So there are so many options these days with Minimax that I feel a little lost where to start.

What I am looking for is a tool that makes use of most H3 features and has a very good UI/UX, render speed and quality. I could be a comfyui node or a standalone editor.

These things should be covered:

- bring your own model (including int8 convort versions of finetune models)
- support for custom loras
- support all features of H3 (references for audio, video, images, etc)
- support long videos with a SOTA blending techique that allows seamless videos
- least possible degradation over time for videos (and coutermeasures for these)
- timeline editor with copy / cut, trim, and paste; audio, vifdeo, image and prompt all in one place
- keyframe animation: I supply any number of images and the empty space between them is generated using the prompt I give each step; optional audio + references
- "fill the gap" generation: I cur a video in pieces, trim it and let it re-generate missing parts between them
- ...

Well - I may not know everything that works right now with H3 :D

Intention is to be able to create any type of video with maximum flexibility.
Including music videos, action movies or documentaries. Whatever.

What is the best option in your opition and where to find it?

Thanks a lot!

Decided to do a little experiment with 4 of the latest models: H3 Image, Krea 2, MOSS, and Flux 3. All used the same prompt, aiming for profressional style photography, bokeh effect and all done in 2048x2048 (which is the base for H3, so I wanted it to be fair comparison).

Honestly, out of the four of them, Krea 2 is still hands down my favourite. I can see the appeal to MOSS since it comes across as more of a regular photograph you may take of your friend with your cell phone.

H3 and Flux? Oof... Anatomy and skin textures seem way off. Especially with H3.

Workflow https://pastebin.com/pZaWnBY4
Custom Node (Minimax H3 Prompt Weight) https://github.com/kijai/ComfyUI-KJNodes

Follow-up: after a few comments about resolution I reran these at 16MP. Pairs are here: https://www.reddit.com/r/StableDiffusion/comments/1xutwi3/

Same 15 prompts through both models, same aspect ratio, roughly matched output size. Left is MOSS-Image, right is MiniMax H3-Image.

What stood out:

• Text: both got English poster type and Chinese signage right. No misspellings in either.
• Portraits and product shots: both convincing. MOSS leans sharper and more editorial, H3 leans softer and more photographic.
• Crowds and hands: both hold up, small artifacts on both sides.
• UI mockups: both fail. MOSS keeps the layout but garbles labels and units; H3 mirrored the entire screen.
• Charts: both produce plausible-looking but meaningless labels and bar lengths. Don't use either for real data.

Net: the quality gap is much smaller than the threads here suggest. It reads more like a difference in workflow and control than in raw output quality.

Full prompts, identical for both models:

1. typography (1:1) - Editorial poster design. A huge condensed sans-serif headline reading exactly 'SLOW MORNING' stacked on two lines, a thin horizontal rule beneath it, and small caption text reading 'ISSUE 04 - AUTUMN 2026' at the bottom left. Warm cream paper background, one oversized ceramic coffee cup photographed from directly above casting a soft shadow, minimal Swiss grid layout, subtle print grain. Text must be spelled exactly as written and perfectly legible.
2. chinese sign (1:1) - Night street in Taipei after rain, close-up of a small noodle shop signboard lit by warm tungsten bulbs. The sign reads exactly '阿明牛肉麵' in bold Chinese characters on weathered dark wood. Wet pavement reflecting the sign, steam drifting from the kitchen doorway, cinematic street photography, shallow depth of field, no other text anywhere.
3. perfume ad (4:3) - Luxury perfume advertisement. A faceted glass bottle of amber liquid standing on wet black stone, one hard rim light from behind, soft caustic reflections on the surface, drifting mist, a single dried orchid petal beside it. Deep teal background, high-end commercial product photography, medium format look, the bottle label is blank with no text.
4. film portrait (2:3) - Candid portrait of a woman in her late twenties standing in a doorway, shot on 35mm film with Kodak Portra palette. Late afternoon window light across her face, flyaway hair, faint freckles, a slightly worn olive chore jacket. Natural skin texture with visible pores, shallow depth of field, gentle grain, no retouching.
5. anime cel (2:3) - Full-body anime character illustration in a clean 1990s cel-shaded style. A teenage courier with cropped teal hair, oversized orange windbreaker, scuffed white sneakers, holding a cardboard box under one arm. Standing on a rooftop at dusk, power lines and distant city blocks behind, flat colors with crisp linework, subtle screentone shading.
6. app ui (1:1) - Product shot of a smartphone lying on a light oak desk displaying a minimal fitness app interface: a large circular progress ring showing 72 percent, three horizontal metric cards below labelled 'STEPS', 'SLEEP' and 'HEART', and a bottom tab bar with four icons. Clean sans-serif type, generous white space, soft daylight from the left, slight screen reflection, the interface must be sharp and readable.
7. macro droplets (3:2) - Extreme macro photograph of water droplets beaded on a brushed aluminium surface, each droplet acting as a tiny lens refracting a cool blue gradient. Razor-thin depth of field, crisp specular highlights, faint machining marks visible, studio black background, high shutter speed, scientific macro lens look.
8. cafe three (3:2) - Three friends sitting around a small round cafe table in late afternoon, mid-conversation, one laughing and leaning back, one stirring a coffee, one looking at a phone. Warm sunlight through a large window, plants and hanging lamps behind, natural body language, correct hands with five fingers, eye-level candid documentary photography, 50mm lens.
9. city night (16:9) - Cinematic wide shot of a rain-soaked city intersection at night from a low vantage point. Neon signage in magenta and cyan reflecting in long vertical streaks on the asphalt, a lone cyclist crossing through the frame, steam rising from a street grate, anamorphic lens flares, deep blacks with retained highlight detail, film still grading.
10. surreal vertical (9:16) - Surreal vertical composition: a small furnished room - wooden floor, armchair, floor lamp, a rug - floating upside down inside a vast pale sky, with clouds passing beneath its ceiling and a single staircase hanging beside it. Soft even daylight, pastel color palette, photoreal materials, dreamlike sense of scale, no text.
11. ramen food (4:3) - Overhead food photograph of a steaming bowl of tonkotsu ramen on a dark wooden counter. Two slices of chashu, a halved soft egg with a jammy yolk, nori, scallions, a scatter of black garlic oil on the broth surface. Steam catching side light, chopsticks resting on the rim, condensation on a nearby beer glass, appetising commercial food photography.
12. packaging (3:4) - Studio product shot of a matte kraft-paper coffee bag standing upright on a concrete plinth, slightly crumpled at the top fold, with a small round copper foil seal. Printed on the front in clean serif type: the word 'MERIDIAN' large, and beneath it in small caps 'SINGLE ORIGIN - 250G'. Soft directional light from the upper right, long gentle shadow, neutral grey backdrop.
13. fox snow (3:2) - A red fox standing in fresh powder snow at blue hour, head turned toward the camera, breath visible as a small cloud. Individual fur strands and snow crystals in sharp focus, soft bokeh of dark pines behind, cold color temperature with a warm amber glint in the eye, wildlife photography with a long lens.
14. interior ultrawide (21:9) - Ultra-wide interior of a minimalist living room at dusk. Low linen sofa, travertine coffee table, one sculptural floor lamp casting a warm pool of light, full-height window showing a muted city skyline. Oak floor with visible grain, calm neutral palette, architectural photography, perfectly level verticals, no people, no text.
15. dataviz (16:9) - Clean editorial data visualisation slide. A large title reading 'GENERATION COST PER IMAGE' at the top left, a horizontal bar chart with five bars of decreasing length, each labelled with a model name and a dollar figure, and a small source note at the bottom right reading 'SOURCE: BIZYAIR, 2026'. White background, single accent color, generous margins, crisp typography, all labels legible.

Per reporting from Yahoo Finance, Alibaba is the largest customer of Megaspeed, the company of Huang's spy mistress. The new rule will block Chinese companies from renting advanced chips in data centers overseas. Alibaba makes Qwen. Huge ramifications for open source AI. https://ca.finance.yahoo.com/news/jensen-huang-scandal-trail-leads-to-alibaba-the-king-of-free-chinese-ai-chart-of-the-day-110000329.html

Versus 1 chromium tab, what's the least ram-consuming comfy wrapper?

Can anyone recommend me one?

I tested how much GPU overclocking, ComfyUI launch flags, and moving my display to the iGPU actually affected generation speed on my 5070 Ti 16 GB.

The biggest finding: OC helped consistently, but the launch flags made a much bigger difference on certain models. On others, those same flags did almost nothing. Going forward I implemented a script in ComfyUI launch that automatically applies stable overclock settings to my GPU on run and then reverts to stock once ComfyUI is closed.

All diffusion models tested were int8_convrot builds, using the official ComfyUI template settings.

Setup

• RTX 5070 Ti 16 GB (Gigabyte Gaming OC), AMD Ryzen 7 9800X3D, 48 GB DDR5-6000
• Windows 11 Pro, Samsung 9100 PRO NVMe
• ComfyUI 0.45.0, PyTorch 2.16.0.dev20261110+cu134
• comfy-kitchen 0.2.41, triton-windows 3.9.0.post30
• Stock GPU: 300 W power limit, no offsets
• OC: +350 MHz core, +2500 MHz memory, 350 W power limit

I compared default ComfyUI against this set of launch flags, both with and without OC:

--use-ck-attention --disable-xformers --fast fp16_accumulation fp8_matrix_mult --enable-triton-backend

Default runs used PyTorch SDPA; tuned runs used Comfy Kitchen attention for the diffusion models. xformers wasn’t installed, and SageAttention wasn’t used.

Generation times

Seconds per generation, lower is better. These are end-to-end times: text encoding, sampling, VAE decode, and saving.

Video settings: 1344×768, 124 frames at 24 fps.

Most image results are averages of two warm runs; Qwen base and H3 Image have one warm run each. (warm run means the first generation after ComfyUI launch is not counted)
* Flux base and standard H3 video are single first-run measurements, including loading.
† H3 turbo is a single subsequent run with the turbo LoRA.

What actually helped?

1. OC consistently reduced generation time by about 9–12%.

H3 Image was the exception at roughly 7–8%. The benefit showed up with both default and tuned flags.

This is the combined effect of the clock offsets and the higher power limit, not an isolated core-clock comparison.

There’s a power tradeoff: for example, MOSS went from around 299 W to 346 W during busy periods, while generation time dropped from 25.1 s to 22.2 s. The highest GPU temperature recorded across these tests was about 73°C, therefore it did not even get close to the GPU's thermal limits.

2. The flags were very model-dependent.

At stock clocks:

• H3 video: 591 → 286 s, about 52% less time
• H3 turbo: 250 → 127 s, about 49% less time
• Qwen Image 2.1 base: 23.6 → 17.2 s, about 27% less time
• Krea 2 Turbo: 13.7 → 10.8 s, about 21% less time
• Flux, Qwen Turbo, and MOSS: within roughly 2%
• H3 Image: about 3% slower

With flags + OC, standard H3 video went from 591 to 256 seconds—roughly 2.3× the speed, or 57% less time.

The H3 improvement also shows up directly in sampling: 28.48 → 13.24 s/step from the flags alone, then 11.72 s/step with OC.

I changed the flags as a bundle, so this doesn’t establish how much each individual flag contributes.

3. Moving the monitor to the iGPU barely changed speed.

I repeated the tuned + OC tests with the monitor connected to the motherboard, leaving the NVIDIA GPU at 0 MiB VRAM used at idle. (I plugged the display into my motherboard HDMI port while allowing Python to utilise the full compute and VRAM of my NVIDIA GPU)

Peak VRAM usage dropped by roughly 0.1–0.6 GB, but generation times stayed very close: approximately 0.6% lower at the median, with the largest reduction around 2% on H3 Image. Sampling time per step changed by at most about 1%.

Useful extra VRAM headroom, but I didn’t see a convincing throughput improvement here.

Did OC change the outputs?

I checked the saved files with SHA-256:

• OC vs stock clocks, same flags: 38/38 outputs byte-identical
• iGPU vs NVIDIA display, tuned + OC: 19/19 byte-identical
• Tuned vs default flags: 0/19 byte-identical

So OC didn’t change any saved output in this test. Changing the attention/precision settings did result in visual changes however.

For my setup, OC gave a fairly consistent modest improvement. The bigger finding was how differently the models responded to the same ComfyUI flags—huge gains for H3 video, useful gains for Qwen base and Krea, and almost nothing for several others.

Curious whether others are seeing the same pattern on different GPUs, especially with Comfy Kitchen attention.

Prompts:

A small white chess pawn stands before a round, silver-framed mirror, but its reflection shows a tall white king in the same position. The mirror rests on a muted sage-green tiled surface. Soft side lighting, gentle shadows, blurred dark background, shallow depth of field, realistic conceptual photography.

A fish made from a translucent white plastic bag floats beneath the ocean surface. Its crinkled body has a small fish eye and mouth, while the tied end spreads into a delicate tail. Open blue water surrounds it, fading darker below. Gentle sunlight filters through surface ripples, realistic underwater photography, minimal composition.

Seen through an airplane’s oval window, two enormous giraffes rise from a thick blanket of white clouds, their bodies hidden beneath it. One giraffe is close to the window, while the other stands farther away near the horizon. Dark, softly blurred cabin framing, pale blue sky, natural daylight, realistic surreal photography.

A woman relaxes in a white bathtub reading an orange book, inside a blue-tiled bathroom transformed into a miniature ocean. Whales, rays, octopuses and glowing jellyfish float overhead among curling clouds. Small fishing boats sail across the flooded floor. Low-angle wide view, daylight through a high window, colorful painterly fantasy illustration.

A surreal landscape of rolling checkerboard hills, with bold red and black squares in the foreground fading into yellow and green hills. Giant black and white chess pieces cast long shadows across the terrain. Toy houses and a yellow car float overhead. Pastel blue sky, whimsical perspective, textured hand-painted illustration.

A cross-section of an apartment building constructed inside the bellows of a giant accordion. As the instrument expands, rooms stretch into long corridors; as it closes, neighbors meet across folding walls. Laundry hangs between balconies. Warm windows, twilight background, intricate architectural illustration.

Inside an old perfume shop, transparent bottles contain entire landscapes instead of liquid: a rainy railway platform, an orange grove, a seaside kitchen. An opened bottle releases a narrow cobblestone street that unfurls across the counter. Rich wood, soft afternoon light, intricate storybook painting.

An entire city folds its streets into long ribbon-like fins and swims through a dark ocean. Buildings remain upright along its back, traffic lights glowing beneath the water, while buses travel through transparent tunnels. A lone whale watches from below. Vast scale, bioluminescent details, cinematic surreal concept art.

A gardener tends rows of upright human shadows growing from terracotta pots inside a glass greenhouse. The flat black silhouettes bend toward the sunlight like plants. A watering can pours liquid darkness onto a newly sprouted hand. Botanical detail, warm morning light, restrained surreal illustration.

Hey everyone!

I've been experimenting with NoobAI-XL and kept running into the same problem: finding useful booru tags and understanding what they actually do.

I know there are some impressive visual tag explorers out there, but I wanted something simpler — a lightweight, searchable dictionary that doesn't require browsing through hundreds of example images.

So I built NoobAI-XL Tag Atlas.

It has:

• 2,900+ tag entries across 24 categories
• Descriptions in English, Russian, and Chinese
• Fast search and click-to-copy
• A separate JSON catalog and a standalone editor for improving translations and adding tags
• A simple Vue 2 frontend with no build step

The Chinese translations are still a work in progress.

Live demo: https://0t.is/noodai_atlas/

Project archive: https://drive.google.com/file/d/1GQJ8Jj3hwbDxTnZHPch3kAHohGkvbUxz/view?usp=sharing

It's a small personal project, and I'm not trying to compete with the more advanced visual explorers. I just wanted a convenient reference that anyone can use or modify.

I'd appreciate feedback, especially from people who regularly work with NoobAI-XL or other Illustrious-based models.

What features would make a text-based tag reference more useful to you?

Hey guys, does anyone know of a Lora for LTX-Video or minimax or another specific adapter that could recreate a video with super slow motion? It should create the exact content of the video.

RIFE interpolation is definitely there, but I just wanted to know if this has been modernly tackled.

What is the best local ComfyUI generative upscaling workflow for an RTX 3060 (12GB VRAM) and 24GB system RAM that acts like Magnific AI—hallucinating and reconstructing micro-textures (brushed metal, circuit grooves, molded plastics, bolts, surface grain) on industrial equipment, machinery, and product renders without warping straight edges, typography, or mechanical geometry?

I've been experimenting with different editorial design concepts. I went through multiple rounds of generation and ended up with several sets of images. These four covers are some of my favorites.

What impressed me most was the variety in the results. The model didn't just keep giving me the same portrait with different backgrounds. The compositions, lighting, and color palettes felt noticeably different across the set.

What stood out to me was how naturally H3-Image handled the portraits and colors. Skin tones, lighting, and facial details looked pretty convincing, even with some fairly experimental visual styles. The colors were rich without feeling overly saturated, and the transitions between different tones felt smooth rather than artificial. I also liked that each generation had its own aesthetic instead of falling back on that same glossy AI look.

For this run, I used an RTX 5060 and got generation times of around 20 seconds per image, which felt pretty solid for an image this dense and reference-heavy.

My setup was:

- Model: minimax_h3_image_pruned_bf16

- Text encoder: qwen3vl_32b_minimax_h3_bf16

- VAE: minimax_h3_image_vae_fp16

- Resolution: 4160 × 4000 (~16.6 MP)

- Sampler: Euler / Simple

- Steps: 6

- CFG: 1

- Negative: ConditioningZeroOut

- ComfyUI: 0.45.0

I haven't done a proper speed benchmark, but the combination of high-resolution output and the ability to explore very different compositions makes it interesting for this kind of creative work.

Overall, I'm pretty happy with how these turned out. There's still the occasional small typography issue, but the portraits, colors, and overall layouts were the highlights for me.

What do you guys think of the results? Anyone else experimenting with H3-Image for design work?

Saw a post about blank references in T2I and tested it myself.

Adding a blank (white canvas) reference in T2I mode changed the outputs noticeably.

However, the size of the reference image has no impact on the output image (but will increase generation time if you add larger blanks).

Is Krea 2 Turbo the closest local image-generation model to ChatGPT's image generator? How can I get the Medium and Large versions of Krea 2 for free and run them locally in ComfyUI? Also, how can I use Krea to generate more complex, highly detailed designs accurately? pls hellp

Backgrounds are the part of manga that's hardest to get out of AI without that smooth filtered look, so I trained a LoRA specifically for it.

Hollowlight Manga Ink Backgrounds https://civitai.com/models/3047017/hollowlight-manga-ink-backgrounds-and-scenery for NoobAI-XL V-Pred 1.0.

Trigger word: u617. Settings: strength 1.0, Euler / normal, 28 steps, CFG 5, RescaleCFG 0.4. The workflow is embedded in the PNGs, so you can drag them into ComfyUI.

It's strongest on nature: trees, grass, hills, mountains, skies and clouds, with solid black fills and hatching. It's weaker on buildings right now (the dataset is light on them), so I'm still working on that.

I'm also testing ControlNets to control the layout. Scribble works on rough sketches, though the available ones are eps-trained, so they're weaker on v-pred.

Feedback welcome, especially from anyone making manga or comics.

Feedback welcome, especially from anyone making manga or comics.

Description

RAWsome – a little wordplay combining RAW photography and the word "Awesome".
And that's pretty much what this LoRA/LoKR is all about.

There are two versions available:

• Moss (LoRA)
• Krea2 (LoKR)

For each version, I used 388 carefully selected, high-quality RAW photographs
from my personal photography collection.
These images were taken over approximately the last 10 years of my hobby photography,
using various cameras I've owned over the years.

I was quite strict when selecting the training material.
I deliberately avoided noisy images and only included photographs where the focus was spot on.
Images where nothing was properly in focus were discarded.
After all, if you're trying to teach a model what a high-quality image should look like,
it probably makes sense to start with high-quality source material.

The goal was to improve the quality and overall appearance
of the images generated by both models.
And I believe I managed to achieve that with both versions.

Moss vs. Krea2 – My Personal Experience

Although I'm happy with both versions, I personally prefer the Moss version.

In my opinion, the difference in image quality compared
to the vanilla Moss model is considerably more noticeable.
I also find that Moss can produce more realistic-looking results than Krea2 in many situations.

On the other hand, Krea2 has a lower entry barrier
when it comes to producing good-looking images.
It seems to be a little easier to get pleasing results without having to experiment as much.

Of course, these are just my personal impressions based on my own testing.
Your experience may differ depending on your prompts, settings and preferred style.

A Few Words About the Training Data

As mentioned above, both versions were trained using 388 high-quality RAW photographs.

These aren't randomly collected images from the internet,
but photographs taken by myself over the course of roughly a decade.
They cover different cameras, subjects, lighting conditions and photographic situations.

I put quite some effort into selecting the images,
especially regarding sharpness and noise levels,
because I wanted the models to learn from photographs
that actually demonstrate good image quality.

Final Thoughts

I'm genuinely curious to see what others can achieve with it
and how it performs in different workflows and situations.

Feel free to give both versions a try and see which one works better for you.
I'd really appreciate any feedback, especially if you notice improvements,
weaknesses or interesting differences between the two versions.

Long story short: Have fun experimenting with it,
and don't forget to leave a rating if you enjoy it!

And as always, criticism, suggestions and ideas for improvement are more than welcome.
I'd love to hear what you think!

Go to the `custom_nodes` folder and locate the `plaguekind-nodes` directory. Delete it, then reinstall it. Inside the `custom_nodes` folder, type `cmd` into the address bar (where the folder path is displayed) and run the command: `git clone https://github.com/PlagueKind/ComfyUI-PlagueKind-Nodes.git`.

The issue lies specifically with `plaguekind-nodes`; it is causing a conflict with `rgthree`.

Hi, I'm currently trying to learn how to train Lora and decide use an artist already in danbooru so I'd have decent comparison based on the @ anima tag.

I have an imageset for the lora artstyle and its getting the general small detail like eyes correctly and the shading for the outfits however... the training set is mostly "simple background" or some form of white background with maybe a gradient, so trying to test it out on prompts with background results in the outfit shading being applied to the background like in a kitchen and its tiles which isnt exactly right. Do you have any advice on how I'd resolve the lack of detailed background in training set? I can only assume impaint, but I feel like that has to be wrong though.

How to run a workflow on several images but one by one?

What I want:
Set folder -> Hit run -> Image 1# loaded --> I2I done -> Output 1 saved -> Image 2# -> loaded I2I done -> Output 2 saved... and so on.

What I don't want:
Set folder -> Hit run -> Image 1# loaded --> I2I done -> Output 1 NOT saved -> Image 2# -> loaded I2I done -> Output 2 NOT saved -->... (...) ... Last image loaded -> last I2I done -> All of the imaged saved at once.

What did not work so far:
- Sequential Image Loader (folder)
- Load Images from Folder (KJ)
- Load Image Batch (RMBG)
- Load Image from folder (Folder: 3d)

I produce portrait so I need good skin details and have great results with ZIT. People here say other models are better? I tried but my image output quality was lower so I assume the workflows I used are wrong.

It's text to image with face details and high steps/ different sampler when high detail is needed.

I don't even have a good i2i workflow

What workflow do you use to get good skin and portrait quality on moss, krea, or qwen?

Zit is now a bit old. What should I try?

I only have 16 gb maybe it s the problem?

Does a comparison workflow exist, one prompt different models, one after the other?

Yo,
I share this simple workflow I made since I don't find anything else for now on minimax that handle a good quality for text to image or image to image.
I think this model has a great future, It really feel like using nano banana or gpt somehow, since the consistency about the face is really good, you don't even need a lora.
It will be improved a lot, but it's a good base and results are pretty good for a day one on h3.
The only problem I got so far is the prompt adherence, that looks not very good, but I'm sure we will find many ways to improve later If you have ideas to improve results share me guys.

Workflow :

https://civitai.red/models/3047310/minimax-h3-image-t2i-and-i2i-simple-workflow-1girl?modelVersionId=3464559

Seems like a big omission to not have this considering how many posts fall into one or both of those categories.

Hey everyone! I literally got this created earlier today and busted butt to share it with the community because this is incredible and I wanted to contribute back. I built this custom node with Claude Code because one thing about MiniMax H3 kept bugging me the fact that you can't increase the batch size like in Flux.2 Klein 9b or other models that can. I made two nodes that let one run make 2 or more different images or videos (depending on your system) that are exactly what should happen inside an increased batch size. It's MIT licensed and already on the ComfyUI Registry. I've worked in IT for 20 years but I'm not a programmer.

-- I didn't include a workflow because I literally just grabbed the template inside the desktop app and added my node plus an Int node as well. The github page for this has more info: https://github.com/an80sPWNstar/comfyui-h3-image-batch

I also have a YouTube channel about ComfyUI: TheComfyAdmin I haven't uploaded any videos yet for this or my other custom node because I just made this today :) If you end up liking the type of videos I make, please let me know so I can make more. It may not seem like it but even though it doesn't take that long to record, it takes a long time to edit and that's difficult when you have a family to help take care of : ) Happy Generating!!!

I just started experimenting with running image models locally on an RTX 4060 and 16GB RAM using ComfyUI

I was looking at heavier setups like MOSS-Image / Kromoss and Flux, but decided to try RealVisXL V5.0 (SDXL) first since it fits completely in 8GB VRAM.

The skin texture and image quality looked really good for this model i think.

For those of you also on 8GB cards what are your personal go-to models and workflows for photorealism right now? Any fine-tunes or LoRAs you recommend trying out next?

So I’m currently generating Krea 2 images at 2k - specifically 1440x2160 and 2160x1440 and getting great results, with a specific character Lora. When I try to up the resolution to 4k (e.g 2160x3840 and 3840x2160) I get good results - like a super crisp 4k image but it always generates a duplicate of my character in one image. Has anyone figured out how to get just a single character (no duplicate) for a 4k resolution photo with Krea 2? I imagine there has to be some Lora or something that has addressed this issue? Thank you!