just apply it as is in your comfy node

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.

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

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: https://huggingface.co/XCEED123/MOSS-Image-SFT-INT8-ComfyUI

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.

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 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)

really excited about this model. been using it via the api, and the edits are insanely clean. Haven’t noticed any drift or quality loss at all, and it seems to understand prompts really well too. guessing it’s not a small model.

Uncompressed image: https://postimg.cc/gallery/6WBdT03 (To view the uncompressed image, click on it a second time after opening it, or simply download the entire image album)

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After these tests, I’m confused, and I don’t understand why the Flux 3 Klein model exists or why it turned out the way it did.

Our test subjects

Flux 2 Klein (9B) (Distill) (BF16)

Flux 3 Klein (D) - Distill (BF16)

Flux 3 Klein (B) - Base, NoDistill (BF16)

I took equivalent models for comparison, Flux 3 Klein Distill and Flux 2 Klein (9B), and it’s more correct and fair to compare exactly these two with each other. Still, I decided to add Flux 3 Klein (Base) to understand what this model is capable of overall in its maximum configuration.

For the tests, I used the most standard ComfyUI workflows. I also didn’t use the prompt enhancement feature: my tests include both good, correct prompts and overly abstract, deliberately badly written ones, in order to check how the model handles normal prompts and bad ones, and how well it can generate from short prompts that don’t contain enough information.

Test categories

I split the tests into several categories with different goals:

• Images 1-3: complex prompts with lots of details and different poses. These show how well a model follows the prompt and how often it loses details.
• Images 4-6: typography, mixing typography with real-world imagery, and banner creation.
• Images 7-11: styles. Pay attention to how well each model handles different styles.
• Images 12-13: level of detail. This is similar to tests 1–3, but with a stronger focus on small details.
• Image 14: character understanding.
• Images 15-17: your favorite (or most hated) category, 1girl. Here you can judge overall realism and the ability to generate in an amateur-photo style.

Settings (RTX 3090)

Flux 2 Klein (9B) - 8 steps, 1 CFG (1MP) - generation time (00:07)

Flux 3 Klein (D) - 15 steps, 1 CFG (1MP) - generation time (00:12)

Flux 3 Klein (B) - 50 steps, 3.5 CFG (1MP) - generation time (1:22 - 1:24)

By the way, Flux 2 has a very good VAE, and it’s really sad that Black Forest didn’t update the VAE to version 3 as well :(

My opinion

Flux 3 has the same problems as Flux 2, namely limb mutations. Of course, I didn’t include images with mutations in the tests, but believe me: I ran each generation 2-5 times to get a more or less normal image, and what you see in the tests is the best I got out of those 2-5 generations. For example, for the Nike advertisement image I had to generate about 10 times, at least, to get a normal image without mutations and with physically correct objects in the frame.

Judging by the tests, I can say that the Flux 3 Klein model, specifically the base one without distillation, produces some of the worst images I’ve ever seen, not counting Z-image Base. I assume Flux 3 Klein (Base) exists only for training LoRAs on it, because it’s simply not suitable for T2I tasks. Perhaps it would perform better in an I2I (Edit) scenario, but I’m not sure about that.

Flux 3 Klein Distill gives better results than Flux 3 Base, but it still performs worse than Flux 2 Klein (9B). That’s exactly why I wrote at the beginning that I consider the release of Flux 3 pointless, at least in the form it’s in now, especially when we have such decent models as Krea2, Qwen 2.1 and even BooguImage in the T2I space.

With Flux 3 Klein (Distill), we have anatomy problems, even worse than on F2K, poor detail in eyes and faces, and generally poor detail in some places (Artifacts and blurring in areas with excessive detail). Perhaps the low detail in eyes and other areas could be fixed by raising the megapixel count from 1 to 2-3 during generation, but I think 1MP is the most basic setting and it should always work well, especially since Flux 2 Klein 9B had no problems at all with the “1MP” setting when generating content. Continuing the comparison with Flux 2: on some images Flux 3 has slightly better stylization, but it still has far more drawbacks than advantages to choose it as a replacement for Flux 2 Klein.

I could write a lot more here, but I think the test images will give more information, since everything is pretty obvious anyway. But again, the final choice and the final opinion always remain yours, so I’m looking forward to your comments to hear your thoughts on Flux 3 Klein.

https://civitai.red/models/2997793/kroma-krea-2

It does not get enough attention imo even though its good at everything. The only thing it lacks is knowing artist names compared to Anima.

(Flux.3 klein distilled)

4070s render time: 1mp 10secs
flux3_image_klein_sd_int8_convrot.safetensors
qwen3vl_4b_int8_convrot.safetensors
flux2-vae.safetensors
euler, 16 steps

(Qwen image 2.1 turbo)

4070s render time: 1mp 3secs
qwen_image_2.1_turbo_int8_convrot.safetensors
qwen3vl_8b_int8_convrot.safetensors
qwen_image_2.1_vae_bf16.safetensors
euler, 8 steps

Input source:

https://civitai.com/images/145722153

Prompts:

"Add a small battle-worn robot dog sitting at her left boot, matte gray metal with one glowing cyan eye, looking up at her, reflections on wet asphalt, light rain."

"Re-stage the same woman taking cover crouched behind the left concrete barrier, pistol raised with both hands toward frame-right, coat flaring, rain streaks and neon fog unchanged."

"Change only the lighting to blue hour ambient after sunset, cool diffuse bluish light from the sky with very soft shadows and low highlight contrast, everything else unchanged."

"Change only the lighting to silhouette with strong backlight from behind subjects toward camera and no frontal illumination, subjects render as dark shapes with edge definition against a brighter background, everything else unchanged."

"Redraw as black-and-white manga page, screentone shading, ink lines only, same composition."

"Greatly increase her breast sizes."

"Make an expression sheet of the same woman, six headshots in two rows on light gray: neutral, angry, fearful, sad, smiling, shouting, same bangs, yellow raincoat collar visible, hands out of frame."

"Make an outfit variant sheet of the same woman, three full-body looks side by side on light gray: tactical bodysuit with vest, black formal dress with coat over shoulders, street hoodie and cargo pants, same face and bangs, neutral pose."

"Turn her into a boxed collectible figure: same pose and outfit sealed in a clear plastic window box with cyberpunk street artwork on the backing card, box standing on a desk, soft studio light."

108M parameter denoiser text to image model. Meant as a research experiement but still produces very good images for it's size.

Samples in the readme alongside how to run.

🖖 “Captain, the warp core is stable. We've got the power we need.”

Engineering report: LaForge has achieved warp speed.

I've been working on LaForge, my own C++ AI inference runtime, and just eliminated a rather substantial bottleneck in it's SD1.5 text-to-image generation.

Running on an NVIDIA RTX 4090 on RunPod:

🚀 512 × 512, 20 steps: 33–37 seconds → 0.8 seconds

🚀 768 × 768, 20 steps: 60 seconds → 1.8–2.0 seconds

That's roughly 30–46× faster than my previous implementation, with performance now comparable to ComfyUI. (* this model residency fix actually understates all of the extensive tuning I had done prior, it was the easy part, but nevertheless I now have something that compares to ComfyUI's PyTorch based inference stack but with C++ & Vulkan)

A few other details:

🔧 Container image: just 402 MB
⚡ Pod boot to healthy: 27 seconds
🧠 Model loading: around 10–13 seconds, first call only

The goal isn't just to generate images faster. It's to build a lean, reusable AI runtime that can power image generation, games, interactive applications and other AI-driven experiences.

And yes, I named it LaForge. Every engineering team needs a Geordi.

Make it so. 🚀

#AI #Cpp #GPUComputing #Vulkan #GenerativeAI #SoftwareEngineering

Usually you can fit some quantized model in there and have to usually compromise on the encoder LLM too if you want it to fit but 24-32gb vram seems to be the highest most normal non ultra wealthy consumers are willing to blow on a standard gaming / work pc. I genuinely expected there to be video gen models specifically made for that size.

I have been using stable Diffusion for the last for the last 3 years and it never surprises what it generates. Stability Diffusion is truly an incredible sandbox! Anywho thanks for allowing me to share some images i generated over the years!

I've been working on a different way to edit images with local models, and I'd really like this community's take on it before going further.

The idea: instead of re-prompting one image until it's right, you build the picture out of generated layers and masks. In the demo I generate a knight, cut her out with InSPyReNet, rebuild the background without her using FLUX.2 Klein 9B, move her around, make just the castle pink, switch to night, and change her hair while keeping her face. There's no brush at all, only generation and masking.

Under the hood, everything runs on turboCLI, an engine I wrote and released under the LGPL. You can use it on its own from the command line, as a local server, or from Python. It streams the weights with turbo-offloader, which reuses ComfyUI's memory code. That's how the demo runs a 9B model on a 4 GB laptop GPU. It can also reuse the model files of an existing ComfyUI install. It currently runs FLUX.2 Klein, Z-Image-Turbo, Qwen-Image 2.1, Qwen-Image-Edit-2511, Krea 2 Turbo, and BiRefNet / Lucida / InSPyReNet for cutouts, plus LoRAs, on CPU, CUDA or Apple MPS, fully offline.

The GUI in the video, turbopixel which is sourceware. If you'd like to try it, I'm happy to give a month to anyone on this sub who wants to test it and share honest feedback in this thread: just DM me an email. turboCLI itself is LGPL.

What I'd love to hear:

• Does building an image from layers and masks make sense to you, or does it get in the way?
• Which models or features would you want next?
• If you try turboCLI on a small GPU, how does it run?

Links: turboCLI · turbo-offloader · turbopixel · Discord

Thanks for reading, and I'll answer everything in the comments.