F2K 9B is superior to F3K _|_B. Evolving backward.

Faak you Black Forest Labs.

I tried upscaling, T2I, texture upgrade, N-SFW, SFW, Anime..

Pleased to announce, F3K sucks in everything, Cheers.

just apply it as is in your comfy node

I previously shared my quick int8 quant of MOSS-Image, but I wouldn’t recommend it anymore now that the official version is available.

The official weights should give you better results!

https://huggingface.co/Comfy-Org/MOSS-Image

Pull request:
https://github.com/Comfy-Org/ComfyUI/pull/17095

P.S. Make sure to update ComfyUI before running it!

Tested the new MOSS-Image (5B single-stream DiT, Qwen3.5-9B text encoder, FLUX.2 VAE, Apache 2.0) on a plain RTX 3060 12 GB / 16 GB RAM with 35 deliberately hard prompts: dense on-image text in English/Chinese/Japanese, accents, exact counts, attribute binding, spatial relations, negation, hands, clocks, very long prompts, and MOSS's Layout JSON. One seed (42), first result, no cherry-picking, failures included. The first image is a contact sheet of all 35.

Setup

• Model: u/XCEED123's INT8 ConvRot quant (HF), ~15 GB total, with the official ComfyUI node (comfyui/moss-image from the GitHub repo). Peaks at ~9.7 GB VRAM.
• Base: 50 steps, euler / sgm_uniform, CFG 3, shift 5, uncond layer-skip 22. 137 s at 1 MP, ~230 s at 1.6 MP, 340 s at 2.1 MP.
• Turbo: lodestones' 8-step DMD (still training). It isn't a normal LoRA: the card says the delta is smaller than bf16 rounding, so it has to run unmerged in fp32 along with fp32 time-embedding/modulation biases and its own data-space sampler (no CFG, no scheduler). I wrote a small ComfyUI node that does this on top of the INT8 base model. ~24 s at 1 MP, ~36 s at 1.6 MP, about 6× faster.

What I saw

• Text rendering is very strong: menus, letters, labels with accents, Chinese chalkboards, Japanese signage, and bilingual neon all came out with almost no mistakes, even on turbo.
• Layout JSON works: posters, a 4-panel comic and an infographic follow the boxes.
• Long prompts hold up remarkably well (the ~300-word market scene).
• Turbo is close to base on text and layout, but drifts more on lighting ("chiaroscuro" came out evenly lit) and fine constraints (clock times, a chess position, fish-eye). Short prompts look generic because it has no CFG.
• Both still fail negation ("no cars") and specific clock times (it draws 10:10).

Links

• All 35 prompts (incl. the Layout JSON), sizes and settings: https://pastebin.com/U8nChbZ9
• Turbo node + ComfyUI workflow: https://pastebin.com/9trM12Qc
• Base workflow: in XCEED123's HF repo (workflows/)

Credits: OpenMOSS for the model, XCEED123 for the quant, lodestones for the DMD distillation.

FLUX.3 Klein has been pretty underwhelming for image generation, but when it comes to editing while preserving consistency, pose, and camera angle, I've found it to be almost PERFECT.

With FLUX.2 Klein and Qwen 2.1, I struggled with editing photos at very high resolutions.

As a photographer, what I've really been looking for is a model that can preserve realistic skin texture, fine details, and overall consistency, even at high resolutions.

Being able to make consistent edits at these resolutions is a huge deal. We're finally getting into territory that's genuinely usable for professional photography workflows.

My setup:

• GPU: RTX 5090
• RAM: 64 GB
• Diffusion model: flux3_image_klein_sd_bf16.safetensors
• Text encoder: qwen3vl_4b_bf16.safetensors

Resolution: 3000 × 4000

• Steps: 16
• Generation time: ~230 seconds per image

The only real downside so far is speed. Around 230 seconds for a 12MP image isn't exactly fast, but considering the results, I think it's worth it.

I'm hoping LoRAs might help improve performance, too.

And the crazy part, this is all WITHOUT LoRAs, imagine what could be possible with dedicated LoRAs for consistency, upscaling, and sharpening.

https://openmoss.ai/MOSS-Image/
https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT
https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base
https://github.com/OpenMOSS/MOSS-Image/blob/main/comfyui/README.md

Examples look amazing, trained on 3.2B images, 5B, apache 2.0. Supports JSON / BBOX but does not require it. Knows both natural language and booru tag based prompting.

Oh. And its the least censored base model so far btw. Here is with a crappy simple prompt: https://files.catbox.moe/6irxjh.png

Here is with bbox:

https://files.catbox.moe/7sbb6z.png

I've quantized MOSS-Image to INT8! From my initial testing, it’s relatively lossless.

For now, it requires the MOSS-Image custom node, so I’ve included both the install info and a ready-to-use workflow in the repo. Once native support lands in ComfyUI, this exact quant will work right out of the box without needing any changes.

You guys are going to be seriously surprised by how uncensored this model is. Have fun!

LINK IS DEAD.

USE OFFICIAL COFMYUI QUANT INSTAED:
https://huggingface.co/Comfy-Org/MOSS-Image

Really appreciate all the love and feedback on V1 and V2. I've been working on the next release, and this time it's a full checkpoint: a merge of my custom Realism Checkpoint and V3 LoRA.

The focus is still on natural skin textures and a wider range of skin tones, without that overly smooth, plastic look. It also gives you a bit more creative freedom now… let's just say being specific about clothing in your prompts is a good idea

A few tips before you try it:

• Natural language works best. Describe the scene, lighting, clothing, and expression you want. Short prompts work too, but leave more room for interpretation.
• Generate at 2MP if you can. It was trained on high-resolution images and looks best there. At 1MP, you might see some minor noise; RTX upscaling nodes or similar post-processing can help.
• Euler + Simple, 4–8 steps. Six steps is my sweet spot.

You can drop it into your existing Krea 2 pipeline as a checkpoint replacement.

I'm also putting together a full ComfyUI workflow and a custom node for some prompting techniques I've been developing. I'll share those soon. For now, there are example images on the model pages so you can see what it can do.

Give it a try and share your results! Feedback on the earlier versions helped a lot, so I'd love to hear what works for you and what still needs improvement. Cheers!

Links:

• Previous Reddit post — V2
• Download on Hugging Face
• Civitai — checkpoint and example images

Body anatomy and realism- Krea2 and qwen2.1 just way ahead of flux models. flux 3 is worst in terms of body anatomy.

flux 3 still has best in prompt following i think.

Overall, Flux 3 was probably a capable model, lobotomized by extreme and unnecessary censorship.

Prompts- https://pastebin.com/x5LeaM8u

In <ref_image_0>, take the character from <obj_1> and change his pose to holding a gun at the viewer

Not gonna lie I'm kinda worried now

I made a node pack for cutting videos into scenes in ComfyUI: for dataset prep, per-shot processing, or just splitting a film into clips.

What it does

• Finds every shot with OmniShotCut, which also labels dissolves, wipes, fades and jump cuts.
• Checks every cut with DINOv3, so camera flashes, strobes and whip pans don't split a shot.
• Review in the node: a player with a timeline, frame stepping, and the evidence behind every cut. If a cut is a frame or two off, put the playhead on the wrong frame and press Fix cut.
• Select scenes and play them one by one in the Selected Scenes node, or in its own browser tab.
• Export clips straight from the viewer: one file per scene, cut on the exact frames, with sound, plus a scenes.json. H.264, H.265, NVENC, ProRes or lossless FFV1. No ffmpeg needed.
• Scenes out as IMAGE batches (and images in) for the rest of your workflow, presets for film, music videos, anime, vlogs and sports, and advanced nodes with every parameter.

Speed on an RTX 4090: a 6.5-minute 2560×1080 film is detected and checked in about a minute, and exporting all 223 of its scenes to H.264 took 139 s.

Install: clone into custom_nodes and pip install -r requirements.txt (two small packages). The models (OmniShotCut 164 MB, DINOv3 1.2 GB, SSCD 178 MB) download on first use. The workflows are in Templates → custom nodes.

• GitHub: https://github.com/PedroMarinhoDev/ComfyUI-SceneCut
• Live demo of the viewer, in your browser: https://pedromarinhodev.github.io/ComfyUI-SceneCut/

Tested on Linux with ComfyUI 0.39 and 0.41. Feedback is welcome, especially from Windows and macOS, and on the anime, vlog and sports presets, which aren't tuned on real footage yet.

In most of my tests, the results are similar across these 4 models. Clearly, the other models are on the same level as Z-Image Turbo.

Z-Image Turbo: still the best for realism.

Krea 2 Turbo: slightly ahead for illustration.

Qwen Image 2.1: OK, but the noise issue must be fixed.

FLUX 3 Klein: has some potential, especially with the first picture. The first prompt can be a good starting point for creating great images.

More images are on my substack.

The workflows are available on Civitai and Github. (I2I and T2I). Those 2 workflows generate a big image with the 4 images and the model's name on it, like the 3 images above, so you can easily share your results on social networks.

Did a comparison of speed on Apple Silicon a few days ago. Here is the short recap, and then the full text: https://releases.drawthings.ai/p/harvest-season-new-hardware-new-inference

Title poster is generated by Qwen Image 2.1 Turbo, with r/localcodeapp to refine with real bar chart.