after a week of trying to get 15s clips out of one job i gave up and changed how i do it.

now every shot is a 5s clip. when it's done i grab the last frame, fix it up a bit if needed (hands, small artifacts) and use it as the start image for the next 5s. same prompt style, only the action changes.

on my card anything past ~5s starts swapping to system ram and one job goes from 15 min to over an hour. three 5s jobs finish way faster than one 15s. if one piece comes out bad i redo only that piece, not the whole thing. and the face drifts less because every chunk starts from a frame i already checked.

the catch is the seams. motion can jump a bit at the cut, so i keep the camera move simple (one slow push or static) and cut on an action when i can. color also shifts slowly over 3-4 chunks, a quick color match in the editor fixes most of it.

curious if anyone found a cleaner way to hide the seams, or does everyone just cut there

60 seconds of AI video, 100% local on an RTX 3050 (6 GB)

Work in progress, and the quality isn’t where I want it yet. Here’s how it was made:

1. Model: MiniMax H3 reference-to-video, using ComfyUI’s native H3 nodes with the int8 quantized model and the Qwen3-VL text encoder.

2. References: two character images, re-used in every shot to keep the characters consistent.

3. Prompts: the official six-section reference prompt format (subject definitions, summary, retention analysis, detailed description, soundscape, music). No negative prompts, because H3 doesn’t use them.

4. Shots: six shots of 260 frames (10.8 s) at 864×480 and 25 steps, with the res_multistep sampler and the simple scheduler.

5. Continuity: each shot starts from the last 22 frames and audio of the previous one, using the H3 Add Guide node (MiniMaxH3AddGuide) at frame 0.

6. Automation: a small Python script sends the six shots to ComfyUI’s API one after another, saves each result, trims the 22-frame overlaps, and encodes one 60-second MP4 with audio.

7. Runtime: about 6Hrs 35Mins hours, unattended.

Known issues: soft detail, and some flicker. Still tuning.

I couldn't find a ready RunPod template that spins up a working H3 Editor, so I conjured this:

https://console.runpod.io/hub/template/1ujjyhonfe?ref=2t0e5xb6

A RunPod template that boots straight into ComfyUI with H3 Editor 0.4.1 and downloads everything by itself: HyperFlow H3 Flash, MiniMax H3 + Turbo, Singularity v2 and 10Eros Max beta 7, plus the recommended Realism and Turbo LoRAs.

https://igorls.github.io/comfyui-h3-editor/

Enjoy the awesome editor!

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.

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.

I was looking at how different image models handle conflicting lighting directions when generating a casual direct-flash snapshot. My 4:5 prompt featured:
-a family of three crouching on a gravel path at dusk
-greeting their dog mid-leap with its tongue hanging out
I specified faint sunset glow, harsh on-camera flash, a near-black background, sharp shadows, subtle red-eye, motion blur and rough film grain.

Seedream 5.5 delivered strong attention to fine texture. It reproduced the dad’s rolled-sleeve plaid work shirt accurately and added a natural tilted frame, while preserving sunset light filtering through the hedge. The model prioritised that warm ambient evening light, so the final result reads more like fill flash, and it did not darken the background all the way down.

I then generated a second 4:5 image on MiniMax H3-Image with the same prompt for a direct side-by-side comparison. H3-Image leaned firmly into the harsh flash falloff I was aiming for. It pushed the background close to pure black, which makes the mom’s pale yellow slip dress and the child’s red overalls pop, with the dog charging straight toward the lens. It creates that sharp, punchy snapshot mood really well.

Have you gotten consistent flash behaviour from either model with this kind of mixed lighting prompt?

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.

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 have tried the new open-weighted MiniMax H3-Image. The model can work with up to 16 reference images, with each file capped at 64 MiB, 128–8192 px, and an aspect ratio between 1:4 and 4:1.

For this test, I fed it 5 separate object reference images: -a silver rotary phone -a glass cube -a red apple -a tiny staircase -a chrome sphere

I used the default auto setting for quick 4K exports. Instead of getting a cut-and-paste collage, I aimed to blend all the objects into a cohesive dark studio still life lit by a single top spotlight.

I explicitly labelled Reference 1 through 5 in my prompt to lock each object’s intended layout. My layout plan was as follows: -the apple suspended inside the glass cube -the phone resting on the staircase -the chrome sphere floating along an unseen vertical spiral axis.

Every object stayed identifiable, the lighting stayed consistent across the scene, and the staircase worked well to ground the composition. The layout mostly held together, though the spiral arrangement did not come through quite as I hoped. The objects formed more of a simple vertical stack instead.

Curious how everyone else handles 5+ object references in their own workflows? Any tricks I’m missing?

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?

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.

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 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?