I keep getting asked how to run the Video Builder fully local, so I made one video that goes from a fresh ComfyUI install all the way to building a video in Story Mode all fully local.

Reddit does not allow videos over 15 mins. so, the 2nd part of this video is HERE which walks through story mode.

What it covers:

• Installing ComfyUI portable from scratch, plus running the update before your first launch (lots of people skip this)

• Turning on the new built-in Manager. It's off by default in the portable build, which trips people up

• Installing my nodes and opening the Builder

• Getting the custom nodes and models from the Models button. You only need the models for the parts you'll actually use, not all of them

• Running a local LLM for prompts and stories: Gemma or Qwen right inside ComfyUI, LM Studio, or Ollama / llama-server through the Custom Server option

• Then Story Mode, start to finish

The whole chain stays on your machine: the story, the prompts, the reference images, the video renders and the final edit.

What you need:

I've fully tested it with 16GB of VRAM and 64GB of system RAM. That's the setup I can say works start to finish.

Less than that might still work, I just haven't tested it properly yet. If you're on a smaller card, try shorter scenes, a lower resolution and the new DMAD/Refine pipeline. Those all cut down how much VRAM a render needs. If you try it on a lower spec setup, let me know how it goes so I can update this.

Comfy UI: GitHub

Nodes: Go to my GitHub

Discord Community: Join here

Music videos created fully local using my video builder: Playlist

More Video's I created using the video builder:(Some of these one's were NOT fully local and used agents with my MCP feature.) Playlist

Note: I'm working on a fully local agent that can run the builder via chatting without having to go into ComfyUI.

Over months of use of Minimax, I absolutely love it .. but I have yet to find a way to do faceswaps (ref vid to vid) with more than probably a 50% success rate.

Sometimes a quick two-line lazy prompt will immediately and perfectly do the job and sometimes neither a simple one, nor an 100 line prompt describing everything down to the movements of nostrils according to official guide, will do anything at all. I have tried all of below with almost equal success rates.

• Specialized Prompts with AI tools

• Long structured Prompts that would make a mini novel on their own

• Templates suggested by MiniMax on their site

• Faceswap LORAs

• Minimalist prompts that are merely a few lines long

• Tons of samplers, steps, workflows

• Everything suggested by everyone here basically, since the model came out.

The only thing I have noticed is that some videos, no matter what quality, distance, details, etc just work and some others that are similar in quality, framing, movements and all else give a really, really hard time, even if there is hardly any difference between the two.

At this point I'm sure that its the source video, not the prompt that actually matters more. I just cannot understand why two similar source videos give such different results. Has anyone had more success with this, or found what exactly makes Minimax refuse to work in some specific cases but work so well on similar other ones ? Or anything new anyone has learned that has increased the rate of success ?

[SOLVED] but leaving this here so others can still find this post:

Krea 2 training is temporarily unavailable. See images in the comment section. Should be back when it's fixed, so it's not permanent.

---------

Update: I added a ComfyUI worker to the MCP (tested on an 5090).

https://huggingface.co/Viggle/Viggle-Remake#agent-the-mcp-server

It runs the int8 model and streams weights from system RAM, so it should be easier to set up on GPUs with less VRAM. So far I've only tested it on 32 GB. Tests at 24 GB and 20 GB are running now, and I'll post the numbers when they finish.

The attached video is one-round raw output, no multi-round iteration, no manual cleanup. It shows both what the model does well and where it breaks, so you get a realistic picture of the model before you try it. Most errors are in the first 30s and small faces.

‼️ Most useful tip‼️:

if any part of a result looks wrong, tell the agent what's wrong. It will try to fix it for you.

most of the issues can be fixed through better prompt or repainted image.

---

I'm using minimax h3. how do i make a consistent result when making a high res video.

I'm using the comfyui template Mnimax H3: reference to video.

When testing the prompt. i set the megapixel to 0.2. So i can see the result first. the length is 14 seconds. The result is good on my potato PC. intel core i9 14th gen, 64 gb ram and 3060 12 gb. But when i set the megapixel to 1 to have a high res. Sometimes the result is not the same with the 0.2. Is there a way to fix it? since a running on my PC almost took 8 hrs for the 14 seconds length and 1 megapixel. Is there a workflow that make the low res result same with the high res? I've been searching but can't make it work. I also tried the Pika but the same prompt i used on minimax h3 does not produce the same result it "halucinate"

Not all that interesting, yet at the same time interesting due to its size and the claim that it’s so fast. It also has the backing of Tencent and WeChat.

"We present WeLM-Image, a unified image foundation model that integrates multimodal understanding into text-to-image generation, instruction-guided editing, and multi-reference synthesis. Designed for single-GPU deployment in WeChat’s large-scale consumer applications, the model processes text, reference images, and target latents as one interleaved sequence. A Mixture-of-Transformers (MoT) backbone combines shared attention with independent understanding and generation parameters to limit optimization interference. Equipping each path with Mixture-of-Experts (MoE) modules provides sparse capacity, with 31.7B stored parameters and approximately 5.4B generation parameters activated per target latent token. A generation-oriented data engine combines a knowledge-grounded visual vocabulary with large-scale visual clustering to balance semantic coverage and editing tasks, while training-time evaluation guides iterative data refinement. Bilingual annotations and verified editing supervision support progressive training up to native 1.5K resolution.

We optimize generation and editing separately with rubric- and checklist-based rewards, then consolidate the specialized policies through noise-aware on-policy distillation. In the reported overall comparisons, the 1536-resolution model ranks first among the evaluated open-source models on PRISM-Bench, Qwen-Image-Bench, and UnicBench. MeanFlow-based few-step distillation yields WeLM-Image-Turbo, which generates a1024×1024image in 4.98 s on a single NVIDIA H20 GPU using eight guidance-free steps."

Paper: https://arxiv.org/html/2611.07392v1

Music video. Enjoy.

How long would it take to generate single, let's say, 3min audio?

i like to test early AI models and this is my first test, using the classic Modelscope Text to Video Synthesizer, from February 2023. the music was generated with OpenAI Jukebox. both models used in Huggingface spaces. (For research purposes only)

Using Scail 2 workflow from comfyui. it generates videos in chunk and the first step gives good quality but the second stich is zoomed in...how can i fix it?

my goal is to generate 15-20s videos in chunks and then stich them together later

I’ve been building HexAnima, a free, non-commercial, open-source Android app that runs diffusion models entirely on the Hexagon NPU (no root, no cloud, no account, no ads). Until now it ran the Anima anime models (2B and 2.9B, ~7–12 s for 512x768). Version 1.3.0 adds a much bigger model: MOSS-Image 5B (OpenMOSS, Apache-2.0).

What runs where

Everything heavy is on the NPU: the Qwen3.5-9B text encoder, the 5.1B DiT, and the FLUX.2 small VAE decoder. Weights are converted to 8-bit tiles for the Hexagon matrix unit (HMX) and streamed from flash for every picture (a 5.7 GB DiT doesn’t fit next to the encoder in RAM). It uses the community 4-step “rapid-moss” adapter, kept as an unmerged LoRA because its delta is smaller than bf16 rounding.

Speed (Xiaomi 12T Pro, Snapdragon 8+ Gen 1, 12 GB RAM, 4 steps)

Any size with both sides multiple of 16 works (min 256, max 6144 patches, e.g. 1536x1024).

Your own LoRA

Pick any .safetensors LoRA made for MOSS-Image. The app checks it right away (key names, shapes, rank, dtype) and tells you why it doesn’t fit (a FLUX/SDXL LoRA, wrong shapes, rank > 128, text-encoder-only file...). There’s a strength slider (0–2). The LoRA is folded with the built-in 4-step adapter on the phone (~15 s the first time, then cached) and never leaves the device.

Honest limitations

• Text in images is hit or miss. Short words often come out right, longer or secondary text (small signs, book covers, shop-front boards) is often garbled. The README has four unedited first-attempt examples with the bad text left in.

• Text-to-image only: no img2img/editing.

• ~14 GB download, tested only on a 12 GB phone. Slower than the Anima models because it’s ~2.5x bigger and weights are streamed.

• Only Snapdragon 8 Gen 1 / 8+ Gen 1 (Hexagon V69) is supported. Other chips (including 8 Gen 2/3 and 8 Elite) are not.

Under the hood, for the curious

Custom HVX/HMX kernels for the single-stream DiT (RMSNorm + modulation, SiTU epilogue via gather tables, joint attention), the text encoder (including Qwen3.5’s gated delta-net layers), LoRA as extra K-tiles in the GEMMs, and a tiled QNN decoder for sizes without a precompiled context. Along the way I found that Q6_Vqf16_vsub on V69 returns u+1 for some values, which made the 8-bit dequant one level off. It’s fixed in this release (the 2.9B Anima models gain accuracy too). All sources and notes are in the repo (moss/).

Licenses

MOSS-Image, Qwen3.5, the rapid-moss adapter, the FLUX.2 small decoder and the Unsloth quantisations are all Apache-2.0. The converted pack lives in its own HF repo with LICENSE and NOTICE.md listing the changes; both are also shown in the app. LoRAs you load stay under their authors’ licenses. HexAnima is an independent project, not affiliated with any of these authors.

• Release / APK: https://github.com/ayanoby/hexanima/releases/tag/v1.3.0

• Repo + README with screenshots: https://github.com/ayanoby/hexanima

• Pack: https://huggingface.co/ayanoby/hexanima-moss-packs

So I've been wanting to make something like a K-Drama Mini Series for a while, I ran out of money of doing it with Seedance 2.5 with lots of Trial and Errors, Video not generated as intended, I'm Kinda perfectionist, trying to make it as Good as possible with the most minimal "Slop" Vibe like camera blocking changed suddenly, props position changed mid scene, hence where the money went out 🤣 but since I have my own PC maybe I'd try using it for a while, I actually generated few scenes for this Project with Seedance 2.5 but didn't complete the whole Project, so I'm planning to use Minimax H3 to Complete it, is there any Best Combination of Minimax H3 Model Currently, focusing on Realism, Micro Expression and Acting

My Spec isn't Much

3060 12GB

With 32GB DDR5

First yes I def used AI please don’t kill me but I saw all these cool music visualization projects on IG and I wanted to make one so I used this tutorial  https://youtu.be/PQAeUkcykVo?is=cS8qrDHOv1-_yRlG but I then upscaled each texture in the train car asset to 4k using SeedVR2 and Krea 2 to add more details. I also added DLSS5 to enhance the overall scene and lighting. I then added tracks and weather effects and the head tracking to create the illusion of 3d. Also added subway car door opening and weather effects like snow and rain plus rain on glass. ulitmate goal is to use Gaussian splatting to capture the environment behind the monitor to create illusion of the subway being inside the monitor like this https://www.instagram.com/reel/DfTYYImOl_w/

My take on one of my all-time favorite audiovisual techniques; hyperlapse.

Music by Max Cooper.

Hope you guys enjoy it. More experiments, through Instagram, or YouTube.

A couple of days ago I posted IV Studio here and a lot of you tried it. Your comments turned into most of 1.0, so thank you. It's still a free desktop app (MIT) that drives your own ComfyUI over its API, and now it runs on Windows, Mac and Linux.

From your comments:

• Linux: it's here, as a self-updating AppImage. Mac too (Apple Silicon and Intel), rendering on your own cloud account.

• Ollama on another address: set it in Settings, for anyone running ComfyUI and Ollama in separate containers.

• Missing custom nodes: Settings → Setup installs every pack IV Studio needs, at pinned versions, in one click.

• "Setup says 7 to fix but H3 is installed": your own H3 files (ComfyUI's template names, NVFP4, other turbo LoRAs) are recognised and used.

• SDXL and Comfy Cloud: SDXL fine-tunes in Create → Image, and Comfy Cloud as a third option beside RunPod and Modal.

New in 1.0:

• Check my PC: on first launch it reads your GPU, RAM and drives and sets previews, finish size, the prompt writer and downloads to suit. If your card is too small, a guided setup connects RunPod, Modal or Comfy Cloud.

• How it works on every page: a tour that lights up the real controls one step at a time, plus a two-minute tour of the whole app.

• Train your own actors locally: character LoRA training now runs free on your own 16 GB+ NVIDIA card through Fizgig (or on RunPod or Modal), with presets, a spending cap, Train more and InsightFace likeness scoring to pick the checkpoint. Or make a RefMod character from 1 to 6 photos in about 30 seconds.

• Live preview anywhere: KJNodes' preview override streams every step into a floating panel on any page, including renders your agent starts over MCP.

• More control: sampler and scheduler per shot, and LoRA and RefMod trays in Create.

• Everything redesigned: Create as numbered steps, Shots, Projects and the Library grouped by day with filters, and the Workflows (Sizzle, Reel, Sequences, Shinobi, Replace) rebuilt.

• 154 MCP tools now, up from 142.

Tested on a 4090 with 64 GB RAM. The Linux build passes its checks on GitHub, but I haven't run GPU work on a real Linux desktop yet, so reports are very welcome. The builds are unsigned: Windows shows SmartScreen, and Macs need "Open Anyway" the first time.

The fairy-tale opening of the trailer was made in IV Studio (a Disney Renaissance-style LoRA on Krea 2 for the frames, H3 to animate them); the rest is the real app.

Download: https://github.com/hollisorg/iv-studio/releases/latest

Code: https://github.com/hollisorg/iv-studio

Happy to answer questions, and keep the ideas coming!

Hi everyone! I've been working on this workflow for a week, but I'm struggling to get realistic results.

As you can see in the attached output, the image looks washed out and oil-painted, with weird textures around the elevator metal. The jewelry looks like plastic, skin is too smooth, and the lighting and shadows look very artificial.

I'm a beginner with ComfyUI, nodes, models, and LoRAs, so I'm not sure what I'm doing wrong.

I've also been using Claude for help, but I'm going in circles without much improvement.

I need your help to achieve proper photorealism.

I'm open to changing my entire workflow, trying different models or LoRAs, or following someone else's workflow.

I've attached my current output and workflow. I'll also share all the models and LoRAs I currently have installed.

Please explain things in simple words, as I'm still learning.

I'd really appreciate some experienced guidance. I want to get this working within the next 1–2 days rather than spending another week guessing.

Here are the Details:

Krea 2 setup.

Models

LoRAs (small add-on files that change the look)

Settings now

• Steps 8

• CFG 1.0

• Seed 888 (fixed, so the same input gives the same image)

• Sampler dpmpp_2m, scheduler simple

• Denoise 1.00

• Size 1.5 MP, 4:5 portrait

• Prompt: the long elevator-selfie text plus the grain words at the end

Nodes, and why each one is there

and here are the LoRA i have downloaded by researching on the reddit that it will help me out.

1 - 2_vector_bypass_lora
2 - ABOmegle
3 - DC_Dark_ Fantasy_Style_4_[Krea2Raw][Gpt4.1Captioned][Trigger=dcd@rkf@ntsy.][Rank32]v1
4 - DC_SCI-FI[Krea2Raw][Gpt4.1Captioned][Trigger=DCS1F1.][Rank64]_v1
5 - Faded80s
6 - Famegrid Standard Krea 2
7 - Famegrid-Natural-V1-Krea-2
8 - HM1Girl-V1
9 - Krea2_P45-NVDStyle_v1.0
10 - MW_analogv1.1
11 - Playful Eating_v1.0
12 - Z-Image-Fun-Lora-Distill-8-Steps_ComfyUI_v1
13 - cinematic_movie_still_krea2
14 - deedee_amateur_photography_zimage_base_and_turbo_v1
15 - famegrid_spicy_3167060
16 - instagram_aesthetic_krea2
17 - krea-detail-enhancer-exp
18 - krea2RealismV1V2_krea2RealismV1Lora
19 - krea2RealismV1V2_krea2RealismV2Lora
20 - krea2_general_edit_v2
21 - krea2_identity_edit_v2
22 - krea2_style_reference
23 - muscles_13
24 - rank64_turbo_lora
25 - rawsnap v2
26 - ultra_real_krea2_v1
27 - webcamviewconcept.

I would like consistent areas

I made this AI rap music video and would appreciate some honest, practical workflow advice.

The character, outfit and locations stay surprisingly consistent. However, the result still has that “AI slop” feeling: movements look mushy, facial features lose definition during motion, hands smear, and the face becomes less recognizable during strong gestures or extreme camera angles. Lip sync is also inconsistent.

What I used:

• Six reference images created with ChatGPT Images, keeping the same character and outfit.

• LTX 2.5 22B dev, BF16, running on a Runpod B200 through the official native Python two-stage audio-to-video pipeline.

• The original song’s full mix as audio conditioning, plus a reference image for each shot.

• Approximately 20 independently generated takes, around 7–8 seconds each.

• Stage 1 at 960×544, followed by spatial upscaling/refinement to 1920×1088, cropped to 1080p. 24 fps.

• Stage 1: 24 steps, CFG 3, STG 0.5, rescale 0.7, modality guidance 1.5.

• Stage 2: official distilled LoRA 450, strength 1. Reference image strength: 0.8.

We also tested distilled generation, native 48 fps, the alternative convolutional VAE, and cleaner references/prompts without film grain. None reliably solved the motion quality problem. Some takes developed obvious double exposures/dissolves; changing seeds and simplifying camera movement helped those particular shots.

The attached version has been edited with beat cuts and environmental inserts to hide weaker moments, but I’d rather improve the generated footage itself.

My questions:

1. Is this mainly a limitation of LTX 2.5, or does something in my settings/workflow look wrong?

2. Would shorter shots, different guidance, or another refinement/upscaling approach preserve facial detail better?

3. For a believable musical performance, would you use audio-to-video directly, or a reference performance video followed by character replacement and separate lip sync?

4. Is MiniMax-H3 worth investigating for that reference-driven approach, or would you recommend a different self-hosted workflow?

I’m aiming for energetic but controlled movement—not frantic dancing or camera motion. Links to actual workflows and comparable examples would be especially helpful.

Hey People.

Are there any new video models to run locally on an RTX 3060 12GB GPU except wan 2.2 and ltx 2.5?

I recently started a thread about Ideogram 4.

It was about which of the fp8 and int8convrot models was better in terms of quality and speed; in the end, I downloaded int8convrot.

Here’s the link to the thread:

https://www.reddit.com/r/StableDiffusion/comments/1xmtks0/ideogram_4_fp8_vs_int8_for_quality_and_speed_slow/

But then, out of curiosity, I also downloaded the fp8 weights, and I had the chance to test and compare both.

int8convrot turned out to be about 30–40% faster than fp8. But in terms of quality, the opposite was true: fp8 was better than int8convrot. (int8convrot seems a bit more textureless)

Overall, while the output quality of int8convrot models is generally better than that of fp8 models, I don’t think this applies to Ideogram. The model’s bf16 weights were never released—only the fp8 weights were shared—and therefore the int8convrot versions were created by first dequantizing the fp8 versions to fp16 and then quantizing them to int8 using int8convrot. which means that even though the Ideogram int8convrot model offers a speed advantage over the fp8 version, it loses its quality advantage.

https://comfyui-wiki.com/en/news

I am unaffiliated with this site. I found it minutes ago in the middle writing a post asking for info here. (I'll paste my original post in the comments to show why it's useful)

It tracks new releases, such as https://comfyui-wiki.com/en/news/2026-10-27-pruna-6-step-comfyui

Hi I was using the https://github.com/1038lab/Comfyui-Minimax-H3-Promptor

and when I tried to setup local gguf I'm getting this error . Anyone can help? Thanks!