LoRA for reducing the soft & smooth AI look and adds more texture and details to images.

Dataset was high-qulality SFW Professional Photography images with wide variety like portraits, lifestyle, fashion, wildlife, sports fitness, architecutre, nature and more.

It is trained on sft version of moss. Samples are generated with Lodestone's turbo (which he said is still in training) lora 14 steps and 1.0 CFG, works very well with it.

Still looking for better turbo worfklow this is the best I had.

LoRA Link - https://civitai.red/models/2462105/ultrareal-moss-krea2-klein9b
Workflow - https://github.com/vizsumit/comfyui-workflows
Prompts are from this free prompt website - https://promptdexter.com

When you try to generate an image on the Krea 3 website, this message appears. I’ve seen many people here hoping that Krea 3 would be a great text-to-image model, but I haven’t seen anyone here mention this message.

*2.25MP, 16steps, distilled model.

First i tried: Woman lying on grass. High quality. 

Second: Woman lying on grass, arms crossed. Perfect anatomy. High quality. Top-down camera perspective.

Then, a challenge: Woman wearing red bikini lying on sand, arms crossed. Beach settings. High quality. Perfect anatomy. Perfect facial anatomy. Top-down camera perspective.

I did NOT create this. It's made by the legend Cyberdelia and it's in early access on CivitAI.

"CyberRealistic Z-Image Turbo is a realism-focused finetune of Z-Image Turbo by Tongyi-MAI.

The idea behind it is deliberately simple: keep what makes Z-Image Turbo good - speed, strong prompt understanding, good composition and extremely efficient few-step generation - while moving the default visual language further toward believable photography.

CyberRealistic doesn't try to turn Z-Image Turbo into a completely different model. The original already has a very capable photographic foundation. The finetune mainly changes what the model considers a "normal" photograph: more natural skin, less synthetic rendering, more believable faces, stronger material texture, more grounded lighting and better anatomical consistency.

Z-Image Turbo already knows how to make a good image. CyberRealistic mainly changes where it starts" - CyberRealistic Z-Image Turbo.

Everyone, including me was super hyped about Qwen image 2.1 but honestly i found the results to be wanting. In this video i show exactly why and also why its still a good model.

I mean it is a different link from BFL...

Some test with 4 step lora.

I've added tcg layout to test his prompt understanding and text rendering.

I'm impressed by the quality of this model.

How to:
For who want to try 4 step lora, i'm using this but i've use only the lora.safetensor loaded as default lora with clip and weight set to 1 (with CR lora loader).

Image are done at 2400x1792, 4 step, beta scheduler, er_sde sampler, cfg 3.

I also using dual cfg guider: positive prompt to both cond, and negative to negative.

Use default sft model, clip and vae.

Use last version of ComfyUI, or update to last.

After many generation, The model seem do better results on landscape image, and seem really like anime.

Actually i've not tested on realistic render, test on it later.

What your impression about this model? i've see many post, some like, some not because is not an edit model, so evalute it for what is it: a text to image model.

View on YouTube while video is pending here if needed. https://youtu.be/acmAZolUw68

I did not do any Post edits on this. At all. This was straight from the video builder.

• The song, made with Suno, lyrics created by Claude.

• I came up with the story and the band's character sheets, and the locations. Used GPT image 2 to create them.

Model settings: MiniMax H3 Singularity, 25 steps on pass 1, then the 8-step 768p turbo LoRA at 4 steps and 0.35 denoise on pass 2 with the latent upscale model.

This used my ComfyUI video builder which has MCP/API support so you can have an agent run it for you and then open up the created project later to see it and play around with it.

The video builder can create all the prompts for you but for this Video I did have Claude use the API / MCP stuff to help create the prompts. You can use any LLM agent to do this.

What Claude did on its own:

• Planned 28 scenes and wrote every H3 prompt shot by shot, with per-shot lyric cues and emotion tags

• Wrote and voiced the spoken intro and outro with ElevenLabs Eleven v4 plus ElevenLabs sound effects (I gave it my API key. I'm only on the $5 plan FYI)

• Rendered overnight through the VRGDG Video Builder's API: 45 renders, about 8 GPU hours on a 5090 and this included re-takes and alternate scene versions.

• Checked every take frame by frame and re-shot the problems.

• Checked every cut, and the audio sync of all 28 scenes came out at 0 ms

• Made optional versions where scenes continue straight from the previous one using latent continuation

You can find my Video builder on GitHub https://github.com/vrgamegirl19/comfyui-vrgamedevgirl
We are working on a new story mode which will allow you to create video's just like this one when it comes to the story flow - But no need for Claude!

You can find a 2-pass workflow in my workflows folder.

Workflows\MiniMaxH3_2Pass_LatentUpscale_Shareable.json

Same prompt. 3 MP. Four generations per model. Best image selected.

The pattern I keep seeing: larger DiTs tend to produce more coherent images. Cleaner shapes, better-defined objects, less visual garbage. Mechanical structures make more sense, spatial relationships hold together better, and fewer generations end up unusable.

This isn’t a conclusion drawn from one prompt. It’s an observation from working with different models over time. This test is simply one example of that pattern.

I’d rather have a larger model in INT4 than a smaller one at full precision.

FLUX 2 Klein 9B is a perfect example: in my testing, it’s better than FLUX 3 Klein in practically every aspect of image quality—even with the updated FLUX 3 workflow.

My working hypothesis is that small DiTs struggle to learn and distinguish enough concepts to be strong general-purpose image models. They can excel within a narrow range and still fall apart outside it.

Based on my experience, my preferred range is:

• 12–16B DiT: the sweet spot.
• 9–10B: already borderline.
• Around 5B: too many compromises for a general-purpose model.
Beyond 16B, I’d expect diminishing returns—but I wouldn’t pretend this test establishes a hard cutoff.
I’m also not convinced we need huge text encoders. For the text-to-image tasks I care about, a capable 4B encoder often seems sufficient. I’d rather see more capacity in the DiT.

Make models accessible through 4-bit quantization. Krea 2 with an INT4 DiT and INT4 text encoder runs well on an 8 GB GPU in my setup.

I've been generating for a year or so, and what hooks me isn't just a pretty face or a cool style, it's a picture with a story in it. A ghost still reading in a library, with a checkout card stamped 1924 on the table. Two gunslingers who've been having the same showdown every night for a hundred years. An old couple dancing outside the house they used to live in.

Scrolling the big galleries, I mostly find portraits and anime, which is fine, but it's not what I'm chasing. So I'm curious who else is out there making images where there's something to figure out, a little detail that makes you look twice.

A bit about me: I'm a longtime photographer and contractor, I trained a LoRA for genuinely see-through ghosts because I couldn't get real transparency any other way, and I've been building my own AI image tool on the side.

Share yours if you make this kind of thing. I'd love to see it, and to know what your process is for finding the story.

First, sorry for my bad English skills; my native tongue is German

So, we are entering a time where it is getting simpler to build software and tools that mimic other software that has closed-source code or is written in a specific programming language.

The main problem with diffusion models is that the tools behind them are 95% compatible only with Nvidia tech.

My wish is that someone uses reverse engineering to rewrite these tools in Rust, making them open-source AND fully compatible with AMD and Intel hardware at the same speed. That way, we don't need Nvidia for everything.

I just wanted to post this here because of the larger knowledge pool when it comes to code and AI.

Have a nice weekend, everyone!

https://github.com/SpendinFR/H3STUDIOFILM

Okay, sorry, but I’ve been busy since last Friday and haven’t had a chance to check Reddit. Now I’m seeing people talking about Flux Klein 3? And apparently it’s terrible (wait, seriously??). And then there’s MOSS Image??

We already have Qwen Image 2.1 and Krea 2, so how do QI 2.1 and Krea 2 against Flux Klein 3 and MOSS Image? Can these two new models actually replace the ones we already have? Too many models, too little HDD space, and too little time, but I am happy :)).

I made a style LoRa based on my own stuff for illustrious and Anima, it works amazing and I've never had any issues with it, but I've been wanting to test MOSS and I saw somewhere that the LoRa training took around 20-40min which I couldn't do with Krea.

I am very interested in knowing how to do that!

i was testing both, but moss are so unrealistic, blurry faces from far way.
flux get more realistic, but, very artifacts like qwen 2.1 too...

so...

Same prompt, same seed, no lora

Flux3 Klein Bf16 in ComfyUi, after the update of the huggingface repo.

First test: a 2x2 character sheet form source image.

Image 1 is source. Image 2 is Qwen-Image 2.1, Image 3 is Flux3 from same source.

Qwen Image is the clear winner IMO.

Second test: a simple color swap on the shirt, from blue to red. Result speaks for itself. Good color swap, but hue shift and introduction of smudging on walls etc.

neat model.

I've only used local LLMs so far, so video generation is completely new to me.

Goal: short promo clips (5 to 15 seconds) to show off a mobile app I built. Think animating app screenshots, a phone in someone's hand at sea, smooth camera moves over a UI. Nothing cinematic, just clean and believable.

Hardware: RTX 5090 (32 GB VRAM), 64 GB RAM.

Questions for people who actually run this locally:

1. Which model gives you the best quality right now for image-to-video and text-to-video?

2. ComfyUI or something simpler for a beginner?

3. Realistic generation time per clip on a 5090?

4. Which of these models have licenses that allow commercial use? These clips would be used in ads, so I want to stay clean on that.

Any workflow you'd recommend for a total beginner is very welcome. Thanks!

Krea 2 and such are great for characters, but what if I just want art?

For example I want a thousand different book covers, each with different designs. Unlit lighting, reusable asset for a game model texture.

How many apps do you know aside from Forge or ComfyUI - that you think is superior or doing decent job?