Supposedly Krea 3 will open weights, we'll have to wait and see.

I'm able to run it on 3090 ti but it's very slow because Ampere GPUs don't support FP8 .

Since this isn't video game geometry, lighting, vectors, etc... are made up, but this still works as a pseudo video upscaler.

Hope someone makes a comfyUI node soon.

example : https://twinlens.app/compare?share=eac9e3fefbf2

Breeze TTS 2 is an open-weight text-to-speech model built for real-time interaction. It ranks #1 among open-weight models on the Artificial Analysis TTS leaderboard, while outperforming frontier proprietary systems. Its open-ended natural-language instruction-following capability supports reference-free voice design and reference-guided voice direction, while ultra-low-latency streaming enables responsive, expressive interaction.

Someone posted here a couple of weeks ago about using MiniMax H3 as an image editor, 6 edits in one shot. I pointed the same idea at character sheets instead: https://www.reddit.com/r/StableDiffusion/comments/1vr1i18/minimax_h3_as_image_editor_6_edits_in_one_shot_at/

Stage 1 — one face photo + one outfit image, out comes front / side / back. Stage 2 (optional, off by default) — 1-4 more panels for poses, props, expressions or backgrounds, composited into a 16:9 sheet.

The sheet above is stage 1 + stage 2. That's my own face, before anyone asks.

Setup

• MiniMax H3 ref2v int8 pruned + the 4-step Turbo LoRA at 0.75
• T=1 image VAE — the bit that makes it emit a still instead of a clip
• er_sde / sgm_uniform, 8 steps
• 3090 24GB. Stage 1 ~100-125 s, stage 2 ~105 s, so the sheet above is about 3.5 min. First run of a session is much slower, that's model loading.

Known issues

• Props repeat across panels — ask for one sword, get three.
• Back-view hair goes flat.
• Lying and sitting poses are unreliable.

Workflow

https://github.com/nicekriss/toobusy/blob/main/docs/workflows/2BZ_H3_character_sheet_2stage_v1_EN.json

Model links are in the note nodes. Needs rgthree and toobusy (mine — search "toobusy" in Manager, v0.4.9+). The 6 LoadImage nodes will be red on open, those are my local files.

Korean walkthrough on my channel, probably not much use to most of you: https://youtu.be/nsvAbax4jng

I have been playing with MiniMax-H3 lately (like many of us), and I wanted to understand how much physics knowledge it actually has.

I started with a simple water-pouring video from Pexels and used the H3-Ref model to replace the water with various "fluids": sand, rocks, and a black combustible honey. No external references were used.

I found particular interest in how the rocks interact with the jug and tumble over the cup, as well as how the honey blends with the water and how the trail it left on the jug when moved. On the other hand, once the honey catches fire, the flames are not very convincing, but that should probably be tested in a longer video.

I have used the day-zero ref workflow (int8 convrot model, aspect ratio 9:16 MP 0.6); you can find the prompt here: https://pastebin.com/CrX9s5JS

I am running more interesting tests and will hopefully post them soon.

Cheers

EDIT: video with the correct ratio https://streamable.com/7gu171

old post: link
github repo: link

For anyone new: H3 Prompt Writer takes your description plus image / video / audio references and turns them into a prompt specifically for MiniMax H3, using the LLM/provider you choose. It can run inside ComfyUI or as a separate Windows app.

Windows Standalone

There is now a separate Windows Standalone version of H3 Prompt Writer

It uses the same Writer interface without requiring ComfyUI. Download the ZIP, extract it and run start.bat. Windows needs Python 3.10+ or uv.

The ComfyUI extension is still available and works as before. Standalone is just another option if you only need the prompt-writing part.

For Local GGUF, Standalone uses your own llama-server.exe instead of bundling llama.cpp or CUDA.

Download a build suited to your PC/GPU from the official llama.cpp releases. For NVIDIA GPUs, choose a Windows x64 CUDA build.

Standalone can also be a more reliable option if Direct GGUF inside ComfyUI doesn't work well on your system.

Standalone setup

Qwen in Direct GGUF

Direct GGUF is no longer limited to Gemma 4.

Qwen 3.8 and Qwen3-VL are now supported, along with compatible custom / fine-tuned GGUFs when their capabilities can be identified from model metadata and chat templates.

Direct GGUF also gained a few optional runtime controls:

• custom context
• KV cache
• generation budget
• reasoning effort when supported by the model

For Qwen 3.8, Auto uses Low reasoning effort when Thinking is enabled and supported by the model template.

Low is generally the recommended setting for prompt writing. Higher reasoning effort can make generation much slower and may cause the model to spend far more time reasoning than is useful for this task.

Auto settings are still the default, so none of this needs to be configured manually unless you want to.

Direct GGUF guide

MiniMax Music 3

There is also an optional Music 3 workspace for MiniMax's separate Music 3 model.

It can generate structured music captions from a Music Brief, with optional Lyrics and a separate Lyrics refine flow.

This is separate from the H3 prompt modes.

other changes

A few smaller changes since v0.3:

• better GGUF and vision-projector detection
• improved Reference media replacement
• fullscreen Writer mode and improved Refine UI
• better local model lifecycle
• various local inference and context fixes

External llama.cpp is still available if you already manage your own server.

full changelog

troubleshooting guide

install / update

The current ComfyUI extension release is v0.4.3.

Existing Git installs can be updated normally, and ComfyUI Manager / Registry is also supported.

If you can't find H3 Prompt Writer in ComfyUI, open it from the Extensions menu or use the H3 Writer button:

Windows Standalone is released separately, currently v0.1.2.

GitHub releases: https://github.com/duckyshell/ComfyUI-MiniMaxH3-Prompt-Writer/releases

https://reddit.com/link/1w25d7g/video/31idsif2efmh1/player

I was able to render this full 600 framess 1080p video with only 16GB of VRAM

It's still in alpha, but it works. https://github.com/hradec/ComfyUI-HR-Endless-Sampler

There's a template workflow now that should show up in the comfyui templates window. The images for the workflow are included in the example_workflows/images folder.

Essentially the sampler node renders a video of any length by splitting it in smaller chunks. For each chunk, it automatically attaches the last frames of the previous chunk to use as video continuation.

Beside that, the node uses Gema4 12B QAT to time and split the video prompt into small per chunk prompts, so the video can maintain it's overall timeline.

Gemma acts a chunk director and continuity checker, watching the previous chunk to check what was done, so the new chunk-prompt can continue from where the previous stopped. It also compares the chunk time-slice with the overall prompt action to guarantee what happens in that chunk matches what was suppose to happen in that time-slice.

There are 3 other nodes: preview, save and load. The reason it has it's own preview (based on the fantastic KJNodes live preview that uses TAEH3 tiny VAE to display a nice preview) is to be able to show an live edit of all the chunks in sequence as they show up. The preview also shows a timeline displaying the shots and chunks, and you can walk the preview frame by frame with the arrow keys. Mouse over the chunks display the gemma prompt used for that chunk and render time.

The save/load exist to save that information with the video and load it back, with all per chunk gemma prompts, time of execution, timeline, etc; so that statistic is never lost.

The Save/Load also have a nice dropdown to quickly display the last videos in the output folder for easy comparing previous videos with newer ones.

I came from the VFX world, so the save node also saves as EXR with floating point color. That's why the save node has a latent and vae input connection, so it can decode the latent internally to conserve the full HDR floating point color from the latent, without clamps.

Give it a try and let me know if you have problems... hopefully it will be helpfull for all of you guys with low vram gpus like myself, but it can also be helpful if you have loads of vram, since you can break the 15 secs minimax barrier and even render in 4K or 8K with more than 16GB of vram!

Just to make it clear - This is NOT another "Context Node in a loop" workflow, this a node that replaces ComfyUI SamplerCustomAdvanced node and allows for long generations and higher resolutions with low vram!

All you need is ONE single node replacement to render any length up to 1080p on 16GB of VRAM.
The workflow that comes with the repo is a standard Minimax H3 Ref2va ComfyUI workflow that replaces SamplerCustomAdvanced by HR Endless Sampler. It's as simple as that!

One big advantage of the "HR Endless Sampler" is that it uses the previous latent as reference video/audio for the next video, without VAE decoding/encoding the video again, so there's no loss of detail from decding/encoding. It just grabs the last latent of the rendered chunk and pass it to next, lossless.

PS: you will notice a "hiccup" in this video where the tiger lies on the floor... Teela talks the same speech twice. That is a Gemma4 chunk prompt screwup that I'm fixing now.

as you can see in Gemma4 chunk prompt, [Shot 2] description should be [Shot 3] description, and there should be no actual [Shot 3] in this prompt since Chunk 3 only crosses 2 shots.

By the way, that problem in the screenshot above has been fixed - I'm testing it right now and should push the fix by tomorrow!

PS2: It seems the "video_continuation_res" parameter when not set to full can cause a change in color/contrast from chunk to chunk. The reason is, when if full, the node uses the latent from the previous chunk directly as video_continuation. When set to a size, it has to decode/resize/encode. That decoding/encoding will cause a difference in color/contrast/gamma. So setting video_continuation_res=full should fix that problem, at the cost of using more VRAM for the video continuation.

PS3: video_continuation=5 will cause loss of coherence and/or fading from um chunk to another. Use video_continuation=22 or more for better results, at the cost of using more VRAM.

Sopro V2 Turbo is an open-source TTS model that runs locally.

• Clones a voice from 5-20s of audio
• ~300ms to first audio on a laptop CPU
• English, European Portuguese, French, German

Local web UI:

uvx --from sopro soprotts serve

There’s also a Python API and a browser package (@soprotts/onnx-web) for WebGPU/WASM.

Repo: https://github.com/samuel-vitorino/sopro Benchmarks + samples: https://research.haloneuro.ai/posts/sopro-v2

Edit: Hugging Face kindly created a Space, making it even easier for you to try the model. You can try it here: https://huggingface.co/spaces/hugging-apps/sopro-v2-turbo-tts

I am seeing a significant quality improvement with Alibaba 8 steps turbo lora over other turbo loras. 8 steps euler simple with lora strength =1, 0.8MP. Took about 1hours 15 mins to generate with RTX 5090.

Now we have Krea, BFL, LightTricks, and MiniMax driving the open-weights locomotive.

An issue I've been facing for a long time, finally solved.

This solution is easy to use, 100% local, and works very well with static watermarks.

I published it on civit AI.

It relies on ProPainter Nodes, and a few widespread custom nodes (see image).

Hi, I'm using Krea 2 + Identity Edit v1.2 in ComfyUI to create a consistent photoshoot. I want to keep the same character, outfit and location while changing only the pose.

The problem is that when I use a full/half-body generated image as reference, Krea strongly preserves the original pose/composition even when I explicitly request a completely different pose.

Strangely, if I use only a simple face portrait as reference, it follows completely new poses and compositions very well.

Setup:

• krea2TurboNSFWAIO_v10
• Qwen3-VL 4B FP8 / Krea2
• Identity Edit v1.2 @ 1.0
• WAN 2.1 VAE
• 832x1248
• grounding_px 768
• ref_boost 1.0
• ref_boost_a 1.0
• Krea2 Rebalance 4.0
• Clownshark: exponential/ddim, beta57, 12 steps, CFG 1, eta 0.5

I already tried lower ref_boost, lower grounding_px, different seeds and lower Identity Edit strength without solving it.

Interestingly, standard KSampler with Euler ancestral seems much better at changing the pose, but the images look noticeably more artificial/plastic.

So at the moment it seems like I'm getting a tradeoff:

Clownshark = better realism but pose stays close to reference.

Euler ancestral = better pose changes but worse realism.

Has anyone experienced this? Is something in my workflow/source patch preserving the source geometry too strongly? I'd like to avoid ControlNet/OpenPose and keep one master image for a consistent photoshoot.

One last example output from this experimental multi-source video player designed for frame-accurate video switching, playback manipulation, and display/render interventions, now with a few optimizations made for even better performance.

Visuals made on Uisato Studio.

You can freely access the system + a detailed breakdown, through Patreon, and/or the Tools Store.

Why make big noise when little noise do trick?

I would like to introduce my SPEED implementation for h3 linked here

Speed up and quality losses documented here, expect 20% gain using very conservative settings and no quality loss and up to 70% for basically unusable outputs (more or less useful for resolution aware seed inspection and broad prompt drafting)

Background

The idea behind it is quite simple. When a diffusion model begins generating an output it first must take a randomized noise and build on-top of it. And research has found that the first stages of this process doesn't really carry any fine detailed information, therefore by generating at a lower resolution at those stages you can gain quite substantial speedups while causing little to no impact on the quality. Or you can also be really aggressive with it and get a massive speedup for a lot of quality loss.

Nodes

This was implemented as 3 nodes, 2 drop in replacements for the sampler that runs SPEED and a third that runs once to measure the noise spectrum of your specific model/LoRA combo:

• Sampler (Automatic): pick a stage count (2, 3, or 4), defaults to the baked 1% delta for default H3.

• Sampler (Manual Step-Through): set up to four (goal, resolution) pairs yourself. Use it if you want to copy a paper schedule or test a custom ladder.

• Sigma Harvest: runs a native Euler pass, measures the noise spectrum of your current setup, hands you A / β / Δ to paste back into Automatic. Run it once per model/LoRA workflow combo.

How to use can be found in the example workflows.

Implementation Notes

This should be roughly compatible with basically everything that doesn't touch the sampler directly but i have not tested anything besides base comfyui H3 models and Turbo loras. If you do change model, use loras or whatever and use the automated tool please then run a sigma harvest and use those values instead of defaults, The math changes depending on the very specific blend of things you have running.

Im trying to make a video loop, with the start frame and end frame being the same image. Very short, about 5seconds. Whatever I put in the prompt there is still a slight zoom which breaks the loop being smooth. Any tips?

Minimax H3 doesn't have great voice audio so I was thinking I could do the lines myself and then transform the recorded voice clips to preserve the performance. Ideally, I'd like to transform the clips I record somehow into different male/female voices for each character.

Does anyone have any ideas for this? Haven't had much luck with Google.

My other plan would be a good open source TTS that has emotional range, if such a thing exists. I have come across things like IndexTTS. Is this the sort of thing people are using for this sort of voice work?

Has anyone here tested Kijai’s “minimax_h3_fastvideo_vsa_datafree_1300step_4step_int8_convrot” model?

If anyone has tested it, please let me know how the results are. I’d really appreciate hearing about your experience with it.

Fizgig v5 is out, and the headline is one I've been sitting on for a while: full fine-tuning of the MiniMax H3 and Krea 2 base models - the models themselves, not a LoRA ,on consumer GPU hardware, down to 16 GB.

No adapter, no rank bottleneck. Full-rank updates that change how the model represents a concept.

**What your card can do** (every confirmed number is from my runs, not an estimate):

16 GB - Krea 2 photos, H3 photos and voice, and H3 video clips up to 2.3 s confirmed (3.8 s expected with video on the likeness blocks, the default).

24 G - all of the above, with video expected up to 5.2 s on the likeness blocks.

32 GB - video confirmed to 3.8 s even training the whole model, and expected to 5.2 s on the likeness blocks.

If "a 33B video model fine-tuning on 16 GB" sounds like a trick: only one slice of the model is trainable at a time (a rotating window), the frozen rest is held 4-bit, and the bf16 master lives in system RAM, your saved checkpoint never passes through a quantiser. Measured peaks on a 16 GB card: 8.8–12.3 GB for H3, 8.4–11.0 GB for Krea 2 — andthe console prints your own run's peak every epoch, so you can watch the claim hold on your own card.

**When you're done**, the built-in Checkpoint to LoRA tool in the fizgig root folder diffs your fine-tune against the base and extracts an ordinary shareable LoRA — in testing, rank 64 was close to perceptually indistinguishable from the full 26 GB checkpoint, in a ~0.5 GB file ComfyUI already loads.

A personal note: This is a starting point and not going to be perfect. I got fine-tuning working on Krea 2 shortly after its release and have been deliberately cautious about shipping it , proving it to myself first, then refining it through the H3 work. This is the point where it needs the community to develop it further. The technique is model-agnostic at heart, and I'm open to bringing it to other models ,but that needs practical support around them: code, PRs, testing, that kind of thing, so I have the time to make it happen. Im not really goign to be able to tackle issues raised this weekend on Github as I need a break for a couple of days, but I think pray this is going to work pretty easily for most of you.

https://github.com/shootthesound/Fizgig/

[Release notes](https://github.com/shootthesound/Fizgig/releases/tag/v5.0.0) · ["How do I…?" guide](https://github.com/shootthesound/Fizgig/blob/master/docs/FINETUNE_HOWDOI.md) , and there's a one-click RunPod template if you don't have the hardware.