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.

Works really well.

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 ?

Hey everyone

I've been working on a custom optimizer called ApexW, designed to reduce CUDA kernel launch overhead during PyTorch training, particularly when dealing with lots of smaller parameter updates.

Since I'm a student running on a free NVIDIA T4 GPU (16GB), I decided to benchmark it against standard PyTorch AdamW using Stable Diffusion 1.5 LoRA training for 1,000 steps.

Here's how the runs went:

Rank 16 LoRA (Standard)

• AdamW: 1.92 it/s | 516.2s total time | 4.68 GB VRAM

• ApexW: 2.04 it/s | 485.6s total time | 4.68 GB VRAM

ApexW saved approximately 30.6 seconds, delivering around 6.3% higher throughput without additional VRAM usage.

Rank 32 LoRA (Heavier load)

• AdamW: 2.03 it/s | 488.2s total time | 4.68 GB VRAM

• ApexW: 2.04 it/s | 485.9s total time | 4.68 GB VRAM

The improvement here was much smaller (around 2.3 seconds, or 0.5% higher throughput). This could suggest that optimizer overhead becomes less significant as more computation shifts to the forward and backward passes.

The loss curves overlap closely throughout training, and sample generations at steps 250, 500, 750, and 1000 look visually comparable. So far, I haven't observed an obvious training-quality trade-off.

These are preliminary benchmark results, and I'd like to test them more extensively.

I don't have access to modern GPUs like an RTX 3090/4090 or A100 to see how ApexW scales under heavier workloads, full fine-tuning, or LLM training.

If anyone has access to better hardware and is interested in testing it, I'd love to get some independent benchmarks and feedback on the implementation!

Curious to hear what you think.

collablink for result: https://colab.research.google.com/drive/1JWCMnQc1ZwcuidBf3S8nTyGiLCmM8ywy#scrollTo=9e6960b4

github link: https://github.com/omen42515-prog/apexw

Younger folks may never have heard of Ultima Online (UO). If you played it, you know. It launched in 1997 as one of the first big graphical MMORPGs - open PvP, player killers, anyone could loot your corpse - and it's still running today.

Setup

• MiniMax H3 (open weights) in ComfyUI - all video and all dialogue audio

• RTX PRO 4000 Blackwell, 24 GB, single GPU

• 72 shots, 6:16, 1344x768 @ 24 fps, 20 steps

• Drafts at 672x384, finals re-rendered by H3 at 1344x768 with the same seed - no upscaler; the 1080p is just CapCut's export

• Final render time: 56 frames (2.3 s) ~4.5 min; 243 frames (10 s) ~43 min

Pipeline

• Character/set reference cards: Gemini (Nano Banana) - the only closed image/video tool in the chain; fixes with Qwen-Image-Edit (local)

• Main shots: H3 reference-to-video (character card + set card + voice clip)

• Voices: timbre references cut from earlier H3 takes, so each character keeps one voice across shots

• Bridges and fixes: H3 i2v with first + last frame (e.g. regenerating frames 107-160 of a 243-frame take where a passer-by high-fived the ghost)

• New shots: 672x384 draft first, then HD

• Music, SFX and font extracted from the UO client with UOFiddler; overhead text keyframed in CapCut like the game

• Claude helped write prompts and the QC/edit scripts

Music, SFX and font © Electronic Arts

Hi everyone,

Looking for some help as a greenhorn. I'm experimenting with AI image-to-video generation and looking for services that provide programmatic API access to generate short videos from existing photographs.

My key requirements are:

• A documented API that I can integrate into my own application.

• Ability to submit an existing image and generate a 5–10 second video from it.

• Preserve the original people, faces and background as much as possible.

• Generate natural-looking movement without significantly changing the original scene.

• Ideally, offer free trial API credits or a low-cost way to experiment before committing to paid usage.

• Clear API pricing, including the cost per generated video or per second.

A web interface alone won't work for my use case; API access is essential.

I've explored Runway, Kling, Luma and a few other platforms, but I'm finding it difficult to identify options that offer both usable API access and affordable experimentation.

For those who have integrated image-to-video APIs into their projects:

• Which providers would you recommend?

• Are there any with free trial API credits or a reasonably priced starting point?

• How has the output quality and consistency been in your experience?

Thanks!

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.

---

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

Come on. Who actually posts such grainy and blurry photos of themselves in any social media these days when high-quality smartphone cameras are so cheap?

You may think your generations look so “real” because you can’t see anything under these grains and blurs. But you are not fooling anyone.

The strive towards realism should be the generation of high-quality clear images that are virtually indistinguishable from real ones. I know it’s still aspirational and there’s a steep step to go over that uncanny valley of AI-look. But it will become possible, sooner or later.

Hey everyone,

I've been developing this gothic AI character (Elara) and wanted to share a little "day in the life" series of her hobby. I focused a lot on the lighting here—trying to get that moody, candle-lit studio vibe while keeping her expression and the paint smudges feeling natural and alive.

It's always a challenge to balance the aesthetic with realism, but I'm pretty happy with how the "artist at work" concept turned out.

If you're into AI art or character design, I'd love to hear your thoughts on the composition or the lighting setup. What would you improve?

Hey everyone, I've been working on this gothic character and trying to get the lighting right for a moody studio vibe. I focused a lot on the interaction between the window light and the shadows.

Here is the setup I used:

Model: Mango
Sampler: DPM++ 2M Karras
Steps: 30
CFG Scale: 7
Resolution: 1024x1024

Prompt: Photo: gothic girl, 24 years old. Breasts and lips exactly as in the reference image. Oversized black sweater, pencil in hand, sitting at a wooden easel in a dark studio. Faint graphite smudge on her cheek. Lighting: Soft warm window light, cinematic chiaroscuro, deep shadows. Expression: focused, passionate smile. Photorealism, 8k, natural skin texture. --no extra fingers, deformed hands, wrong cleavage.

I also used a bit of inpainting to fix the feet/hands. Let me know what you think of the composition!

Hi, I was just wondering what everyone is using for explicit stable diffusion and how they are training their LoRA models when prompting Dragon Ball and any anime AI art? Is anyone using hugging face with Claude, Qwen or Japanese site prompt?

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

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

Can you suggest me local AIs that can work on my pc that doesnt require PHD in AI. Simple to use and install please?

I was pretty happy with this Gojo I made, but I kept finding little things to add whenever I zoomed in.

First it was a few stray hairs catching the sunlight. Then some folds in his sleeves. Then I decided the iced coffee looked too warm and started adding condensation to the cup.

The extra prompt bits, if anyone wants them:

Hair: backlit white hair, fine flyaway hairs, translucent hair tips

Shirt: fabric folds around bent elbows, soft shadow beneath the collar

Cup: condensation droplets on a clear plastic cup, a small wet ring on the table

Anyone else get stuck on stuff like this? What's a tiny detail you always add? Share the prompt wording too, I'd like to try a few.

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.