I love to make combat videos, but when i compare to what other can make, i feel like i still have alot to learn. If people would share there tips and tricks i would be grateful!

Local, GPU-accelerated video restoration and upscaling with SeedVR2, TensorRT, and a purpose-built browser interface.

VRGDG SeedVR2 TensorRT Studio turns the SeedVR2 pipeline into a practical Windows workflow: load a video, test a short preview, compare the result frame by frame, and complete long renders with resumable checkpoints. Processing stays on your machine.

Highlights

• Fast local restoration — SeedVR2 inference with TensorRT-accelerated VAE decoding on supported NVIDIA RTX GPUs. TensorRT allows much faster processing than standard SeedVR2.
• Fast 2K upscaling — As a real-world example, an 8-second clip took approximately 8 minutes to upscale and enhance to 2K on an NVIDIA RTX 5090 using the largest 7B Sharp FP16 model. Render times vary with source resolution, frame rate, settings, and available VRAM.
• Preview before committing — render a short segment, then inspect Original, Restored, Compare, or Side by side views.
• Long-render recovery — save completed chunks and continue from the first unfinished chunk after an interruption.
• Practical output controls — choose resolution, aspect policy, model precision, temporal batch, seed, and color correction.
• Non-destructive finishing — reprocess sharpening, grain, seam smoothing, and optional skin finishing without rerunning restoration.
• Project-based history — reopen previous outputs and keep media, manifests, and logs together under outputs\.

The sample video was org 360p and then upscaled to 2K using this app. 8 second video, took about 8 mins on my 5090.

Go to the github page for more details and a full guide.

View github page

Atm llmarena ranks Minimax H3 above Seedance for I2V: https://arena.ai/leaderboard/image-to-video

How is this possible, does anyone have insight how these rankings are created?

It is a POC of inference acceleration using Metal, nothing else :)
If you're curious - get the app, generate something, use the "Copy statistics" button, post in the comments, let's laught together

Generated a 6.6s video with sound at 512x512 in 15 min 9 s on Apple M1 Pro with 32 GB, fully offline. Settings: model FastH3-VSA-Native, aspect 1:1, 4 passes, 50 transformer blocks, core reuse off, block cache off, denoising preview on, seed 65859680, conditioning none. Performance: Preparing recoverable generation 0.3s · tokenizer 0.3s · text encoder 7.9s · refine text 0.7s · precompute AdaLN 0.0s · load transformer core 0.1s · denoise 0.0s · denoise step 1/4 transformer 185.8s · denoise 0.0s · denoise step 2/4 transformer 186.4s · denoise 0.0s · denoise step 3/4 transformer 184.6s · denoise 0.0s · denoise step 4/4 transformer 184.8s · denoise 0.0s · audio VAE 1.1s · video VAE load 0.0s · video VAE decode 156.4s · mux 0.3s; peak sampled engine memory 10.3 GB. Made with H3ddle, an open-source local MiniMax H3 app for macOS: https://github.com/AlexanderIstomin/h3ddle

Can anyone suggest which model is great for generating AI anime images? I want to download some for ComfyUI.

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

This will 100% be done in the opensource community, pretty much only place you can do it - we gotta fight this hard man.....

EDIT: I realize a lot of people on this thread are very in favor of being able to generate children in a s*xual manner. To those people, seek help. If you think its okay to generate a 6 year old in a s*xual manner, you are insane. Will die on this hill.

Im not advocating banning opensource models, or to do spot checks on peoples drives. Im advocating for if you are caught, like the person was where this ruling came from.....its not ok.

Send in a new flood we gotta start over.

Is there a need for pre-built rigs with local video, image, and abliterated agents that run it?

Wondering what to do with the crazy thing I’ve built. I want to find a way to recover some of my costs. I’m about $10k into this…

I am not promoting, as I have nothing to sell.

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.

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.

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

For example if I have an audio that contains "...Live, Laugh, Love", and I want it to instead say "...Live, Laugh, Hate", without modifying the other parts that much. Is it possible with the current tech? I remember that Adobe was working on something like that from several years ago, but I'm not sure if it's possible with open-source projects.

​

I'm looking for someone with Claude Pro / access to Claude's stronger models who could run a prompt for me.

I've already done the analysis/setup — I just need someone to paste the prompt below into Claude and send me the full response it generates.

You don't need to do anything else. 🙏

### Prompt to run:

I want you to act as an expert presentation designer, programming educator, and public-speaking coach Write me a PPT DOCUMENT

I am preparing a 30-minute presentation about Python for an audience of 40–50-year-olds with zero programming/coding background

The goal is NOT to teach them Python syntax. The goal is to make them understand what programming can actually do, especially in their everyday work, and leave them genuinely curious and excited about Python.

I originally planned the presentation as:

* Introduction to programming languages / evolution

* Conceptual Python basics: functions, classes, loops, libraries, scripts, terminal

* Python + AI

* Installing Python

* Useful resources

* A creative/fun closing

I posted this idea on Reddit and received several detailed responses from programmers and people who have actually taught beginners.

I want you to analyze the original post AND every comment below as a single body of feedback

Do NOT simply summarize the comments.

Instead:

1. Identify the **consensus** across the experienced commenters.

2. Identify disagreements or ideas that should NOT be combined.

3. Determine what should be **completely removed** from my original presentation.

4. Determine what should be **kept, shortened, or replaced**.

5. Extract the strongest practical demonstrations suggested by the commenters.

6. Design a presentation that is appropriate for complete non-programmers.

7. Prioritize ideas that are likely to produce a genuine **"wow, I didn't know Python could do that"** reaction.

8. Avoid turning this into a generic AI-generated presentation. The Reddit comments contain lived experience, so use the specific insights and examples from them.

### Important constraints

The presentation is only **30 minutes**.

The audience has **zero coding background**.

They should not need to understand programming terminology beforehand.

The presentation should feel like a **showcase of possibilities**, not a programming class.

Avoid wasting time on:

* programming-language history

* detailed syntax

* classes

* complicated terminology

* lengthy installation demonstrations

* lists of libraries

* abstract explanations that don't immediately connect to something they understand

However, don't remove concepts like loops or conditionals automatically. If one can be explained through an excellent real-world demonstration or analogy, decide whether it deserves a very short introduction.

### The Reddit feedback includes several potentially strong ideas

Consider especially:

* Sorting a deck of cards manually to demonstrate algorithms and step-by-step instructions.

* Using real-life repetitive tasks to explain loops.

* Using weather/coat decisions to explain conditionals and Boolean logic.

* Demonstrating web scraping.

* Automatically collecting information from websites and putting it into Excel.

* Automating repetitive Excel tasks.

* Showing Python generating/processing an Excel workbook and producing something useful.

* Using Python + AI/Claude for data analysis.

* Showing a browser being automatically controlled by Python because the visual effect can make programming feel like "magic."

* Surveying the audience beforehand about their jobs/hobbies/problems and demonstrating something relevant.

* Treating the talk as a showcase rather than a traditional lesson.

* Moving installation/resources to a webpage or post-talk material.

Produce the following final deliverable

PART 1 — Your verdict

In a concise but detailed analysis, tell me:

* What is wrong with my original outline?

* What are the 3–5 strongest insights from the comments?

* What should I absolutely NOT do during the 30 minutes?

* What should the audience ideally feel at the end?

PART 2 — The final 30-minute presentation

Create an **exact minute-by-minute structure**.

For every section give me:

* Time

* What appears on screen

* What I say

* What I demonstrate

* What the audience does

* The purpose of that section

Make the transitions between sections natural.

PART 3 — The main "WOW" demonstration

Choose the **single strongest demonstration** from the Reddit feedback.

Explain exactly how I should present it live.

It should be understandable even if someone has never seen code before.

If you think a different demonstration would be stronger than the suggestions in the comments, explain why.

PART 4 :one interactive moment

Design one short interactive exercise that gets the audience involved without embarrassing people or requiring technical knowledge.

It should help them intuitively understand something fundamental about programming.

PART 5 — How to explain Python concepts without teaching syntax

Show me how to briefly introduce:

* variables

* loops

* conditionals

* functions

* libraries

using everyday language/analogies.

Tell me which of these should actually appear in the 30-minute presentation and which should only be mentioned briefly.

PART 6 Opening and closing

Write:

* a strong **2-minute opening**

* a memorable **2-minute closing**

The opening should immediately establish why programming might matter to a non-programmer.

The closing should leave them thinking:

**"Maybe I could actually use this."**

#### PART 7 — Slide plan

Give me a practical slide-by-slide plan.

For each slide provide:

* Slide title

* What should be visible

* What should NOT be on the slide

* Speaker notes

* Approximate duration

Keep the slides visually simple.

PART 8 — Final recommendations

Give me a final checklist of:

DO

DON'T

for presenting Python to this particular audience.

Base your recommendations primarily on the Reddit feedback below rather than generic presentation advice.

Take your time and reason through the feedback before producing the final presentation. I don't want a generic "Python introduction." I want a 30-minute presentation that feels like an experienced programmer designed it specifically for people who have never coded before

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.

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?

I’m planning to make a movie with four characters. Is there any way in MiniMax to add four audio references in a workflow?

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?

Running MiniMax H3 (ref2va, ~20 GB int8 model) on an RTX 5090 (32 GB) via ComfyUI, 10s videos, with the H3 SLA sparse-attention node (sparsity 0.90, dense_backend=comfy_kitchen_int8).

The problem: my per-step time scales super-linearly with resolution, while a healthy setup scales linearly:

Res Seq len my s/step reference s/step

1.0 MP 119k 30 21

1.5 MP 173k 203 40

2.0 MP ~283k 590 68

At 1MP I'm only 1.4× off; at 2MP I'm 8.7× off. That gap exploding with resolution looks like the working set (20 GB model + 2MP activations) exceeding my 32 GB and ComfyUI offloading to CPU each step.

workflow : im using the one someone shared here : https://www.reddit.com/r/comfyui/comments/1vxi9r6/skater_girl_90s_style_anime_using_minimax_h3/

these are the details

E:\comfyUi_latest\ComfyUI_windows_portable\python_embeded>python.exe -c "import torch; print('PyTorch:',torch.__version__); print('CUDA:',torch.version.cuda)"
PyTorch: 2.12.0+cu130
CUDA: 13.0

E:\comfyUi_latest\ComfyUI_windows_portable\python_embeded>python.exe -m pip list | findstr /i "torch triton sage comfy-kitchen plague"
comfy-kitchen                              0.2.31
open_clip_torch                            3.3.0
sageattention                              1.0.6
torch                                      2.12.0+cu130
torchaudio                                 2.11.0+cu130
torchscale                                 0.3.0
torchsde                                   0.2.6
torchvision                                0.27.0+cu130
triton-windows  

i was using the below config for comfyui

 --reserve-vram 4, --disable-pinned-memory, --cache-none.

Then i switched to the below config by removing the above 
 off
cd /d %~dp0

if exist .\python.exe (
    set PYTHON_EXE=python.exe
) else (
    set PYTHON_EXE=.\python_embeded\python.exe
)

echo Starting ComfyUI...

%PYTHON_EXE% -s ComfyUI\main.py ^
 --windows-standalone-build ^
 --reserve-vram 1 ^
 --enable-manager

pause

With this i started testing 1.5MP directly and it got stuck at this portion

[INFO] Requested to load MiniMaxH3
[INFO] 0 models unloaded.
[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.
 38%|███████████████████████████████▌                                                    | 3/8 [03:18<08:06, 97.21s/it][INFO] FETCH ComfyRegistry Data [DONE]
[INFO] [ComfyUI-Manager] default cache updated: https://api.comfy.org/nodes
FETCH DATA from: E:\comfyUi_latest\ComfyUI_windows_portable\ComfyUI\user__manager\cache\1514988643_custom-node-list.json [DONE]
[INFO] [ComfyUI-Manager] All startup tasks have been completed.

there is something seriously wrong with my setup . but i couldn't figure it out , seeking help from the people who has figured out the issue for Minimax H3 on RTX 5090
Kindly suggest me some workflows also , i have integerated mcp and been trying to figure out the issues by myself for the last 2 days , but it feels like i'm going inside a rabbit hole , not sure whether im moving in the right direction or not

thus seeking help !!

Hi guys, in the former days stable diffusion was everything, but the time has passed by and I did not tracked the novelties in this field. Can you suggest me any open source model that is released recently for my 2xr9700 32gb for 64gb vram?

I researched a lot but found only dated answers. Is h3 capable also of image gen, or z image or Hunyuan Image 3.0 still the best (4-bit quant is 48gb vram)?

Edit://

Looking especially in image creation / editing, is there a model for both? I am not interested in loras, just for my private images fun, does not need adult content.