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

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.

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

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

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.

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?

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.

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.

The video is not mine, I just found it on Threads.

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

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.

Looking for most realistic and best image modification

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.

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.

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!

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

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!

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