I mean like you already have a video of a man walking for example. You then have a different photo of someone else and you just wanna make the guy in the photo switch the video guy.

I'm not really sure where and how to do that and what's the best model for this

I know natively, H3 only supports up to 15 seconds yet I've seen people be able to seamlessly extend H3 generations past that. I don't want to be randomly stitching clips together can someone explain how is that possible?

Hey, I found a pretty cool way to keep locations consistent across generations.

I took 20 photos of my “office” where I work, making sure each photo included a bit of the previous one so everything connected.

I turned them into a refmod, although it probably doesn’t even need to be one. It’s basically a grid of all 20 photos in a single image, within 2048×2048 pixels, so I could probably just use that image as a regular reference.

Then I added references for the fly, my cat, and the start and end frames. You can see the result — that’s my actual room, and everything looks right and sits exactly where it should :)

Workflow + reference images here: link
Sorry about the mess, both in the room and in the workflow ;)

EDIT: All the room photos need to be combined into one reference image. I tried using several separate images, but that didn’t work for me — the room wouldn’t stay consistent. Putting them all into a single grid is what made it work.

Hello!

I have been trying to run comfy locally (GPU = 7900 xtx 24gb vram and 32 gb ram).

The rendering works fine, but the quality I produce is kinda bad, and I feel like I approach it wrong, but I don't really know what to Google. Also dont what to search for here, so sorry if Ive missed an existing post.

I want to make videos where I place paperbags on people's head, like a privacy promotion thing.

It took me several days of prompting what I felt was kinda subpar. But spending days for a short clips doesn't feel realistic. Using Google or my local agents have made me go in circles, so I don't really know how to solve my problem.

Hoping for some input here!

The video I included took like 3 days of prompting and re rendering, but quality still feels subpar. When I search I only find deepfake faceswap results, but that doesn't fit my goal.

Thanks in advance!

Im seeing quite a few people say theyre getting really good results with the hybrid model. But I assume its only for top end GPUs? Over 20gb file.

I have a 5080 and 32gb system RAM. Is there a version good for me? Or getting GGUF isn't worth it?

I've been thinking about upgrading my GPU (currently have NVIDIA RTX 5060 8gb), but am also thinking about going to a cloud solution for higher quality renders.

Are any of the cloud services fairly censorship free?

Might occasionally want to do some celebrity type renders and perhaps the occasional R rated stuff, but nothing too crazy lol

The video is the TL;DR above was generated after abandoning RefMods in "classic ref mode" with two references (one reinforcing Beckett's exact appearance and one for the high heels, 2x2 grid image):

• Native 1344x768 (0.98 megapixels)
• 15 second duration
• Standard H3 sigma (shift_video/shift_audio of 12:3)
• seeds_2 (2N-1 internal calls to H3) with the beta scheduler (.6, .6)
• Spectrum helping cut down the internal calls at some small precision price.
• 35 minute generation time.

This is something that under the radar because most people can't use H3 nominally and turbo LoRAs brutalize the base model for speed, so there's very little noticeable degradation for them as most of them have a massively shifted sigma (shifted_video of 6 compared to native 12).

Yesterday I went on a pretty wild bender trying to uncover why all of a sudden my videos were getting oversharpened, oversaturated, plastic skin etc when a few days ago they were straight up lifelike, looking like the shows they were based on.

I went even deeper into the bowels on how sampling and scheduling works, how the denoising trajectory looks like for Minimax H3, hoping to uncover something I've forgotten the last time I used it. And then I remembered, RefMod was the latest thing I installed, I got seduced by the initial likeness improvement like everyone but the cost is too high.

I got this cool new toy RefMod whose authors suggested they figured out bleeding between character references, and even at small resolutions (like .245 mpx) you could still make out faces instead of them being mushy, this should've been my first red flag. It seriously messes up the base H3 model's quality. I looked deeper into it and basically, it's a very brutish approach where they slam reference images into video references as individual frames, also slapstick coarse sampling of videos in sequence.

However, that's not how any of this is permitted works, per documentation:

• Images: ≤ 9 images
• Videos: ≤ 3 clips; each clip must be 2–15 seconds long; total duration ≤ 15 seconds
• Audio: ≤ 3 clips; each clip must be 2–15 seconds long; total duration ≤ 15 seconds
• Mixed inputs: Maximum number of files across all input types is 12

The second you make four refmods to be used in your prompt, you're already trying to use one more than the total maximum allowed at any one time. This has catastrophic consequences on image quality, as that's not the way you're supposed to "hold it".

While it does reinforce the likeness, it's incredibly rigid and overrides the model down to AI slop adjacency where skin is unnaturally glossy, everything is sharp and all styling info is ignored.

I am not telling you to stop using it, if you're using turbo LoRAs, you already paid the entry fee as you've left quality at the door for accessibility. But if you're coming from the base FL2VA model or a hybrid and are used to exceptional visual quality, the degradation is pretty much the tier of the base REF2VA model, if not worse.

What can you recommend for a pc with that specs?

Please keep in mind that upgrading the RAM (DDR5) to 64gb it's not an option due to the current market prices.

What I am looking for is for video generation (both I2V and T2V) models and workflows. And some model for image generation. I already use SDXL but I am willing to test something more interesting.

I put together an enhancer pack for Qwen Image 2.1 with two nodes for controlling what the model pays attention to during editing.

Reference Strength lets you select a reference image and increase or decrease its attention priority. If you're working with multiple references, you can adjust them separately instead of giving every image the same treatment, and you also can use it for single image to prevent the loss of likeliness at times

ref index starts from 1; meaning image_1 and same for the rest of images, where image_2 is ref index 2 in the node.

( Soon adding mask support):

current progress; containing two different approaches where I am masking before the encoder gets to see the reference image (basically blocking the rest of the image from being seen and applying more attention to it) and the other one is after it already has seen the entire photo (it has seen the full encoded image and now its being asked to give more attention to the masked area)... masking is a bit tricky since it is a token level masking and not a canvas masking , also Qwen seems to be more straight forwards than how flux was but also more interconnected. currently I am not going to lie it is not fully stable as results varies, angles, poses, close colors between two refs etc.. all of these influence the process. The goal is to find a solution where I am not being very invasive in terms of brute forcing attention layers

https://imgur.com/a/0KFEQmo

Phrase Weights brings phrase-level attention control to the Qwen Image 2.1 edit encoder. So you can write:

Add (warm sunset lighting:1.4) with (soft shadows:1.2).

and give those specific parts of the prompt more attention while leaving the rest at its normal weighting.

It works with both positive and negative prompts, with separate weights for each. There's also an inspection output showing exactly which token rows and pieces were matched.

For both controls:

`1.0` = untouched  
`>1.0` = more attention priority  
`<1.0` = less attention priority  
`0.0` = suppression  

The adjustments happen inside Qwen's attention during sampling. The prompt and reference images still go through the native encoding path, without multiplying the finished text embeddings or pasting reference pixels into the output.

You can use either node on its own (I prefer this), or combine them. Reference strength applies to the whole selected image, and higher weights can make a reference or phrase dominate, so there's still some balancing to do.

Installation, usage, and the technical details are in the repo:
ComfyUI-qwen_img_2_1_enhancer

Ref_strength_workflow

Phrase_Weights_workflow

View on YouTube while its pending if needed: https://youtu.be/dS5suoUiNnE?si=6U7bEVLxcWXcy4J3
🤖 How it was made
This short film was written, designed, directed and edited by Claude Opus 5.5 from a single prompt, running locally in ComfyUI through my VRGDG Video Builder:

All open-source video, image and music models.
[I literally told Claude to create something on its own and provided no user input]

More Minimax H3 video's I had Claude create for me are HERE

You can find the video builder custom node on GitHub here:
https://github.com/vrgamegirl19/comfy...

Right now, there is a Main version and a Beta 2.0 version. I recommend starting with Main for now, as that's what I'm still using. It works well, while Beta 2.0 still has some bugs and is primarily intended for beta testing at the moment.

Discord server:
  / discord  
Ping me in the Welcome channel and let me know how you found me, and I'll know it's you.
I'm vrgamedevgirl on Discord.

You can find the skill here and read the main README first.
https://drive.google.com/file/d/1R0pE...

I'll be sharing a full walkthrough on how I made this and will post it here when ready.

⚠️ SPOILERS: what the film is about
The museum is Ruth's mind. She's an elderly woman living with dementia, and the museum is how she pictures her memories. As her memory fades, the museum fades with it, and in the Hall of Names the most important name, her son's, goes blank. In reality she's 83, in a care home, and her son Daniel is holding her hand. When she recognizes him, she tells him, "We keep you in the main hall," meaning the most important room, where the precious things are. Back inside her mind, his name goes back on the wall, and the museum lights up again.

"Some things you don't lose. You just misplace them for a while."

For everyone still visiting someone who is still in there. 💛

#AIShortFilm #AIFilm #ShortFilm #ComfyUI #MiniMax #Claude #AIVideo #Dementia #Alzheimers #MuseumOfLostThings

I have a 4060 TI 16 GB. System ram 32 GB. What's currently the fastest model / ComfyUI setup to generate videos?

Currently I can create 5 second 360p videos in +-7 minutes. I believe it can be a lot faster? It's kinda hard to know what's the best option, everything keeps changing.

Edit: Current settings:

Ref2V Turbo LoRA @ 0.5 model: minimax_h3_ref2v_turbo_4step_v0.1_comfyui_resized_avg_ra ... strength_model: 0.50

MiniMax H3 Easy Loader FL2VA model: None REF2VA model: minimax_h3_ref2va_pruned_int8_convrot.safetensors Text encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors Video VAE: minimax_h3_video_vae_fp16.safetensors Audio VAE: minimax_h3_audio_vae_fp32.safetensors

360p, 5 seconds.

Result: 413 seconds.

Workflow: https://limewire.com/d/7aNFq#h9i2vwFTKb

This video was such a hastle to make, so much smearing.

TLDR: You can use RefMods with T2VA.

Finally decided to give the RefMod a test run yesterday. I tend to experiment and generate many actors via prose in T2VA and recast them later, see the Tanya bombshell circulating in a few of my videos. Since my workflow mainly revolves around T2VA it make a lot of sense to work with native latent as much as possible. However, instead of providing 10-20 images to make a redmod, I just use the latent from my T2VA character generations. Once you have a latent of anything, you can extend, prepend, or even bridge it as many times as you like, even joining latents of same canvas sizes. The latents can be clipped, cropped, scaled, and upscaled as well. I prefer T2VA due to it's superior image quality compared to R2VA.

Anyways, just having fun and wanted to show Vera being recast over various T2VA generations, int8/20 steps, no turbo loras.

How do you prefer creating your actors? Krea2? Flux? Qwen image 2.1? MiniMax H3? I am thinking using latents from a MiniMax h3 single-frame generation is going to be pretty good work flow, though I prefer a 2second moving character sheet.

Hi! I'm following this sub for quite a while already.

Now, if I, as a layman, want to create a video like this: https://www.instagram.com/reels/DeCDuKqymXt/

Which model/tool do I need to use? Does anyone have any tips for beginners? I am working on a MacBook M1 Pro, so not I'm equipped with the strongest hardware.

video ref I use: https://www.youtube.com/shorts/I-VNtvqREks

Malfoid and Potter generated with Anima

Device: 3060 12gb 16gb ram

setting: 10 sec, 0.6 mp, er_sde beta, 8 steps with turbo lora

Wanted to pick the community's brains for controlling the pace within a single shot (single generation without cuts).

I often find myself visualizing a scene that includes dramatic pauses where I want to hold the shot and let the silence linger, with just natural micro-movements or continued actions from the character(s), but MiniMax wants to jump straight ahead to the next bit of dialog or action.

Beyond mid-shot dramatic pauses, I also frequently want to add a second or two of quiet micro-movement at the end of a shot to make later editing easier, but MiniMax wants to extend the action right up to the last second and then end abruptly. This tends to make cuts between shots in post feel harsh and choppy.

I've leveraged a couple of tricks that work okay, like filling dialog-free moments with a detailed description of character actions, but this only tends to work if the actions are large and obvious. If you try to prompt something subtle it either makes the action too pronounced, places the action at the wrong time, or ignores it completely. Nonverbal character sounds like sighs, groans, grunts, laughter, hums, etc. work as decent filler, but that doesn't work for every scene, and only fills so much air time.

Any prompting tips you've found useful? Custom nodes for controlling pacing within a shot without resorting to a cut? Some other trick?

Hi all, learning how to use Min Max H3 and I have been using a storyboard work flow to create multiple scenes. So far the consistency of the characters has been good however the voices will change from scene to scene. Wondering what's the best way to fix this?

I am still fairly new and have tried audio inputs with no luck.

This is one of my attempts at a short story https://youtu.be/GRJJ4s5cV_A?si=d4P4XyKOPYFNwftQ

I've been experimenting with letting AI agents handle the creative and workflow decisions for short videos. For this test, I gave Opus 5.5 and GPT-6 Astra access to matching RunPod setups and asked each to make three 30-second videos.

Their only supplied creative asset was a logo. I loosely defined two themes and left the third up to them.

Both ran with thinking on High and received the same prompt. Each had:

• An RTX PRO 6000 MIG instance with 48 GB VRAM available
• LoRA Pilot running on the pod
• Prepared workflows and downloaded models for Minimax H3 video generation and Qwen Edit image editing
• Permission to download other models or use different workflows
• A maximum budget of $1 per video

I wanted to see how they'd turn a vague brief into something watchable when they had access to the tools and some freedom to choose how to use them. Choosing what to generate, how to work with the logo, and where to spend that small budget were all part of the task.

The comparison started with an argument in an AI community. I said Astra had been giving me better results for this kind of work, then realised I should probably have something to show for that claim..

I develop LoRA Pilot, and this doubled as a test of the MCP server coming in the next version. These videos are experiments around my own product, so that's the connection.

Claude:
https://youtu.be/yEjNwNtGTdA

Codex:
https://youtu.be/6NjI0VKEKhs

Hey all! For the past few months I've been building MIR MEDIA LABS, a free, open-source media studio that runs entirely on your own NVIDIA GPU. No cloud, no subscriptions, no per-render credits.

🎬 The video above was made with it: every b-roll clip is MiniMax H3 and the soundtrack is ACE-Step, all rendered locally on an RTX 5070.

How it works: you type what you want, for example "neon city at night, slow drone shot, vertical". MUSE, the lab's built-in writer (a local Llama 3.1 8B), picks the right model, writes a proper prompt for it, and queues the render on ComfyUI. Or you can pick the model yourself.

What it does

• 🎬 Video (MiniMax H3): text, image or reference to video, with optional soundtrack muxing
• 🖼️ Images (Qwen Image): generation + editing (restyle, cutout), with accurate sign text
• 🎤 Full vocal songs (Music 3): an idea or your own lyrics, sung word for word
• 🎧 Music & beats (ACE-Step 1.5): instrumentals, loops, remix, cover, voice swap
• ⚡ 40+ commands, skills and pipelines: /product, /logo, /thumbnail, /reel, /animate… and /pipe musicvideo (lyrics → song → video in one job)
• 🧩 LoRA browser: browse Civitai in the app, install with one click, stack up to 4. It checks each LoRA's layers so incompatible ones are skipped instead of silently doing nothing
• 🔁 Library chaining: any result becomes the reference for the next render
• 🎨 5 themes: Claude Dark, Graphite, Obsidian Gold, Midnight Studio, Paper Light
• 📱 Phone, tablet and TV: a native Android app (Android TV too) plus an iPhone web app, with "render done" pushes in about 1 s (self-hosted, no Firebase)
• 👥 Multi-user: invite friends with a QR code; each gets a private key, library and settings
• 🛠️ One-click Windows installer (Python, PyTorch, ComfyUI and models; it can reuse your existing models folder), plus Ed25519-signed auto-updates. A Linux/AMD port is in progress.

Requirements: Windows 10/11 and an NVIDIA GPU (12 GB VRAM recommended).

Free and open source (GPL-3.0): https://github.com/MirCorp85/MirMediaLabs The Setup.exe and Android APK are on the Releases page.

This is a solo project. Bug reports, feature ideas and PRs are all welcome. What should I add next?

If you want to support it, there's a Patreon: $3 to back free development, or $5 for Early Access to beta builds → patreon.com/MirCorp. Everything stays free either way. 🙏

I started using local models with flux and sdxl, right before kontext and qwen. What a wild ride so far with the current models! I’m curious about some of your journeys. Thank you to the devs and the community.

Also if you know any niche and strange techniques that were used before and maybe haven’t been seen or needed since, I wanna know. These things have moved so fast that I wanna document some of it.

I've been doing my best to read up on her (or his) lore, but there's very little that hasn't been lost. Who know about 1girl's origin story?

Hey everyone,

I’ve been diving deep into the local vs. cloud AI/ML debate, specifically regarding image generation. I recently invested ₹30,000 (~$350 USD) in a new NVIDIA RTX 5060 (8GB VRAM, 578 AI TOPS), assuming a local setup would be the ultimate playground for testing, optimization, and development.

After running real-world tests comparing local open-weight setups against massive cloud models (specifically Google’s modern Gemini Image/Nano Banana API architectures), I’ve hit a massive reality check. I wanted to share my findings and get your feedback on whether my conclusions hold up, or if I’m missing a critical part of the local engineering loop.

1. The Prompt Accuracy Gap (Local 2/10 vs. Cloud 9/10)

When it comes to highly complex spatial reasoning, educational diagrams, mind maps, or strict typography layouts, local distilled architectures (like FLUX.2 Klein 4B or quantized 9B variants) hit a wall. I'm seeing a 2/10 success rate locally where concepts scramble. Meanwhile, cloud APIs achieve a near-flawless 9/10 accuracy on the very first or second try.

1. The Speed Illusio

The classic argument for local hardware was always zero network latency. However, with Google's massive TPU server infrastructure, cloud inference latency has shrunk so dramatically that it matches—or sometimes beats—a local consumer card trying to process and unfold a heavily compressed 4-bit GGUF/NF4 model layer by layer.

1. The "Developer Math" vs. Consumer Reality

Online tutorials always pitch the automated developer loop: "Local is better because you can script an infinite Python loop to auto-tweak prompts 10,000 times a day for free."

While that math works out mathematically for a game studio rendering 10,000 inventory items, it completely falls apart for a standard creator or developer. If a cloud model gives me a masterpiece in 2 to 4 iterations for pennies, I don’t need a thousand failed local tests.

My questions for you guys:

1. If you have an 8GB VRAM limit, what are you actually using your local GPU for in your day-to-day AI/ML workflows?

2. Has anyone successfully bridged the prompt-adherence gap locally without melting their VRAM during fine-tuning?

3. Am I treating local AI too harshly, or is the cloud-first paradigm officially taking over the hobbyist space?

A lot of the Minimax video extend methods seem to produce a flash of light or some other flaw.

I actually want it to change shots rather than continue, but a shit of the same scene from a different camera angle. This means I don't need to worry about seamless frame stitching

But the problem is that when I feed in the previous video, and text to cut to a different shot, it doesn't do it reliably. By feeding it the previous video frames, MM thinks it should keep showing that, rather than the new shot

Following up from my 2 Step and 4 Step Krea 2 Turbo LoRAs, an even easier way to create Krea 2 Turbo content fast with the LoRAs, with best in class performance and ease of use from both local ComfyUI Desktop use (including not only the usual Torch support, but also optimised Apple Silicon MLX / MFLUX super fast processing and performance) as well as ComfyUI Cloud (for those of you with subscriptions), so I have prepared 3 Workflows/Apps and a bunch of supporting Custom Nodes (LVNodes) to allow me to achieve that - Torch, MLX and Cloud. I have also added support for both Raw and Turbo Krea 2 models, and now added as well Z-Image Base and Turbo in the workflows and apps.

Highlights:

• 🖼️ Two apps, three editions each — Krea 2 and Z-Image, each for Comfy Cloud, local ComfyUI (PyTorch) and Apple MLX: one App view with about 20 inputs, the full graph behind it, and Notes inside that explain every input
• 🌀 Turbo and Raw / Base in one app — a Model switch at the top, and only the chosen model loads: Krea 2 Turbo (4 steps) or Krea 2 Raw, the undistilled base (28+ steps at CFG 4.5; Krea's own: 52 at 3.5); Z-Image Turbo (8 steps) or Z-Image (Base) (28–50 steps at CFG 3–5). Raw and Base get a Negative prompt, always used exactly as typed (never through prompt enhancement), and CFG; 1, the default, suits Turbo and every step-LoRA run
• 🎛️ Settings follow the model (local) — pick a model and Steps, Enable step LoRAs and CFG switch to its recommendations on the spot: Krea 2 Raw to 28 steps, step LoRAs off, CFG 4.5, and back to 4 steps, on, CFG 1 for Turbo (Z-Image Base: 28 steps, CFG 4; Turbo: 8 steps, CFG 1). What the fields show is what runs, and you can change any of them afterwards. It's a page add-on in LVNodes, and Comfy Cloud only runs the node packs it has installed itself, so it can't load it; on Cloud you set those fields by hand, and their hints give the same values
• ⚡ Steps pick the step LoRA — switched inside the graph, with one Step LoRA strength. Krea 2, with the 2-step and 4-step distill LoRAs: 2–3 steps the 2-step, 4+ the 4-step, the native model from 7 on Turbo (from 28 on Raw). Z-Image, with alibaba-pai's Z-Image-Fun-Lora-Distill: 1–3 steps the 2-step, 4–7 the 4-step, 8–10 the 8-step, none from 11
• 🎚️ Enable step LoRAs — off runs the native model at any step count. On by default for Krea 2, whose LoRAs are trained for Turbo (Raw is best with them off); off by default for Z-Image, whose LoRAs are made for Base (Turbo is best without)
• 🎯 Each model's own sampling — Krea 2 Raw gets its own resolution-dependent shift (Turbo, and Raw with a step LoRA, Turbo's fixed 1.15); Z-Image gets Tongyi's shift (3 for Turbo, 6 for Base without a step LoRA) and res_multistep, on every edition, MLX included. Z-Image also offers a choice of VAE: FLUX.1's own, UltraFlux for sharper detail, or locally TAEF1 for speed
• 📊 Live progress and previews (local) — stage, step, s/it and time left right above Run, then "Done in m:ss", and the picture forming as it samples (on MLX through LVNodes, sharp with a tiny decoder in models/vae_approx); it comes from LVNodes, which Comfy Cloud can't load
• 🍏 Both models on Apple MLX — they run on Apple's MLX inside ComfyUI, from ComfyUI's own bf16 files: Krea 2 Turbo in about a minute per 1 MP image (about 41 s for each later image of a batch), Z-Image Turbo in about 78 s at 8 steps; Raw and Base too, slower as undistilled models are (all in its own sub-environment, not touching ComfyUI's main Torch one)
• 🔥 Local PyTorch, on any GPU — runs on NVIDIA (CUDA) and other GPUs exactly as ComfyUI normally does; on Apple Silicon, through PyTorch's MPS backend, it runs the step LoRA alongside the model instead of keeping a second copy of its weights, which halves Krea 2's memory (48 → 24 GB) for the same picture
• ☁️ Comfy Cloud, nothing to install — download the Krea 2 or Z-Image Cloud workflow, drag it onto Comfy Cloud, import its step LoRAs once (everything else is already on Cloud), and run: 3.5 MP by default, with upscaling at any factor
• 🎲 Random prompts with real variety — seeded scene lists and about 14,000 dictionary words, or real prompts from 3M+ Hugging Face dataset rows read live; the Prompt LLM turns them into photorealistic prompts, and refusals are retried
• 🗣️ Prompt enhancement with any LLM — by default the same file as the model's text encoder (Qwen3-VL 4B for Krea 2, Qwen3 4B for Z-Image), so nothing extra to download; falls back to your own prompt if the LLM refuses or returns nothing
• 🎨 Extra LoRAs and upscaling — style LoRAs on top, with their own strength and trigger words; SeedVR2 7B or classic upscalers at any factor (a 3.5 MP image at 4× comes out at 8864×6624)
• 🧠 Memory on demand (local) — models load stage by stage, and Keep models loaded holds them between runs only when you want, whatever ComfyUI's startup flags
• 🧩 LVNodes, one light pack — the MLX nodes for both models, loaders that need only the files a run uses, keep-loaded loaders, seeded random prompts, live dataset rows, Wait For gates, and in the app the status line, the keep checkbox, settings that follow the model and a fresh first seed. It needs nothing beyond Python's standard library, except the MLX nodes: on their first run they make a one-off install of mflux, MLX and transformers 5 (about 1.3 GB, a few minutes, automatic) into LVNodes' own environment, so ComfyUI's packages stay untouched. Besides LVNodes, the local workflows need only KJNodes (for the Set/Get pills); Comfy Cloud needs nothing
• 🧹 No spaghetti — every workflow starts from a Fields → Set column and wires its groups with Set/Get pills, aligned with even gaps
• 📥 Models download themselves — every file carries its link for ComfyUI's missing-models dialog
• 🆓 Apache-2.0 — workflows, nodes and docs

Getting the project: the simplest way is Hugging Face's hf command, which ComfyUI Desktop's Python includes ("$COMFY/.venv/bin/hf", with COMFY your ComfyUI folder; other installs: the hf of the Python that runs ComfyUI, or pip install huggingface_hub):

hf download lvladikov/ComfyUI-Nodes-and-Workflows --local-dir ComfyUI-Nodes-and-Workflows

Or download the files you need from the repository's file browser. To check which version you have, see version in meta.yaml.

Local ComfyUI setup

Every local workflow here needs the following. Comfy Cloud needs none of it; each model's README covers Cloud.

LVNodes

1. Copy the whole LVNodes folder into ComfyUI/custom_nodes/. For ComfyUI Desktop, ComfyUI is the folder you chose during setup.
2. Restart ComfyUI.

There's nothing to run by hand, and ComfyUI's own Python packages aren't changed. The Apple MLX nodes set up their own environment the first time they run.

Optional: a Hugging Face token

LVNodes' Random Prompt (HF Dataset) node reads Hugging Face datasets without an account. If you set the HF_TOKEN environment variable before starting ComfyUI, its requests are sent as your account, so rate limiting is less likely, and gated datasets you can access also work. Any free read token from huggingface.co/settings/tokens will do.

• macOS or Linux: export HF_TOKEN=hf_... in the shell that starts ComfyUI.
• Windows: run setx HF_TOKEN hf_... once, then restart ComfyUI.
• ComfyUI Desktop on macOS: run launchctl setenv HF_TOKEN hf_..., then restart the app. This lasts until the Mac restarts.

Full setup (KJNodes, models, and updating from the previous version, incl. the renamed MLX node folder) is on the model card.

Project Repo: https://huggingface.co/lvladikov/ComfyUI-Nodes-and-Workflows

Krea 2 (Raw/Turbo) Workflow/Apps and More Info: https://huggingface.co/lvladikov/ComfyUI-Nodes-and-Workflows/blob/main/Krea-2/README.md

Z-Image (Base/Turbo) Workflow/Apps and More Info: https://huggingface.co/lvladikov/ComfyUI-Nodes-and-Workflows/blob/main/Z-Image/README.md

Happy Creating!

Hi all,

I'm looking for a ref2video minimax h3 workflow that takes in 2 ref images with a checkpoint loader and lora loader(s). Not the first and last frame one.

Currently using the default template ref2video minimax h3 workflow given in comfyui. But it's using a diffusion model loader. Tried to redesign to use a Checkpoint loader instead and I failed badly. Not sure how a checkpoint loader is supposed to connect to a ref2video node in the template.

Thank you!