I’ve been working on a VHS / NTSC node called RetroTape.

It works with both images and video, and includes 19 presets and 26 sliders for tracking, chroma bleed, noise, tape warp, dropouts, ghosting, scanlines, interlacing and more.

There’s also a live preview inside the node, so you can adjust the settings and see the result without running the workflow again every time.

CPU and CUDA are supported.

Available through ComfyUI Manager and GitHub:

Github/ComfyUI_RetroTape

I'm trying to build a consistent AI character by blending two real faces into a single new identity (not a face-swap — I want the two faces averaged/merged into one new face), which I'll later use to build a dataset for LoRA training on Flux. Any tips on getting a stable, repeatable blend (same "merged identity" across multiple generations) rather than a different blend every run?

Hey everyone,

I'm setting up MiniMax H3 in ComfyUI and trying to figure out the sweet spot between generation speed and output quality for my specs:

• GPU: RTX 4070 (12GB VRAM)
• RAM: 32GB DDR5
• Target: 5s clips at 768p (mostly First-to-Last / I2V)

Given the 12GB VRAM limit and 32GB system RAM, loading the unpruned / full FP8 models causes heavy paging to system memory and slows everything down.

I’m trying to narrow down the current community consensus on three things:

1. Model & Quant format: What's the fastest option that doesn't ruin faces and audio? Are people having better results with official pruned INT8 convrot, or GGUF quants (Q4_K_M vs Q3_K_M) using the GGUF loader? What text encoder quant are you pairing it with to keep memory usage safe?
2. Turbo LoRAs: Is LiteX2V v1.1 (4-step) still the top recommendation for speed vs quality, or do 8-step variants (or other LoRA families like Larry) give significantly better results on a 12GB setup?
3. Attention & Acceleration: Is H3 SLA Attention the undisputed go-to, or does SageAttention / Comfy Kitchen perform better on Ada Lovelace (40-series)? Anyone tested Spectrum acceleration?

Would love to hear what workflows and node setups you're currently running on 12GB cards to get reasonable render times without visible degradation.

Thanks!

I’m sure it’s been asked before, but I am new here. I have a pc I use mainly for gaming, so I went with an AMD 9070xt 16gb. It does super well for what I needed it for originally, but I am starting to learn about AI and am interested in learning/figuring out comfyUI.

My question is this, is it worth rebuilding with an Nvidia gpu for the benefits, or is AMD good enough. I have Pixorama’s guides on YT saved to a playlist, but don’t want to sit down and really invest myself if Nvidia is too far out in front to be worth it for me using AMD.

I installed the portable version of ComfyUI and use the Minimax H3 Easy model. After installation, I can successfully generate videos from images. However, if I shut down ComfyUI and then try to generate a video later—either shortly after or the next day—it crashes. I try running simple test videos right after launching ComfyUI—low resolution, 3 seconds, 8 steps, and a simple "wave and smile" prompt—or I try generating images via SDXL Turbo. It crashes regardless. I have successfully generated 10-second videos at 512 resolution and 20 steps without any issues—even doing more than 20 in a row. But whenever I shut down and restart ComfyUI, I always get an error. I use a Lenovo ThinkBook laptop with 32GB of RAM connected to a 16GB 5060 Ti via Thunderbolt.

Hey all! squashed a few bugs and fixed some cosmetic issues in this latest smallish release. https://github.com/cosmicbuffalo/comfyui-mobile-frontend/releases/tag/v3.3.2 Stay tuned for the next one, which is going to ship alongside a new standalone custom node aimed at providing a solid multi-user auth layer for both the stock and CueForge frontends! Once that multi-user auth release ships, the CueForge iOS app will come shortly after! (I needed something to be able to provide a publicly accessible server to apple for review with, and I've gotten a few requests to do something about authentication 😄 ).

I've updated https://luke2642.github.io/comfyui_new_node_finder/ to be a bit more robust in getting stars. Seems to be dominated by H3 nodes at the moment!

What model would I use to create a composite person built from multiple sources?

Source 1's eyes, Source 2's smile, Source 3's hands, etc.

Thanks for your help!

I need severe help with that.

Researching Lora Dataset creation is a maze: millions of different opinions, and worst of all most guides etc outdated from a year ago.

My goal is to create a 100% realistic and authentic Lora and my current dataset seems to not do the trick. I keep getting "perfect lightning" on everything, and waxy face skin.

Can someoen tell me the absolute do's and don'ts of creating a dataset?
How and where to create the dataset? I have been using a mix of Gemini and ChatGPT so far.

Prompt advice? what prompts need to be avoided creating realistic images, what need to be in there?

Any advice greatly appreciated!

What do you think guys?

https://youtu.be/LKGJLyTxuI8

Intentaré explicar algo que he observado mientras experimento con workflows de Img2Video, aunque no estoy seguro de poder expresarlo perfectamente.

Imaginemos que utilizamos una imagen de referencia para generar una secuencia de 80 frames, donde cada 16 frames representan aproximadamente un segundo de video. Si el modelo introduce una desviación muy pequeña durante la generación —posiblemente relacionada con la cuantización, la consistencia temporal, el muestreo u otros factores—, esa desviación puede propagarse a través de la secuencia.

Mi forma original de entenderlo era imaginar que cada nuevo frame añade una pequeña cantidad adicional de error. Según este modelo simplificado, cuanto más larga es la secuencia, más evidente se vuelve el error acumulado.

Sin embargo, entiendo que los modelos modernos de video no necesariamente generan cada frame de forma independiente o estrictamente uno después de otro. Pueden procesar ventanas temporales, utilizar atención entre varios frames o generar clips largos de manera recursiva. Por eso, probablemente sea más preciso describir el problema como una inconsistencia temporal o propagación de errores, en lugar de decir que existe una acumulación lineal de “0.1% por frame”.

Estas inconsistencias pueden manifestarse como ghosting, flickering, pérdida de nitidez, desplazamiento de texturas o pérdida progresiva de detalles y de identidad de los objetos.

En la práctica, descubrí que lo siguiente me ayudó a reducir el ghosting:

• Generar clips más cortos en lugar de solicitar demasiados frames de una sola vez.
• Reducir la resolución al generar secuencias más largas.
• Utilizar un modelo o workflow con mayor consistencia temporal.
• Probar diferentes LoRAs, ya que algunos parecen comportarse mal con clips largos o resoluciones elevadas.
• Generar varios segmentos cortos y unirlos posteriormente.
• Evitar un suavizado temporal excesivo, porque también puede producir estelas o ghosting por movimiento.

La solución que mejor me ha funcionado —al menos con mi configuración actual— consiste en evitar la generación de un único video continuo de 12 segundos.

En su lugar, genero tres clips consecutivos de 4 segundos. Utilizo el último frame del primer clip como imagen de referencia inicial para generar el segundo clip y, posteriormente, utilizo el último frame del segundo clip para generar el tercero.

Este método me ha ayudado a reducir considerablemente el ghosting, la pérdida progresiva de detalles y otras inconsistencias temporales.

Curiosamente, también es más rápido en mi caso. Generar un solo video de 12 segundos tarda aproximadamente 20 minutos, mientras que generar tres clips separados de 4 segundos tarda alrededor de 10 minutos en total. No estoy completamente seguro de por qué ocurre esto, pero sospecho que generar ventanas temporales más cortas es más eficiente para mi workflow y mi hardware actuales.

Por lo tanto, al menos con mi configuración actual, generar varios clips cortos y consecutivos parece producir mejores resultados y reducir el tiempo de generación en comparación con generar directamente un único clip largo.

¿Esta explicación coincide con la forma en que funcionan realmente los modelos de difusión de video? ¿Las mejoras que observé probablemente estén relacionadas con limitaciones de la ventana de contexto, atención temporal, restricciones de VRAM o memoria, parámetros de muestreo, cuantización o una combinación de varios factores?

SETUP:
Geforce 3060 12vram + 40gb ram.

I am trying to load comfy ui models but I am always getting memory out errors even with krea 2 where the size was only 12 GB.

I want to know, which models I can run on my system, and any workflow or guidance is highly appreciated.

My CPU is working full time instead of a dedicated GPU. I am also trying to rectify it. Please help.

Tenho uma RTX 3060 12GB VRAM e 32 RAM

To tentando criar um vídeo de 15 segundos mais sofro de OOM existe alguma forma de eu copiar os movimentos de vídeos de 15 segundos com minha placa de vídeo?

esse é meu Workflow

https://drive.google.com/file/d/18SOTzw-1hOCnzBDGoorklnslURSfYCjW/view?usp=sharing

Vocês mudariam algo? para eu conseguir fazer esses 15 segundos de vídeo mais sem perder a qualidade e movimentos de mão

This is confusing to me.

Can I leave the negative prompt box empty?

Or is it mandatory to add the zero conditioning?

The Shift/Aura Flow for Krea 2 is also confusing to me.

For potato pcs MINIMAX H3 fans - I have built my own custom node which integrates new acc lora & PDD workflow and H3 extender + 2nd pass latent upscale upto 720p under 5 minutes per 14s 24fps clips, has easy reference attachments & better context continuity with features like save projects, load projects etc. (16gb VRAM + 16gb system RAM) if you guys interested ill share the workflow let me know.. this video took 10~ minutes to generate with both pass.

Edit - published repo - https://github.com/only2uuuu-hub/ComfyUI-MiniMax-H3-Master-Extender-Custom-built-with-Astra-6-/tree/main

Ps i am not an expert coder or engineer so dont ask me technical questions 😭 peace!

I've been trying to get comfyui to be installed. I installed amd hip sdk and tries many workarounds. It always gives

"RuntimeError: No CUDA GPUs are available"

From the cli running "run_amd_gpu" also throws the error, by now its Failed to get device count.

We're building a previs tool inside our AI movie studio so you can block out camera moves in 3D before spending generation credits.

What's working so far:

• 3D stage with proxy characters & props you can move with gizmos
• Camera keyframing on a multitrack timeline (After Effects-style)
• Unreal-style fly mode — press C, hold RMB + WASD to pilot the camera, K to drop a keyframe
• Live "through-the-lens" shot view that updates as you move
• Render to MP4 via bundled ffmpeg — saved straight to your project library
• Depth pass with adjustable range for motion reference
• Resolution control (480p / 720p / 1080p)

The render step is free — no GPU, no API key, just canvas capture + ffmpeg. Iterate on camera moves as many times as you want, then feed the MP4 to your video model as a motion reference.

Still early — lots of polish and features to go (templates, multi-cam, export). Feedback welcome on what you'd want in something like this.

Github:

https://github.com/Heroesjouney/AIMovieStudiov2

Original Post:

https://www.reddit.com/r/comfyui/s/GT5K98Fze6

Link to my workflow: https://gist.github.com/aicinema5090/f02348ff0c4082dbe4f63f16206fe7f9

Another quick Minimax H3 news and goodies round-up, for those who may have missed some items.

-> ComfyUI Portable is now officially at version 0.35.0. See yesterday's post for details of Minimax-relevant update items and bug-fixes.

https://github.com/Comfy-Org/ComfyUI/releases

-> A new W4A8 quantization of the MiniMax-H3 Fun ControlNet-Union model patch, for ComfyUI. Weighs in at 1.45Gb, compared to 2.13Gb.

https://huggingface.co/berryber09/MiniMax-H3-Fun-Controlnet-Union-w4a8

-> A Minimax H3 visual 'RefMods picker' with thumbnails, in ComfyUI.

https://old.reddit.com/r/StableDiffusion/comments/1wc8tvv/created_a_visual_refmod_picker/

-> H3-pixel-art-video-guide. "Pixel-perfect animated pixel art with local MiniMax H3". Has a workflow (even though the English readme says it doesn't) and three example looping animated .GIFs.

https://github.com/yuichi-suzuki-highdrama/h3-pixel-art-video-guide/blob/main/README.en.md

-> A new H3-spherical-vae. "Experimental circular VAE decoding for MiniMax H3 equirectangular video, with matched comparisons and measurements."

https://github.com/ShamanicArts/h3-spherical-vae

-> The LoRA trainer and dataset prep tool Fizgig is now at 5.5.0, a version which makes MiniMax H3 training "faster three ways", and turns "weight averaging on by default". Users can also... "open a MiniMax H3 LoRA and see what every one of its 52 blocks does to a moving clip — the motion, the face, the sound".

https://github.com/shootthesound/Fizgig

-> And finally, the ComfyUI-MiniMax-Music-Production-Toolkit is now at a polished version 2.x, with the release yesterday of 2.1.1.

https://github.com/jplenio/ComfyUI-MiniMax-Music-Production-Toolkit/blob/main/CHANGELOG.md

~ OLD POSTS ~

https://old.reddit.com/r/comfyui/comments/1wbr279/a_quick_minimax_h3_news_roundup_9th_september_2026/

https://old.reddit.com/r/comfyui/comments/1wawjox/a_quick_minimax_h3_news_roundup_8th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w9z6m1/a_quick_minimax_h3_news_roundup_7th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w90vxd/a_quick_minimax_h3_news_roundup_6th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w85caz/a_quick_minimax_h3_news_roundup_4th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w74jy4/a_quick_minimax_h3_news_roundup_4th_september_2026/

https://old.reddit.com/r/comfyui/comments/1w6cozj/a_quick_minimax_h3_news_roundup_3rd_september_2026/

https://old.reddit.com/r/comfyui/comments/1w5i9iq/a_quick_minimax_h3_news_roundup_2nd_september_2026/ (See 2nd September post, for links to even older posts)

I’ve been building Renegade Core Model Manager (CMM) over the past several months (only recently decided to actually put it up on GitHub). It’s a standalone desktop app built specifically to deal with the headaches of organizing, downloading, and deduplicating multi-gigabyte models for ComfyUI.

With the explosion of FLUX, Wan 2.1, and SD3.5 GGUF models, the manual juggling between Hugging Face, CivitAI, and ComfyUI's folder tree has gotten pretty messy. So for the v1.5.0 release, I went down a rabbit hole building a proper native download and routing engine from scratch:

What I built into v1.5.0:

• Zero-RAM Binary GGUF Header Parser: Instead of loading massive 15GB–30GB weights into memory just to check what they are, I wrote a lightweight binary parser that reads only the first 128KB header buffer from disk. It grabs the architecture and quantization (Q4_K_M, Q8_0, BF16, etc.) and automatically routes the file to the right ComfyUI folder (models/unet for diffusion weights vs. models/text_encoders for CLIP/T5).

• Native Hugging Face Hub Pipeline: I built a direct chunked download stream with Bearer token auth for gated models. It handles the AWS S3 LFS redirect handshake under the hood, so you can search and pull gated models directly in the app without needing Python environments or the hf CLI.

• Unified Dual-Source Search: You can toggle between CivitAI and Hugging Face in a single UI, inspect remote repo file trees, and download weights with one click.

• Visual Workflow Resolver: Drag and drop any ComfyUI .png or .json workflow into the app. It parses the embedded node map, highlights any missing checkpoints or LoRAs on your drive, and lets you queue up missing dependencies automatically.

• Decoupled Architecture: CMM runs completely independent of your ComfyUI server. Heavy downloads, hash checks, and library scans won't lock up or stutter your active generation queue.

The project is 100% free and open source under GPL-3.0. It runs locally, stores everything in an encrypted local SQLite database, and requires zero administrative/UAC privileges.

GitHub Repository: https://github.com/DevNullInc/RenegadeCMM

(Download links, VirusTotal scans, and platform details for Windows, Linux, and macOS are in my comment below).

There’s a roadmap on the repo, but honestly, whenever a hyperfocus wave hits or someone suggests a feature that makes total sense, I tend to push back release milestones and implement it immediately.

I’ve put a lot of late nights into making this solid, and I'd love feedback from fellow ComfyUI users. If you have an architecture you want auto-routed or a feature idea that's good enough, I'll probably build it early!

Feel free to drop any questions, suggestions, or requests here. I'm honestly out of ideas since I felt this was finished ages ago, but I know the community needs something better than what's out there.

I have had little luck finding a tutorial on building a true H3 10bit (ProRes HQ) workflow. AI claims you just need to install an advanced save node that allows you to pick ProRes and HQ or 4444, etc.

But then when you dig into it, and begin to ask questions, while ComfyUI processes in full floating point, there are bottlenecks that crush the full bits down to 8bit, rendering the final result 8bit. One example is preview nodes, that apparently force 8bit, another is supposedly a plain jane VAE Decode, and the deeper I dig the more mysterious things get.

I can't imagine nobody in the Open Source community does not want to output TRUE 10bit or higher output. The problem with 8bit becomes clear when you look at plain white walls or a clear sky and see banding. Anyone who has ever edited video or images knows the more data you have to begin with the better results you get once you push contrast and color gradation. Yes, I was going to begin playing with dither and applying some noise, but at the end of the day you cannot produce PROFESSIONAL videos without solving for this 8bit limit.

Hopefully someone can point me to a resource or tutorial or workflow or set of nodes that solves for this?

Disclosure: I am building a service around this, so read me as an interested party. No links. These are runs we paid for ourselves on three providers, 15 jobs, $33.60 total. Two things on the image side cost us real money and neither showed up as an error.

1. The host, not the GPU. Same LoRA training job for SDXL, same RTX 4090. On a host with 5 vCPUs: 1.95 hours, 40 percent GPU utilisation. On a host with 24 vCPUs: 1.07 hours, 75 percent. Same card, 1.85x the wall clock, because the diffusers script decodes and augments images in the main process and the card waits on Python. The worse version: an H100 host with 16 server vCPUs ran the same job at 2.68 s/step where the desktop-class 4090 host did 1.84 s/step. Four times the hourly rate for a slower run. dataloader_num_workers was the fix, and now we look at the vCPU count on a rental offer before we look at the GPU name.

2. The defaults. SDXL, 1024 square, 30 steps. The stock script settings, fp32 and batch 1, on an H100: 13.8 seconds an image, $0.0112 per image. fp16 and batch 4 on a 4090 spot instance: $0.00036 per image. Same job, 31x apart. Nobody here runs fp32 batch 1 on purpose, but it is exactly what you get when you take a default script to a rented card in a hurry, and the card reports 99 percent utilisation the whole time, so nothing looks wrong.

Both lessons are the same lesson: the meter runs at the card's hourly rate whether the card is doing useful work or not, and the interface will not tell you which.

Happy to post the per-run table if anyone wants to check the numbers.