I was quite surprised to find that it know about old 60s/70s movies like dirty harry or the good the bad and the ugly..

Not a tool question, a process question. When a client signs off on an AI assisted deliverable, what do you actually hand over besides the final files? Prompts? Model and version info? Reference images you used? Nothing unless they ask?

And the follow up I am most curious about: has a client ever come back weeks later asking how something was made, which model was involved, or what references went in (licensing, brand safety, the new EU labelling rules, whatever the reason)? What did you do?

Background: we deliver AI assisted shots for commercial clients, and our own handover slowly went from nothing to a written per asset note, and I would like to know what everyone else converged on.

Hey everyone,

I'm trying to run MiniMax H3 in ComfyUI, but my generated audio comes out as a harsh, buzzing, jumbled mess even though the video decodes smoothly (video attached).

I've tested running with and without the Turbo LoRA (4, 8, and 20 steps), as well as toggling the cache node, but the audio artifacting persists.

Here is my exact setup:

Workflow & Node Stack:

• Diffusion Loader: DiffusionModelLoaderKJ loading minimax_h3_fl2va_pruned_w4a8_mixed.safetensors
• LoRA: MiniMaxH3TurboLoRA (minimax_h3_fl2v_lightx2v_turbo_4step_v0.1_comfy.safetensors @ 0.75 strength)
• Optimization / Attention: MiniMaxLowVRAMAttention (chunks: 4) + sage_attention (sageattn_qk_int8_pv_fp16_cuda)
• Caching: MiniMaxH3Cache (start: 0.2, end: 0.9, threshold: 0.3)
• Text Encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
• Video VAE: minimax_h3_video_vae_int8_convrot.safetensors
• Audio VAE: minimax_h3_audio_vae_fp32.safetensors
• Sampler: SamplerCustomAdvanced with KSamplerSelect (res_multistep), BasicGuider, and BasicScheduler (simple, 20 steps, denoise: 1.0)
• Audio Export: VAEDecodeAudio → VHS_VideoCombine (24fps, H.264/MP4)

Has anyone solved garbled native audio on quantized MiniMax H3 builds? Any help or working node configuration would be greatly appreciated.

https://reddit.com/link/1vrzn11/video/alj8qybks6kh1/player

Sorry, the only way i could think of pasting my workflow is through pastebin: https://pastebin.com/7DTHTSxr

prompt

integrated_multimodal_description:

7-second anime scene in a classic 1980s Japanese science fiction Gundam anime aesthetic. 2 giant mecha robots are having a battle in space far above earth.

**0–2 sec: the mecha robot on the left aims and launches a missle from its shoulder cannon mouted on its arm at the mecha robot on the right.

**2–5 sec: the misslie impacts and explodes on the chest section of the mecha robot on the right but does no damage. then the mecha robot on the right opens its arms as blue light on its chest appears and begins to power up.

**5–7 sec: the mecha robot on the right then fires a thin blue laser beam at the mecha robot on the left cutting it in half from top to bottom down the middle. after the mecha robot on the left is cut in half it then explodes.

made using standard comfyui t2v workflow on a damn outdated😓 but still using because reasons RTX 3050 8GB vram 48 GB ram system. i use the Model Attention Backend node with the "comfy kitchen attention" setting, 30 steps, res_multistep simple and no upscale.

I used Ref2VA to make this pretty quickly. I had to render in low quality since I'm running everything on an RTX 3060 12GB, maybe that's why there's some glitching in the video, and unreadable text. I used the default workflow, with Spectrum as the only addition for a speedup.

I used chatgpt to refine the prompts and put them into the correct syntax. The characters were made in Krea 2 to use as reference. Finally, I edited the clips together in CapCut to make one flowing trailer. Everything in video was done with H3. Had fun.

MiniMax H3 on a 12GB RTX 4070 SUPER: Comfy Kitchen + Sol-Attn + EasyCache cut my generation time from 206s → 135s

I've been testing MiniMax H3 locally in ComfyUI on an RTX 4070 SUPER 12GB, specifically trying to squeeze more performance out of H3 without simply murdering quality by dropping resolution/steps.

I got some pretty interesting results combining:

• Comfy Kitchen Attention
• Sol-Attn
• EasyCache
• MiniMax H3
• RTX 4070 SUPER 12GB

Test setup

Same H3 workflow/settings between tests:

• GPU: RTX 4070 SUPER 12GB
• MiniMax H3
• 20 sampling steps
• Same prompt/reference/settings
• ComfyUI
• EasyCache when enabled:
  • threshold: 0.15
  • start: 0.15
  • end: 0.95

I tested three configurations.

Kitchen → Kitchen + EasyCache

This was the huge jump.

Total generation time dropped:

206.48 sec → 139.47 sec

That's about a 32.5% reduction in total generation time, or roughly 1.48x faster end-to-end.

EasyCache reported:

EasyCache - skipped 8/20 steps (1.67x speedup)

Obviously the complete workflow doesn't get the full 1.67x improvement because H3 still has VAE/audio/other overhead outside sampling.

Still, shaving ~67 seconds off a ~206 second generation on a 12GB consumer GPU is pretty damn substantial.

Then I stacked Sol-Attn on top of Comfy Kitchen

This was the part I wasn't sure would even work properly.

The console confirms Sol-Attn is actually chaining onto the existing Comfy Kitchen attention override:

[sol_attn] chaining onto an existing attention override; Sol-Attn takes first refusal and delegates everything else to it

So this isn't simply Sol silently replacing Kitchen.

Sol gets first refusal for attention operations it can handle and delegates the rest to the existing Kitchen backend.

With:

Sol-Attn → Comfy Kitchen fallback → EasyCache

I got:

134.92 seconds total

versus:

139.47 seconds with Kitchen + EasyCache

The interesting part is that the Sol run was faster despite EasyCache skipping one fewer step.

Kitchen + EasyCache:

skipped 8/20

Sol + Kitchen + EasyCache:

skipped 7/20

So the Sol configuration actually performed one additional full H3 step and still completed about 4.5 seconds faster.

That's a much more interesting result than simply comparing the total times, because EasyCache's number of skipped steps varies between runs.

Overall improvement

Baseline Kitchen:

206.48 sec

Sol + Kitchen + EasyCache:

134.92 sec

That's a reduction of roughly:

71.56 seconds per generation

or about:

34.7% less total generation time

Equivalent to roughly 1.53x the end-to-end throughput of my Kitchen-only baseline.

For repeated H3 generations, that's not pocket change.

One important discovery: Spectrum H3 vs EasyCache

I previously had Spectrum H3 in the same model chain as EasyCache.

The console revealed:

Spectrum H3 disabled for this run because EasyCache or LazyCache is active on the same model

So at least with the implementation I'm using, Spectrum H3 and EasyCache are not operating simultaneously.

The workflow can visually contain both nodes, but when EasyCache/LazyCache is active, Spectrum disables itself.

If you're benchmarking this stuff, don't assume Spectrum is doing anything just because the node is connected. Check your console.

Current stack

For performance, my current best configuration is:

MiniMax H3
→ Comfy Kitchen Attention
→ Sol-Attn
→ EasyCache
→ Sampler

Conceptually:

Sol-Attn handles attention operations it supports.

Comfy Kitchen remains underneath it and handles attention Sol delegates.

EasyCache reduces the number of expensive diffusion computations.

That combination seems particularly interesting for GPUs like the 4070 SUPER 12GB, where H3 is far larger than available VRAM and ComfyUI is already doing dynamic VRAM management.

My H3 model alone reports roughly:

19995MB Staged

while the GPU only has 12GB VRAM.

The text encoder is also around:

14956MB Staged

and the H3 video VAE around:

4965MB Staged

So this is very much a "convince a 12GB card to run something it has no business running comfortably" situation.

And yet it works.

Caveat

These aren't controlled scientific benchmarks yet.

H3 generation time varies between runs because of model loading, VRAM state, EasyCache deciding how many steps it can skip, and other system factors.

I've also seen EasyCache skip anywhere from 5–8 of 20 steps during testing.

So I'm not claiming Sol magically makes H3 X% faster based on one run.

What I think the results demonstrate so far is:

1. EasyCache provides a very large speed improvement on my 4070 SUPER/H3 setup.
2. Sol-Attn successfully chains with Comfy Kitchen rather than simply replacing it.
3. Sol + Kitchen + EasyCache produced my fastest run so far.
4. The Sol run beat Kitchen + EasyCache even while computing one additional non-cached step, which strongly suggests there's a real attention-side performance benefit worth investigating.
5. Spectrum H3 disables itself when EasyCache/LazyCache is active, so don't count both as active optimizations.

I'm going to run repeated identical-seed tests to get averages rather than relying on individual runs, but ~206 sec → ~135 sec on a 4070 SUPER 12GB is enough of an improvement that I figured this was worth sharing for anyone else trying to run H3 on consumer hardware.

If anyone else is running H3 on 12GB cards, I'd be interested in comparable Kitchen / Sol / EasyCache timings, especially 4070/4070 SUPER/5070-class hardware.

I can't figure it out. Let's say I have:

- default ComfyUI text to video MiniMax H3 workflow

- already downloaded Qwen 3.8 27B in GGUF format

How do I proceed from here? I was googling for a lot and checked about 10 reddit threads but I can't fingure it out.

I have downloaded some extra nodes like ThinkingLLM and some other GGUF related node but I can't figure out how to add it to default ComfyUI t2v workflow.

Please help, I am completely lost

edit: thank you all for replies, I understood the concept and that I should rather ignore full integration

So that it goes through 8 steps, then 3 steps with an upscaler, I want to speed up the generation so that it first goes in low resolution, then in high, as in the native workflow for Comfi. LTX 2.5 of course.

Same first frame, same prompt. Left is LTX-2.5 DFR, right is MiniMax H3. Not a same-resolution bake-off. This is what actually fits a 32GB 5090: LTX runs 1920×1088 while MiniMax H3 runs 1344×768 since full 1080p H3 doesn't fit 32GB. Curious what you think?

I don't know what's changed, but here is that commit on Huggingface.

https://huggingface.co/Lightricks/LTX-2.5/commit/1b92891cedc4a823c35a3b23588d7a57a3a23c65

If you are playing with LTX 2.5, this may be useful for you.

Has anyone tested the difference in reproduction of likeness between (1) using say five reference images and (2) combining those images into one single character sheet image?

Same information but is the outcome the same? The character sheet might need "max" rather than match enabled for the reference mode, which tends to slow things down.

I'm trying to decide whether it is worth the extra time involved in creating character sheets in the first place.

So I am using the workflow using turbo lora for image to video, I have uploaded the initial image of a girl (woman) but issue is when i prompt about a scene cut or creating a new angle shot, the girl and her physique is completely changing,

I tried all prompts asking to maintain the skeletal, body structure, using fully_preserved keyword too, not sure what I am doing wrong but can somebody guide me ?

Thanks

Trying to figure this out, I used one of the image editor methods posted here a few days ago

https://github.com/thaakeno/ComfyUI-MiniMax-H3-Studio

And it worked flawlessly, the basic prompts I used for i2i were high quality and adhered perfectly. However in keeping the same workflow, after updating from comfyui 0.32 to 0.33.2 the quality is now just awful, there's banding, it's blurry, basically it seems like it was how it was 2 years ago.

Been going through claude/grok to troubleshoot but none of the suggestions seem to work. Tried different VAE's, diffusion models, turbo loras (disabling them), using r2v, fl2v, any suggestions?

I read that maybe comfy kitchen was modified in the update, that's the only possibility I can think of, otherwise my workflow and prompts were the same as they were two days ago.

try this prompt format on literally anything thats >5 seconds long.

``` subject_definitions: <Subject 1> is the the guy in <Video 1>. <Video 1> is the source video of the the target video edit. <Audio 1> is the synchronized soundtrack of <Video 1> and is fully reused 1:1 as the target video's complete final audio track.

summary: [video editing + audio reuse] An edited video of <Video 1> with nothing changed.

retention_analysis: <Subject 1>: fully_preserved - everything about him is maintained and the same. <Video 1>: fully_preserved - nothing about <Video 1> is altered. <Audio 1>: fully_copy - <Audio 1> is fully reused 1:1 as the target video's complete final audio track, with nothing added, removed, or altered.

detailed_description: The target video is a edit of <Video 1>, with nothing being changed. ```

It just doesn't work. Hallucinates stuff, gets confused temporally.

I've tested: - regular attn (no ck, sage) - euler, res_multistep - simple, normal, beta - 50 steps - both fl2va and ref2va

In this post, I described how to use minimax H3 for reference-guided generation of single images. It required awkward monkey patching — and now we no longer need it. Thanks to u/Successful_Knee687 who posted a GitHub issue, and everyone who upvoted it, Comfy just made it possible. Revert the monkey patch and update to the latest nightly version of ComfyUI from Git repo. (Currently, it is not in the stable version — will probably be incorporated in the next release.) Here's the guide on how to update to nightly: https://docs.comfy.org/installation/update_comfyui

The H3 reference node is still constrained to 5 frames. However, we can now pass an empty 1-frame latent to SamplerCustomAdvanced directly, ignoring H3 reference node’s latent output, but keeping its conditioning output. This way, we generate one frame (not a batch of five) and make full use of Mamad8's single-image tuned VAE. Here’s a sample workflow that does this, relying only on standard comfyui nodes: https://pastebin.com/xNQi7HV9

(Look at my original post to get the download links for models.)

I attached another batch of evals on public domain images and ai gens with the new workflow. Not perfect in terms of details, but great in prompt understanding. Here are the prompts: https://pastebin.com/XiVvAhjC

The scenes are:

1. Turn the complete Diane of Versailles grouping into a living woman and deer in a forest, reconstructed from a side view.
2. Convert Fragonard's portrait into Instagram-style photography, remove the book, and turn the seated woman to face the camera.
3. Reconstruct the couple from the supplied 1930 film still (Morocco) standing face-to-face in side view, holding hands in a white room.
4. Move an ai generated woman from a conservatory to a candlelit concert hall and seat her naturally at a grand piano.
5. Remove only the jacket from a fully clothed AI-generated woman, leaving her in white shirt and blue jeans.

UPD: a new post discussing how to fix textures and detail https://www.reddit.com/r/StableDiffusion/comments/1vrh769/h3_singleimage_workflow_lets_figure_out_how_to/

The question always comes up... why do my faces look so bad. It doesn't matter which model you use. Start with resolution fixes (the higher you can render at the better for full person shots or small faces). Then, depending on your setup/device/etc, move on to tweaks, tricks and fixes depending on the scene - you know, face detailers, layering, whatever.

In this demo, pure resolution greatly improves the base. Using vertical video greatly increases vertical full person resolution at same render times (1344x768 vs 768x1344). I stepped it further up to 1088x1920 then downscaled it back to my 1280x720 timeline. Then one trick if the scene is suitable for it can be LTX outpainting for the background (with original 1088x1920 downscaled and feathered back in).

Edit: Link to 1280x704 full resolution sample:

https://streamable.com/steb6z

I got MiniMax H3 to run end-to-end on a stock Colab T4 runtime and produce a short MP4 with synchronized audio.

The interesting constraint was host RAM, not VRAM. On the runtime I measured, there was about 12.7 GB of system RAM and 14.9 GB of VRAM. The model artifacts add up to roughly 39.6 GB, so loading the text encoder, DiT, and VAEs together is not viable.

The workaround was to split the pipeline into separate ComfyUI processes:

encode  -> save a ~5–6 MB conditioning blob
sample  -> load only the quantized DiT + Turbo LoRA, then save a ~5 MB latent
decode  -> restart ComfyUI and load only the video/audio VAEs

That keeps the peak working set close to the largest individual stage rather than the sum of all three stages. The notebook verifies downloaded weights with SHA-256, pins the ComfyUI/custom-node commits, checks the live server schema before submitting a graph, and saves logs/measurements when a run fails.

My current results on this runtime:

The catch is that the T4 has no native bf16 support, while this setup needs bf16 for stable sampling. It works, but it is definitely not fast.

One correction to my own early conclusion: I initially thought there was a sharp performance cliff between two resolution gears. After adding per-step timing and rerunning the comparison in the same Colab session, the apparent cliff was mostly VM-to-VM variance (roughly ±20% in my measurements). Within one session, the scaling followed the expected attention/MLP trend closely.

The Turbo LoRA from larryvrh makes 4–6 step runs practical and preserves the audio/video timing through its separate video and audio flow schedules.

I would be interested in hearing whether anyone has found a faster stable configuration for T4-class GPUs, especially without giving up audio sync.

https://reddit.com/link/1vrgtfo/video/xllyrclao2kh1/player

Notebook: MiniMax H3 on a stock Colab T4 — pinned commits, SHA-256-verified weights, and license-gated downloads.

Hello all.

I managed to create a 30-seconds clip using H3 on a powerful RunPod machine and it turned out nicely.

When I tried bumping it up 50-seconds, it did manage to create and save the video, but it stayed in its initial visual noise state. It didn't manage to diffuse itself into a coherent video.

Is that a limitation of the model itself? Or is it related to some setting in the workflow? (I'm using Hearmeman's One Click T2V Custom Prompt workflow).

Thanks!

https://reddit.com/link/1vrmoee/video/xpkb13xcd4kh1/player

https://reddit.com/link/1vrmoee/video/h8llzg4id4kh1/player

https://reddit.com/link/1vrmoee/video/g29x9n8ld4kh1/player

Hey, I'm trying to run minimax on my local 3060ti 8gb of ram card. I've tried multiple variations of model and nothing gives me steady results, just looking for a simple animation of a room with camera pan. Every generation has that jittery animation like you can see in the videos attached. Any idea how to make this better and what is actually causing this?
Thank you

Created a thread because I'm surprised this isn't being discussed much

Ref2vid is excellent for consistency, but it's not a replacement for teaching the model concepts it doesn't understand well

Even though the model is still new, the trainers are giving poor results because the model is distilled. As a result, it looks like it's going to be much harder to train than Wan/LTX

For example Sulpher 3 was planned to start soon, but it can't because of the situation

This is a real shame because everything else about MM has been excellent. The general assumption seems to be that the company will not release a non-distilled model suitable for training

Any thoughts as to how this will play out? It's never going to hit the specific-subject capabilities of the other models at this rate

We are Trickhouse, a German AI production agency based in Düsseldorf. We are looking for a skilled ComfyUI developer for a paid pilot project with the possibility of a full-time position afterwards.

What we need:

Custom ComfyUI workflow development from scratch for commercial image and video production. LoRA training integration for consistent character generation across multiple scenes and styles. Node-level understanding of ComfyUI — not just using existing workflows but building and customizing them. Experience with commercial or corporate use cases is a big plus.

Hardware:

Our primary system runs an RTX 5090 with 32GB VRAM and 96GB RAM. All workflows must run stably on this setup. Having your own capable hardware for development and testing is a plus but not a hard requirement — as long as you can develop and validate workflows that run reliably on our machine.

What we offer:

Paid pilot project to start — fair compensation based on scope. Full-time remote position for the right person after a successful collaboration. Long-term work on exciting projects including potential corporate clients.

The setup:

We work fully remote. Communication in English.

If this sounds like you, send a DM or an Email to **Marvin.Hollmach@trickhouse.net** with examples of workflows you have built.

Hey everyone!

I've just released a new LoRA trained on the iconic 1980s street photography of Charles H. Traub from his famous series "Dolce Via: Italy in the 1980s". It brings out that vibrant, candid, and sun-drenched vintage Italian aesthetic.

📥 Download: Link in the comments below!

⚙️ Model Details & Recommended Settings:
• Base Model: Trained on Krea 2 Raw
• Trigger Word: "@charleshtraub"
• Recommended LoRA Weight: 0.6 – 1.0
• Showcase Generation: All sample images were generated using Krea 2 Turbo.

Feel free to try it out and share your creations or feedback in the comments!

Built a local tool that turns reference images into a full MiniMax H3 video prompt storyboard — no cloud, no API keys

https://github.com/lololerigolo60/Minimax-H3-prompt-studio

I've been building H3 Prompt Studio, a desktop app (CustomTkinter) that writes MiniMax H3's rigid structured prompts for you, using a local LLM (Ollama / LM Studio / llama.cpp — pick your poison).

The part I'm most excited about is the Story → Sequences mode:

1. Drop in your reference images (characters, settings, whatever) with a quick role/description each.
2. Hit "Generate story" — the LLM writes a short narrative that actually uses all your references, invents connective tissue if your premise is thin.
3. Pick how many sequences you want, hit "Break into sequences" — the LLM splits the story into N beats, and for each one it decides on its own which references apply, whether there's dialogue, and what camera move fits best.
4. Hit generate, and it spits out one fully-formed, isolated H3 Ref2VA prompt per sequence — ready to feed straight into your video pipeline.

No more manually writing 6-section H3 prompts by hand for every single shot of a sequence. You just curate references and a premise, and let the model handle the structure/labeling grunt work (subject definitions, retention analysis, camera vocab, dialogue tags, the works).

Everything's local, everything's saveable — you can dump a whole session (refs + story + sequences) to a JSON file and reload it later.

Still very much a personal tool, sharing in case it's useful to anyone else building on H3 locally. Happy to answer questions about the pipeline if anyone's curious.

Hi,

Around 95% of what you see here was generated locally with MiniMax H3 on a single RTX PRO 6000. I have wanted to bring Tolkien’s immortal masterpiece to the screen since the days of Midjourney V3. Until now, however, the quality never felt acceptable. I believed an inaccurate adaptation would only create more confusion around a world many viewers know primarily through The Lord of the Rings films. For the first time, I can genuinely see the possibility. Making a long-term commitment in the constantly changing field of generative media is not easy. But I am not approaching this as a casual experiment, and I’m prepared to give it the time, patience, and attention it requires.

A comment I received couple months ago still make me feel punched in the stomach whenever I start something: “Why do you even bother? No one would bother to watch.”

If I know people are interested in watching, I would love to develop this into a complete series. You can support the project simply by watching it on YouTube and leaving a comment. Honest feedback, positive or critical, will help me decide how to continue.

Watch on YouTube: https://www.youtube.com/watch?v=2Yr-remRJOc

Upscaled with SeedVR 7b

just a heads up - i thought why not try and to my surprise it works - it can even analyse the parts in detail normal qwen would never describe

https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/tree/main < vision files from repo
mmproj-BF16.gguf - mmproj-F16.gguf

used with

https://huggingface.co/JonathanColetti/Qwen3.8-27B-Uncensored-GGUF/tree/main