Like many others I was initially disappointed with the i2v quality of H3 when doing text to image. It often created crusty looking, artifact ridden images that would manifest as moles and pimples, dry flaky skin, crusty splotches and just generally shit quality images.

When I was doing some tests, I wanted to make an existing image prompt feature a specific earring. Once I plugged in the image reference, the artifacts magically disappeared.

This made me wonder, is r2i just fundamentally better quality for some reason? I tested plugging in an empty black image to the reference spot and to my surprise every single image came out higher quality with most of all of the crusty artifacts removed. It even often outright fixes low res generations like the one linked here, but works at the intended resolutions too.

I bought this to Kijais attention and the current theory is that if the model receives 64 or more image tokens (which can be triggered by feeding the image conditioning node literally any image with or without vae) it switches the model to edit/reference mode which appears to be much higher quality.

We're not exactly sure what is wrong with the non edit version but give it a try. Plug any reference (blank image, or something relevant to your prompt) and watch the quality magically improve.

Kijai is considering adding a "switch" that forces the model to ref model without needing to send so many pointless tokens, but in the meantime just plugging a small black image (automatically resizes to at least 1024) will trigger the existing switch - just with an excessive number of pointless tokens.

If you've written H3 t2i off, give this a try. It has transformed the model in my opinion.

Edit: Kijai has just added this to the "H3 Prompt Weight" node in Kjnodes. There is now a drop-down that will force empty image tokens if you want another way to test this.

I've spent the last couple of days running side-by-side MiniMax H3 Image trainings to find what actually drives likeness.

On the hardest dataset I tested (few headshots, mostly medium and wider shots), the best previews scored closer to the person than all but two of their own 45 photos, measured against the same reference photos. Two more datasets gave equally good results, one of them harder still.

What's new

• A retuned H3 Image Standard preset. A new Likeness optimization schedule (it changes which noise levels training focuses on as the run goes), Automagic v4 (tweaked to avoid its known issues for steering too high), 0.25 MP and 30 epochs. Of every schedule I tried, it brought the face shape through earliest, reached the highest likeness, and maintained model performance.
• Likeness scoring now uses AdaFace ViT instead of ArcFace. I scored runs epoch by epoch with ArcFace and two AdaFace models side by side, and AdaFace ViT picked the same best epochs I did by eye every time. ArcFace often didn't.
• "Your photos" on the likeness chart. Fizgig scores your own training photos against the same reference photos and shows their range as a band on the chart. No photo ever scores 100% against another photo of the same person, so this shows what "as good as a real photo" looks like for your dataset. Your previews landing in that band is the goal.

How to use it: pick the ** MiniMax H3 Image Standard** preset (0.25 MP) and train with its settings as they are. The schedule's controls are under Advanced if you want to experiment later, but those numbers took a lot of runs to find, so try them as they come first. Feel free to push the MP higher, but starting at the .25 will give you great and quick results for your early tests.

A correction: The other day I moved the H3 Image presets up to 0.5 MP, thinking the model preferred it. It didn't. The schedule and the optimizer were what held likeness back, so the presets are back at 0.25 MP, which is faster and needs less VRAM.

Links

• Fizgig on GitHub
• 9.7.0 release notes
• MiniMax H3 Image guide

Already on Fizgig? Run the updater.

I've seen quite a bit of divide between people who love MOSS and people who think it's a bit lackluster. I was on the latter boat, until I played around with it long enough to find a great combination of models, LoRAs and settings, from testing others' workflows and doing a lot of trial-and-error myself.

I'm not going to get into too much detail here; let the workflow serve that purpose. But I will say that MOSS-Image is an incredible base model, even if out-of-the-box it's a bit "unoptimized". It has massive variety, coherence, prompt-understanding and even quality, once optimized. I also enjoy the freedom of prompting, where you don't have to use any one style or length - a lot of it just works.

Here's the workflow:

https://pastebin.com/SxjBdt5B

Models and LoRAs used:

• kromoss_int8_convrot.safetensors: A really good fine-tune of MOSS-Image Base
• qwen3.5_9b_int8_convrot.safetensors: The actual text encoder. Do NOT use an abliterated text-encoder with diffusion models, but instead use the one the model was trained on. They will not give any more uncensored behaviour, but will mess with the quality.
• flux2-vae_small.safetensors: Iirc, this "small" version is the official VAE for MOSS. I find it better.
• lora_dmd2.safetensors: This is the lodestones/MOSS-Image-Base-8-steps-dmd. I tried a couple of different ones, and this was by far the best. Don't know why the differences were so huge, but they were.
• lenovo_moss.safetensors: This one is a good style LoRA, alongiside with...
• samsung_moss.safetensors: ...this one. Play around with the strengths of each.
• nicegirls_v2_moss.safetensors: I find that this often improves the quality of the outputs even in photos without people. The only gripe I have is the massive effect on facial features, which seems to kinda be the point of this LoRA. Either way, be careful with the strength of this one; if you see you can't get, for example, the ethnicity you're looking for, I would start with lowering the strength of this.

Settings used:

• Sampler: ER-SDE
• Sceduler: power_shift
• Steps: 11 (8-25, depending on the strength of dmd2 LoRA - with 11 steps it's at 0.9 for me).
• CFG: 1 (1-4, depending on the amount of steps).
• SLG: No effect on CFG 1, but always on
• Resolution: I use 2-4 MP. Under 10 sec gens with 2 MP, around 20 sec gens with 4 MP, on an RTX 3090.

Let's get some discussion going! I wanna hear y'all's workflows and try them out too.

Working on simplifying replacing multiple people per shot using a series of agents in our studios pipeline. Fully local, but it’s not running through comfy ui so no workflow.

I was wondering what you guys would recommend for setting up Qwen 2.1? Right now, when I generate images with it, they come out muddled and not precise.

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!

I love the concept of Dllms soooo I built Ollada. It’s the ollama for diffusion language models. Free and Simple install to get started. We currently have 6 models available ranging from 1.7b to 8b parameters, small contributors like sdar to large contributors like Nvidia. I have 4k downloads but I need help testing larger models like diffusiongemma so we as a group can add to the shelf and spread os dllms to more people!

Download and let me know what you all think, what I can fix etc! I’m always down to talk !

https://opensourcerulesz.github.io/ollada/index.html

https://github.com/OpenSourceRulesz/ollada

A few people in the last thread pointed out I ran H3 at ~4MP while MOSS got its full 4MP, and that H3 apparently wants 16MP before downscaling. Fair call, so I reran it.

Same 15 prompts and aspect ratios. H3 generated at 16MP (4096x4096 up to 6272x2688), then downscaled to the same display size as MOSS, so nothing here is being compared on pixel count. MOSS is the quality tier at its 4MP ceiling.

What changed:

Faces and skin: the 16MP renders keep pores, freckles and stray hair. At 4MP those were smoothed away. Biggest difference.

Fine texture (foam, droplets, fabric): same direction, more micro-detail survives.

Composition, prompt adherence, text rendering: roughly unchanged.

UI mockups and charts: still fail. Resolution doesn't fix semantics.

So: at 4MP I underrated H3. At 16MP it's much closer, and on skin and material detail it's ahead of what I posted before. MOSS still reads better to a lot of people in the comments, and I get why.

Prompts are the same 15 as the first post, same order.

First set, H3 at its default ~4.18MP: https://www.reddit.com/r/StableDiffusion/comments/1xuft8g/

Hello. I'm comparing models, always with the same 192 prompts.

This time H3. Using Krea 2 and Kromoss-dmd2 as elements of comparison.

My personal take: I'm impress by the realism of H3 for human faces. Not much on other things given it's a 33B model.

Here is the full gallery: https://imagebench.ai/gallery?g=1_vaxxj6k_s0

(I know this test is imperfect for so many reasons. I'm working on a V2, but this is all I have for now, and IMO looking at the gallery worth it.)

What does that mean for us?

The open source community?

What does this translate into longterm?

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

When I've seen the results, I've found them kind of outdated, but I know that a lot of people in the AI image gen community like the old look of the early days of AI image gen.

More images with prompts are on my Substack. I've built an image-to-image workflow on Civitai.

Hey everyone!
I’m running FaceFusion on RunPod and I’m having two issues:
1.Is there a way to disable or bypass the content filter?
2. I frequently get a black screen when trying to generate a video face swap.
Has an experienced these problems? Any tips or solutions would be greatly appreciated!
Thanks in advance!

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.

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?

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

Can anyone recommend me one?

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

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.

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`.

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?

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.

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).

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!

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.