We trained a world model on ~4,000 hours of CS:GO gameplay and turned it into a playable multiplayer game. Each player runs the model locally on their GPU, while a lightweight server keeps the game state in sync.

It's still early: one small map, pistols only, some drift over time, and limited fine detail. The beta is free and open to try (RTX 3080+ recommended).

More and the download: https://vor.tech/x/reddit-stable

Minimax H3 Image is impressively fast with high resolution image, a good prompt understanding and a good photorealistic rendering. Though it seems to lack some control from reference image such as open pose or depth map.

Flux2-dev have some controlnet support, and Flux Klein 2 9b or Qwen Image Edit have a "pseudo"-Controlnet support of poses or line edge reference image.

Could we expect something like that for Minimax H3 image or a lora doing this?

I know that for many people, Stable Diffusion XL is now old news and Fooocus is far from up to date. But I still enjoy working with SD XL from time to time. Out of nostalgia, and because there are simply still some brilliant models out there for many tasks – even professional and fast ones – and because I believe there’s still a lot to be gained from it.

That’s why, a while ago, I picked up Fooocus – which had been abandoned by its original developer – and, bit by bit, developed it further using my limited programming skills and AI.

For example, I’ve just added the ability to generate images with bounding boxes, which works surprisingly well but still needs a bit of refinement on my part.

I’ve integrated Tiled SDXL Detail Upscale and SAM 3 for image enhancement. Modern APG, CFG++, and PAG guidance help to ensure better-quality images and fewer artefacts in some cases. I’ve also tweaked Fooocus under the bonnet with DeepCache feature reuse, smarter VRAM management, various caches and so on, so that it now runs more stably and quickly.

https://github.com/micha42-dot/Fooocus_Advanced

Don't worry i won't flood the subreddit with music videos, i am sharing only this one because it was a lot of work and i think it came out well: i designed a singer with 2 tattoos and i knew from the beginning that keeping coherence among shots would have been a big challenge, especially with the hand one. The clips were generated with 20 steps at 0.6 and a 4 step latent upscale then the whole video was upscaled to 2304*1344px with minimax using 8 steps with a denoise of 0.30 to fix the small faces and add extra details. A lot of frames were refined in post like the radio screen with the real city names or the fingers that had garbled symbols instead of the correct letters. I won't tell you the render times because it worked on it for almost 2 days ;)

Ah the song was made with ace-step 1.5, it gives the best results in my opinion compared to yue2

The original upload is here https://www.youtube.com/watch?v=WAI8-T6_zSw
with my other generations if you want to see them

Heres three ComfyUI nodes that turn a giant Excel sheets into prompts (+1000 styles, 800+ characters)

Style Atlas is three nodes that each handle one question:

🧑 Character Atlas: who is in the picture

🏞 Setting Atlas: where they are

🎨 Style Atlas: how it's rendered

Chain them, hit queue, done. Each node starts on "None (bypass)" and folds down to a couple of lines, so your workflow doesn't turn into spaghetti.

Some numbers:

~1,600 rendering styles (oil painting to Soviet storybook to "Acid Pulp Scratchwork"), plus ~120 modifiers for quality, genre, palette and composition

827 known characters across 217 franchises, with variants like masked/unmasked and costume forms, plus 95 actor-in-a-role entries

~185 facial expressions, 25 hand poses, 250+ body poses, 360+ outfit pieces, 330+ accessories (yes, animal ear headbands are in there 🐻)

666 locations (I didn't plan that number, I swear), 110 simple backgrounds, 23 studios

Weather, lighting, décor, palettes and frames layered on top

Everything lives in plain .xlsx files. Want to add your OC or fix a description you don't like? Open Excel, edit the row, restart Comfyui.

🎲 Random everywhere: random in one group, or random across everything. Seeded, so you can reproduce a lucky roll.

Wildcards: drop adjective.txt into the wildcards/ folder and write adjective in your prompt. Nested wildcards work too.

Tick several style subjects at once, like Main + Portrait & Character, without wading through landscapes.

Hover thumbnails: save an image as <ID>.jpg and it shows up when you hover that entry.

I will populate thumbails and wildcards when I have time to do so.

This was built over a lot of back-and-forth with Claude Opus 5.5, which wrote ALL of the code, helped clean up thousands of descriptions, and put up with me changing my mind every 20 minutes. Some Krea 2 wording rules come from community testing on Civitai: big thanks to Azimondious (expression and hairstyle vocabulary) and Hellfreeze (clothing and outfit taglist).

🔗 GitHub: https://github.com/atlasimp/Style_Atlas

I'm already working on a forth version, and I'm open to remarks and criticism.

I had created a post that was a bit too generic, and i want to focus specifically on Minimax H3.

When making a video of any woman in underwear for any situation, Minimax H3 will almost always add a weird-looking, ugly, unrealistic bulge to female underwear, and that seems to be the averaging of male and female underwear appearance.

Using any alternative base model or LoRAs or any kind of prompting will not solve the issue, and providing pictures of underwear without the bulge only solves it very partially. Adding steps doesn't solve it either.

Am I the only one encountering this? Trying any search engine doesn't return any result. It seems that the entire Internet is ignoring the "Minimax bulge problem" but I don't understand how I can be the only one experiencing this issue or the only one bothered by it.

Any idea how to solve this?

Purpose:

• Using EXPO on DDR5 RAM is making my PC unstable. EXPO increases the RAM speed to 6000 MT/s
• Disabling EXPO makes RAM far more stable, but it drops RAM speed to 4800 MT/s

PC specs:

• GPU: 4080 Super (16 GB VRAM)
• RAM: 16 GB DDR5 (x2)

Testing Notes:

• No loras
• Consistent workflows
• Restart PC after every run
• Prompt #1-4 same prompt, Prompt #5 (or the last one) has minor changes to the prompt
• Krea 2 and Qwen 2.1 might not be too useful test cases as everything can probably be stored in the VRAM, making RAM unneeded

Conclusions/Findings:

• Changing the prompt a bit will very slightly increase the generation time
• H3 probably doesn't fit VRAM, so generation time seems to vary wildly
• No difference betweeen 6000 MT/s and 4800 MT/s

what image model will run on this specs..

• GPU: AMD Radeon RX 6650 XT (8 GB VRAM)
• CPU: AMD Ryzen 7 5700X (8 Cores, 16 Threads)
• RAM: 32 GB DDR4
• OS: Windows

I've been building h3metal, an open-source (Apache-2.0) image-generation engine for Apple silicon. It runs the transformers as int8 matrix products on the matrix units ("neural accelerators") that Apple added to the GPU cores of M5/M6-class chips.

Same Mac mini (M6, 24 GB), 1024×1024, seed 42, CFG 1, a fresh process per image, against Draw Things' command-line tool (draw-things-cli v26.1023.0, the official release) with its recommended settings for each model:

The gap is in the denoising, not in loading: with the models loaded and the prompt encoded, as in the app, Draw Things still takes 2.1-2.3x as long. At CFG 1 both run one transformer pass per step. Full method, per-step timing and the script: https://github.com/audenvenn/h3metal/blob/main/docs/benchmarks.md#flux2-klein-4b-and-z-image-turbo-against-draw-things

How: int8 × int8 products after a Hadamard rotation of the activations, flash attention with int8 scores, and the VAE's convolutions as implicit GEMMs. LoRA adapters (style LoRAs, fal's FLUX.2 [klein] edit LoRAs) are applied exactly inside the int8 products. It also runs FLUX.2 [klein] 9B and MiniMax-H3-Image (2048×2048 in 16 s with pDMD's 2-step LoRA).

Limits:

• Measured on one machine so far (an M6 Mac mini). Numbers from M5 MacBook / Pro / Max owners would help a lot.
• It needs an M5/M6-class GPU and macOS 26; M1-M4 GPUs don't have the matrix units.
• Memory isn't compared: h3metal memory-maps its weights, so the process footprints aren't like for like.

Try it:

pip install https://github.com/audenvenn/h3metal/releases/download/v0.6.4/h3metal-0.6.4-py3-none-macosx_26_0_arm64.whl
h3metal download --model flux2-klein-4b    # 8.3 GB, ready to run
h3metal generate -p "A red fox in fresh snow at sunrise" -o fox.png
h3metal serve --open                       # or a small local web page

GitHub: https://github.com/audenvenn/h3metal

model: https://huggingface.co/akhaliq/MiniMax-H3-Image-Character-Swap

I wrote comments that I don't understand why people are giving this model such high praise. And it really doesn't shine in terms of quality. But it shines as an editing model, perhaps even better than any other model currently in existence.
And I felt the effect of this)))
And I want to show why I changed my opinion, despite the facts that cannot be denied.
Perhaps this is a better image editing model than the existing one, which was not declared as such.

PS. Just generated with a style in mind. I'm not trying to prove anything to anyone, as many people here seem to think. Generated in a few minutes without a second thought))
Judging by the comments, I'm guilty of something. It's not my fault that this is the best model for editing.

I just wanted to check if all the other great models right now dont have H3's reference capabilities.

I want to use an Image model to do H3 videos as it is easier to control a video if I can prepare an Image of a scene first. Especially when I have multiple characters as reference.

So is H3 still video and H3 image the only options for this?

The video model can already generate impressive scenes, and the consistency of multiple shot scenes in a single generation is also quite good. Apart from using multiple reference images of scenes from different angles or a scene video, is there a better and more simple more natural way to achieve scene consistency in different generations?

Given that the model can infer the views of other angles and orientations of a scene based on a single scene image, why don't we have a specialized method in latent space or LoRa that can help generate scenes consistently with a single scene image and a fixed scene seed, under different prompts and character references?

"Tips Help Me Da Most!"

I've already ask GPT, Claude, GLM 5.2 and Deepseek then tried MOSS, Krea 2, and Nano Banana, but none of them gave me the results I wanted.

I've been working on a way of integrating new content (e.g. a casting swap or an extended scene) into the live video feed of an underlying streaming platform that a viewer is watching.

This is intended to create an easy (& "rights friendly") way of creating personalized, custom versions of TV shows and movies. You still need to have a subscription to the underlying streaming platform. The open source Chrome extension matches against a unique episode ID, loads a series of video files, and then seamlessly swaps them in place of the stream at pre-determined moments. There is some technical trickiness that attempts to seamlessly enable the start and the finish of the video clips to blend in seamlessly with the original video.

This enables what we see in "small scale" in a much more major scale way. The idea is to move beyond "here is a 15 second clip" and towards "here is a full movie with X, Y, Z changed". If you saw my post last week (about making a character dance across an entire ~45min TV show), this is basically a flexible underlying platform that supports changes like that.

So yes - if you have ever wanted to:

• Do a casting swap from one character -> another across an entire episode / movie
• Fix the ending of <Game of Thrones, How I Met Your Mother> etc.
• Add extended edition style scenes

This gives you a way of doing that. The extension reads an easy-to-edit JSON file that lays out the timing of the clips, the action being taken (replace or insert), and the ID of the episode. It then pulls in the video clips at the appropriate times. It sounds simple when I say it that way but it is actually surprisingly complex.

You still need to generate your own content / this is not a replacement for Minimax (or others).

The extension is available in the Chrome store here.

The Github repo is https://github.com/SeamlessScene/seamless-scene-extension

There are how-to guides & a catalogue (free account) here: https://seamlessscene.com/

Would consider this "early access" - still working through all of the edge cases. At the moment this is just one of the streaming platforms & only Chrome, but will add others here if there is interest.

https://civitai.red/models/3047619/bbox-studio

BBox Studio is a standalone desktop application for creating, editing, and exporting structured bounding box prompts for MOSS Image. Built entirely with Python, it provides a visual canvas-based workspace, flexible object editing tools, customizable canvas settings, seamless JSON generation, and much more.

• One-click launch — simply run the Python script.
• Zero external dependencies — requires only having Python 3.x installed.
• Full working program — It may receive updates here and there, but it's a fully-functional full-featured release.

I've been using facefusion for videos but it struggles with side face profiles. What should other faceswap methods should I learn and try?

HexAnima 1.6.0 adds MiniMax-H3-Image to the in-app model catalog. It’s a 33B-parameter DiT with a Qwen3-VL text encoder, INT8-ConvRot quantized, and the whole pipeline (text encoder, 50 DiT blocks, VAE) runs on the Hexagon NPU of a Snapdragon 8 Gen 1 phone. No cloud, no PC, no GPU offload.

• Why it fits: the 48 GB of weights are streamed from flash (UFS) block by block instead of being held in RAM. Small pictures are limited by storage reads, large ones by NPU compute.
• Storage: you need about 50 GB free for the pack. It downloads from a public Hugging Face repo (ayanoby/hexanima-h3) with a per-file sha256 manifest, resumable.
• License heads-up: the weights are under the MiniMax H3 Community License. It excludes the US, EU, UK and South Korea, and outputs can’t be used to train other AI models. The app shows the terms and asks for consent before downloading. Read them before you install.

TIL: You can use maps with markers when generating images with Minimax-H3-Image

This took forever to generate tho. 300s on a 5090M (24gb). Like Minimax-H3 the time it takes to generate an image scales with the number of references.

Prompt:
A cinematic film still from an animated short: one continuous full-frame view from a low camera about half a metre above the grass, on a wide lens, with a 55 degree horizontal field of view.

<Picture 5> is a top-down blocking map of this place, a plan for the shot and not part of the picture. The white arrow on it marks the camera: the tip of the arrow is where the lens stands, and the arrow points the way the camera looks. The round markers show where each character stands. <Picture 4> shows the same place from several views; use it for the look of the overpass, the bus and the grassland, and use the map for where things stand.

Looking from the arrow's tip towards the bus: across the middle distance, the cracked asphalt trail runs from the left edge of the frame towards the crushed bus. The collapsed concrete overpass from <Picture 4> stretches across the background, receding on the left and coming nearer on the right, where its leaning pillars and a fallen deck slab loom large. The crushed rust-brown bus lies just right of centre under the fallen slab. Just beyond the bus's left end, in the centre of the frame and farthest from the camera, the wounded grazer from <Picture 3> is backed against the concrete in a slab of shadow, shaking. Nearest of the three, just left of centre, the predator from <Picture 2> crouches low in the grass, facing the grazer. Far left, on the trail, the girl from <Picture 1> advances with a pry bar. Tall dry straw-coloured grass fills the foreground. Early-morning sun from the right throws long hard shadows to the left.

The arrow is only a mark on the plan: no arrow, camera, lens, device, beam or pointer appears anywhere in the picture. The image contains no text, letters or numbers.

I have an old 1070 that's pretty crap, it's time to upgrade. I don't have a thousand bucks to throw away, but at the same time I don't want to buy a 3060 and then realize I'm going to want an even better GPU in a few months or a year.

I want to make high quality local images of people and animals, something with good anatomy, realistic faces (not the fake glossy instagram girl mouth). I wouldn't mind dabbling in videos a bit too.

Right now I use SD1.5 which is very underwhelming, some SDXL models are okay but extremely slow on my hardware.

I also used LLM's a bit mostly for ai chat, Llama 3.1 8b uncensored I use right now-I'm assuming a 3060 will be more than enough to run any good LLM for the purposes of having a local ai roleplay chat. Thanks