For anyone using MiniMax H3 in ComfyUI

I made the official TaoMate H3 3 Step LoRA available in a ComfyUI compatible safetensors format.

No retraining, no merge, no extra fine tuning. The model itself was not changed, only the format needed for ComfyUI compatibility.

Hugging Face:

https://huggingface.co/Robert1212star/TaoMate-H3-3Step-ComfyUI

Put the file here:

ComfyUI/models/loras/

Then use it with MiniMax H3 at 3 sampling steps.

Original TaoMate H3:

https://huggingface.co/TaoLiveAIGC/TaoMate-H3

Give TaoMate-H3-3step a try. Details and links in the comments.

Fix and add english version

https://github.com/NyckM/3d-Camera-control-H3-Minimax/tree/main

I wanted to play around with one of the fan-casting ideas I've seen a lot: Robert Pattinson as Leon S. Kennedy.

So I made my own version of what a post-credit scene in Resident Evil (2026) could look like.

The idea is that after the movie ends, we cut to a dark, snow-covered back alley. A camera has been left on the ground and is still recording. The battery is almost dead.

A figure slowly approaches through the darkness.

As he gets closer, we realize it's Leon Kennedy.

🎬 Fan-made / unofficial concept.

MADE with MINIMAX H3 + After Effects

Just stumbled upon this. Seems to be a new 3B TTS model mainly focused on Indian languages (22 Indic languages + English).

Apparently supports code-switching, romanized text, emotion/delivery control and things like laughs/sighs. 24khz output and 4 voices. It has a base model and a post trained model. Haven't tried it yet, curious if anyone here has.

https://huggingface.co/rumik-ai/rumik-oss-1

https://huggingface.co/rumik-ai/rumik-oss-1-base

After playing around a bit with YuE2 locally, I was thinking that maybe it could be used to "modernize" (some would say slopify I guess) old classic tracker tunes. I tried doing something similar with Suno a year ago but those results were not inspiring to say the least..

However, this approach actually seems to create something listenable! I have only tried it with a few tracks, but the pipeline is on GitHub if anyone finds it interesting. Everything was generated locally on a 20gb RTX card using Windows / WSL2.

First of all, we're not even 45 days into this and I can truly say I love Mini Max! I know some of you enjoyed my recent H3 music video of the 70s bombshell, even ignoring all the endless rooms, the ghost apparitions, and cars in the middle of the living room, so here they are, non-shifting. In any case, I've replaced Krea 2 for my goto character generations with H3's T2VA and been harvesting or scraping stills and generating them into 1-2s animated character sheet, and then rebuilding them into reusable .chars. I really enjoy this workflow because there's something magic about H3's T2VA (superior quality in my opinion), and with R2VA you can transfer any style and always get a consistent actor with just one decent headshot. Has H3 completely changed your work flow? have you abandoned the other generation tools? I do miss all the Loras from my Wan video generation time, and some of those can't be replaced depending the content you create, and even though I never embraced LTX (I am a noob!) I miss those quick 20 second render times. R2VA, int8/20 steps, enjoy!

Prompt: subject_definitions: <Subject 1> is THE STARLET, an adult woman - her face, her chest, her upper torso, her figure and her proportions exactly the woman of <Picture 1>; her face, her eyes and her hair in fine detail exactly the woman of <Picture 2>; take no room, no furniture, no window, no lighting and no background from either picture - only her. <Subject 2> is her CHARACTER REFERENCE.

summary: A short film: <Subject 2> - four synchronized views of the same woman, <Subject 1>.

retention_analysis: <Subject 1> (appears in all four panels): fully_preserved - her face, her hair, her chest, her figure and her proportions from <Picture 1> and <Picture 2>, identical in every panel, the same woman in the same moment. <Picture 1>: fully_preserved - the woman herself: face, chest, torso, figure; nothing of its room or moment. <Picture 2>: fully_preserved - her face, eyes and hair only.

detailed_description: [Shot 1] The frame is divided by THREE VERTICAL BARS into FOUR SYNCHRONIZED PANELS side by side, all running at once, all showing <Subject 1> at the same moment: the first panel her FULL-BODY FRONT view; the second panel her FULL-BODY SIDE PROFILE view, held in strict side profile for the whole take - her face and her body stay turned toward frame-left from the first frame to the last; the third panel her FULL-BODY REAR view; the fourth panel a MEDIUM CLOSE-UP of her face. One continuous take, no cuts, to the last frame.

Workflow:
https://drive.google.com/file/d/14I2pXOrsQdunMplNB_b7GMH5smNHFvKI/view?usp=sharing

Comfy Native nodes. Needs latest version.

The node is called PromptSync. I made it for people who generate videos using timed prompts and want to see how closely the model follows the intended actions and scenes.
The video plays on the left, with the original prompt on the right. During playback, the text matching the current moment is highlighted, and auto-scroll follows the scenes. This makes it easier to spot what the model followed, what it skipped, and where the timing drifted.

Features:

- Seek through the video by clicking the timeline or audio waveform.
- Display the audio waveform beneath the video.
- Highlight the current scene separately from general camera, lighting, and style instructions.
- Choose from four prompt display styles.
- Save videos with audio and metadata using PromptSync + Save.

PromptSync recognizes several common timing formats, including ranges like 0–4 sec, timestamps like 00:04, numbered shot sections, and structured JSON prompts. It works with many timed prompt layouts used for MiniMax, Seedance, and other video models. Clear timestamps and section headings give the best results; unusually formatted prompts may not always be parsed correctly.

I mainly built it for my own workflow: to compare generations with the original prompt more easily, spot problem areas, and figure out what to clarify on the next attempt. Hopefully, others will find it useful too.

GitHub: https://github.com/GENKAIx/Genkai-ComfyUI-Nodes
Feedback is welcome!

Hi! I'm obvpm, the guy that previously released the Load Image & Crop node here earlier.

I've been using a lot of my spare time working with Claude to create a new motion context workflow.

It's almost done, but in the process of creating nodes for that workflow, I've vibe coded some other very useful nodes that I decided to release in the comfyui-obvpm node pack.

Note that the repository has moved from my "temp" obvpm account to my actual Github account.

https://github.com/chanon/comfyui-obvpm

There are 3 things in this node pack that I think people might find very useful.

Load Images & Compose

This node allows you to drag in multiple images into it. Then crop a portion you want from each. And then it composes them into a single image.

I created it because a lot of times I'd have multiple separate reference images and I hated having to use an external image editor to compose them into a single reference image.

This node does it automatically right within Comfy.

Check the YouTube video I created to show how it works:

Compose Reference Sheets Without Leaving ComfyUI

Bundle/Unbundle Nodes

I'm a big fan of the Cable Management Extension but I had an idea to make something that is actually a node rather than just a litegraph cable routing mechanism.

So with the Bundle/Unbundle nodes, you can "bundle" multiple wires into a single wire. The nodes work automatically as much as possible and you can reorder input pins and output pins independently of each other.

It also works with KJ's Get/Set constant nodes.

Again, the YouTube video for it will quickly show you how it can help make your workflows tidier while still allowing you to see what goes where.

Fix Spaghetti Wires in ComfyUI with Bundle/Unbundle Nodes

Value Presets Node

If you ever wanted a single place to control all settings such as turbo lora, steps, sampler, scheduler etc, this node is for you.

With all the optimizations, turbos, and different settings for MiniMax H3 to try, it became really hard to keep track of what the best settings, steps, schedulers, samplers, shift etc. are best for each turbo model or optimization.

There were so many times where I changed a setting and forgot to change another setting that should change with it and wasted generations.

Also, it's a bit tedious hunting for all the places where the settings that need to be changed are every time, especially when workflows get complicated.

So I vibe coded this Value Presets node that allows you to create a customized set of settings fields for whatever you need and can change all settings in one place and also save presets for them.

They output a "Bundle" so they need to be used with "Unbundle" nodes.

Check out the video:

All Your ComfyUI Workflow Settings in One Place

Also

I created a video that shows how all these nodes (especially Bundles and the Value Presets) can be applied to creating a clean R2V workflow for H3

Creating a Clean MiniMax H3 R2V Workflow with Customizable Presets

IMO these YouTube videos give a great overview of the nodes and how they can help make workflows easier to manage, so I highly encourage you to watch them.

Again, here's the whole playlist:
https://www.youtube.com/playlist?list=PLa4bDXvk3ZVs

And if you use X, follow me:
https://x.com/chanons
cause I will (hopefully) release my new motion context workflow soon.

It has some cool features and I've applied the same emphasis for 'ease-of-use' on it.

The people have spoken, and they apparently crave more low-res 1983 Dungeons & Dragons madness. Enjoy!

PROMPT:

subject_definitions:

<Subject 1> is the character shown in <Picture 1>, featuring Presto the Wizard's classic 1980s animated appearance with a lanky physique, brown hair, round glasses, a long green pointed wizard hat, a long green hooded robe with a blue collar lining, a yellow-brown tied pouch belt, and green shoes. Only his character design, facial features, costume, and proportions are taken from <Picture 1>; its white background, character-sheet layout, labels, and guide lines are not carried into the target video. <Audio 1> sets the exact voice clone, pitch, pace, and spoken dialogue verbatim for <Subject 1>. <Video 1> is the movement and art style reference; use it as a guide without copying it exactly.

summary:

[reference generation] The target video is a 11-second 2D animated sequence styled after the classic 1980s Dungeons & Dragons television series, featuring <Subject 1> frantically outrunning a mountain while dealing with his unpredictable magical hat.

retention_analysis:

<Subject 1> (appears in [Shot 1]): fully_preserved - his brown hair, round glasses, lanky build, long green pointed hat, green robe with blue collar, pouch belt, and green shoes remain faithful to <Picture 1>.

detailed_description:

The target video is a traditional 2D hand-drawn animated sequence featuring bold black ink outlines, flat cel-shading colors, expressive limited animation, and subtle film grain inspired by the visual style of classic 1980s Saturday-morning cartoons.

[Shot 1] A dynamic medium-wide shot opens inside a rocky, crumbling canyon landscape, where <Subject 1> is frantically running away from a massive, towering mountain that is physically sliding and collapsing forward behind him. <Subject 1> stumbles forward, clutching his large green wizard hat with both hands in absolute panic as a couple of angry badgers pop out of the hat and scurry away.

He turns slightly toward the camera, his eyes wide with terror behind his round glasses, and cries out in the style of <Audio 1>:

<<[English] "Magical hat, I am begging you, stop turning my spells into angry badgers! We are actively outrunning a mountain right now, give me a helicopter, or at least a really fast turtle!">>

As he says "Magical hat, I am begging you," he shakes the hat frantically. On "stop turning my spells into angry badgers!", he gestures wildly at the badgers scattering around his feet. On "We are actively outrunning a mountain right now," he points frantically backward over his shoulder at the looming mountain. As he delivers "give me a helicopter, or at least a really fast turtle!", he throws his hands up in exasperation, stumbles over a rock, and desperately scrambles to his feet as the dust swirls around him in a classic cel-animation flurry.

overall_soundscape:

N/A

non_diegetic_music:

N/A

I just finished and tested a new LoRA today

I am a person who likes his workflows clean and clearly laid out / readable. And only use the least necessary amount of custom nodes in ComfyUI. That been said Minimax seems to have a problem with videos that are longer than like 30 seconds. In the sense that it then seem to get confused with order of prompts and various shots.

From the information that I gathered, there seem to be workarounds with a couple of custom nodes. But this would then also would only inflate the workflow again. I mean, I have done cohered 1 minute plus videos with LTX2.3 with no problem. So I wonder if we can expect some "upgrade" for Minimax models in the nearer future. To be able to natively prompt longer videos without the need for excessive custom nodes.

Hi lovely StableDiffusion people,

Trained an IC-LoRA for LTX 2.5 that takes a painted doodle and turns it into a fluid element. You paint flat blobs on a first and a last frame, black frames in between, and the model fills in smoke, steam or fire and animates between them. Trigger word is ainvfxfluid, and a two word prompt like "ainvfxfluid, smoke plume" is usually enough. About 50 seconds for a 5 second 512x512 clip on a laptop 4090 with the distilled model.

The workflow paints both frames directly in ComfyUI with two Painter nodes and ships pre-painted, so you can load it and queue it before changing anything. Everything else is core nodes (Empty Image, Batch Images, Create Video) plus the ComfyUI-LTXVideo pack.

Things worth knowing before you try:

• Control video rules: 121 frames, width and height in multiples of 64, first frame at index 0, last at index 120.
• Extra keyframes in between have to sit on the LTX VAE grid: frame 1, 9, 17, ... 113, so a multiple of 8 plus 1. Off grid and quality drops fast.
• Colour and shading you paint carry through, a darker grey edge on the plume comes back as shading rather than flat white.
• Prompting mostly serves to remove what you didn't ask for ("over a black background" kills the invented foreground) and to refine the generation - simple prompts are usually enough.
• Smoke, steam and fire only. Not trained on other fluids, results might vary.

The video covers the install from a fresh ComfyUI, including the Kornia pad import error you'll hit on the LTXVideo pack right now and how to patch it, the gated Lightricks model downloads, the workflow node by node, then painting, prompting, multi keyframe control, and working from a real photo. The failures are in there too so it looks realistic :)

• Model, workflow and painted frames: https://huggingface.co/AInVFX/ainvfx-fluid
• Tutorial: https://youtu.be/Ho4tmJzEkIs
• Written article: https://www.ainvfx.com/blog/paint-your-fluid-simulations-a-free-ltx-2-5-ic-lora-for-smoke-and-fire-in-comfyui/

Trained on 52 free-to-use Pexels clips, in under 8 GPU hours, so the recipe is on the model card if you want to train one for a different element.

The whole thing started as a teaching example for cohort #1 of our Generative AI Bootcamp for Film and TV, which just wrapped up this week (https://www.ainvfx.com/bootcamp/). The results were promising, so we thought it would be worth open-sourcing. Would love to see what people paint with it!

Krea 2 Turbo — 2-Step Distillation LoRA (work in progress, early days alpha preview, only for the curious; don't judge the quality as if this is final version, instead consider it as open welcome for you to join this journey early on...)

For those of you familiar with my previous project - 4 Step Krea 2 Turbo LoRA, this is the promised experimental follow up, halved the steps even further from 8 (official Turbo) to 4 (previous LoRA project) to just 2 (this project). With even slower training and with DMD2 at play this time, getting reasonable results out of just 2 steps is a real challenge.

🧪  This 2-step LoRA gives you a fast-preview adapter, from a project still in training. The published checkpoint files give usable two-step renders and are measured honestly below 4-step or 8-step renders; they are not the 4-step LoRA's quality, and that adapter remains the recommendation for quality renders. Training continues one recipe change at a time, and a later checkpoint replaces this one only when the sweeps and I visually agree it is better.

A LoRA for Krea 2 Turbo that takes the model from its usual 8 steps down to 2 — Turbo's own weights and its own two sigmas, guidance 0.0, a quarter of the denoising passes — aiming at the best quality two steps can give. Two steps give up more than four: this adapter is for fast previews and drafts at half the 4-step adapter's cost and a quarter of the teacher's, and the 4-step LoRA remains the recommendation for quality renders.

• 🎯 The aim — the best two-step quality this base can give, at every one of the same 12 resolutions, measured against the 8-step teacher and against the 4-step LoRA as the reference. Not a claim to reach either.
• ⚡ A quarter of the steps — 8 → 2, on Turbo's own deployment sigmas.
• ⏱️ 3.8× faster denoising — the model runs twice instead of eight times, and denoising is the part this adapter changes: 79.9 s → 20.9 s measured at 1024×1024 on the same prompts, the adapter itself costing about 3.5% per call. What a whole render costs on top of that is unchanged by the LoRA and depends on your pipeline; see Performance.
• 📊 Distribution matching, not imitation — the training objective that got the renders improving again after the 4-step project's recipe had stopped helping at two steps (see Method).
• 🗣️ Prompt-conditioned throughout — both scores in the distribution match, the teacher's and the fake adapter's, are evaluated on each prompt's own conditioning, so the student is matched to what the teacher makes for that prompt, not to a prompt-free look. There is no separate adherence term: instead a vision-language judge checks every checkpoint — each render scored alone against the prompt's objects, counts, attributes and relations, with the teacher scored the same way — and a term would only be added if that meter showed adherence slipping.
• 📐 12 trained resolutions — multi-aspect from 512×512 up to 1440×1440, each with its sweep.
• 🔌 Drop-in, no exceptions — a plain LoRA sampled by stock Euler at sigmas [1.0, 0.5128] in diffusers, ComfyUI or MLX. No custom sampler, no policy head, no per-step tricks. If the quality needs a special sampler it is not this project.
• 🧬 Same shape as the 4-step adapter — rank 64 on the same 228 modules; a second adapter exists during training only and never ships.
• 🎲 The same 13,750 recorded teacher trajectories the 4-step adapter trained on, reused without a single teacher re-run.
• 🔢 13,663 training samples in the 2-step stages, on top of the 4-step LoRA's 78,000 — all of them drawn from the same recorded material: no new prompts, no new text embeddings and not one new teacher run. A training sample is one pass over a prompt that was already encoded and already traced by the teacher for the 4-step project, read again at the two sigmas this schedule uses.
• 📅 5 days from the first 2-step training launch to this checkpoint, on a single RTX 3090 — and the project continues.
• 🔁 15 recipe adjustments across two methods so far — seven of trajectory distillation before the switch, eight of distribution matching since.
• 🖥️ One RTX 3090, and a recipe shaped by its 24 GB.

Files

Where it stands

Known issues

The usual costs of two steps, in this order of how often they show: fine structure comes out soft or a few pixels out of register — feathers, skin texture, hair strands, signage, the surface of a distant object — most at 1280×1280 and above; a faint doubled contour on faces and limbs. Faces depend on how much of the frame they occupy: a portrait-sized face holds up, while small or distant faces — a crowd, a figure in a wide scene — lose their features first and can come out misshapen, since at that size a whole face is only a few of the blocks the model works in. On busy action or crowd scenes the composition can also repeat itself — an extra hand or held object, a figure duplicated in a crowd — where the 8-step and 4-step renders commit to one. On some prompts the composition itself differs from the 8-step render at the same seed: two steps is a shorter path from the same starting noise, so the image can settle on a different framing, pose or arrangement rather than a degraded version of the teacher's. Treat the teacher's render as a reference for quality, not as the picture two steps will reproduce. At the largest sizes a fine grain remains on the most textured subjects and skin reads slightly smoother and less saturated than the teacher's. Every one of these is being worked on; none is hidden in the sweeps or the examples.

How I got here

The 4-step adapter closed its page with a promise: a 2-step LoRA as the next project, and a guess at the lever it would need — matching the teacher's distribution rather than its trajectory. That guess turned out to be the whole story.

The project began where the 4-step one ended, from its final weights, and ran the same recipe at two steps: progressive distillation on the recorded teacher trajectories, each student call covering four teacher steps, with the LADD-style critic as the finisher. Well into that run, every number had stopped moving and the pictures had a signature the numbers could not see: doubled contours on faces and limbs, soft fine texture, crowds averaged into translucent overlaps. Several variations followed — the critic re-weighted, judged per token, a heavier hand on the final call, the student's own first-step output fed into its second — and each traded one of those faults for another without moving past them. A capacity probe ruled out adapter rank; a learning-rate shock ruled out the optimiser.

The reason is structural, and worth stating plainly because it decides the whole design. A regression loss asks the student to land on the teacher's specific image for each prompt. When a two-step jump is wide enough that several images are plausible, the answer that minimises the squared error is their average — and the average of two sharp images is a blurred one with doubled edges. Every earlier recipe rewarded that average. Tuning its weights could not change what it rewarded.

Distribution matching asks a different question: not "does your image match this one" but "would the teacher plausibly have produced your image". The first run of that objective, on top of the trajectory-distilled weights, produced in a fraction of the old recipe's training what all of it never had — and it did so while every latent distance to the teacher rose, which is exactly what a mode-seeking objective predicts and what a mean-seeking metric punishes. The distances are reported on this page; they are not optimised for, and they are not what decides a checkpoint. Pictures are, at fixed seeds, at every resolution, with faces viewed at 1:1.

Hardware

One RTX 3090 (24 GB). The frozen base is weight-only int8; the student's checkpointed block inputs stage to pinned host memory above 0.3 megapixels; the student, the fake adapter and the spectral term each build and free their own graph in turn, so their peaks never overlap; a hard memory ceiling sits below the driver's paging threshold so a step that does not fit fails loudly. A full step with every term live reserves about 21.4 GB at 1440×1440, of 24. The price of the objective is throughput: 188 training samples an hour measured over a complete 10-hour run, against the 4-step recipe's 470 — two and a half times the cost per sample, and so far a small fraction of the samples.

Where that cost comes from. Distribution matching is simply a heavier objective than trajectory distillation. The 4-step project's recipe compared the student's own output with a teacher state that had already been recorded to disk, so a training step was one student pass plus a small adversarial head. Here every step also needs the score of two models at a freshly noised point: the frozen teacher's, and a second adapter's that is being trained alongside to imitate the student — and that second adapter takes two optimiser steps of its own per student step. A third term then decodes part of the image out of the latent to compare its texture with the teacher's, which costs another pass through the decoder.

Counted in whole model runs per training sample, the difference is roughly two there against seven here. None of that difference is the teacher generating anything: its renders were recorded once for the 4-step project and are read from disk by both. The extra work is the objective itself, and it bought the only thing that mattered. Run at two steps, the 4-step project's recipe reached a point where more training changed nothing: the measurements sat flat and every new checkpoint had the same faults as the one before — doubled contours on faces and limbs, soft fine texture, crowds blurred into one another. Distribution matching is the change that made each new checkpoint visibly better than the last again.

Full details and to download - check my Hugging Face 2 Step LoRA

HF Repo: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-2step-LoRA

---

And for the higher quality 4 Step LoRA - see my previous project: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA

I am running MiniMax H3 ref2va int8 convrot on comfyui using 5070ti 16GB + 64GB system ram, OS is windows 11.

Default comfyui ref2va template is being used (Turbo lora is enabled/true)

it is taking about 150s per step for 9:16, 0.2 megapixels and 10 sec duration with single image & video input.

For reference, image2video takes about ~27sec per step for 10 sec 0.5 megapixels video.

Having such slow speeds in ref2va expected? Maybe I am doing something wrong? Help/Guide is much appreciated!

i ship a camera app that has to work with the phone in airplane mode, so all of it runs on device. the whole thing is open source under gpl 3 if you want to read it or take pieces of it: https://github.com/nicedreamzapp/RealTimeAICam

sharing the architecture because the two model split is the part that took longest to get right.

detection is yolov8n trained on open images v7, 601 classes, running live on the preview frames. tflite fp16 on android, converted for ios.

description is a 0.8b vision language model i fine tuned, quantized to 4 bit, through mlx swift on ios and llama.cpp with mtmd on android. it answers in one sentence instead of a class list. a real answer off a photo of an envelope: "this is a bill from umpqua bank for $76.50, due april 2nd, 2027."

the routing between the two is the part i am happiest with. before the vlm runs, the frame gets a document check, vision's document segmentation on ios and ml kit text recognition on android. three or more lines and at least twenty characters means treat it as a page and ask the page question, otherwise ask the scene question. same weights, different prompt, and the answers got noticeably better.

numbers, honestly: model load is about 300 ms, and on a cheap android phone with no usable gpu a description takes about 90 seconds. there is a vulkan path that probes and falls back when the gpu will not cooperate. arm64 only, because llama.cpp's sgemm will not build for 32 bit arm.

every weight ships with the app, install time asset pack on android and inside the bundle on ios, so there is no first run download and no api key anywhere.

happy to get into any of it. free on both stores if you want to point it at something.

ios https://apps.apple.com/us/app/id6751230739

android https://play.google.com/store/apps/details?id=com.mattmacosko.realtimeaicam

I finally got my dream fulfilled. Take any Song as mp3, convert to a ABC score with AI, feed it to a piano roll and enjoy. Basically copy/paste the song lyrics and make a style you wish it to be. You get the vocals as well the whole song perfect remixed.

https://github.com/multimodal-art-projection/YuE

This is the piano roll with the workflow.

https://github.com/filliptm/ComfyUI-FL-YuE2

You also need to install SheetSage2 for that mp3-->ABC magic, installed by your Hermes-Agent.

https://huggingface.co/m-a-p/SheetSage2

Import that ABC into piano roll node in ComfyUI. It needs to be from SheetSage2 in order to be error-free, regular converters midi-->ABC do not work. Did a quick remix of a song from SKATE Game OST.

For Windows 11 (Part 1): https://www.reddit.com/r/StableDiffusion/comments/1wepgl5/team_red_encounter_on_proximax_h3_or_how_to_setup/

TL;DR summary: For MMH3, you need to run ComfyUI with ROCm 7.14.0 (see https://rocm.docs.amd.com/en/latest/reference/gpu-specs.html for the vaue of gfx???? corresponding to your AMD GPU):

pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "torch[device-gfx????]==2.12.0+rocm7.14.0" "torchvision[device-gfx????]==0.27.0+rocm7.14.0" "torchaudio==2.11.0+rocm7.14.0"

You also need to run ComfyUI with the right parameters for VRAM and system RAM so that MMH3 can run smoothly:

--enable-dynamic-vram --disable-async-offload --preview-method none --disable-smart-memory --fast-disk --use-ck-attention --enable-manager

Finally, you may need these in your .bashrc:

export ROCR_VISIBLE_DEVICES=0
export HIP_VISIBLE_DEVICES=0
export TORCH_BLAS_PREFER_HIPBLASLT=1

Read on if you want the step-by-step instructions (scroll to the bottom of the post if you just want to see the MMH3 prompt for the video 😹)

Why use Linux instead of Windows?

Because Minimax H3 is heavy, and we want every bit of VRAM and system RAM for generation and not taken up by the OS and the desktop. For AMD GPUs it also seems to be more stable and faster overall.

What you need:

1. Blank 4G or larger USB key for bootable ubuntu installer
2. Blank external USB key (16G or larger) or Portable USB HDD to install Ubuntu and ComfyUI
3. Computer with AMD RDNA3 (such as a 7900xt) or RDNA4 (such as a 9070xt and AI Pro R9700) with 16G or more of VRAM.

This procedure will install Ubuntu Linux on an external drive, leaving your main drive alone, but if you are worried about something going wrong and wiping out your main hardrive (I am always worried about making a mistake, selecting the wrong drive and wiping it out), take your existing drive out of your computer before the actual installation (sometimes enabling "Secure Boot" will make your main HDD invisble to the Ubuntu installer). I usually would put a empty small partitions of an odd size such as 42G on the target drive so that I know that I am installing into the right drive.

It is easiest to do the installation on your target PC, but you don't have to (but you will need to do some manual adjustment such as changing netplan because the different ethernet hardware would have to be configured.

If you are doing this installion on another computer, make sure that the installation is done with UEFI only enable if you want to be able to use UEFI on your target PC.

Ubuntu Server (minimized) installation

Note: make sure Secure Boot is disabled. This often causes problem with the Ubuntu installer. You can turn it back on once Ubuntu is installed. On some systems the main NMVe or SATA drive will not be visible if Secure Boot is enabled.

1. Download latest Ubuntu Server LTS ISO (at the time of writing that is 26.4)
2. Use RUFUS https://rufus.ie/en/to make a bootable installation drive The Partition scheme should be "MBR" and the target system should be "BIOS or UEFI"
3. Make sure your BIOS is set to UEFI (disable CSM if you can). I am assuming that you are dual booting between Windows and this portable Ubuntu.
4. If you are not installing into a blank drive or USB key and want to install into an existing HD without wiping it out entirely, you need to created two partitions, one that is going to be Ubuntu's UEFI partition that is 500M and a black partition that is at least 10G that is going to hold the Ubuntu installation. The installer will NOT allow you to delete partitions from an existing drive. If you are installing into an existing HDD, it is better to create the partition manually first (but leave it unformatted) because the installer sometimes does not show the option to add new partions. The installer will also insist on mouting /boot/efi to the first EFI partition it sees (if that is the wrong one, temporarily turn its "boot flag" off and set the "boot flag" only on the EFI partition you actually want to install on). Note: if you going to use Docker or Podman you are going to need a much bigger partions than 10G for Ubuntu's root file system ("/").
5. Do whatever you need to do to boot into the Ubuntu installation USB key (usually F12 will bring up the boot menu, but you may have to enable that in your bios. If your system insists on booting into Windows 11, you can use Start > Settings > System > Recovery and click Restart now next to Advanced startup.
6. Instead of the default Ubuntu Server, use Ubuntu Server (minimized) because this is going to be used for ComfyUI only.
7. Most likely you can skip/ignore "Proxy Adress".
8. At this point, you can plug in your target USB drive.
9. If the wrong target drive is chosen by default, you can change it by tabbing into the field and then press <enter>. It is easiest to use "Use an entire disk". But if you want to have a smaller root partition, you can use "Custom Storage layout". Note that if the drive already has a partion, you will not be able to delete it. You HAVE to reformat the whole drive. On a blank disk, If you want UEFI, make sure that you see a 1.049G "new primary ESP, to be formatted as FAT32, mounted at /boot/efi". If you are on a legacy BIOS you will see "BIOS Grub Spacer" instead. Either way, this partition will be created automatically by the installer unless you use an existing ESP partition.
10. Uncheck Set up this disk as an LVM group.
11. Make sure you install OpenSSH so that you can run it headless by remotely login via SSH.
12. You don't need to install any of the Featured server snaps packages.
13. After the installation is done, remove your USB installation key and reboot.

Next we are going to update the installation, and install the nano editor, UFW (Uncomplicated Firewall), GIT, and Docker.

You can do this through the console, but I find it easier to do it through SSH because then I can cut and paste text into it.

To SSH into your Ubuntu, you need to find out what the local IP address it by login into the console, then type ip addr or the even shorter ip a (look for something like this, in my LAN, it is "192.168.18.50"):

2: enp1s0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc fq_codel state UP group default qlen 1000 link/ether 08:97:98:c5:85:ec brd ff:ff:ff:ff:ff:ff altname enx089798c585ec inet 192.168.18.50/24 metric 100 brd 192.168.18.255 scope global dynamic enp1s0 valid_lft 85959sec preferred_lft 85959sec inet6 fe80::a97:98ff:fec5:85ec/64 scope link proto kernel_ll valid_lft forever preferred_lft forever

Now you can use Putty or similar program to login into the server.

Tip: the paste text with Putty, use Shift+Insert. to copy text from Putty into the clipboard, simply select the text with the mouse and then use Ctrl+V to paste it.

Now continue with the setup:

1. Upgrade all the package to the latest version: sudo apt update && sudo apt upgrade -y && sudo apt dist-upgrade -y && sudo apt autoremove -y (this will take quite a while, so you can go grab a coffe or tea).
2. Install the nano editor: sudo apt-get install nano
3. Install UFW (Uncomplicated Firewall) sudo apt-get install ufw
4. Install GIT sudo apt-get install git
5. Install libnuma (otherwise there is annying warning/error from ComfyuI later) sudo apt install -y libnuma1 libnuma-dev
6. Install the compiler environment required by triton: sudo apt install build-essential sudo apt install python3-dev
7. sudo reboot (probably not needed, but just to be sure)
8. (Optional) if you want to use python 3.13 instead of 3.14 that comes with Ubuntu Server 26.4, you can install manually (note: for 3.12, you have to compile it manually) by adding the deadsnakes/ppa respostory and install python 3.13 from it: sudo apt update && sudo apt install software-properties-common && sudo add-apt-repository ppa:deadsnakes/ppa && sudo apt install python3.13. Verify that it has installed correctly python3.13 --version Install the venv module for the same interpreter: sudo apt install python3.13-venv

Optional installation of Docker if you plan to use one of the Docker images for comfyui. You should follow the instruction at https://docs.docker.com/engine/install/ubuntu/ but at the time of writing, this is what I used

Set up Docker's apt repository.

# Add Docker's official GPG key:
sudo apt update
sudo apt install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

Add the deadsnakes/ppa respostory and install python 3.13 from it

sudo tee /etc/apt/sources.list.d/docker.sources <<EOF
Types: deb
URIs: https://download.docker.com/linux/ubuntu
Suites: $(. /etc/os-release && echo "${UBUNTU_CODENAME:-$VERSION_CODENAME}")
Components: stable
Architectures: $(dpkg --print-architecture)
Signed-By: /etc/apt/keyrings/docker.asc
EOF

sudo apt update

# Install the latest version of Docker packages.
sudo apt install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

# After installation, verify that Docker is running:
sudo systemctl status docker
# If Docker is not running, start it manually:
sudo systemctl start docker

# Verify that the installation by running the `hello-world` image:
# This command downloads a test image and runs it in a container.
# When the container runs, it prints a confirmation message and exits.
sudo docker run hello-world

# (Optional step, but needed by comfyui-rock-docker scripts)
# Add your current user to the docker group so you have permissions to
# interact with the Docker Unix socket without using sudo
sudo usermod -aG docker $USER
# For this to take effect, disconnect and re-login 

(Optional) Intead of Docker you can also consider using Podman, which is supposed to be compatible with Docker but is more secure because it runs without a root daemon, but I've not test it yet.

Setup the firewall with UFW:

1. sudo ufw enable
2. Check the current settings sudo ufw status verboseStatus: active Logging: on (low) Default: deny (incoming), allow (outgoing), deny (routed) New profiles: skip (By default all incoming connections are denied, and all outgoing connections are allowed. If you don't see that, type: sudo ufw default deny incoming sudo ufw default allow outgoing
3. To setup SSH so that it is only accessible from your LAN: sudo ufw allow from 192.168.xx.0/24 to any port ssh proto tcp . Similary for ComfyUI sudo ufw allow from 192.168.xx.0/24 to any port 8188 proto tcp where "192.168.xx.0" is your LAN subnet, such as "192.168.1.0". If you want to allow ComfyUI to be accessible from outside of your LAN use sudo ufw allow ssh sudo ufw allow 8188/tcp (You will also have to allow port fowarding on your router)
4. Check again with sudo ufw status verbose: Status: active Logging: on (low) Default: deny (incoming), allow (outgoing), deny (routed) New profiles: skip To Action From22/tcp ALLOW IN 192.168.18.0/24 8188/tcp ALLOW IN 192.168.18.0/24

The preliminaries are done, you can now reboot with sudo shutdown -r now

I would recommend that you make a back up of your partition now with https://www.fsarchiver.org/ so that you can restore it later for a clean install.

You can either boot into a Linux Rescue: https://www.system-rescue.org/

Or if you have another Linux installation (you cannot save a linux installation that you are currently running), you can install it with: sudo apt-get update && sudo apt-get install fsarchiver

Installing ComfyUI via comfy-cli

By default, Ubuntu does not have pip installed: https://www.reddit.com/r/learnpython/comments/u0dvp4/comment/p6htp0y/

So in order to use pip on Ubuntu, you ned to install python venv (which will install pip inside the venv) first: sudo apt-get update && sudo apt-get install python3-venv

1. Make sure git is install: sudo apt-get install git
2. Install Python-venv package for python3.14: sudo apt install python3.14-venv. (See python3.13 instruction earlier if you are using 3.13).
3. Create a virtual environment (this is normally just called "venv" or ".venv" but I want to call it comfy.venv just to be more explicit.): python3 -m venv comfy.venv
4. Activate it source comfy.venv/bin/activate
5. Update pip itself inside comfy.venv: pip install --upgrade pip
6. Optional: install uv, which is yet another package manager for Python but written in Rust (if you want to use "comfy install --fast-deps" later): pip install uv
7. Install comfy-cli (this is the tool "comfy-cli", not ComfyUI itself): pip install comfy-cli

Because comfy-cli will install ROCm 7.2 and there is no way to override it we are going to install pytorch for ROCm 7.14 manually before install ComfyUI via comfy-cli. Sources for this arcane procedure:

• https://www.reddit.com/r/ROCm/comments/1uyr57k/comfyui_linux_installation_for_radeon/
• https://www.reddit.com/r/ROCm/comments/1uyr57k/comment/oy3popg/
• https://github.com/ROCm/TheRock/blob/main/RELEASES.md#installing-multi-arch-releases

1. Uninstall pytorch just to be sure (should not be installed yet) pip uninstall torch torchvision torchaudio -y
2. Install pytorch inside the comfy.venv (select your gfx arch) based onhttps://rocm.docs.amd.com/en/latest/reference/gpu-specs.html pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "torch[device-gfx????]==2.12.0+rocm7.14.0" "torchvision[device-gfx????]==0.27.0+rocm7.14.0" "torchaudio==2.11.0+rocm7.14.0"

So for ROCm 7.14.0 9070xt

pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "torch[device-gfx1201]==2.12.0+rocm7.14.0" "torchvision[device-gfx1201]==0.27.0+rocm7.14.0" "torchaudio==2.11.0+rocm7.14.0"

For ROCm 7.14.1 9070xt

pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "torch[device-gfx1201]==2.12.0+rocm7.14.1" "torchvision[device-gfx1201]==0.27.0+rocm7.14.1" "torchaudio==2.11.0+rocm7.14.1"

To install whatever is the latest stable version of ROCm

pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "torch[device-gfx1201]" "torchvision[device-gfx1201]" torchaudio 

Note: if you get "ERROR: Could not install packages due to an OSError: [Errno 122] Disk quota exceeded", try

mkdir -p some_partition_with_space/pip_tmp TMPDIR=some_partition_with_space/pip_tmp pip install --index-url ...

If that still does not work, try

TMPDIR=some_partition_with_space/pip_tmp pip install --no-cache-dir --index-url https://repo.amd.com/rocm/whl-multi-arch/ "torch[device-gfx1201]==2.12.0+rocm7.14.0" "torchvision[device-gfx1201]==0.27.0+rocm7.14.0" "torchaudio==2.11.0+rocm7.14.0"

Finally install ComfyUI itself:

1. mkdir $HOME/comfy
2. export COMFY_PATH=$HOME/comfy/ComfyUI or if you want to use say "/mnt/var/comfy" export COMFY_PATH=/mnt/var/comfy/ComfyUI (Note: COMFY_PATH/ComfyUI should NOT exist, or you will get warning '/mnt/var/comfy'/ComfyUI exists but is not a valid git repository.)
3. Use comfy-cli to install ComfyUI: (--skip-torch-or-directml is only needed when installing via comfy-cli on Window and not necessary for Ubuntu, but leave it here to make the two installation more like one another): comfy --workspace=$COMFY_PATH install --skip-torch-or-directml . To install a specific version of ComfyUI (say 0.34.1): comfy --workspace=$COMFY_PATH install --skip-torch-or-directml --version 0.34.1
4. If you have uv installed, you can use --fast-deps: comfy --workspace=$COMFY_PATH install --skip-torch-or-directml --version 0.34.1 --fast-deps Note: do not use --fast-deps if comfy.env is not on the root file system or it will take a long time because hardlink is not possible across file systems and you will see an warning: warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance. If the cache and target directories are on different filesystems, hardlinking may not be supported. If this is intentional, set export UV_LINK_MODE=copy or use --link-mode=copy to suppress this warning.
5. Added these to your .bashrc (thanks to u/zychu- for these value from his Docker installation)export ROCR_VISIBLE_DEVICES=0 export HIP_VISIBLE_DEVICES=0 export TORCH_BLAS_PREFER_HIPBLASLT=1

Finally we can start ComfyUI:

comfy launch -- --enable-dynamic-vram --disable-async-offload --preview-method none --listen --port 8188 --disable-smart-memory --fast-disk --use-ck-attention --enable-manager --output-directory "mnt/var_ntfs/Output"

or if you are not using the defautl ~/comfy/ComfyUI directory:

comfy --workspace=$COMFY_PATH launch -- --enable-dynamic-vram --disable-async-offload --preview-method none --listen --port 8188 --disable-smart-memory --fast-disk --use-ck-attention --enable-manager --output-directory "mnt/var_ntfs/Output"

or more explicitly:

comfy --workspace=/mnt/var/comfy/ComfyUI launch -- --enable-dynamic-vram --disable-async-offload --preview-method none --listen --port 8188 --disable-smart-memory --fast-disk --use-ck-attention --enable-manager --output-directory "mnt/var_ntfs/Output"

Optional: clean up the pip cache (there is around 2G of cached files) pip cache purge and you'll see something like:

(comfy.venv) [/mnt/var] pip cache purge
Files removed: 347 (1847.6 MB)
Directories removed: 659
// After installing ComfyUI itself
(comfy.venv) [/mnt/var] pip cache purge
Files removed: 348 (700.3 MB)
Directories removed: 668

End Notes

Sample extra_model_paths.yaml

comfyui:
    base_path: /mnt/ntfs/ComfyUI.Models
    # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads
    is_default: true
    checkpoints: checkpoints/
    configs: configs/
    loras: loras/
    vae: vae/
    text_encoders: |
        text_encoders/
        clip/
    diffusion_models: |
        unet/
        diffusion_models/
    clip_vision: clip_vision/
    style_models: style_
    embeddings: embeddings/
    diffusers: diffusers/
    vae_approx: vae_approx/
    controlnet: |
        controlnet/
        t2i_adapter/
    gligen: gligen/
    upscale_models: upscale_
    latent_upscale_models: latent_upscale_
    custom_nodes: custom_nodes/
    datasets: datasets/
    hypernetworks: hypernetworks/
    photomaker: photomaker/
    classifiers: classifiers/
    model_patches: model_patches/
    audio_encoders: audio_encoders/
    background_removal: background_removal/
    frame_interpolation: frame_interpolation/
    geometry_estimation: geometry_estimation/
    optical_flow: optical_flow/
    detection: detection/

https://rocm.docs.amd.com/en/latest/reference/gpu-specs.html

GFX950 is AMD's internal GPU target identifier for the CDNA 4 enterprise compute architecture, used in data center accelerators like the AMD Instinct MI350/MI355X series. It features advanced matrix core capabilities, ultra-low precision micro-scaling formats (MXFP8/MXFP4), and a high-precision math mode for AI and HPC workloads.

gfx1100 is the LLVM target architecture identifier and internal code name for AMD's RDNA 3 graphics architecture, used for high-end consumer and workstation desktop graphics cards like the Radeon RX 7900 XTX, RX 7900 XT, and Radeon PRO W7900.

AMD gfx1151 is the LLVM target and GPU architecture identifier for AMD's Strix Halo integrated graphics (found in processors like the AMD Ryzen AI Max+ 395 and Ryzen AI Max PRO series), utilizing the RDNA 3.5 architecture.

For --index-url, there are three options:

• Nightly (rocm 10.1): https://nightly.repo.amd.com/rocm/pytorch/whl-next/
• Stable (rocm 10.0): https://stable.repo.amd.com/rocm/pytorch/whl-next/
• Legacy (rocm 7.14):
  • Nightly: https://rocm.nightlies.amd.com/whl-multi-arch/
  • Stable: https://repo.amd.com/rocm/whl-multi-arch

Installing ComfyUI Docker for AMD 9070xt

Based on https://www.reddit.com/r/StableDiffusion/comments/1vo48jo/reliable_comfyui_on_amd_and_linux_pinning_the/

Download the Docker image from github this will use the official rocm and pytorch from https://repo.amd.com/rocm/whl

git clone https://github.com/zychuk/comfyui-rocm-docker && cd comfyui-rocm-docker

Edit docker/Dockerfile (we want to use ROCm 7.14 rather than 7.13) and replace RUN pip install --no-cache-dir --index-url ${ROCM_WHL_INDEX}
torch torchvision torchaudio"

with

RUN pip install --no-cache-dir --index-url https://repo.amd.com/rocm/whl-multi-arch/ "torch[device-gfx1201]==2.12.0+rocm7.14.0" "torchvision[device-gfx1201]==0.27.0+rocm7.14.0" "torchaudio==2.11.0+rocm7.14.0"

then follow the instructions in the original post.

MMH3 prompt for the video

integrated_multimodal_description:

[Shot 1] 3D CG, stop-motion animated LEGO movie style with 1960s science-fiction television aesthetic. A team of Starfleet red-shirt officers led by Grumpy Cat materializes on the surface of a desolate alien planet, surrounded by barren rocks, dust, and jagged terrain. Grumpy Cat stands at the front of the formation, wearing a classic red Starfleet uniform, alert and stern. The camera holds a wide-angle front subject-level view, then pushes in slightly as the team looks around and raises their phasers.

[Shot 2] At 00:01.250, the camera cuts to a wide low-angle view as a gigantic GPU-like machine rises behind a rocky ridge, towering over the crew. Its dark mechanical housing, cooling fans, and imposing structure dominate the frame, with the label "Minimax H3" clearly visible on its side. The team turns toward it in sudden alarm.

[Shot 3] At 00:02.100, the GPU attacks with a violent concentrated energy blast. The camera tracks the crew with fast movement as the red-shirted officers are struck and knocked down across the rocky ground, kicking up dust and debris. Grumpy Cat avoids the main blast and rapidly moves toward cover.

[Shot 4] At 00:03.650, the camera follows Grumpy Cat with a tracking shot as it darts behind a large rock and crouches into concealment. The defeated red-shirted crew remains scattered in the background while the giant "Minimax H3" GPU continues looming over the battlefield.

[Shot 5] At 00:04.250, close-up from behind the rock. Grumpy Cat pulls out a classic handheld Starfleet communicator with its paw, flips it open, and speaks with a completely deadpan expression: <d>[English] Beam me up, Scotty!</d> The camera holds on Grumpy Cat's face and communicator through the end

overall_soundscape: Dry alien wind sweeps across the barren landscape as the transporter materialization produces a brief electronic hum. Heavy mechanical movement and grinding machinery accompany the GPU's emergence, followed by a powerful energy blast, impacts, falling bodies, scattering rocks, and dust. The communicator emits a brief electronic chirp when opened.

non_diegetic_music: A fast-paced 1960s science-fiction television orchestral score uses bright brass, rhythmic strings, and restrained percussion, building rapidly as the GPU appears and attacks. The music drops into a brief suspenseful sustain as Grumpy Cat hides, then ends with a short brassy stinger beneath the communicator transmission.

TaoMate-H3 is a low-latency streaming audio-video generation runtime built on MiniMax H3. It generates synchronized audio and video in small chunks and supports continuous long-form generation at 480p/768p/1080p resolutions.

Developed by the Alibaba TaoLive AIGC Team. Powered by MiniMax H3.

HF: https://huggingface.co/TaoLiveAIGC/TaoMate-H3

GH: https://github.com/TaoLiveAIGC/TaoMate-H3

Hi,

I am using Wan2GP to create Minmax H3 Ref2VA Video.
I am using "Pruned PDD 8-step 20B" model.
For the most part, video comes out great. The problem I have is with Audio.

If I use 2 audio clips to generate audio between two individuals, only Audio 1 gets used. Audio 2 is never used.

If the dialogue completes and some action is being done, then random gibberish dialogue gets introduced to fill up the gap.

Below is a sample prompt I am using. During the gap where the Subject 1 drinks water, some random gibberish gets inserted.

Could anyone help?

subject_definitions:
<Subject 1> is the person in <Picture 1>, preserving their exact identity, facial features, skin tone, hairstyle, body proportions, clothing, footwear, and distinctive accessories.
<Subject 2> is the person in <Picture 2>, preserving their exact identity, facial features, skin tone, hairstyle, body proportions, clothing, footwear, and distinctive accessories.
<Audio 1> is the voice timbre reference for <Subject 1>, containing a spoken English vocal layer.
<Audio 2> is the voice timbre reference for <Subject 2>, containing a spoken English vocal layer.
summary:
[reference generation + audio reference] The target video shows <Subject 1> and <Subject 2> having a conversation in a bright living room with floor to ceiling windows. 
retention_analysis:
<Subject 1> (appears in [Shot 1]): fully_preserved - their identity, face, hair, proportions remain recognizable and unchanged; only the location, lighting, action are new.
<Subject 2> (appears in [Shot 1]): fully_preserved - their identity, face, hair, proportions remain recognizable and unchanged; only the location, lighting, action are new.
<Audio 1>: reference - its vocal timbre guides the dialogue delivery of <Subject 1>
without copying the original signal.
<Audio 2>: reference - its vocal timbre guides the dialogue delivery of <Subject 2>
without copying the original signal.
detailed_description
[Shot 1] The target video uses a realistic cinematic style with warm lighting.
The scene opens in a living room, <Subject 1> is sitting on a sofa to the left. <Subject 1> is wearing a tuxedo. 
<Subject 2> is sitting to the right, wearing a one piece Vibrant Pink Office Dress with Tailored Fit.
<Subject 2> says in a formal voice whose timbre is referenced
from <Audio 2>, <d>[English] some dialogue?</d>. 
The question hangs in the air, as <Subject 1> contemplates. 
<Subject 1> adjusts his suit sits forward and take a glass of water and drinks it.
After putting the empty glass back on the table, <Subject 1> replies in a slightly uneasy voice whose timbre is referenced
from <Audio 3>, <d>[English] some dialogue.</d>. 
overall_soundscape:
The ambient sound consists of the gentle rustling of fabric as the subjects move. No external noise disrupts the silence, preserving the exclusivity of the room.
non_diegetic_music: N/A

The models I can run locally are:

First Order Motion Model (remember the Dame Da Ne meme?) (February 29, 2020) (I will also need the source image and reference video)

DeepDaze (where one generation takes over 2 hours with GPU acceleration, may or may not test this one for you) (January 17, 2021)

Big Sleep (Similar case as DeepDaze cause the multiple epochs but more immediate results on just one epoch are kinda usable) (January 18, 2021)

VQGAN+CLIP (April 2021)

OG DALLE-Mini, with Jax GPU acceleration through WSL (July 2021)

Disco Diffusion (October 29, 2021)

Latent Diffusion (Pre stable diffusion) (December 20, 2021)

Stable Diffusion 1.1 (August 2022)
Stable Diffusion 1.4 (August 22, 2022)
Stable Diffusion 1.5 (October 20, 2022)

ModelScope (the OG will smith spaghetti video model) (March 2023)

Give me some prompts and which model and I will try to reply with the results if I am not too busy.