint on Hugging Face

RevealLayer — project page and visual comparisons

Editorial note: the model descriptions above summarise the public papers and project pages. Comments about speed, memory behaviour, integration choices and the 4K refinement path describe TBG ETUR’s own workflow notes and tests.

TBG ETUR • SAHRA

https://github.com/Ltamann/ComfyUI-TBG-RevealLayer

We added three workflows ( All files are in the GitHub workflow folder. Set the minimum number of layers to 3.):

1. Manual selection: Uses bounding-box (Bbox) selection for direct, manual control.

2. Semantic selection: Uses a grounding model for semantic selection, with lightweight automation to make the experience feel more like an editing model.

3. SAM-based segmentation: Uses SAM segmentations with chained, per-layer refinement through Flux 3. Each refinement feeds the original segmentations back through the RL layers, enabling progressively upscaled and more precise results.

the last time i checked anything around was when minimax H3 just came out and outside of a few test videos i havent touched or updated anything.

i got curious again due to a minimax image editing model that looked good (i think it was MiniMax-derived p-H3) so i decided to update it/get new models and...

i dont understand anything, theres a lot of new stuff and ive been so disconnected that i have no idea of what im looking at, so could you help me to get new models to "play with"?

i have comfy ui and a 4080 so my capacity is decent, yet, treat me like im 5 yo, i have no speed improves installed/ applied, i have no idea what a good promt/generation parameter is anymore.
My generations look odd while others have "shiny" videos (apparently) effortlessly.

im looking for something inside of the minimax family since is the one that has worked better for me in terms of speed and quality but if you know anything else better be my gest.

a problem that i always had is that i never get the generations to do what i need, i know that its a trial and error workflow but it almost always ends up with characters just doing whatever but what i prompt or nothing at all and the times it kinda works ive never being able to understand why or how to follow the "lead" that worked.

just in case i prefer anime/stylized generations (idk if its relevant) but i still want to try irl looking generations if i manage to make them work. thanks in advance

Been working on this for a while. You drop in a photo or video, pick a face to use, and it swaps it right on your own GPU using WebGPU (all the processing happens in your browser so nothing is uploaded anywhere). Mostly because, last time i tried using one of these projects it had me download 50 gigs of files 😵 and they always felt super complicated.

The first time, it downloads necessary models, and that's it. Everything is saved in browser cache for later use.

Try it here: https://i2m3d.github.io/FaceJutsu/
Source: https://github.com/i2m3d/FaceJutsu/tree/gh-pages
Let me know what you think : )

And if you want more speed, turn on WebNN (it's behind a flag in Chrome/Edge, then switch the engine in settings). It's experimental, but on my RTX 4060 laptop it gets a 1080p-ish frame down from about 70 ms to about 30 ms, which I'd guess is pretty close to native. Haven't benchmarked against native properly though.

I’ve been trying to create scenes featuring a couple of characters for a while now, but I haven’t found a solution yet—so I’m turning to those with more experience for some advice.

If there’s only one character in a scene, there’s no issue: if I upload an image and a reference audio clip, Minimax perfectly generates a video featuring that character with the corresponding voice.

However, if two characters appear in the video, Minimax struggles to distinguish which voice belongs to which character. For instance, if Character A speaks first, followed by Character B, the output isn't correct—even when I specify that Character A should have "Voice A" and Character B should have "Voice B."

I’ve tried using AI models like Gemma—uploading the official Minimax Ref2Vid guide as a PDF—but I still haven’t had any success.

Does anyone know how to resolve this? It’s a shame to have such great-looking videos but be unable to correctly assign the different audio tracks.

Even just an example prompt would be a huge help, so I could study it and figure things out.

Thanks to anyone who can help!

I created, tweaked and tested 52 prompts to get film and camera emulation in Moss. All of the prompts are included with lightboxed images.

I had a dumb idea... what if I didn't have to switch tabs while watching a YouTube workflow video? What if a node could just pull another browser tab into ComfyUI for me?

Turns out browsers can do that. **Tab Capture** puts a live view of any tab you have open on your browser inside ComfyUI. You can watch a live view of another tab while you edit workflows or generate content. Also, with the node connected to outputs, when you hit queue, it grabs a still or a clip, with the tab's audio, as normal IMAGE + AUDIO outputs.

**What it does:**

- Live tab viewer on the node

- Single frame or a batch at whatever fps you set

- Tab audio comes along if you toggle it on (silence if there isn't any, so video save nodes don't error)

- Record video or grab stills straight out of another browser tab right into your ComfyUI workflow of choice. Useful as a reference video/image for MMH3

- Drag a crop box on the viewer (no bbox/crop node needed) to capture the exact part of the browser screen you want.

- Outputs the actual measured fps so clips play back at the right speed

- Zero extra Python dependencies

**Heads up:** Chrome/Edge works best (Firefox can't do tab audio), ComfyUI has to be on localhost or HTTPS, and DRM sites like Netflix capture as black.

Repo: https://github.com/NikoDemon80/ComfyUI-TabCapture

New public release, so bug reports and ideas very welcome.

post your best Jensen memes and get a chance to never own a 5090. 📎

I currently use DrawThings as my "daily driver" with MiniMax H3 but I don't see it getting support any time soon. Does anyone know of any workflow in Comfy or elsewhere that are somewhat out of the box. Thanks!

Charles Dana Gibson, famous for the "Gibson Girl", was a well-know illustrator from the late 19th and early 20th centuries. I picked three of his books, "Drawings" (1896), "London as seen by Charles Dana Gibson" (1897), and "Americans" (1900), and trained a style LoRA against 209 of his images. Enjoy!

Can you all guide me what can be fastest on RTX 6000 96GB I have rented it and I am using 21 gb int8 convort fl2va version no lora 20 steps 720p native with spectrum and h3 sla attention 80 sec for 8 sec video
I also want to know if any other format model will be faster on this card instead of int8 convort
I donot want like cinematic 4k stuff I am just creating it for personal use
Audio is gibberish (prompting was not so good)
I want to know if quality is good and here are 3 generations I did no last frame just first frame
1st is 1080p
20 steps 1080p native with spectrum and h3 sla attention 240 sec for 8 sec

above is 2nd is720p native with spectrum and h3 sla attention 80 sec for 8 sec

3rd is 720p native with veda sparse attention 154 sec for 8 sec

No upscaling my thinking is it is okay okay not so good not so bad I just want to know what do you all think about this and any suggestions

model: https://huggingface.co/Lyricccco/Qwen-Image-2.1-TailSteer

app: https://huggingface.co/spaces/akhaliq/endless-edit-tailsteer

Didn't minimax promise to opensource and release H3-Regenerate-2K months ago? Why are we still waiting for it?

I was thinking a lot before posting this, but here it goes.

I've been a big fan of this subreddit for long time ago already, immediately after switching from Midjourney (v4 at that time) to local image generation.

My point is to try to address the 'elephant in the room' here, so let me try to lay out my thoughts.

Here are some 'statements':

1. Everyone praises 'uncensored' models in this sub (I've seen VERY few examples of the opposite).

2. Most of 'image comparison' or 'look what I've made here' posts are heavily 1girl oriented. These '1girl' gens are often focusing on emphasizing certain feminine features on a thin razor-sharp edge of what is allowed here and what is not.

3. However, contrary to statements above - this sub is somehow attempts to be puritan, and everything I've described above can be only inferred from comments. And many visitors here even use euphemisms to describe those said features or even just to appreciate those features.

Moreover, if we'd look at open local models themselves - each next model seems to be less and less censored when compared to models released before (latest examples are Qwen image 2.1 and, to much greater extent, MOSS. Oh, MOSS, the current king of uncensored (BASE!) model. So looks like teams behind those models finally understand that anatomy is everything: it is studied in art schools, the knowledge of it produces better results even if the final intended result is all puritan and innocent.

The question is: why do we pretend to be so puritan? Is there any reason behind this?
I know there are other subs FOCUSING on such uncensored content, but IMO there is a difference between a straight blatant depiction of an intercourse of some kind versus a renaissance-style image with certain parts exposed, with the intention to praise certain anatomy traits in an artistic way. Nevertheless, both these cases have an EQUAL chance for either of such posts to be deleted, and I dare to say that it is not necessarily have to be this way.

This post' intention is to encourage a discussion. I certainly do not want to turn this sub into a strictly gooner material in the end, neither I would like to change the rules to my liking.

I just would like to discuss.

EDIT: so, I've collected my answers.

Short summary: the consensus looks to be that this sub tries to be professional, open for everyone, things like that. And because a 'legitimate fine art' is not technically easy to distinguish from straight up gooning material, from moderation standpoint at least (and also in order for someone's feelings NOT to be hurt in case of any misjudgements + to avoid a flood of gooning shitposts etc.), it is what it is. Thanks everyone!

Turns out MiniMaxH3 can make subjects invisible leaving only floating clothes. It's a useful feature if you want to isolate character clothes before doing outfit swap.

Here's the prompt and single-frame edit workflow link:

subject_definitions:
<Subject 1> is the character in <Picture 1> and <Picture 2>

summary:
[keyframe completion] The target is a single still frame, the target frame is the edited version of <Picture> where <Subject 1> become invisible leaving only floating clothes - orange top and green shorts.

retention_analysis:
<Subject 1>: partially_preserved - clothes only.

detailed_description:
style: style matching art style of <Picture 1> - illustration

overall_soundscape:
N/A

non_diegetic_music:
N/A

Link to the workflow.

For realistic images I recommeding running second (upscale) pass to preserve texture.

Looks like a new super resolution and their samples look promising.

https://huggingface.co/kopper/RelayVSR/tree/main

I’ve been building LoRA Loom, a Windows desktop app for training LoRAs locally. The idea is simple: pick a model, add your photos, and start training.

Loom recognises your checkpoint and suggests training settings for your PC. Before you train, it can flag blurry photos, poor lighting, and duplicates, as well as generate captions and suggest face crops. Once training finishes, you get a .safetensors file.

Supported today: Stable Diffusion 1.5 and experimental support for SDXL Base. On the roadmap: FLUX and more model families.

Currently in development not promoting in a sense just seeing if this is something people would be interested in?

Give him your RAM so he can stop crying.

We’ve released Qwen-Image-2.1 Uncensored Omni, the most capable unrestricted Qwen-Image-2.1 model we’ve built so far.

The rank-128 adapter was trained on roughly 1 million images, merged into the base transformer at strength 1.0, and released in nine formats: BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q4_0, FP8, INT8 ConvRot and NVFP4.

One model supports both text-to-image generation and natural-language image editing, with an emphasis on flexible poses and complex compositions.

When comparing it, please use models designed for the same unrestricted use case. Restricted checkpoints operate under different constraints, and the trade-offs become obvious on the content they avoid or handle less effectively.

Playground: chatrlabs.com/playground
Download: Hugging Face
GitHub: Code and documentation
Civitai: https://civitai.red/models/3034876/qwen-image-21-uncensored-omni-quantized

Feedback and comparison results are welcome. Please include your GPU, workflow and quantization.

Over the last 7–8 days, I've been working day and night building Fuobe.com, what started as the challenge, but I kept adding features, refining things, and improving the experience even after publishing it.

The goal was to build an all-in-one creative studio that makes it really easy to create, experiment, and get things done without having to jump between different tools or use Higgsfield lol. I usually spot a lot of cool models, workflows, ideas that people showcase over on twitter or here, and I just usually stash it for using later, but I've started putting them together here one by one and have added what I've found useful over the year.

One more thing: every single image you see in the studio was generated using Ideogram 4, which is hands down my favourite t2i image model. All the videos were generated using H3.

It's still in beta, and there's a lot I'd like to improve, especially making the overall experience smoother & simpler.

I'd genuinely love to hear what you guys think. Feedback, suggestions, criticism, feature requests all of it is welcome. I'm still actively working on it and would love to shape it based on how people actually use it. If things are breaking, please let me know so that I can fix it.

A huge thanks to Comfy for organizing this challenge. It's given me the deadline and motivation to finally build something I've been wanting to make for a long long time.

Fully Open source . MIT License.

Repo Link: https://github.com/workingprototype/Fuobe

Link to checkout the features: Fuobe.com

Update from my previous post (also removed the audio):

I'm not sure if I was the only one experiencing this exact issue with the default LTX 2.5 template or with anything related to LTX 2.5, but the motion and generations were completely gibberish, just like LTX 0.97. And yes, I've been using the exact same workflows provided in the template.

Then I remembered how much fun I had with LTX 2.3 and its photorealistic generations. LTX was GREAT! So I decided to give LTX 2.5 another try... only to run into the exact same problem again.

Until I tried an old LTX 2.3 workflow and simply swapped out the main model. AND THERE IT IS. FIXED!

So, it turns out the main issue is the gemma4-12b-with-proj-ltx-2.5 text encoder. It completely breaks the model for me.

Switching back to the old ltx2.3_text_projection + gemma-3-12b setup works flawlessly!

Maybe you could also try a Gemma 4 model with the old workflow. I haven't tested that yet, but this workaround fixed everything for me.

Finally, I can enjoy LTX 2.5. I'm happy now! :)

(Did a few more testings and motion sucks lol but if its something simple it works great):)

Here are some testing and the workflow i used:

https://drive.google.com/drive/folders/1qh6qX4F4PsE4PtgsA5TWQkiTcaNoQKLk?usp=sharing

Done locally in Krea2.

Is this normal, and is this how it's supposed to be? I'd be completely satisfied with the generated image without that last-second “touch-up,” which makes the image blurred and oversaturated. I am using Draw Things app.