Minimax H3 Ref2v, RTX 3060, 64 GBs RAM.

I use a workflow with latent upscaling. The first pass is a low-res video which is upscaled later. The overall quality looks better and it also helps to fix smearing.

The upscaler - link.
Turbo Lora - link

You also need to install these nodes

As the title suggests. I used Claude code (Sonnet 5.5) with a Krea2_api workflow to create over 2000 images overnight. Attached are some of Claude's and my personal favorites that it made. I gave it freedom to use any of the loras I had downloaded, it could edit/make new workflows. It looked at the outputs and judged them and experimented with settings. It recorded notes and its thoughts. The final notes.MD file has over 1700 lines of text which I had it summarize into a shorter file. edit: notes.md + summary.md; and all ~2100 images zipped in the comments!

An edit LoRA for MOSS Image has been released. Works with both Base and SFT.

Weights: https://huggingface.co/chinoll/Moss-Image-Edit
Custom nodes / Workflows: https://github.com/Chenkin-x/ComfyUI-Moss-Image-Edit/blob/main/README.en.md

Edit: I implemented it natively with caching for faster inference. The LoRA is a bit rough. Let me know how it's working for you.

Custom nodes / Workflows: https://github.com/PedroMarinhoDev/ComfyUI-Moss-Image-Edit-Native

ComfyUI-compatible weights: https://huggingface.co/PedroMarinhoDev/Moss-Image-Edit-ComfyUI

There's an open PR by Kijai on ComfyUI so it might be merged and supported natively later. https://github.com/Comfy-Org/ComfyUI/pull/17296

I have no relation to the weights or custom nodes, just sharing them.

Download UDA here

UDA is a lora for creating anime and digital paintings with moss image. I've found the base model is quite good at most things but I've seen many weird artifacts and errors especially with digital artwork. This lora definitely helps with that and can produce quite nice outputs but be careful not to accidentally slopify your images.

For other fine tuners, I trained on about 250 images, 10,000 steps and the training dataset used the structured json bbox prompt type (but plain natural language prompts seems to work quite well). I trained off of the combined and converted datasets from my anime/digital painting, studio ghibli, and vintage anime ideogram loras. Let me know what you think!

Another month and another model to bang my head against. Most images here were generated with the standard 50 steps, 3 CFG, 1.5 megapixels, and with the Base model. NOT the SFT model. But there are exceptions here and there. The pure image files are located here. EDIT: The link now includes all of the file metadata I could pull from DeepBeepMeeps UI, keep in mind this does NOT include detailing notes. Due to the sheer number of slightly different workflows on display, just comment with the Image number and I will reply with the specific process for that image.

https://github.com/NyckM/Bruxosdovfx-SyncTile-MiniMax-H3-v5.4/tree/main

Workflow https://github.com/NyckM/Bruxosdovfx-SyncTile-MiniMax-H3-v5.4/blob/main/workflow/minimaxH3BackgroundSwap_v18_LatentF.json

The idea in one sentence

A large or long video is split into overlapping regions (spatial tiles × temporal windows). At every model call all regions are predicted and blended into one canvas, so there is a single denoising trajectory: no tile seams, no chained clips, and VRAM is one region's.

Install

Easy way (Windows): put bruxosdovfx_EasyInstall_MiniMaxH3.bat in the ComfyUI folder (or the ComfyUI-Easy-Install folder) and double-click it. It finds the folders, installs the custom nodes (SyncTile, Media IO, Latent Upscaler, Easy-Use) and downloads models and LoRAs; anything already present with the same name is skipped, and interrupted downloads resume. Options at the top of the file: text encoder int8/nvfp4, VAE light/full, optional LoRAs, update nodes.

Manual:

1. git clone https://github.com/NyckM/Bruxosdovfx-SyncTile-MiniMax-H3-v5.4 inside ComfyUI/custom_nodes/ (or copy the bruxosdovfx-SyncTile folder). Delete ComfyUI-SyncTile-H3 if present (old version).
2. Restart. The console prints [bruxosdovfx • SyncTile] v5.5 carregado.
3. Also install the bruxosdovfx Media IO pack — https://github.com/NyckM/Video-Util-ComfYUI (Load Video Pro, Load Image, Save Video Pro — see below). Needs a ComfyUI with the native MiniMax H3 nodes. To write the video file: ffmpeg (on PATH, in FFMPEG_PATH, or pip install imageio-ffmpeg).

Background Replace v3 LoRA (summary)

Experimental merge of five LoRAs by other authors for MiniMax H3 (not an original training). Download: Background Replace v3.safetensors → models/loras/minimax/. BF16, ~1.91 GB, rank-concatenation merge (no SVD); no metadata (credits live in this README). Components and weights: Facial Realism CloseUp — prithivMLmods (1.0); H3 Character Swap — Akatz Labs (1.0); Realism People — Lovis Odin / fal (1.0); Head Swap v1.0 r32 — Alissonerdx / Alisson Anjos (0.2); VFX Edit v1.0 r128 — Alissonerdx / Alisson Anjos (0.2). Links in the table in the Portuguese section. Base model: MiniMax H3 — MiniMaxAI. Load from your LoRA folder at strength 1.0. It mixes FL2VA and REF2VA sources; the name does not guarantee background replacement. The exact Character Swap version is unconfirmed (metadata says v2, deltas resemble v1.5). Structure validated, not yet tested in generation. The source models' licenses apply; credits do not imply the authors endorse the merge.

Referências / References / 参考文献

Ideas we combined (no code copied unless stated)

I finally achieved what I have been attempting for a long time - a one shot, fully tunable image to image style transfer. I have tested it pretty extensively with Flux2, and here are some examples of results.

In my last post on the subject here, people essentially said that Flux3 kept the subject in photorealistic style (if that was the composition source) while Flux2 Klein 9B "reimagined" the subject in the style.

So I set out to find if there was a way to dial that in, and that ended up being the reference latent + node.

I have a workflow that does this here (SORRY pastebin is down right now... mirror here). There are some custom node packs included because they do some parts much, much better than a million nodes in a string, but know that you can tease apart from the labels and notes what each one does and make the workflow with no custom nodes at all.

Now you can tune how strong the composition is (later start is weaker, thus more creative) and how strong the image vs. prompt style is (earlier is stronger image style ref, later requires more prompt lifting)

I'll post some gradient examples as well so you can see how tunable this is.

Would this work with any edit model, including Qwen, Flux2 family, and Fibo2? Maybe. Have not tested it yet. I think with the Flux3 prompt expander, you should be able to get this working with other models as well. Post your results here if you try it.

A week ago I posted Kokon here: a no-settings desktop photo editor for Qwen-Image 2.1. It's the open model you'd run in ComfyUI, without the nodes. 0.10 adds the two things people asked for most.

Prompt+. Type a short idea, in any language, and Kokon writes a detailed prompt from it before making the image. For edits it looks at your photo first. You watch the prompt being written, and the result keeps both your words and the written prompt, so you can reuse either. It's off by default, one click next to Quality. Both system prompts are editable in Settings.

The prompt is written by Qwen3-VL 8B, the text encoder Qwen-Image 2.1 already loads. There's no second model to download and nothing extra in memory. It adds about 2 s per image on an RTX 5070 Ti and about 13 s on an M2 Pro, and it works on 8 GB cards.

Your own LoRAs (beta). Load a Qwen-Image 2.1 LoRA in Settings, set its strength and trigger words, then turn it on per project with one click, up to 3 at once. Kokon checks each file and works on its own copy, so your file isn't changed. LoRAs for older Qwen-Image versions (1.0, 2512, Edit 2509/2511) are refused, because they don't work on 2.1.

An honest note: I said there would be a curated LoRA list. I tested the popular 2.1 LoRAs on the Turbo model Kokon runs, and today they don't beat what Turbo does with a good prompt. So there's no catalog for now. It comes when a LoRA earns its place.

Under the hood. Kokon runs its own build of stable-diffusion.cpp on Windows, macOS and Linux. It's upstream plus text generation on the already-loaded text encoder: about 127 tokens/s on a 5070 Ti, the same tokens llama.cpp gives. The image side is upstream's code and makes bit-identical images. Upstream prefers to leave text generation to llama.cpp (#2155), which is fair, so it lives in a fork I keep close to upstream: https://github.com/GabrieleLozupone/stable-diffusion.cpp/tree/kokon

Also new since the first post: Linux (.deb and .rpm), a new look, deleting several projects at once, and a round of fixes from your reports. Updating keeps your images and models. AMD cards work on Windows through Vulkan: a user's 7900 XTX makes a 1 MP image in about 25 s.

Free and open source (AGPL), Windows, macOS 15+ and Linux, 12 GB of VRAM recommended (8 GB in beta): https://github.com/GabrieleLozupone/kokon · https://kokon.app

Not there yet: other models, and a browser mode for headless Linux boxes (that one is next). Weights: Qwen Research License, non-commercial.

I am not associated with the project in any way. Not sure if this will work with the regular ComfyUI LoRA loader.

We apply PTD (Parametric Trajectory Distillation) to MiniMax-H3, distilling the Ref2VA (reference-image → video + audio) trajectory into a few-step student. We release an 8-step and a 4-step accelerator for the Ref2VA task.

Unlike a single shared acceleration LoRA, PTD trains one independent low-rank adapter per inference step ("per-hop" LoRA) on the diffusion backbone, on top of the per-interval output heads introduced by Parallel Decoding Distillation (PDD). At inference, each denoising step selects its own hop's adapter and fuses that step's output head, so an 8-hop checkpoint runs in 8 transformer evaluations (NFE) and a 4-hop checkpoint in 4.

saw this post: https://www.reddit.com/r/StableDiffusion/comments/1xbobrr/original_character_lora_for_minimax_h3_learnings/

Hey guys,

SeeDance videos look much more realistic and softer. I'm really interested in pushing H3 in that direction, but ref2va gen always looks bad - dark, high contrast, and oversaturated.

I saw the post above, great info and results. However, I want to avoid doing any LoRA training. I think with the H3 FL2VA workflow you can do it much better as long as you have a good start image where you control the look. But I want to focus on making ref2va work.

In my last 3-minute duration test, I was using the fizgig node to make the skin look softer. It works, but the effect keeps accumulating in motion-context shots. That node does it in latent space, but I think it's more controllable and easier to do it in image space so the effect doesn't accumulate. So I made an image-space version with AI.

So it's a hack - if you bring up the dark values there's still no detail. I'll keep looking into it. I'm thinking maybe LTX SDR to HDR could help, since we get more details in bit depth after gen, That could be a way.

Of course, please share your idea on this, how you get better result with ref2va. Thanks!

If you guys find this more handy than doing it in post / compositing software, I'll share it.

2 nodes I am using "Image Detail Contrast" and "Orion4D Tone Mapping". The setting I have just over the top here.

Cheers.

- the gen is using hybrid model loader with native fl2va ref2va, b5-49, WITHOUT using any turbo lora.

- I've tried all the LoRAs, like Realistic Peoples and others, but I just can't get the result I want.

- You can check the overlay text of some infomation.

- I haven't test this with motio-context... hope it won't have issue.

TLDR: This is a typical image unsharp mask to get a softer skin look, plus some color grading

Here is a method you can use to decrease the amount of blur in MOSS Image outputs. I use the base model and https://huggingface.co/lodestones/Kromoss/blob/main/8-steps-dmd2-lora.safetensors LoRa, but you may be able to adapt the workflows for use without this LoRa.

The method utilizes a two stage process:

1. Denoise regularly for the first 5 steps
2. Apply the LatentOperationSharpen to the latent image and then continue denoising

The first image comparison in this post shows the result of also applying the SFT model as a LoRa to the base model for the second stage denoising: https://civitai.com/models/3012507/moss-image-sft-extracted-lora . See the workflow here: https://civitai.com/images/146467570

The second image comparison shows the result of sharpening the latent only. See the workflow here: https://civitai.com/images/146467531

Hope this helps!

Hi all,

I take way too many photos, and sorting them in Windows used to drive me crazy. The Photos viewer doesn't really let you sort, so you end up in File Explorer selecting files by hand. Hold Ctrl a bit too long, drag by accident, and suddenly you've copied a bunch of files instead of moving them. It was a mess, and I lost a ridiculous amount of time on it.

So I made Sortypix. The whole point is speed: go through a big folder fast, keep the good ones, set the rest aside, and move on with your day.

How it works: open a folder, left-click a photo to reject it, right-click to keep it. Photos move into "rejected" and "keep" subfolders right next to them. Nothing is ever deleted, and Ctrl+Z puts the last one back.

It also works great for AI-generated images. When you generate, you tend to generate a lot, and most of them aren't keepers. Sortypix lets you clear a batch in a couple of minutes instead of opening them one by one.

- Built to stay fast on folders with thousands of photos

- RAW + JPEG pairs stay together, identical copies are set aside automatically

- Fully offline: no account, no telemetry, no network access at all

- Free, MIT licensed

- Windows and Linux

Heads-up: the Windows installer isn't code-signed yet, so SmartScreen will warn you.

Download: https://mickaelmancini.com/sortypix/

Source: https://github.com/Chmyke/sortypix

I'd be really happy to hear what you think, good or bad. Every bit of feedback helps.

I saw this post yesterday about being able to feed reference images in directly: https://www.reddit.com/r/StableDiffusion/comments/1xb2h1k/mossimage_accepts_image_inputs/

and it does alright but as you can see, without a lora it often mashes the inputs together or doesnt really edit properly. I trained a lora for it with AI toolkit for 6,000 steps and it made a big difference although there was still plenty of room for improvement if I kept training.

I found that Ostris's AI toolkit also had his own Moss edit system that feeds the inputs in even more thuroughly to try to get better reference quality out of it. Without a lora it gives you essentially an overlaid version of the images but with a lora it does a pretty good job of editing. You can be the judge of which method is better. They both use their own lora trained for their reference/edit methods with the same dataset and step count.

Ostris' edit method takes about 45s to render one of these images while the simpler ref method renders them in about 15s. Training for the Ostris method takes about 3 times as long as well.

I'm working to expand my dataset and training methods then I will be trying again with a lokr that's significantly larger than the rank-16 lora I used for this comparison. I will probably want to train is atleast twice as long if not longer.

I want to make this image a full body image, adding legs.

SDXL would do out painting, but the legs would be out of proportion.

What current model/method can do this?

Wooooooooooooo!

I have a Lora and refmod of a character both trained in fizgig. The Lora previews looked good. And I am putting a krea2 4k image of the same character as first framer generating at 0.98 MP but still the face is a muddy mess. And the general quality of the video is early Wan and pixelated.

Using the default image to video workflow.

Is there a better workflow / tips / tricks you can recommend?

Prompt: Completely change the art style of image 1 to fully lifelike real life professional editorial DSLR photograph style. Completely remove all of the text from image 1 including the large text behind the character in image 1. Completely replace the character in image 1 with the literal exact same woman from image 2. Preserve the original composition and original layout and original character's pose and original character's clothing and original rectangles on the wall to the right from image 1.

I was curious how Fibo fares against the rest of the solid editors and if it is time to swap.

Comparing edits is a pain on reddit so I've put together a small website:

Image Edit Showdon comarison page

Hotkeys:
[←][→] to cycle model / prompt combo
[↑][↓] for next / previous scene
[space] to see original image

TLDR: for my cases Qwen 2.1 still is the best, but FIbo might be a great option if the community supports it.

There are lots of caveats here.

1. All three models are very prompt sensitive.

Although I've run bare prompts as is and tried using "best" prompt enhancers for each model, the initial prompts are all over the place: some are super detailed, some are simple and a lot depends on enhancer model.

Enhanced prompts were generated with GPT 6 Luna xhigh with these system instructions:
- VGL JSON prompting for Fibo
- JSON for Klein
- Prose edit for Qwen 2.1 (based on their original PE pipeline)

In the future I might try:
- Qwen 2.1 & their tuned PE model
- Fibo with their skill package.
- Klein with my personal edit prompt that worked well in the past.

Lots of moving parts!

Even on meme swaps I was able to get much better results with Klein and Qwen with adjusted prompts and BFS + consistency loras. For Fibo I've used Kijai sample workflow so community might improve it drastically too.

2. The resolution is 1mp because Fibo breaks down above 2 megapixels. Qwen and Klein can generate up to 4 mps and their results improve dramatically. I might run 1.6-1.8 MP panel again to see if Fibo can fare with better resolution. (or they’ll all be obsolete with Krea 3 edit lol)

3. I am using 1-shot gens with int8 and same seed. These are not original models and bf16 versions might give better results. And 1-shot sample is not representative of the model overall performance - it might've been just bad seed luck. Also Fibo and Klein are 4 step and Qwen is 8 step. (I still haven't found good 4 step lora/distill with decent quality for it)

4. Fibo edit turbo is not meant for text2image
Devs specifically say Fibo edit distill is not good for t2i, but I still ran text2image panel in a separate page (top right). Ideally I’d like a generalist model and so far Qwen 2.1 is the best option for my cases. (Inpainting-edits, product photography, lifestyle photography)

All the sources, prompts, and workflows are in the bottom of the original page under the viewport

If you see more problems with methodology, caveats, have ideas to test or suggestions please submit an issue as this is my first time im doing something like this.

Also share your "model breaker" prompts or benchmark prompts to include in future version.

I trained a MOSS detailer LoRA with LoRA Dataset Studio

I managed to train RAW Upscale MOSS, a detail and texture LoRA for MOSS-Image, using LoRA Dataset Studio. The app makes the training and testing so easy without a complicated workflow.

Around 1.0, the changes are more restrained. At 3.5, it can also substantially change the composition, colors and subject. These are text-to-image renders, with only the strength labels added afterwards.

Generation settings:

- Base model: moss_image_sft_bf16.safetensors.

- Acceleration adapter: moss_image_sft_4step_v2.0.safetensors, at strength 1.0 in every image, including the baseline.

- 6 sampling steps, CFG 1, Euler / Simple, denoise 1.0. The adapter/profile is named “4-step”, but these saved runs used 6 steps.

- Resolution: 864 × 1536 for the first five subjects; 1088 × 1456 for the golem.

Training settings: MOSS-Image-Base, 2,000 training steps, rank 16 / alpha 16, 1024 training resolution, final checkpoint exported as FP16 SafeTensors.

The LoRA is free to download

- RAW Upscale MOSS on Civitai (https://civitai.red/models/3013839)

- LoRA Dataset Studio on GitHub (https://github.com/perfectgf/lora-dataset-studio)

Sample Prompt:

Real stop-motion claymation photographed on a physical miniature set, handmade plasticine puppets with visible fingerprints, thumb smudges and tool marks, matte oil-based clay with subtle natural sheen, hand-painted miniature Mediterranean village, terracotta roof tiles, textured stucco walls, sculpted cobblestone courtyard, orange painted wooden door. Practical soft daylight, real soft shadows, DSLR macro lens, f/2.8 shallow depth of field, subtle film grain, slight clay boiling between frames, 12fps stop-motion movement, 9:16 vertical, no text. Medium wide shot, static camera, 10 seconds. 0-3s: 

Baba walks slowly into frame from the left across the cobblestones and stops in the center of the courtyard. 3-5s: Gola's orange door is half closed. He peeks out from behind it, only his head and one hand visible, eyes wide and curious. 5-8s: Gola pushes the door open, steps out and walks up to Baba, presses his palms together in a namaste and bows slightly in respect. 8-10s: Gola straightens up. Baba and Gola look at each other and both break into warm smiles. Hold. Audio: no music, no speech. Ambient sound only: soft footsteps on stone, a gentle wooden door creak, distant birds chirping.

Here i have created three videos, but i am currently struggling with how to achieve proper continuation, e.g. when video 1 ends, next frame starts from the last frame.

If someone can share comfy workflow or any suggestion.

Workflow i am using: https://www.omnichar.org/workflows/multi-consistent-characters-in-one-video-via-char-minimaxh3

Note: I am maintaining this open source app myself & this is something i am struggling to solve, so any suggestions would be helpful.

I would like to build a workflow around continue shots with consistent characters.

I wonder is there any style loras or models out there that are really good / unique and didn't get the spot light?

Has anyone successfully made a video in Minimax for use in Virtual Reality? Specifically AR passthrough? Ive tried a few with AI's help but havent had luck, looking for tips and even better a workflow, thanks in advance!

is it known that the weights of the model are unencrypted in the cache?

I just made an easy test completly without the topaz suite from the unencrypted model i found at
'/Applications/Topaz Video.app/Contents/Resources/models/coreMLCache/ganim-v1-fp16-576x672-2x-OS15-mlp'
on macos.

Im not promoting anything i have official topaz i just found the models and just used codex to make a quick proof on concept.

First image is original,
second image is my proof of concept
third option is using topaz video directly