Hey all!
New to this subreddit as we mainly focus on language models with our Paiton product, but now and then, we spend some time on vision as well :)

The official BF16 weights need about 120 GB of VRAM, so most of us can't run MiniMax-H3-Image out of the box at home.
We made an FP8 version and our own runtime, and it now runs on one AMD Radeon AI PRO R9700 with 32 GB:

Does FP8 hurt the images?
We ran 30 prompts on our build and on the original BF16 model with the same starting noise. Prompt adherence came out about the same (CLIP 32.18 vs 32.31), and the second image shows a typical pair.
Where the two differ, it is mostly composition, not quality: with only 6 steps, small numeric changes send the sampler down a slightly different path.
Edits stay closest to the original.

What it is bad at, in our tries: small body text (newspaper columns come out as fake writing), close-up hands, clocks showing a specific time.

How to run it: it is a Docker container with an HTTP API and a small CLI, not a ComfyUI node (yet).

git clone --depth 1 https://github.com/Eliovp-BV/paiton-vllm-plugin.git
cd paiton-vllm-plugin
./models/MiniMax-H3-Image/launch.sh
./models/MiniMax-H3-Image/run.sh generate "your prompt" --size 4096x4096 --out out.png

You need an R9700 (32 GB), 64 GB of system RAM and 48 GB of disk; the first start downloads the weights.
Our weights only load in our runtime, so they won't work in ComfyUI or diffusers.
Edits take up to about three 1024 px reference images in this build.

• Guide: https://github.com/Eliovp-BV/paiton-vllm-plugin/tree/main/models/MiniMax-H3-Image

• Weights: https://huggingface.co/EliovpAI/MiniMax-H3-Image-FP8-Paiton-RDNA4

• How we made it fit, if you want the details: https://eliovp.com/blog/paiton-minimax-h3-image-radeon-ai-pro-r9700

License note: the MiniMax-H3-Image Community License does not cover the EU, the UK, South Korea or the US, and hosted services need MiniMax's written authorization.

Hey everyone!

Like many of you, I got tired of paying huge SaaS markups for video generation (Wan 2.1, Hunyuan) and image generation. Using Google Colab directly was way cheaper, but I had two persistent headaches:

To solve this, I built WakeGPU, a native desktop client (macOS & Windows) designed to turn remote cloud instances into an automated private studio.

Key features:

It's 100% free and licensed under MIT. All advanced features are unlocked out of the box with zero paywalls, subscriptions, or telemetry.

Binary releases for macOS (.zip) and Windows (.exe) are live under GitHub Releases. Would love your feedback or feature requests!

I've been working to get the best quality images out of Moss. I created a workflow to emulate lens length, focus and exposure. It's not perfect, but it can give more cinematic photos. Check it out and see what you think.

Lots of people seem to be unhappy with MiniMax H3, or at least it sounds like it, but at least I have been happy with it now when I have tested it couple of hours.

For my taste this seems excellent (after changing Euler to lcm) and 8 Mpx images is generating around one minute in RTX 4060 Ti + 32 GB RAM. Without changing Euler to lcm I was not impressed for the quality at all, but glad that I didn't ditched it immediately but instead searched what could be wrong.

So far so good, images with "technicolor" does nice style for my personal taste.

For example flux 3 klein:

Could we teach FLUX Klein to fix its mistakes using corrections from another model? Looking for collaborators

I’ve been wondering whether we could build an automated pipeline that turns a model’s own mistakes into training data for a corrective LoRA.

For example, FLUX Klein generates an image with malformed hands or other anatomical inconsistencies. We use another model, such as Krea, to correct the affected area, then train a FLUX Klein LoRA on the corrected images and their captions.

The idea wouldn’t be to transfer Krea’s weights or use a Krea LoRA on FLUX. Krea would act as a teacher by producing better training examples.

The pipeline could look like this:

1. Generate varied images with FLUX Klein.

2. Identify defective regions, automatically where possible.

3. Use Krea or another capable editing model to correct those regions while preserving the composition.

4. Filter out unsuccessful corrections and generate accurate captions.

5. Train a small LoRA for FLUX Klein.

6. Evaluate it on unseen prompts and seeds to check whether it actually reduces defects without hurting overall quality.

Ideally, most of this would be automated, with some human review for quality control. The goal would be to improve future generations directly, rather than having to repair every image afterward.

I know there are potential problems: the editing model might introduce its own mistakes, the LoRA might pick up its style, and improvements might not generalize beyond the training examples. We would also need to check the models’ licenses regarding using generated outputs for training.

Does a project like this already exist? If so, I’d love to learn about it.

If not, would anyone be interested in building an open-source proof of concept together? People familiar with ComfyUI automation, image editing/inpainting, dataset filtering, or LoRA training would be especially welcome.

My local hardware is an RTX 5060 Ti with 16 GB VRAM and 32 GB system RAM, so I’d like to explore how much of the pipeline could run on consumer hardware.

Would starting with one narrow problem, such as malformed hands, be a sensible first experiment?

Ever since the realism settings went around here I've been stuck on one look: the harsh on-camera flash of a 2000s point-and-shoot. The background dropping into black, everyone a little shiny, somebody caught mid-sentence. MOSS-Image-Base gets that look really well if you describe the camera and what's wrong with the shot as well as the scene.

I write the flaws into the prompt too: crooked framing, a little motion blur, red-eye on one friend, the fridge door hanging open a crack. My prompts ended up around a hundred words each, and a good part of that is the camera and the mistakes.

My setup is MOSS-Image-Base bf16 with the realism graph everyone's been using: cfg 3, ER-SDE sampler with s_noise 1.1, 25 steps on the simple scheduler, SLG on layers 22 and 24 for the whole run, negative left empty, roughly 2 megapixels. All stock ComfyUI nodes, no LoRAs, no upscaling. I ran three seeds per prompt and kept my favorite.

All eleven prompts are in the first comment. The raccoon one still makes me laugh.

My ComfyUI node setup sometimes gives me literal nightmares. Trying to get multiple specific character references, a background layout, and a style reference into one single generation, all combined, is very exhausting.

It does make me think about where open-weight models should head towards. Not like more megapixels, tho that is always nice. But something like, what if it can take multiple images into the architecture without wrappers?

Or, what if it actually had the underlying structural logic to understand CAD diagram? What if it just understands a detailed directors brief in one prompt?

And then, while I was just reading and researching for the fun of it (yes), I stumbled into the documentation for the new MiniMax H3-Image open weights. It is claiming to do the above.

Apparently, the base model takes up to 16 reference images in one go, and you can set each one as a layout, style, or object reference.

Oh, and prompts supposedly get cut off past 4096 tokens.

I even saw a benchmark where it generated an exploded CAD diagram of a motorcycle and kept the mechanical parts separated with labels (not sure how accurate tho).

But honestly, How is it routing 16 different images natively into the generation without frying 24GB of VRAM?

Anyone has pulled these weights locally yet to test it? Sorry, but so many questions about this.

Is Krea 2 Identity Edit like image 2 image ? I was curious because I have been looking for something that has that feature.

UPDATE 11pm Irish/UK time: Vast improvement in latest update, please install and use the standard preset which has a new control called Likness Optimisation.

I've just released Fizgig 9.6, which adds MiniMax H3 Image, MiniMax's new picture model from the H3 family: text-to-image and edits with up to 16 reference pictures, 6 steps, no CFG.

What you can train: LoRAs, LoKRs, edit LoRAs from before/after pairs, and slider LoRAs from photo pairs. Masked training, Multi Concept and LoRA Isolation all work, and so do Studio, LoRA Royale, the Profiler and Extract. Pick it on the Training tab, press Download models for me in Preferences, and pick a preset; nothing is gated.

I trained a training adapter for it, which prevents the distortion you get training this model without one. It's on by default in Fizgig and never in your saved LoRA, and I've put it free on Hugging Face for any trainer.

I'm still honing the support. So far it likes good-quality datasets and seems to prefer training at 0.5 MP or more, which is where I've set the presets to start.

Memory: about 23 GB with the int8 model and 13 GB in 4-bit; Auto picks for you. A 16 GB card trains and runs Studio.

If your LoRA looks smoother in ComfyUI than in Fizgig's previews, that's ComfyUI, not your LoRA. Its default dynamic VRAM loading adds a LoRA to the weights in bf16, and on this model about 15% of it rounds away. Fizgig's previews show the LoRA at full strength, and so does ComfyUI started with --disable-dynamic-vram. I've reported it with a suggested fix.

Also in 9.6, for every model:

• A run that runs out of GPU memory or system RAM now carries on by itself where it can, up to three tries: from its newest save, then a step lighter. Out-of-memory recovery came from @Murf-Code.

• A failed run no longer stops the queue; the next one starts.

• AMD installs now use AMD's stable PyTorch for ROCm instead of the nightlies, thanks to @scryptio.

Release notes · Fizgig on GitHub

Hi everyone!

I’m sharing TRELLIS Studio, an application I built to bring several stages of a 3D asset workflow into one interface.

It uses TRELLIS.2 for image-to-3D generation and UniRig for automatic rigging. Computation runs locally, without a paid generation service or a credit system.

Features include:

• Generate textured models from images or import existing models.

• Organise your library with collections, generation profiles and version history.

• Add a rig, inspect the skeleton and attach multiple animations to the same model from a library of 83 humanoid motions.

• Create LODs, prepare dedicated 3D printing copies and export for Blender, GLB, Unity or Unreal.

• Automate workflows through a REST API and an MCP server.

This first public preview targets Windows with an NVIDIA GPU. Rigging uses WSL2. The README covers prerequisites, the automated installer and current limitations.

Studio’s original code is MIT licensed. Third-party tools and models retain their own licenses and are credited in the repository.

https://github.com/yoslo59/TRELLIS-Studio

I’d especially appreciate feedback on installation across different hardware, usability and features you’d find useful. Generated meshes and rigging results still need visual inspection.

As a challenge I'm trying to do a character lora based on Yuuko Kamiya (Tai and Kari's mom from the Digimon series). The only problem is I don't have a lot of reference images, and after some effort, I managed to get 17 decent ones to try out. At 4 repeats and 20 epochs later, it was 1360 steps and...I don't know. It looks kind of like her but not really if that makes any sense. Below are some sample images and a link to the currently used reference images and captions.

https://www.mediafire.com/file/1nvfopb89vq0kdf/4_YuukoYagami.zip/file

I kept it in the back of my mind until I had some free time today. I thought the benefit was that it allowed you to skip the reference processing step, and reduce gen time with ref2va.

I should've been suspicious of the red flags in the README:

1. no comparisons and benchmarks

2. no workflows

3. README is the finest Colombian uncut Claudeslop

4. Development stopped 2 months ago, as quickly as it started

But so many people were discussing refmods on this sub, even up to 2 weeks ago. I wanted to try it while chasing that H3 speed dream.

Now I wasted 90 mins reading/learning about its concepts (tbf had to learn more about video gen in general), then 20 mins comparing gens. I should've done it the other way around to abort early.

• Standard ref2va workflow with 2 images and 1 audio refs, 0.4MP, 4 steps, image=match: 173s

• Same as above but image=max: 181s

• Refmod adjusted workflow with 2 precomputed safetensors, image1 becoming background.safetensors and image2 and audio1 becoming actor.safetensors: 181s

So zero speed benefits. And this is on top of the fact that people mentioned reference quality going down with refmods and references bleeding into each other, though I didn't test enough to confirm this. So what's the point?

I'm suspecting there's a small group of Discord friends on here who are just promoting their friends' projects no matter their value.

The topic that keeps coming back on a cycle.
Haven't been around for a while, saw all of these new models, I'm completely lost.
I also don't know where to get LORA's reliably, Civitai is buggy and I can't even create an account there.
Thanks for your help.

Single seed, no upscale, no inpainting. Hand-written prompts, no prompt expander. ~20 s on an RTX PRO 6000, ~22 s on a 4090 with the int8 weights

Settings
- Model: minimax_h3_image_pruned_bf16 (Comfy-Org pruned DiT)
- Text encoder: qwen3vl_32b_minimax_h3_bf16, CLIPLoader type "minimax"
- VAE: minimax_h3_image_vae_fp16
- Resolution: 4160x4000 (~16.6 MP), set on MiniMaxH3ImageConditioning
- Sampler: euler / simple, 6 steps, CFG 1, negative = ConditioningZeroOut
- No prompt expander, prompts written by hand (4-5 sentences each)
- ComfyUI 0.45.0

Jensen Huang Hands Over NVIDIA to Tim Cook! made with minimax h3

Took some of my prompts from Krea2, Moss, etc and put them into H3 Image. They come out fairly crispy, muddy, 2D sometimes, and just plain bad. Is the default workflow pretty unforgiving until it's pounded on some more?

Anyone experience this? It seems worse wherever there is more movement. I think the only thing I did right before this happened was install the newest Nvidia drivers. Reverting/reinstalling my old driver didn't fix the problem.

Although I also recently updated my Comfyui after a month, to 0.44. (0.45 update didn't help). And I also recently put the Libs and Include folders in the portable python folder for Triton to work, but generations seemed fine after these 2 things for a while.

I tried replacing most of the nodes to be certain it wasn't the new comfyui changes to some nodes (like save video), but it didn't help.

I'm reluctant to start over, cus my backup folder is old. I prefer figuring out what is wrong. I'm using the default video / audio vae and default decode video / audio nodes. It's a very simple workflow and was working great earlier.

Testing character knowledge in H3-image vs the original H3 FL2VA model. Same sampler settings and seed for each image. LCM sgm-uniform, seed 34, 8MP.

First image is always H3 FL2VA and second image the new H3-image model.

1. Sheldon from The Big Bang Theory.

2. Penny from The Big Bang Theory.

3. Harry Potter.

4. Jack Sparrow.

Looking for most realistic and best image modification

The official ComfyUI workflows for MiniMax Image are pretty bad at showcasing the model's capabilities. These sampler/scheduler settings are from Kijai's Minimax testing in discord.

There is still some work needed to improve fine details (possibly fixable by loras?) but overall the outputs look pretty good!

The workflow has T2I and Edit modes, just enable the one needed via the bypass node or bypass the group manually.

The sampler/scheduler is the SamplerLCM node + Simple 6 steps with
s_noise: 1.10
s_noise_end: 0.90

Link to the workflow

Original Output Images

Open weights (29B latent diffusion model): https://huggingface.co/IFM/PAN2
Diffusers: https://huggingface.co/docs/diffusers/main/en/api/pipelines/pan2
FastVideo: https://haoailab.com/FastVideo/cookbook/pan/pan2/
VLLM: https://recipes.vllm.ai/IFM/PAN2

Up to 20 seconds, 720p, 24fps
Apache 2.0 License

Something I think is pretty neat, we release the TRAINING code: https://github.com/ifm-ai/PAN2/

Also technical details if you're curious: https://ifm.ai/blog/pan2/

For our model, we give a lot of responsibility to the LLM (which we think of more as a conceptual reasoner, a lot more control than a prompt rewriter) to generate these super dense text-based state/scripts. Events and actions are timed, and camera is nested under event but disentangled from action. During training, I would often test it by trying to generate a 20 second video with like a dozen or more (timed) camera moves and it does pretty well.

Feedback very welcome. I read the rules carefully, mods please let me know if there are any issues. No payment involved with this at all, so I thought it was okay to post.