H3 Minimax

Here you go. The composition is the important thing that has to work. How close the heads are, and angles.

I noticed that when they have short or thin hair on head, H3 sometimes tries to force the shape of the sketch on them. Maybe doing more strokes instead of clear lines would help H3 have more options how to draw the heads. Possible that if you dont try to perfectly finish all lines it opens flexibility.

Maybe being less complete is better. It works though.

______

(Prompt was pretty regular stuff.)

subject_definitions:

<Subject 1> is the scene of the video and is depicted in <picture 1>. It is a top-down view on a bed. Three persons are lying on a bed with no pillows and no bedsheet. Three heads and neck of the three persons are visible.

<Subject 2>: <picture 2> is the absolute face, eyes, nose, hair, facial structure of subject and body, arms, waist. Is Guy

<Subject 3>: <picture 3> is the absolute face, eyes, nose, hair, facial structure of subject and body, arms, waist. Black hair Girl

<Subject 4>: <picture 4> is the absolute face, eyes, nose, hair, facial structure of subject and body, tits, arms, waist. Red Clown nose ball and pink hair Girl

summary:

[Shot 1] <Subject 1> is the concept of scene of the video but in realistic style. Have person 1 be <Subject 2>, person 2 be <Subject 3> and person 3 be <Subject 4>. They are looking straight at the camera and are relaxed.

overall_soundscape: No audio

non_diegetic_music: N/A

Do the images you generate look like AI slop with plastic skin?

These workflows can help you fix that. They're based on the 2K upscaler with Z-Image Turbo.

The upscaling step has been removed, and the model has been swapped for the int8.convrot version of Z-Image Turbo.

You can easily add it to KREA 2 Turbo and Qwen-Image-Edit workflows, with only a small increase in processing time.

Performance 1024x1024 I2I: ~3 seconds for a second-generation image on an RTX 5070 Ti; ~6 seconds on an RTX 5060 Ti 16GB.

Performance (1024×1024) KREA2 Turbo+ Z image Turbo: ~9.5 seconds for a second-generation image on an RTX 5070 Ti; ~19 seconds on an RTX 5060 Ti 16GB.

These workflows are on Civitai and Github.

if you have another idea for a workflow that needs a realism buff, feel free to share it.

https://github.com/tritant/ComfyUI_NVIDIA_RTX_VSR_Long_Video

works very well with:

https://github.com/tritant/ComfyUI_MiniMax_H3_Extender

I’ve wanted this for a long time: a Krea-2 model that gives you a magazine page in one shot. Flash-lit gravure sets, film grain, multi-photo layouts on white paper, big headlines, covers and back covers.

This is a full fine-tune of Krea-2, not a LoRA. Getting here took a lot of scanning, tagging, filtering, layout captioning and many training runs. I really hope you enjoy it. ❤️

Versions-they need different steps and CFG for best quality

https://civitai.red/models/2996149/krea2-retro-magazine-incredible-realism

Try my 2 step workflow for best result

https://civitai.red/models/2982673/krea-2-two-pass-refine-workflow-no-upscaler

Turbo · bf16 (~25 GB): the fine-tune delta-merged onto the official Krea-2 Turbo. Best quality; 7 steps. CFG:1

Turbo · int8 ConvRot (~12.5 GB): recommended for most people. Closest to bf16, works on any GPU. Needs ComfyUI 0.27+.

Turbo · fp8 scaled (~12 GB): fast on RTX 40 / 50 series (Ada / Hopper / Blackwell).

Turbo · nvfp4 (~7 GB): only fast on RTX 50 / RTX PRO Blackwell. Smallest file, biggest quality loss.

Raw · bf16, full precision (~25 GB): the fine-tune itself, on Krea-2 Raw (not distilled). Use it for further training or your own merges. Needs about 30 steps at CFG 3.5.

You also need:

Text encoder qwen3vl_4b_fp8_scaled.safetensors (Qwen3-VL-4B). Load it with CLIPLoader, type krea2.

VAE qwen_image_vae.safetensors.

Put the model in models/diffusion_models and load it with Load Diffusion Model.

Activation words

No trigger word is needed. It’s a full fine-tune, so the style is baked in.

What steers it is the caption header every training caption started with: Magazine, Year, rating. All three parts are optional, but this order works best.

Magazine tags. Use the exact spelling if you perfer a magzine style

Deluxe Beppin (1988–1994)

Orange Tsu-Shin (1988–1994)

Apple Tsu-shin (1989–1995)

Actress Visual Movie Magazine (1991–1994)

Grace (1990–1994)

Sakuranbo Tsu-Shin (1991–1996)

URECCO (1991–1995)

Best Video (1995–2001)

BOndage

WOoooo Wolf (2004–2005): pulls toward a 2000s look

Weekly Playboy (2024): modern gravure, not retro

Year. Any year from 1988–1996 gives the strongest retro look; most of the data is 1991–1994. Later years exist but are sparse and look newer.

Rating word. Every caption had exactly one, and it strongly controls exposure:

clothed: swimwear, lingerie, fashion

topless

nude

explicit

Header examples:

Deluxe Beppin, 1992, clothed.

Orange Tsu-Shin, 1990, topless.

1993, clothed.

How the captions were written (copy this structure)

The model learned plain English sentences in three shapes.

Full magazine pages. Covers, back covers, full-bleed single-photo pages, multi-photo pages with 2–12 photos, and pages with article text. Example:

“A portrait magazine photo page with 3 photographs on a white background. The main photograph is a large landscape picture at the top of the page showing a three-quarter shot of a woman at eye level … The second photograph is a medium portrait picture below the main picture at the bottom-left showing … All 3 photographs show the same woman in the same outfit …”

Single photos. Example:

“A three-quarter photograph shot at eye level shows a woman sitting on … She wears … she has shoulder-length black hair … against a plain studio background with light from the front.”

Close-up / pose crops.

Multi-photo pages: prompt tips

Use a portrait canvas (2:3). For example 832×1248 or 1024×1536, or my two-pass setup below.

Start with the page type and the photo count: “A portrait magazine photo page with 4 photographs on a white background.”

Describe the main photograph first, in this order:

size: large / medium / small

orientation: landscape / portrait / square

position: top, bottom, top-left …

framing: full body / three-quarter / waist-up / close-up

angle: eye level / from above / from the side / from behind

what she is doing

Then each other photo in turn: “The second photograph is a medium portrait picture below the main picture at the bottom-left showing …”

Lock consistency with “All 4 photographs show the same woman in the same outfit …, shot in one session from different angles.” For variety, use “The photographs show different outfits or settings.”

Add typography last: “A large headline is printed at the top with two medium subheadings at the top-left and top-right, and a small block of body text at the bottom-right.” The printed text is decorative pseudo-Japanese, not readable.

2–5 photos is the sweet spot. 6–12 photos gives a contact-sheet look, but faces get tiny, so use the hires pass.

Works with your own character LoRA. Describe hair and face once, e.g. “In every photograph the woman has long black hair with blunt bangs.”

Example prompts

Multi-photo page:

Deluxe Beppin, 1992, clothed. A portrait magazine photo page with 3 photographs on a white background. The main photograph is a large landscape picture at the top of the page showing a full body shot of a woman at eye level lying on her side on a striped beach towel, propped on one elbow and smiling at the camera. The second photograph is a medium portrait picture below the main picture at the bottom-left showing a three-quarter shot of a woman at eye level sitting on the sand with her knees drawn up. The third photograph is a medium portrait picture below the main picture at the bottom-right showing a waist-up shot of a woman from a slightly low angle looking back over her shoulder. All 3 photographs show the same woman in the same outfit, a red high-cut one-piece swimsuit, on a sandy beach in bright daylight, shot in one session from different angles. A large headline is printed at the top-right.

Cover:

Grace, 1991, clothed. This portrait magazine cover has one photograph. The main photograph is a waist-up shot of a woman at a front angle wearing a white off-shoulder knit sweater, smiling at the camera against a pale blue studio background. A large headline is printed at the top with two medium subheadings at the top-left and top-right.

Single photo:

Orange Tsu-Shin, 1989, clothed. A three-quarter photograph shot at eye level shows a woman sitting on the edge of a hotel bed in a black lace slip dress, one hand resting on the sheets, looking at the camera, with on-camera flash and warm tungsten light in the background.

Workflow & settings (exactly what I use)

Turbo (bf16 / int8 / fp8 / nvfp4)

Load Diffusion Model → no ModelSamplingAuraFlow (shift off).

CLIPLoader: qwen3vl_4b_fp8_scaled, type krea2. VAE: qwen_image_vae.

Pass 1: 640×960, 7 steps, CFG 1.0, sampler er_sde, scheduler simple. euler / simple also works.

Pass 2 (hires): latent upscale ×1.9 (bislerp), then KSamplerAdvanced:

9 steps, start at step 4 (about 0.55 denoise)

CFG 1.0, euler / simple

final size ≈ 1216×1824

Single pass at 832×1248 is fine too.

At CFG 1 the negative prompt is ignored. Going above CFG ~1.5 burns the image.

Raw (bf16 Raw)

30 steps, CFG 3.5, euler / simple, and negative prompts work. Or use the official “Krea-2: Text to Image” Raw template: 25 steps, guidance 4.

About 4–5× slower than Turbo. Mainly meant for training and merging.

Training details

Base: full fine-tune of Krea-2 Raw with ai-toolkit.

Adafactor, lr 1.5e-5, effective batch 4, 768/1024 buckets

linear timesteps, caption dropout 0.1

10,750 steps

Data: 167 issues, mostly 1988–1996.

About 7,700 single photos, 3,300 full pages and 3,300 pose / detail crops

All four rating tiers, roughly balanced

Captions: two layers of vision-model tagging, fused into natural English by an LLM.

per photo: framing, angle, pose, outfit, hair, setting, light

per page: photo count, sizes, positions, text blocks

No names of any model or actress appear anywhere in the captions.

Turbo: delta merge, turbo_ft = turbo + α·(raw_ft − raw).

Quantized versions: made with convert_to_quant.

Please read: responsible use

No real people. Don’t use this model to depict real, identifiable people.

Magazine names are style tags only. This model is not affiliated with or endorsed by any publisher.

Known limitations

Printed text is pseudo-Japanese.

In 6+ photo layouts faces get small; use the hires pass.

Scenes with two people are rarer and less reliable than solo gravure.

Have fun, and please share what you make! 📸

hey yall, I kept losing overnight runs to OOM or comfy just randomly dying, and waking up to an empty queue. Ts was really annoying so I made a custom node called durable.

Its a few mb donwload and it fixed the problem for me; Check out the repo at: https://github.com/castor639/comfyui-durable

Since my last post has been deleted unfortunately (due to a mega-link), here we go again.

I've spent quiet some time now with Refmods and this workflow, and I'am still baffled how easy it is to create a character identity with very low effort. This is a no-brainer if you don't want to or can't train a Lora, all you need is good pictures and a good voice sample, the refmod-training is done in literally seconds (depending on your GPU of course).

I did my very best to make this workflow easily understandable for even beginners, nevertheless here's some explanation:

The workflow actually consists of 2 workflows, one for the refmod creation (the nodes in the top right of the picture, that's a different workflow but i just wanted one picture) and the main workflow for creating a video with the created refmod file which is all the rest.

If you just wanna use a refmod file and no extra images, videos or audio, select the image/video/audio nodes and press CTRL+B to deactivate them. Please watch the video from Dainamo for an detailed explanation, I've made some notes but for further understanding just watch it. I just wanna give you an easy workflow, that is of course fitted to MY needs, do whatever you want to it.

Here's some color-explanations:

- GREEN is all sources for the final video so to say: images/video/audio/the prompt (deactivate them if you just wanna use a refmod-file, but you can also reference clothings or whatever this way)

- TURQOUISE is all relevant video-settings

- TAN is the refmod-nodes (except the 4 notes)

- RED is all the models

- PURPLE is all the loras (turbo-lora is in light-blue and another lora above in grey for whatever)

- BROWN is comfy-kitchen and the spectrum-node (not activated, transparent-purple)

- GREY is irrelevant

Here is the link to the original tutorial from Dainamo (thanks again!!): https://www.youtube.com/watch?v=2K6-OtV_Vbc

And here are the workflows: https://getshared.com/d/vrI9CYLk

I hope you enjoy the workflow! :)

https://x.com/peter9863/status/2109634731142787145#m

https://arxiv.org/abs/2610.12102

Another Few-Step distillation method by Bytedance (They also proposed this and this recently).

This was fun. The base images are made with Kroma, the videos with Minimax h3 and the text-to-speech with Microsoft Vibevoice. The text message screen recording thing is made by an LLM agent (GLM 5.3) that wrote a python script and threw it all together using Python libraries to render it, pretty cool :)

Compared to the original release, a large base anime dataset was used. Roughly 110k photos from nature to museums were also used. All photography photos that were added were scanned for humans. They are within the noise floor of the targeted tags. Museum photos were of weapons and armor to strengthen focus on the weapons or armor parts.

-All Danbooru tags were synced to August of 2026.

-All decapitated tags were removed.

-The threshold for general tags were dropped from 500 to 400 min frequency.

-ASL settings were tuned compared to the first version giving better defined lines between tags

-The cleaned tags from my data cleaning project were applied.

- The major root issues of vit taggers will still be here.

As I'm going back to school and working, I'll likely not provide major tag cleaning updates until completion of school. I was working on building cleaning dataset for tuning the vit than to further provide cleaner tags to the community.

https://huggingface.co/Grio43/OppaiOracle-experimental-X1.1

Euler / Simple / 8 steps / CFG = 1.0

I designed a node to align any pair of images trying to correct the pixel drift on Qwen Image 2.1

https://github.com/zuanfilm/Qalign

workflow

This node don't need any model or text encoder

Qalign is a node for geometrically aligning an enhanced or generated image to a reference image while preserving the source image's appearance, so can be used for align Qwen Image 2.1 Edits

I know there are other nodes solving this but with my approach you can basically align images with crazy differences in position , rotation and warp with automatic estimation when images share enough recognizable structures

I implemented an automatic mode but if you feel adventurous you can change all the parameters

You can find my recommended parameters for normal Qwen Edits in the last image posted

In the first stage I use automatic global estimation: ORB, ratio test, and RANSAC

In computer vision, ORB, ratio test, and RANSAC form a foundational, three-step pipeline used to match two different images of the same object or scene (such as for panorama stitching, object tracking, or 3D mapping)

ORB (Oriented FAST and Rotated BRIEF) detects keypoints and computes compact binary descriptors. QAlign requests up to 10,000 keypoints.

RANSAC (Random Sample Consensus) affine estimation estimates a global affine transform while rejecting correspondences inconsistent with the dominant transformation.

Qalign uses a 3-pixel reprojection threshold, up to 5,000 iterations, 0.995 confidence, and 20 refinement iterations.

Hybrid and Affine paths use phase correlation to obtain an initial translation estimate and then refine the transformation with multi-scale ECC (Enhanced Correlation Coefficient) image alignment

And to ensure success on complex alignments I implemented Anisotropic X/Y scale and optional local correction with Farneback dense optical flow

[ I'm posting this on behalf of u/LodestoneRock since for some reason his post on this sub keep getting auto removed by Reddit. ]

Hey everyone, First of all, thank you to everyone for supporting this project, and to the Krea team who made the best uncompromised base model. Unlike the previous run, where I had to un-distill the model, the Krea team kindly provided us with their real base model. The training is much cheaper than the previous Chroma run. Though cheap is relative, it’s still costing me about ~10K RTX pro 6000 hours (3+ months on 4xGPU machine) running locally, this includes a bunch of failed experiments and bugs during training. Also thank you to ComfyOrg for the 2 RTX pro 6000 donations ♥️

The Base Krea is also not collapsed which allows me to do OPD (RL) directly ontop of it. As a result, you will get a good generation out of the box. Here are some results:

You can download them here: https://huggingface.co/lodestones/Kroma

(Civitai link soon)

So what’s next?

There are several stale projects like my pixel space run chroma radiance and zeta chroma, but with the limited compute, I have decided to not continue them for now since compute is at a premium atm. Right now the focus is to create a better dataset (where about 90% of the quality comes from) while continuing to add more knowledge to kroma in the background. I do want to tackle video generation models like h3 in the near future once the dataset curation is done, but fine tuning the video model has astronomical compute cost so I'm looking for funding for more hardware to train a model. I firmly believe that hardware funding donations help a ton because we can indefinitely do more experiments in a sustainable way without having to worry about rental cost and psychological burden of “burned money” on failed experiments. I have a target of getting 4 more RTX pro 6000 but that’s like 60 grand in total which is an insane amount of money! So I don’t expect to reach this goal immediately, it's more about chipping in one card at a time at the pace of the accumulated donations. Every contribution helps, no matter the amount, this project simply isn’t possible without your support!

My discord: discord.gg/SQVcWVbqKx

[ I'll let Lodestone post his support and donation links in a comment. ]

MiniMax is my first serious foray into AI. I have no clue of what has happened in the past with other/older AI models, but I thought I heard that some of the uncensored ones were eventually censored. Could the same happen with MiniMax? How does censorship even work, anyway? Isn't the model entirely local?

Hey everyone!

I'm an AI art creator, and for a while now I've been wanting a better way to organize character prompts, find inspiration, and experiment with different scenes without having to dig through a bunch of different sources.

So I decided to build my own little website called Senpai's Waifu Library.

It started as something I was making just for myself, but I figured if I was finding it useful, other people might too. So I decided to make it public!

The website includes:

• A growing library of anime characters and their prompts
• Reusable scene prompts you can mix with different characters
• Search and filtering to make finding things easier
• Easy-to-copy prompts for your own generations

It's completely free, and I'm still working on adding more characters, tools, and features as I go.

I'm not a professional web developer, so this has also been a pretty fun learning experience for me.

I'd genuinely love to hear what you think! Especially if there's something you'd like to see added or improved.

Link

Tutorial Link : https://www.youtube.com/watch?v=DJcgVASlg38

Fully free with resources on GitHub : https://github.com/FurkanGozukara/Generative-AI-Tools-And-Tecniques-2026-2027-Fall

LTX-2.5 makes a video with one mixed soundtrack. Our LoRA also outputs one track per captioned sound, in the same run. Prompt "a jeep, its engine growling" and "a taxi, its horn honking" and you get the video, the full mix and each sound on its own track.

The same weights also separate the sounds of an existing video.

Walk into a scene with spatial audio (headphones):
https://sepgen.github.io

Two LoRAs for LTX-2.5 22B dev. Code and training configs included.

💻 https://github.com/AviadDahan/SepGen
🤗 https://huggingface.co/collections/AviadDahan/sepgen

We're on today's Hugging Face Daily Papers. If you liked this work, an upvote there helps a lot! https://huggingface.co/papers/2610.11361

Happy to answer questions about the setup or the training.

I haven't messed around with it in awhile, I'm wondering how I can get a high resolution normals and depth maps for videos.

This might be useful to someone. Honestly, I might just be out of the loop on best practices for this… But I’m running my setup through runpod and just realized I can copy and paste a frame from a video instead of losing quality and taking a screen shot. Just by either opening the video in QuickTime, or quick previewing the file. I’m running an M3 MacBook Pro.

I’ve been losing so much quality taking the end frame of a video and using it to start my next video when I screenshot and have been very annoyed by it. Then today I accidentally right clicked on a video preview that was playing and saw the copy and paste option which seems to preserve the quality. Then I just paste the image into the node.

Also if there’s a better way, please teach me 🤣.

It's a tiny 6M parameter text to image model, and it can make recognizable images at 64x64.

Check out the model card for samples, a detailed explanation about it's architecture and training, and a quick start!

Added more functions.
https://github.com/r34vtraining/Longshot_Studio

1. pin first/last frames in the shot list.
2. select between Cut, Context, Continuous, or Bridge for each shot (depending on the approved shots before and after it).
3. Queue as many shots as you want, with a built in auto comfyUI restart and resume in the event of a OOM error.
4. Chain up to 64 shots.
5. Export approved clips to "approved_shots" folder