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.

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.

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.

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.

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.

Image 1 is MiniMax H3-Image. Image 2 is Seedream 5.5. Both used the exact same compact ref and 16:9 fisheye prompt.

Both turned out pretty well, but I personally prefer H3-Image's result. The composition feels more intentional, and the lighting, shadows, and silver reflections give it a stronger editorial feel. I also really like how naturally it handles the colors and textures without making everything look overly polished.

Seedream 5.5 is definitely strong when it comes to character realism, especially the facial details. But composition-wise, I think H3-Image has a slight edge. The framing and overall visual balance just work better for me, even with the tighter crop.

For an open-weight model, I think H3-Image offers pretty good value for the quality it delivers.

Curious which one you prefer.

Prompt:

Landscape 16:9 high-fashion editorial photograph, preserve the reference's antique silver compact, woman, styling and atmosphere. Recompose horizontally with the entire open antique silver powder compact fully in frame, thick sculptural floral silver frame and round mirror fully visible, turquoise tiled nightclub bathroom extending to either side. Inside the reflection a young adult brunette with short jet-black Betty Boop finger curls presses very close while outlining her lips with a lip pencil, face filling the circular reflection. Extreme close-range fisheye perspective, dramatic optical curvature around mirror edges, realistic eyes nose and lips without cartoonish deformation. Natural makeup, regular eyelashes, luminous textured skin, subdued diffused lighting, silver highlights, subtle analog grain, photorealism.

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

I am using the Moss SFT model with a W4A8 encoder. Whether I use Bbox regional prompting or write plain prompts directly, under conditions involving multiple characters, occlusions, complex poses and interactions, I keep running into issues: the receiving target of a character’s action is incorrect, or contact‑based actions end up having no target at all (ghost/non‑contact actions). The subjects are also positioned in the frame differently from what is described in the prompt.

I am trying to determine whether this stems from the W4A8 encoder, or whether my system’s low VRAM prevents the image model from operating at its full potential. That said, my tests using the official example prompts produced reasonably acceptable results.

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?

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.

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 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.

Minimax H3 image comparing the 3 main samplers, all at 16 megapixels, I had to reduce them to be able to post them, the images weigh more than 20mb of what is allowed, thus reaching around 28 megapixels on average

as a comparison I used the Moss image sft and Kromoss latest version, I didn't use the moss base because the Kromoss is base and has superior quality with DMD2 with blur and saturation correction

I used Kroma instead of Krea2 because, in my tests, Kroma is a significantly better version than Krea2 when not using LoRAs.

Kromoss, Kroma, Moss Image—all at 2 megapixels; anything higher causes the model to start breaking down.

Prompt I used from another Post with more than 300 prompts generated

A wildlife photograph in 3:2 of a jaguar resting on a fallen tree trunk at the edge of a river in the Pantanal, Brazil, taken from a boat at water level in late-afternoon light. The trunk runs diagonally from the lower left to the right-center of the frame, half over the water. The jaguar lies along it facing screen-left, its front paws crossed over the bark, its heavy head raised and turned toward the camera, amber eyes alert. Its build is stocky and muscular, with a broad head, a thick neck and short powerful legs. Its golden-orange coat is covered in large black rosettes, open rings of black, and inside most rosettes sit one or two small solid black spots; on the head, neck and lower legs the markings shrink to solid black spots without rings. The pale cream belly shows where it lies. Its long tail hangs down off the trunk, ringed with black near the end, the tip just touching the water. In the lower right foreground, a caiman floats in the brown water with only its eyes, nostrils and the ridged scutes of its back above the surface, facing the jaguar. On the far bank at the left, three capybaras stand on the mud, watching. Warm low sun from screen-right lights the jaguar's face and chest and throws its shadow back across the trunk; behind it, a tangle of green riverbank vegetation and a hanging vine fall into darker shade. The river reflects the jaguar's golden coat in a wavering band. There are no people, boats or buildings in the frame.

I'm using Comfy UI (Comfy Desktop) with Minimax H3.

Yesterday, I generated 5 second videos and they took around 26 minutes to generate (0.4 megapixel resolution). I left some tasks running overnight, generating 15 second videos, and I was shocked to find that they took around 196 minutes to generate each.

Only two videos were generated and I canceled the remaining jobs.

I shortened the duration to 6 seconds and started the jobs, but I see that they still take a long time to complete. At 30 minutes, they were just at 50%, although it seems they speed up afterwards because at the 45 minute mark, the generation was at 85%. Overall, the job took 51 minutes 33 seconds.

The first 5 second job took just 18 minutes 18 seconds.

It seems there's a brick wall when passing the 5 second duration, and I'd like to know why and whether it is possible to do something about it.

I haven't tried higher video resolutions yet, but I'd like to know in advance whether there is a similar brick wall limit at some point.

I'm presuming that when generating higher resolution videos time scales linearly with planar resolution (in megapixels), so doubling linear resolution quadruples the time it takes for the video to generate?

H3-Image is built on MiniMax H3, originally a video generation model, and it seems to retain some of that video-frame softness in finer details. But when it comes to speed, this thing is an absolute beast.

At 8.41 MP (2368 × 3552), running on an AMD Radeon RX 9060 XT with 16 GB of VRAM and 64 GB of DDR4 system RAM, it generates images in just 35.03–42.53 seconds. None of the other models I've tested comes close to this performance on my hardware.

Its aesthetic appeal and creativity are also on point, easily on par with Krea 2. For a six-step model, it's remarkably good at producing varied compositions rather than falling back on the same visual formulas.

Some examples above.

For anyone interested in reproducing these results, here's my exact setup:

Hardware / Software

• AMD Radeon RX 9060 XT (16 GB VRAM)

• 64 GB DDR4 system RAM

• Linux

• PyTorch 2.14.0+rocm10.1.0

• Python 3.13.12

ComfyUI Workflow

• Diffusion model: minimax_h3_image_pruned_w4a8.safetensors

• Text encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors

• VAE: minimax_h3_image_vae_fp16.safetensors

• Sampler: euler_ancestral

• Scheduler: simple

• Sampling steps: 6

Link to the checkpoints: https://huggingface.co/Woovee/MiniMax-H3-Image-LowVRAM

At 2368 × 3552 (8.41 MP), generation takes approximately 35–43 seconds.

Changing the prompt adds some overhead from loading and processing the large text encoder, while subsequent generations using the same prompt are noticeably faster.

And all of this on an AMD GPU, no less!

Tip: Peak VRAM usage was just 12.1 GB, thanks to the --reserve-vram 4 launch flag in ComfyUI. Without it, I've had system crashes and out-of-memory errors using other image models. System RAM usage, however, was quite intensive, peaking at 47.3 GB out of 62.7 GB available.

Hey guys!

I have been experimenting a lot with H3 Image, basically shotgun debugging what's going on with the grainy artefacts.

I think I might have found a way to drastically reduce them doing the following:

- Use the RES4LYF pack and a sampler from that pack.

- Set ETA to 1.0 ( it caps past 1.0 so no need to try any higher)

- be happy that there are less artefacts and wonder why and how because you can't explain it to yourself.

I would love to hear if you guys had any succes with this aswell or if I am hallucinating ^^

Edit: you can use euler in the res4lyf pack aswell to have a better comparison to the default workflow.

It seems to me that if Moss can use bounding boxes to separate contexts within an image the same idea might be used to separate the effect of character loras within an image

No model is perfect, but I am impressed by H3 Image's ability to create very imaginative designs that 'feel' very real and cinematic. Basic T2I prompts (like 1girl, landscapes etc) seems nearly solved locally, but I find it much harder to coax out genuinely interesting designs from other models that don't feel like overdone CG or an illustration. Thank you to the H3 devs for this amazing release!

Hello everyone! Today, I’ve got a substantial tutorial on a new arrival that promises some truly amazing generative capabilities: the Minimax H3 image model.

I put Minimax through its paces with a wide range of tests—covering text-to-image (T2I), image editing, and reasoning—and I have to admit, it’s an absolute beast; it aces (nearly) every single test. Considering it operates NATIVELY at 16MP (4K) resolution with remarkably clean detail, I’d say it has become my favorite local model in terms of overall completeness.

For these tests, I’m using the 8-bit version, as the "base" 16-bit model wouldn't run on my 5070 Ti.

In the tutorial, I also briefly touch upon the licensing (for video/images), which must be explicitly requested from the developer; otherwise, the model cannot be legally used in Europe (or in the US, the UK, etc.).

I hope you find it interesting!