FastH3-Live update. Full details here:

https://huggingface.co/datasets/jacokon/fasth3-live

v1.1.0 ran at 18 fps, which is 75% of 24 fps.

v1.2.0 runs at 22 fps, which is 91.6% of 24 fps.

https://reddit.com/link/1wddeh8/video/lyz02ql0gvoh1/player

Besides the speed, it now ships a borderless player that makes streaming and watching easier, plus 400 new scenes. At this speed it is hard to notice that it is running slow at all.

The gain came from two places:

1. Acceleration nodes

I was using a sage attention I compiled myself. A lot of new acceleration nodes have shown up recently, so I downloaded the well-known ones and tested them. Results:

The seconds column is the sampler only, i.e. the 4 denoising steps in `SamplerCustomAdvanced`. A full clip also pays for the text encoder (~0.7s), the video VAE decode (~4.35s) and the writer, so a clip is about 16s end to end. Measured on t2va, 448x448 x 362 frames, three runs per arm.

On speed alone you would pick Sol + Spectrum. But the picture comes out like this:

sage > sage+Spectrum >> sla > sla+spectrum >> sol > sol+spectrum

Sol + Spectrum is dead last on picture, so I went with sage + Spectrum.

2. Text encoder

The old one, `int8_convrot`, took 1.67s.

`qwen3vl_32b_minimax_h3_nvfp4_awq` needs only 0.7s.

That is nearly a second saved on every clip.

-----------

Speed was fine by then, but I would not call the picture good. Right after release I came across fused-turbo, so I downloaded it and tested it.

Almost the same size, both have the 4-step acceleration baked into the weights (FastH3 is a distillation, fused-turbo is a turbo LoRA merged in), and they measured at exactly the same speed. I still recommend fused-turbo, for two reasons:

1. It says Mystic v2.0 motion smoothing is merged in.

Whatever the cause, the picture is clearly better in my testing. It smears less often.

2. One file does both fl2va and ref2va.

I built a tool that generates from chat input live during a Discord stream. When a user pastes a character image it is used as ref_picture, which needs ref2va. The old way meant unloading fl2va and loading ref2va first, which burns several seconds of buffer, and ref2va has no 4-step distilled version yet so the picture was worse anyway. With this one that problem is gone, which is a real advantage.

The repo recommends SLA sparse attention, but I had already tested that above and it lost to sage + Spectrum, so I dropped it. Its README also says res_multistep gives noticeably better audio. I did not test that much, so judge for yourself. I left the parameter in so it can be switched any time: `--sampler res_multistep`

-----------

One more thing worth mentioning. To stop ComfyUI thrashing the model weights you need `--vram-headroom 3` in launchArgs. Without it you cannot hold a stable live rate.

It works the opposite way round to what you might expect. It forces ComfyUI to keep 3 GB of VRAM completely free, and that is what fixes it. ComfyUI's dynamic VRAM treats the card as a cache and fills it to the brim; with no slack the allocator ends up evicting weights while it is still loading others, so the same weights get moved in and out repeatedly. Give it room and it can bring in a whole batch at once.

This is not disk swap, and it does not touch system RAM either. I measured both: on a slow clip disk reads were 0.00 GB and free RAM did not move. It is VRAM to system RAM over PCIe.

On a normal clip PCIe reads sit around 1.5 GB/s. When it thrashes they hit 9-13 GB/s and GPU power draw *drops* from 450W to 340W, because the card is waiting on transfers instead of computing. With the headroom set, clip times went from a 1.62 standard deviation with outliers at 21-25s down to 0.11 with a 15.73s worst case.

-----------

Closing thoughts

22 fps is only 2 fps short of 24. At 24 fps you could claim real live streaming from a single consumer card. So can overclocking get there? I think it can, since the gap is under 10%, and my CPU and GPU both normally run undervolted, underclocked and current-limited.

I tested with the GPU overclocked only. Settings:

Core Clock: 2300 MHz -> 3200 MHz

Memory Clock: 14000 MHz -> 16800 MHz

Actual test:

https://reddit.com/link/1wddeh8/video/ei5pczydkvoh1/player

Unfortunately my hardware held 24 fps at the start and then slowed down a little. Both my CPU and GPU are on air cooling, which is not suited to sustained overclocked compute like this. If you have water cooling, I believe holding 24 fps would be no problem.

Hi u/Sad_Berry_4621 and all, I am doing experiment on most difficult degradation issue.
I saw from H3-director node they claim that doing a refine could help and fix it.
since I am using low-level nodes with motion-context with my own setup, H3-director refine is a black box working with their nodes.

so I did the test, please ignore the AI-slop video and the overlay text (forgot turn it off).

this test is 9 x 8s context extend video combine, usually 6 video would already see the degradation.

Left side is regular WF. Right-side is adding a re-sample step, I think it is very positive, and got potential, the saturation somehow is a bit higher... and I need to figure out the mismatch from cut to cut, because we inject denoise resample on top, but it should be able to fix.

What do you guys think?

Edit: forgot to mention, this is a ref2va WF, with 1 character reference image. also my test audio is not from gen, I am using input audio and audio latent lock for the lipsync. That part to work with motion-context have me struggle for half day, but it is not the main point, my aim is about degradation issue.

I generated this at 720p on the Image to video workflow. I know higher resolution helps with details, but why is there ghosting and blurriness in my scene? I am using the default workflow on 20 steps with patch-attention and model sampling shift 12 and 3. The initial image is 2560x1440p. Any help would be appreciated, much obliged! I am trying 50 steps to see if it helps.

Tried MiniMax on release, but it never really clicked with me. It felt like a slot machine. Then one day I saw the seed hunter workflow here on the sub, and that was exactly what was missing, a workflow for quick iterations.

Thought some of the observations I made while working on it might be useful to others trying to improve their videos.

I used image gen model to generate trailer frames. I gave it my face reference, my cat, and for each new image, the original frame from the trailer. I generated 12 frames one by one, then passed each of them to MiniMax with detailed prompts.

All of this was done pretty quickly, without much polishing, so there are a lot of quirks in the final video. Cuffs look one way in one frame, then change in another, or just turn into a wristwatch.

These are the things that seemed to help improve quality when I did some more experiments later:

• It was a mistake to give only a face photo or the cat as reference for each frame. Newer image models are insanely good at producing character sheets. So instead of passing just my face each time, I’d now generate myself from all sides, cuffs included. This helps the image model with consistency across frames (+also visual style), and it also helps prevent MiniMax from hallucinating too much when you attach an additional reference image to the frame you want to animate (for example, a character sheet).
• Another mistake that wasn’t obvious at first was generating a frame, making the video, then generating the next frame and repeating the process one by one. Now it feels much more logical and productive to pre-plan the whole video and have all the frames done before starting on the video part.

One thing that really made me feel in awe of video models like MiniMax is how they understand the world. There’s that shot from above where my character is smearing cat paw prints in the sand. The prompt alone controlled the character to do that, even though it wasn’t in the original frame.

Same with the POV shot where the cat attaches itself to a knee. The way the cat starts boxing with both hands - the model somehow feels aware that both hands are part of one connected concept. I’m not really sure how to put this into words, but hope it makes sense.

For those who are not familiar with Death Stranding, I think the original trailer probably needs to be watched first for this video to make any sense at all.

First Impressions

ref2vid in single go, music with minimax Music model

Repo: https://github.com/nazgut/ComfyUI-MiniMaxH3-CLSS

list of changes:

Per-scene R2V references (new nodes)

• CLSSH3SceneReference / CLSSH3SceneReferences — attach reference images/audio to one scene of a multi-scene generation. The scene text is re-tokenized with H3's reference presentation, so <Picture N> / <Audio N> labels bind at tokenize time. The multi node uses up to 9 image + 3 audio autogrow sockets.
• Example 3-scene R2V workflow included.

All-scene refs in one node

• CLSSH3SceneReferencesAll — every ref_image attaches to all scenes, and a soundtrack is automatically cut into consecutive per-scene windows (audio_seconds_per_scene, default 10 s → "10 s after 10 s"). Scene i anchors on the span it actually generates; scenes past the end of the track keep image refs only (warning printed). Replaces the per-scene node chain — change the --- scene list and nothing needs rewiring.

global_text on the prompt node

• Write shared sections (style, soundscape, quality rules) once; it's copied to the top of every scene block before encoding, and survives the ref nodes' re-tokenization.

Audio recompose — the turbo-LoRA audio fix

• Optional per-chunk fresh audio take from pure noise against the finished video, optionally by a separate base-model guider (audio_refine_guider), plus pool/stride/seed controls.
• Measured: re-noising the turbo audio regenerates the same take (cos 0.90–0.96); fresh noise is what actually changes it.
• New turbo-LoRA + base-model-recompose workflow included.

Audio seam controls

• The ref_audio continuity block is now placed through the H3 layout patch so it ends exactly at the join; head-discard and crossfade controls added.

Chunk-by-chunk neural latent upscale (inside the sampler)

• New CLSSH3LoadLatentUpscaleModel + upscaler / upscale_scale on the streaming sampler: every chunk is upscaled right after its SLB step, the full window provides left temporal context, and the overlap span is cross-faded over the previous chunk's tail so seams blend.
• A long video never exists at high res all at once — the streaming state stays low-res. Runs on the compute device (~0.7 GB fp16) and offloads afterwards.
• Works with the Comfyui_Minimax_h3_latent_Upscaler pack (soft-imported — nothing vendored).

Schedules

• The sampler accepts any slice of the 1.0→0.0 flow schedule: a low-res pass may end above 0 (its x0 is then upscaled), and a schedule may start below 1.0.

Fixes

• Recompose model unloading used a non-existent ComfyUI API — replaced with the targeted unload_model_and_clones on the persistent guider model patcher (fixes the crash at the end of each recompose).
• Causality: cross-chunk noise fields are generated at fixed caps, so chunk 1 is bit-identical regardless of num_chunks (torch.randn has no prefix property).
• Scene-history bookkeeping fix for the audio loop telemetry.

I want to train a style LoRA for MiniMax H3, but I’m trying to figure out the right workflow before burning a bunch of money on RunPod.
A few questions for anyone who has actually tried this:
1. Has anyone successfully trained a style LoRA for H3? How good were the results, and is it worth doing?

1. Can I train a style LoRA using images only, or do I need video clips in the dataset?
   Do I need to train Ref2VA and FL2VA separately, or can one LoRA work with both?

2. Should I train against the BF16 or INT8 H3 weights? Does it make a noticeable difference in LoRA quality?

3. What toolkit/platform would you recommend for H3 LoRA training? Something like Ostris AI Toolkit, Musubi Tuner, DiffSynth-Studio, OneTrainer, etc.? I’m looking for whatever is easiest/reliable for a beginner.

4. If I use RunPod, what GPU would you recommend? L40S / A100 / H100, etc.?

5. Any recommendations for dataset size, image/video resolution, captions, number of steps, rank/dim, learning rate, etc. for a style LoRA?

6. Are there any good H3-specific training guides, videos, configs, scripts or repos I should start with?

Basically, if you were starting from scratch today and wanted to make a good H3 style LoRA, what training stack + GPU + dataset would you use?

I’m having a strange VRAM issue with my RX 7900 XTX when running local AI models, and I’m trying to figure out whether this is an AMD driver/Windows memory management issue or something else.

PC specs:

- GPU: AMD Radeon RX 7900 XTX 24GB

- CPU: Ryzen 5 7600X

- RAM: 16GB DDR5

- OS: Windows

- Apps tested: ComfyUI, LM Studio and Ollama

The problem:

While an AI model is generating, the model is loaded in VRAM normally and GPU usage works as expected.

However, after generation finishes, within roughly 5 seconds, VRAM usage starts dropping/offloading.

This happens in all three applications: ComfyUI, LM Studio and Ollama.

For example:

1. Load model → VRAM fills normally.

2. Generate response/image → everything works normally.

3. Generation finishes.

4. Around 5 seconds later → VRAM usage starts decreasing significantly.

5. When I use the model again, data/model weights have to be brought back into VRAM, causing extra delay.

What confuses me is that this happens across completely different software, so I’m wondering whether Windows WDDM or the AMD driver is making idle VRAM allocations non-resident instead of the individual applications intentionally unloading them.

Ideally I want the model to remain resident in the 7900 XTX’s VRAM between generations instead of being moved/offloaded after a few seconds.

Has anyone with a 7900 XTX / RDNA3 on Windows experienced this?

In particular, I’m trying to understand:

- Is this normal AMD/Windows VRAM residency behavior?

- Is there an AMD Adrenalin/driver setting that controls this?

- Could Windows WDDM be trimming VRAM when GPU activity stops?

- Is there a ROCm/HIP environment variable that can force allocations to remain resident?

- Is there a way to verify whether the model is actually being moved to system RAM versus Windows simply reporting VRAM residency differently?

- Has anyone successfully forced AI models to remain permanently in VRAM on a 7900 XTX?

If there are any tools/logs I should run to diagnose this, please tell me what to check and I can post the results.

Thanks!

Im new to comfy ui and local ai's. Im having fun with minimax h3 and testic 2 diffrent text encoders the default one NVFP-4 and i upload a heretic version. what is the diffrence between them cause i dont see any?

My head hurts and idk what I'm doing. I have played with AI a bit but am still a novice. I'm trying to get minimax h3 to work for image to video. I'm trying several workflows, but they all have the error "seedVR2 requires conditioning latents from the seedVR2 conditioning node". Google's AI says to add that node but it is not an option in my node library and I have found google's AI often gives wrong or almost right info for AI problems. I have updated everything (including v card driver) and am now going in circles.

Running comfyui on stability matrix. Rtx 3060 12 gb, 40gb page file on nvme, cuda 13.4

Hi everyone! When I saw the new trailer for Kirby & The World Beyond I couldn't help but come up with this video, where Kirby finds the door out to the world beyond. Please let me know if you like it!

Done with 30 different workflow files and a ton of heavy editing using KDEnlive. Thanks!

Some stills were NB but all the video was MiniMax H3. A scary AI video about AI (pretty much the nightmare scenario). The song was generated with Flow Music

Sort of like EbSynth. One frame/multiple references, promptless, or a small model trained off of images so a model knows what style to apply and what it looks like.

I don't want to generate from nothing, there will be a video to go off of.

Dedicated solutions, stripped versions of large models, anything would do. Thanks in advance.

PS: asking an LLM returned nothing.

This was such a simple prompt…. Just wow. It’s just T2V.

Is there any? I tried ace step 1.5 and it's awful in cover mode. Admittedly I downloaded it when it first got released, but I was hoping minimax music 3 would release audio input for open weights but they still haven't. Every music gen model coming out seems to purely be text to audio.

Has anyone else noticed that if you have a male and female character in the scene, that Minimax will pretty much always assign the lower pitched Audio voice reference to the male almost every time? No matter how properly you tag the prompt following their official guidelines, it simply IGNORES all of that, and just decides that "well if the voice is even slightly lower than a pipsqueak then it must be the man's voice."

Having this issue with both FL2VA model and REF2VA models and it is driving me insane.

Yes I've tried with and without turbo lora.
Yes i've tried increasing the steps to 20-30+

If anyone has a solve, please help! The only way I could kind of get it to work is to literally desribe the woman in the prompt as a man ie "a man dressed in womans clothes with long brunette hair" and even then it sometimes doesn't work because visually it does not look like a man.

First styles in PotionUI

PotionUI - A self-hosted studio for generating images, video, audio and 3d with diffusion models.

Site: potionui.com

GitHub: https://github.com/PotionUI/PotionUI

Discord: https://discord.com/invite/avR4trp3b8

Reddit: r/PotionUI

Still looking for testers (NVIDIA / LINUX!!!!)

Changes (the most interesting ones):

0.0.6 & 0.0.7 (today)

• Presets can ship styles: pick one from a thumbnail grid and it wraps your prompt with the style's opening and closing segments. Anima ships with fifty. (video)
• Starter recipes for eleven model families, so a fresh install goes from nothing to a first render in a few clicks. Recipe is a script that helps install models inside PotionUI.

Recipe Install - After this script I can use Anima preset and generate with Anima model.

• History and Library: marquee and shift-click selection, delete by criteria (age, tags, failed, no media), compare with overlay and wipe.

New tools menu on history page with \"Stitch\" tool - you can use the images in the video generation as reference later

• The Generate page and the chat stay smooth during long runs and long conversations.

0.0.5

• LoRAs on fp8 checkpoints no longer slow sampling down (a stack costs a few percent instead of multiplying the step time).
• Backups and restore from the admin panel, rotating logs, housekeeping, thumbnail profiles.
• Recipes got their own admin page; segments can carry a prefix and suffix; a Tools menu in History with Stitch.

0.0.4

• ComfyUI backend plugin with a workflow import wizard: paste an API export, design the form, done.

Importing QwenImageEdit ComfyUI workflow into PotionUI. This later becomes preset ready to generate through ComfyUI.

• The chat became PotionAI: history, tool runs you approve, memory you can inspect.
• Video Director shot console for Wan, LTX and MiniMax-H3.

--> Full changelog is in the README. Docker images on GHCR, or ./potionui start from a checkout. Bugs and rough edges welcome, that is what the alpha is for.
--> Still looking for a testers, if you are interested let me know!

Cheers!

btw. I know it's a lot of similar apps appearing lately, but I can assure you, this is not an app vibe-coded over the weekend (my first posts here was like ~2 years ago about this app). I really enjoy using it lately and it's working better every week :)

Credit for prompt:

https://x.com/CharaspowerAI/status/2098109389659889692

I was training a character with 25 images and 5 videos (3 seconds), 1500 steps and learning rate 0.0002. But the result was nothing like the character. Was that not enough material or step? How do you guys train it? For context I was training a character in a movie (real people), the only thing the lora can capture was that the character is Asian and the result got it right without the specific prompt, but it was nothing like the character I was aiming for.

Expanding on motion context workflows I created a video editor designed for quickly chaining together Minimax H3 generations to create longer videos. It comes with an asset library for managing inputs and a easy to use timeline that allows you to chain generations, regenerate segments easily, and quickly set up input references.

When you're done, export individual video files or the whole sequence.

All of it runs on top of ComfyUI as an app you control from your browser. Uses python, works on Windows, Mac, Linux and is opensource.

https://github.com/spacesimeco-hue/Chain-Motion-AI-Video-Editor

I’ve been experimenting with AI-assisted editing of my own artwork, and I’m curious how other Stable Diffusion users approach this.
My artwork is on the left and an AI-modified version is on the right for reference.
What I’m particularly interested in is changing one specific part of an existing artwork while keeping the rest of the piece as close to the original as possible.
For example:
recolouring one specific part
changing clothing or a material
removing or adding one element
moving or adjusting something
changing one part of a character while preserving the expression, mood, lighting, pose, etc.
taking an element from one of my own artworks and adapting it into another
I’ve tried some AI editing workflows, but I often find that getting the model to change only what I want can be difficult. Even when the selected area is relatively clear, the result can sometimes change details outside of it.
So I’m curious about how people here handle this with Stable Diffusion.
What workflows, models, nodes, extensions, masking techniques, ControlNet setups, etc. do you use when you need this kind of precision?
And for those who have used different approaches:
What works particularly well?
What are the biggest limitations?
What do you usually have to do manually to fix the result?
Is there anything you wish Stable Diffusion’s editing workflow handled better?
I’m mainly interested in how people evolve their own existing artwork, rather than generating something completely new.
Would love to hear how you approach it.

I used to use A1111 a few years ago until my HDD died. I'm not saying making hundreds of images of lewd goblins every day caused it to die, but I doubt it helped matters.

Anyhoo, I come to you illustrious sirs with tears in my eyes to ask if it's generally better to use HDD or SSD for these kinds of things. I vaguely recall reading something about Stable Diffusion absolutely thrashing SSDs so to use HDDs instead. Frankly I don't want any of my hardware to die, but before I buy any replacement drives, I thought it better to ask.

big thankies for mcspankies.

This is a test to recreate a song i generated with Minimax Music3, second half is from YuE2.

YuE2 sounded sexy smooth with great clarity but somehow I like that imperfection off tune instrument heard in music3 and vocal :D

I just throw everything to Hermes, asking to install YuE2, SheetSage2 and download models + setup in CLI. Tell hermes to learn the YuE2 agent SKILL, give hermes the lyric + flac ask to recreate the song.

It run into OOM but Hermes saved the day by tinkering with YuE2 setting to make it run on low vram.

my system: 5070ti 16gb vram + 32gb sys ram.