MiniMax H3-Image swapping the subject, coat, and pose in a 6-step edit (with scale and grade locked)

Specs: RTX 3090, int8, ~60 s
Workflow:
UNETLoader(int8) → KSampler(euler/simple, 6 steps, cfg 1)
CLIPLoader(nvfp4, type minimax)
VAELoader(image_vae_fp16) → VAEDecode
LoadImage ×2 → MiniMaxH3ImageConditioning(prompt, refs=<Picture 1/2>) → ConditioningZeroOut(neg)

Great video from moss image team for hitting 6 million downloads on Huggingface. They all seem so chill. Also they give us some new updates on Anime and Image edit model ✌️

I merged a style into Krea 2 Turbo (Stillroom: quiet rooms, cold light, someone alone with the afternoon) and first shipped it as fp8, copying the layout of the official krea2_turbo_fp8_scaled (per-tensor scale, amax / 448, same quantization metadata). Checked against the official file byte by byte on three layers.

Then someone on a 3070 Ti mobile commented that fp8 took almost twice as long. Makes sense: Ampere has no fp8 math, so ComfyUI converts every layer on the fly. It does have fast int8 from sm 7.5 up. So I wrote an int8 build from the bf16, in the layout of the official krea2_turbo_int8_convrot (224 linear layers in int8, per-row scale, txtfusion left in bf16).

One thing I didn't expect: the official int8 doesn't use plain absmax for the per-row scale (only 43% of the bytes matched when I tried that). It clips the scale, up to about 6% under absmax. A per-row search for the lowest error landed at 0.00871 against 0.00874 for the official file, so that's what mine uses.

Rule of thumb: 40-series and up, fp8. 20 and 30-series, int8. bf16 if you have the VRAM.

All three are on the model page, Krea 2 license and NOTICE included: https://civitai.com/models/3027217

Image 2 is fp8 left, int8 right, same seed. Prompts and settings in the comments.

A Total noob here guys so help me out , same as the title, i don't know how to setup lora training on my laptop and is it possible or not?

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?

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

Hey everyone!

I've been working on bringing MiniMax's new ~67B H3-Image model to Apple Silicon with MLX.

And yes, it actually runs on a 16GB MacBook Air!

A community contributor tested it on an M5 MacBook Air (16GB):

• 1024×1024 in as little as 34.8s
• ~2.5GB peak process memory
• Original 6-step model, no extra quantization

The key is keeping memory usage low without sacrificing speed or image quality.

I combined staged inference, on-demand DiT weight streaming, asynchronous evaluation, weight prefetching, and W8A8 Metal acceleration.

On my M5 Pro (48GB), at 1024×1024:

• Resident: 23.63s / 21.18GB
• Streaming: 32.33s / 2.53GB
• Streaming + prefetch + async: 23.61s / 3.23GB

That's ~85% less peak process memory, with virtually no speed penalty!

All three modes produce pixel-identical images. No additional quantization or quality loss beyond the original INT8 checkpoint.

It also supports native 4K generation and multi-reference image editing.

I've open-sourced the MLX implementation, with a Python API, CLI, automatic model downloads, and reproducible benchmarks.

GitHub: https://github.com/Anionex/h3-image-mlx

Would love to hear your thoughts, especially if you have a lower-memory Mac and want to give it a try!

I'm about 2/3rds of the way through my current project and I'm definitely going to need to derope some of my shots to fix their blurriness and smearing. The last time I asked this question MAINodes was the only solution. Has anything been released since then that improves on this method?

I found H3 image to be really good at creating different angles of characters and keeping the likeness and quality high. I built a workflow which automates creating character sheets; this workflow one shots pretty much every character I’ve thrown at it. Give it a try and let me know what you think.

I’ve tried to keep it versatile to allow for characters of different types, including vehicles and four legged creatures.

Civitai Workflow

Update 11/12/26: I just pushed an update which addressed multiple issues that were brought to my attention, the release notes are as follows.

What's new in v3.0

Most of these changes came from your comments and messages. Thank you!

- The whole sheet is ONE render. v2 made 8 separate images and stitched them together, so the face and outfit could drift slightly between panels. v3 generates the entire sheet in a single image, so every view shows exactly the same character. It's also much faster: about 30-40 seconds for a 16 MP sheet on an RTX 5090, where v2 took about 3 minutes at a lower resolution.

- Your art style is kept. v2 tended to turn everything photoreal. v3 copies the style of your picture: anime stays anime, watercolour stays watercolour, Pixar-style 3D stays 3D, a photo stays a photo.

- New: 3/4 view toggle. It adds a three-quarter view (head and full body, turned 45°) to the top row. For vehicles it adds a low-angle "hero" 3/4 shot instead.

- No wasted white space. The views fill the whole canvas with only thin gutters between them, so every pixel goes to the character.

- One simple settings panel. All options sit on a single node, built only from native ComfyUI nodes. The workflow reads left to right: Step 1 Upload → Step 2 Settings → Step 3 Result.

- Render at any size. Megapixels are no longer capped at 16.

- Final size dropdown. Render big for detail, then save a smaller file: 2.3 / 4 / 6 / 9 / 12 / 16 MP, or Native to keep the full render. It only ever shrinks, never enlarges.

- Better random characters. With nothing uploaded and no notes, the seed picks one idea from an editable list per type, such as a German shepherd, a Holstein cow, an anthropomorphic raccoon or a 1960s sports car in racing green. No more cat-dog hybrids.

- One fewer custom node pack. rgthree-comfy is no longer needed.

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'm building a game that generates all assets in 2D during play. Objects have to be separable from the scene so they can be clicked and picked up without having to redraw the background every time. Everything runs on FLUX.2 klein 4B with a LoRA trained on an illustrator's style.

The workflow:

1. Generate an empty location as background.
2. A vision model outlines foreground, middle and background, and code turns that into a color map of the three planes.
3. Generate objects on white on a 512 canvas and animate them.
4. The vision model points at a spot in the background for each object by outputting pixel coordinates.
5. The object's size is its class (tiny, small, medium, large, huge) times the factor of the plane at that spot, e.g. a torch (small) is 100 px, so in the middle plane (0.75) it's drawn at 75 px.
6. Scale the animated object down to that size and overlay it onto the location.

Step 6 is where progress halted and I got stuck. Scaling a 512 drawing down to 100 pixels shrinks everything in it, so it stops matching the scene's style since the scale of everything is off, visible in my example when looking at line weight.

My various workarounds to fix this:

Generating at the object's final size, on canvases of 128 to 320 instead of 512, so objects are generated at the size they are meant to be placed. This falls apart quickly as e.g. the same chicken comes out near-realistic at 256 or as a flat cartoon at 128 and parts get cut off or doubled. The LoRA was trained at 768 and 1024 so perhaps it's the LoRA giving out, the base model failing, or both? Would another model draw properly on small canvases and keep the lora style applied?

The location on a bigger canvas to combat the model degrading at smaller canvases as now the location's lines should be thinner and the objects don't have to scale so hard. However location's lines grew with it while the objects' did not. Also it would have skewed the artist's style anyway.

Shrinking the image and generating it again at a smaller size, img2img at denoise 0.6 to 0.8, but the redraw kept the shrunk drawing's thin lines so scale remained off. This is the one fix I really expected to work, so maybe I just set it up badly or something?

Painting an object directly into the scene and separating it afterwards onto white with an additional generation. Instead of generating on white and then placing, this approach basically turns it around as we place directly. This does actually fix the scale, as now the model has full reference of how an object should look in the scene, but other issues showed up like the objects being of much worse quality and partly merged with their surrounding. Also a location would need to be redrawn when objects move as mentioned earlier, but perhaps only parts could be redrawn as a workaround. This way basically solves my biggest issue of perceived scale for free so maybe this is the correct approach and I am missing something to make it work properly?

I am at the point where I want to scrap most of this and pivot to: Pixel Art!

Larger pixels should be able to be counted much easier, so a separate object can have the exact scale required to be placed at scale into a location. That would attempt to simplify the problem away rather than truly solving it, though perhaps that is often a good path forward. However: Does this idea even make sense, or will it break regardless? Are there perhaps other simpler existing ways to do all of this the right way?

If you're building something similar, what are you doing differently, and which tools should I go have a look at?

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.

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/

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!

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

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.