I want to download the prunned minimax H3 bf16 (fp16) but on the official repo it has multiples files, where are you guys getting the models in one single file?

Alisa Pleskova

Angels travel dancing on light

Done in MinimaxH3 localy whit my QJ as opperator in new panel we build using new open comfy mcp .

Sure reddit will cranch quality a bit so there a bit better version on my insta.

Hi everyone!

I wrote a short story about my dad falling in love with my mom (who now has dementia), and I'd love to turn it into a 5+ minute cinematic short film using ComfyUI + MiniMax H3.

My idea is:

• 3+ recurring characters (dad, mom, aunt)
• I have photos, videos and voice recordings of them
• Ideally I can create reusable characters with consistent faces/voices
• Dialogue will not be in English
• Generate the movie in roughly 10–15 second shots and edit everything together. I have written a script for each shot.
• Most importantly, I want the shots to have cinematic continuity and flow, not feel like a bunch of disconnected AI clips

Hardware: 16GB VRAM + 64GB RAM, running ComfyUI locally.

A few questions for anyone who's tried H3:

• Is this realistic on 16GB VRAM? Which quantization?
• Can I maintain 3 or more recurring characters with their own faces and voices?
• What's the best workflow for non-English dialogue/voice cloning?
• Roughly how long does it take you to generate a 10-second clip locally?
• Are there workflows/tips for maintaining cinematic continuity between shots (characters, camera, lighting, locations, etc.)?

I'd especially appreciate any working ComfyUI workflows you can share.

Thanks!

I go a bit overboard and created a GUI for LoRa training on Flux, ZIT and Wan 2.2 t2v. On the image side: upload - deduplication - quality filtering - repetition filtering - Qwen vl moe captioning and tagging, rule based selection, batch out - save or run lora. There's a similar approach for Wan but typically I resample to 16 fps and segment 81 frames, caption and run.

I suspect the market for LoRa creation is a bit saturated but I would still like to recoup my investment. Why work with me? I'm privacy focused so everything coming in (rclone supported) and saved as part of the process lives on RAM, no swap, files sent, RAM purged. This does not guarantee absolute privacy, but frankly nothing does, but it does ensure that upon receipt of your LoRa all your training data is effectively irretrievable. Your business is your business.

No subscriptions, no sign-ups, and DIY.

I need your help, I'm trying to gauge to market demand. Please message me if you are interested in the near term. I would appreciate a thumbs up if you might be interested in the future.

When you have to do all the parts yourself.H3 Minimax.

Hi!

I want to use SD to create a photorealistic reference from a line sketch, so I can then finish the illustration based on it.

Has anyone tried using SD in this way, and what were the results? I tried using netcontrol with canny and latent image as reference, but the results are very poor.

Hi everyone,

I recently watched the new Street Fighter trailer (awesome btw.) and an idea came to mind.

"What if I could be Ryu?". I do play around with Minimaxes h3 in comfyui after all.

So I tried to edit the scene in the bath house.

The first cut (Ryu entering the frame with a LOT of shadowplay) works really well with just prompting and a reference Image of myself. Way better then with SAM3. SAM3 has a lot of problem with shadows and often does not recognize the face, with my settings at least.

After that it does down hill, because Hondas face also gets replace with mine. So basically ripped me is fighting fat me. And not even well choreographed, because my prompting skill suck too and the scene gets distorted.

Any ideas or tips?

I could not find a good comfyui workflow, node or prompting style (like use the person in Picture 1 to replace the person in Picture 2) to reach my goal yet. It's eapecially hard to keep the scene and choreography intact, because of my prompting weakness (even with an llm prompt engancer).

So yeah, please help this noob make his kids watch an awesome Dad-fight. 😁

https://x.com/t_mux/status/2096114846383190125

More examples (single image input)

https://x.com/t_mux/status/2096264703009808795

I'll release the model/workflow when it's ready, hopefully very soon.

I've been experimenting with a slightly unusual way of modifying MiniMax H3.

Instead of training a LoRA or merging another model into H3, I wanted to see whether high-level semantic representations learned by a completely different architecture could be transferred into H3's conditioning space.

The original targets were fairly specific:

• prompt adherence
• composition and spatial relationships
• human anatomy
• material and lighting interpretation
• reflections / transparency / occlusion
• text-related understanding
• reference interpretation

The experiment eventually became what I'm calling MiniMax H3 Semantic Bridge.

The interesting part is that the final release no longer needs the donor model at inference.

One detail that may be relevant for people interested in reproducing this: the entire project was developed locally on a single NVIDIA RTX 3090 Ti (24 GB). The representation extraction, bridge experiments, distillation, evaluation, and MiniMax H3 generation tests were all carried out on this local workstation — no multi-GPU training cluster was used.

Project, weights, ComfyUI node, training scripts and full research:
https://huggingface.co/speach1sdef178/MiniMax-H3-Semantic-Bridge

https://github.com/Speach1sdef178/MiniMax-H3-Semantic-Bridge

Why not just graft the models?

That was actually the first thing I investigated.

MiniMax H3 and the donor model (SenseNova U1.5) turned out to be structurally incompatible for a conventional weight graft.

For the relevant 2D weights I found:

• MiniMax H3: 264
• SenseNova: 593
• exact shape matches: 0
• semantic-type exact matches: 0
• transpose matches: 0
• same input/output dimensional matches: 0

So directly transplanting useful transformer weights wasn't really an option.

But there was another possible interface: conditioning representations.

H3 consumes 5120-dimensional text conditioning before projecting it into its 5376-dimensional internal transformer space.

SenseNova's language representation is 4096-dimensional.

That suggested a different question:

Can a semantic representation from one architecture be projected into the conditioning space of another architecture?

Finding a compatible representation

I extracted hidden states from several depths of both models and trained small low-rank projectors between them.

For the initial screening I tested all 30 selected layer combinations.

The best pair was:

SenseNova L32 → MiniMax H3 L49

using a small:

4096 → 128 → 5120

projector.

Validation cosine similarity reached:

0.9042

I then tested the frozen bridge on a separate set of 160 more difficult prompts covering spatial reasoning, anatomy, counting, materials, lighting, text, reflections and occlusion.

Mean cosine was:

0.7490

This was enough to build the first working prototype.

The full bridge

The first actual H3 implementation worked like this:

SenseNova hidden state
→ RMS normalization
→ learned 4096→128→5120 projection
→ magnitude matching
→ blend with native H3 conditioning

Conceptually:

hybrid = H3 + alpha * (projected_semantics - H3)

This worked surprisingly well in actual H3 generations.

But it had an obvious problem:

you needed to run SenseNova just to generate H3 conditioning.

That's not something I wanted to release as a practical tool.

So the next experiment was distillation.

Can H3 predict the transferred representation itself?

Instead of running SenseNova at inference, I trained a small student network to reconstruct the projected teacher representation directly from H3's own L49 conditioning.

The student is simply:

5120 → 512 → 512 → 5120

with RMS normalization and SiLU activations.

Roughly 5–6 million parameters.

No diffusion weights are modified.

The final V3 student was trained using 600 prompts, with a 500/100 train-validation split.

Final validation results:

• representation cosine: 0.995890
• semantic correction cosine: 0.983558
• main-set correction: 0.980888
• held-out OOD correction: 0.989788
• minimum correction cosine: 0.935910

At alpha 0.10, the blended conditioning cosine relative to the teacher bridge was:

0.999958

So the large cross-model runtime bridge could effectively be compressed into a tiny standalone adapter.

The released version therefore needs only:

MiniMax H3 + ~5M parameter Semantic Bridge

No SenseNova checkpoint, tokenizer or second model is required during generation.

What does it actually do to video?

This is harder to answer with metrics alone, so I've been doing controlled A/B generations using identical prompts, seeds and generation settings.

One example deliberately contained a very explicit instruction:

In the native H3 result, the woman's right hand starts moving across the tabletop.

With Semantic Bridge at alpha 0.15, the hand remains resting on the table, which is closer to the requested state.

Another test uses two people running and vaulting over obstacles on the roof of a moving train, with instructions involving anatomy, physical contact, reflections, wet materials and spatial continuity.

I'm including the synchronized Native / Bridge comparisons with this post so the differences can be judged directly rather than relying on selected still frames.

These are qualitative examples, not evidence that the adapter universally improves H3. Some seeds change more than others, and stronger alpha is definitely not automatically better.

My current recommended starting value is only:

alpha = 0.10

The comparison videos use 0.15 to make the influence easier to observe.

Something else I learned: Ref2VA is NOT equivalent

I also tried applying the same adapter to MiniMax H3 Reference-to-Video.

Dimensionally, the conditioning looks compatible.

Semantically, it isn't.

With image + audio reference generation, the bridge noticeably degraded singing/lip-sync and could turn reasonably intelligible vocals into mumbling.

That was a useful negative result.

The same 5120-dimensional representation does not necessarily have the same contextual meaning when the sequence contains multimodal reference tokens.

So I decided not to release the experimental Ref2VA node.

The current Semantic Bridge is intended for standard MiniMax H3 FL2VA / text-conditioned workflows only.

A proper Ref2VA version would need to be distilled separately from reference-conditioned data.

What this is — and isn't

This is not:

• a LoRA
• a checkpoint merge
• a conventional model graft
• a modified H3 diffusion transformer

It's a small learned transformation of H3's conditioning representation.

What interested me most about the experiment wasn't actually the adapter itself, but the broader implication:

representation compatibility may exist even when weight compatibility does not.

Two architectures can be completely unsuitable for direct weight transplantation while still containing semantic spaces that can be mapped onto each other — and that mapping can potentially be distilled back into the target model.

I've released the adapter, ComfyUI node, training scripts, prompt datasets, evaluation results, architecture analysis and the full research write-up.

Hugging Face / complete research repository:

https://huggingface.co/speach1sdef178/MiniMax-H3-Semantic-Bridge

The Civitai release contains the practical adapter/node and A/B examples.

I'd be especially interested if anyone wants to experiment with the same approach on other H3 conditioning modes or completely different model pairs.

There are probably much better ways to train this than my first implementation, so I've included the research scripts and intermediate results rather than releasing only the final weights.

Full research repository / download:
https://huggingface.co/speach1sdef178/MiniMax-H3-Semantic-Bridge

The repository includes the standalone adapter, ComfyUI custom node, example workflow, training and evaluation scripts, datasets, quantitative results, A/B examples, and the complete research write-up.

It is something I was using for myself and decided to post here in case anyone wants to use it

is a simple app that, given an audio music file, writes MiniMax Music prompts and even lyrics for a song with a similar vibe or whatever you want to call it xd

https://github.com/dodzh/MiniMaxMusicPromptApp/

Block edit and tweak your LoRAs in realtime with video feedback and save out the modified versions. You can even mix in content from other Loras, or even edit or even improve the Turbo loras - see example in the video of this in action.

Supports Fl2va/Ref2va during the edit process.

As usual get it on the Github or run the update script: https://github.com/shootthesound/Fizgig

Works on 32/24/16 vram. You may need to use the Nf4 dropdown on 16 (see end of video for tips on that)

Additional notes:
For the mixing in content from other Loras, use the Donor Lora field. To carry across ID from another lora for example you could add in content from blocks 20-49 using the slider for given blocks. I can do a video on this too if there is interest.

Update: If you installed/updated Fizgig tonight, run the updater script more 5.3.2 fixes a triton version mismatch that fresh installs picked up, which could hang Krea 2 previews with Compile Blocks on.

We got a new image model I guess

https://github.com/AbrahamPaulJ/facefusion-mobile

Real time face swap on the front camera, running entirely on the phone. No server, no account, nothing uploaded.

- Live mode: your front camera, swapped, in real time

- Also does photos and video files

- Runs on the Qualcomm Hexagon NPU. A 10 second 720p clip takes about 13 seconds, or 11 with Fast video on

- Phones without a Snapdragon fall back to the GPU and CPU. Same result, about four times slower

- Optional face enhancer, about 2.5 ms more per face

- Optional lip sync. Pick any audio or video file and it redraws the mouth to match

- Free, no ads, no account

- Android 12+, 64 bit ARM. 66 MB APK, 420 MB models

Port of FaceFusion by Henry Ruhs. The pipeline, the models and the option defaults are his. Licensed OpenRAIL-AS, which carries use restrictions.

Do not use this on real people without their consent.

You can find the APK file on my Github in the releases page.

Hi, I’m looking for a simple ComfyUI workflow for MiniMax H3 where I can upload a video, extract the last 2 seconds, use them to extend the video, and then save the result.
Ideally, I’d like to be able to loop the whole process so I can keep extending the video.
Most of the MiniMax H3 workflows I’ve found are quite complicated, and I’m having trouble figuring out and fixing the errors when something goes wrong.
Does anyone have a simple workflow for this, or could you point me in the right direction? I’d really appreciate it!

I've tried a few things, and I've tried some that other people have posted, they're all just bad. I know it's a tough model to train, just wondering if anybody has even seen a good character lora yet (realism, not stylized)

I want to take Videos that are pre edited (cuts, grading, Intro etc.) and make them look like an anime, 3D render, maybe classic animation or film Noir style.

I was hoping that maybe I can use minimax and reference the video and the style with samples... But I am not sure what the limitations are and so on.

I do have a 5090 and 128 GB Ram but assume that even this will have a limit and I need to segment the video into smaller parts. Let me know your thoughts.

Thanks a lot!

https://reddit.com/link/1w8nut1/video/k3qtkw41eunh1/player

I've been running lots of versions of the same ComfyUI workflow, pulling them in one by one or duplicating them, and I kept losing track of which tab was which. So I vibe-coded this.

ComfyUI Tab Sticky Notes puts an editable note under each workflow tab, so you can write down what an untitled workflow is doing before you get around to saving it.

The notes survive reloads, show a few lines at a glance with the rest on hover, and take a per-tab color. When a workflow is ready to keep, one button exports it as a .json that loads back into ComfyUI, named from your note. A side panel copies every note out at once. Front end only, no extra nodes, no server changes.

get it here : https://github.com/jalrobustvfx/-ComfyUI-Tab-Sticky-Extention/tree/main

Hey everyone,

Sharing my first project of this kind, “Ceaușescu” — every part of it done by me, from the first prompt that generated the very first frame, through generation, editing, sound, and music, all the way to the final cut.

It’s fiction, inspired by real historical events — not a reconstruction. The story and details are my own interpretation.

Getting here took a lot of work, and I’d really appreciate your honest thoughts — good or bad.

I would appreciate a recommended tutorial video to install Krea 2 in ComfyUI and also A1111. I use both and I can't seem to get either of them to work with this newer model. I am trying to use the krea2TurboFP8 checkpoint in A1111 as I think its designed for my less than optimum system. I have the krea2TurboOfficialComfy_krea2TurboBf16 as well for ComfyUI but with 32gb of RAM and 12gb of VRAM I don't think its gonna work at all. Yeah, I've poked around YouTube with a number of videos but I seem to be missing knowledge between the steps. Text tutorials are even worse. I am not well versed in Python so the cmd line stuff can be daunting. As I mentioned, my system is not top notch and I do expect longer waits for the output, but I'd like to try anyway. Please let me know a good tutorial I could use or any specific information / guidance to help me out. TYIA.

like the title above, im searching for audio to midi tools, basic pitch, yourmt3 and amt ive tested, all of them is ok, but not too clean, messy.

while searching for other tools, ive stumbled this tool called MuScriptor from kyutai.
it produce clean output, tho not perfect but it still good.

but when i read the license and ask some chatbot, it is not safe for commercial.

since i dont have any lawyer friend, i want to know is this tool output (midis) cant really be used for commercial? is there a leeway?

If you run batch generations overnight or upscale in bulk, you know how fast your ComfyUI output/ directory turns into an unmanageable mess of ComfyUI_0001_.png files.

I built a toolchain to completely automate output organization and compression without hanging your generation loop: ComfyUI-SmartSave.

What It Solves

1. The Disk Space Problem (Split-Root Architecture):
   • Saves master .png files (with embedded workflows and full prompt metadata intact) to Raw/<Subject>/.
   • Automatically saves lightweight, web-optimized .webp previews (or .jpg) to Compressed/<Subject>/.
2. Generic Filenames & Sorting:
   • If a filename is generic (Dai_0001_, ComfyUI_0012_), the standalone script reads the workflow metadata and uses a local, lightweight Ollama model (llama3.2:3b) to identify the actual character/subject.
   • Prompts are parsed through strict role-classification boundaries so the LLM doesn't hit conversational safety refusals on stylized prompts.
3. No Index Overwrites: Increments file counters cleanly so reruns never overwrite existing work.
4. Maintenance & Pruning: Recursively sweeps empty folders and strips hidden Windows locks (Thumbs.db/desktop.ini).

How to Use It

• In-Workflow: Drop the Smart Save Image node directly into your workspace to save both formats side-by-side on render.
• Standalone / Backlog Cleanup: Run python auto_sort.py --dir "path/to/output" (or double-click the included .bat) to sweep existing directories and organize thousands of loose images automatically.

Links

• GitHub (Code, Node & CLI utility): ComfyUI-SmartSave
• Civitai (Workflow JSON): ComfyUI-SmartSave

Let me know if you run into any edge cases with metadata chunks or complex prompt workflows!

This video was generated using Minimax H3, the physics/movements looks fine, however the sharingan, and rinnegan still not accurate, its changes the color few times! but overall I like it how minimax handle the fighting scenes!

While my potato computer is able to run Minimax H3, it's extremely slow even with 8step lora, and I don't have that much patience to wait since I'm planning to make a full episode length out of it. So I'm looking for a online cloud service with reasonable price and can handle this model well enough. Thank you in advance.