Hello everyone

Want to get faster MiniMax H3 video generation without sacrificing quality? In this tutorial, I’m testing the new H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes to find the best combination for speed and quality. The goal is to push MiniMax H3 as far as possible while cutting generation times by up to 2×, then upscale the results with LTX Upscaler to reach a stunning 2432 × 1344 (2K-class) resolution. By combining both H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes I generated video at 0.8 megapixel using "**RTX3060 6GB 16GB RAM "**and I got

 13 minutes vs 41 minutes at 8 steps

 27 minutes vs 52 minutes at 20 steps

LTX 2.3 Upscaler 11 minutes to get 2432 × 1344 resolution

Workflow link

https://civitai.com/articles/34028/comfyui-tutorial-minimax-h3-4-steps-lora-upscaling-2x-faster-generation-best-settings-for-2k-ai

Video Tutorial link

https://youtu.be/ZUzeM9OEJ4Y

I have been working on this last week.

• What is this node ?

- One node with different MiniMaxH3 workflows.

• What its for ?

- If you hate spaghetti and hate doing workflows and dealing with errors.

https://github.com/LeonQ8/ComfyUI-ALLinONE-MinimaxH3

There always appears to be something missing from comfy templates. I don't see a spot to add a reference video or audio? And had to figure out that you have to duplicate image ones and connect them to get up to 9... Totally confused where to input the reference video. Load video node doesn't connect.

Workflow: https://drive.google.com/file/d/1B4kODxXQgJ1QOKRsEIkxHbgYmdruPpTK/view?usp=sharing

I made this experimental short scene using ComfyUI.

I generated the characters and backgrounds using the Krea 2 open-weight model with a Turbo LoRA, then animated them using MiniMax H3 with a reference-to-video workflow. With the Turbo LoRA, each 5-second clip took around 2–3 minutes to generate.

I edited everything in CapCut, added some color grading, bloom, and film grain, and it all came together nicely.

For the lo-fi track, I produced it on the Maschine MK3 using a free sample pack.

This is a generative re-render, not restoration or sharpening. It invents fine detail. In every test with one person it added freckles that weren't there.

The comparisons use MiniMax H3 clips at 640x384, 10 seconds long, upscaled 2x. They're Lanczos versus ReDetail at the same output size, so there isn't any bigger image sleight of hand.

On a motocross clip it redrew the jersey graphic and number plate. The new markings stayed fairly stable between frames, but they weren't the original markings. Logos, numbers and text are all fair game.

If reddit compresses this video to the afterlife again, see: https://civitai.com/models/2857731/redetail-ltx-25-generative-video-upscaler-workflow-cli

So it's useful for AI-generated or generally soft footage, where there isn't much real detail to recover. It's a bad fit if a face, label or logo has to be 100%.

• Silent clips fail because the model encodes audio and video jointly. Add a silence track first.
• Both output dimensions must divide by 64, not 32. Clip length must be `8n+1` frames or the model silently drops the tail.

I like 1.5x, not 2x. On one clip, 243 frames from 768x1408, 1.5x took 7 minutes and peaked at 65GB. 2x took 17 minutes and 80.5GB. The 2x result carries maybe more detail, but check between the two and it's hard to tell imo. On skin most of that extra is invented, not recovered. Faster render, less made up texture.

UPDATE! NOW WITH CACHED CONDITIONING

The text encoder is now optional. The graph runs with empty prompts, so its conditioning is a constant. It ships pre-computed at 26KB, which skips the 15GB download and takes peak VRAM from 30.4GB to 24.8GB on a 5090.

There's a Mac build in there now too, ReDetail_LTX25_upscale_MAC.json. It runs the GGUF transformer with no text encoder at all (the cached conditioning replaces it), so it's about 17GB of models total. On an M5 it did 33 frames from 640x384 to 1280x768 in 4.4 minutes. Per frame megapixel that's roughly 6x slower than a 5090, not the 30x I was expecting, so a 10s clip lands around 34 min at 2x or 19 min at 1.5x. Quality holds.

Repo: https://github.com/Bambushu/redetail

Crappy demo clip as I was short on time, but handy to see in context of the screenshot of this post

https://github.com/shootthesound/ComfyUI-H3Studio

Lots of hopefully clear instructions in the Github link and a basic example workflow.

Its my first time making a video editor after 15 years of using one every day, so there is a lot of carried over UX, and more I'll refine.

If you fancy it, this plays nicely with what is now a fast and high quality results Minimax Lora Trainer (getting good quality training to work in minimax has been a nightmare, but its there now): https://github.com/shootthesound/Fizgig

Is there a way to convert the last frame to a true image like a png?

I noticed I could create longer vids by taking the last frame and use that as a refrence image, but when I do that like in my screenshot, generation time gets much larger, I think it's b/c internally Minimax reads that last image actually as a video, thus behaving much differently.

I wanted to test it a bit with creature animation, snow, wind, and atmosphere. I started by generating still keyframes with Seedream pro, then used image-to-video to generate videos in .

Prompts and keyframe here :https://www.patreon.com/u8638148/posts/minimax-h3-and-166452641?utm\_medium=clipboard\_copy&utm\_source=copyLink&utm\_campaign=postshare\_creator&utm\_content=join\_link

Finally got the flow and prompt structure figured out for H3- now I'm having so much fun.

Will post specs in comments.

Summary made with help of AI:

Original video: https://www.youtube.com/watch?v=BCqp2xnUeKk

📌 General Overview & Main Points

The State of AI Video: The stream focused on the rapid advancement of AI video generation, showcasing a side-by-side comparison of four state-of-the-art models: Flux 3, Seedance 2.5, MiniMax H3, and LTX 2.3.

Open Source vs. Proprietary: The team expressed huge appreciation for companies releasing models as open-source (like MiniMax H3), allowing the community to run them locally, tweak them, and dig into the "guts" of the tech using node-based workflows in ComfyUI.

Model Personalities: The team noted that models are developing distinct "personalities."

Flux 3: Praised for its incredible sense of humor, absurdist comedy, and "editorial voice." It understands jokes, world-building, and retro aesthetics perfectly.

Seedance 2.5: Highly literal and cinematic. It produces gorgeous, high-fidelity footage but struggles with complex camera movements (like 360-degree orbits) and abstract concepts.

LTX 2.3: Very raw and direct. It takes prompts incredibly literally, sometimes to a fault (e.g., literally narrating the text of the prompt in the video or turning characters into physical objects).

🚀 Everything About MiniMax H3

Availability: MiniMax H3 is an open-source and API-based model, meaning users can run it locally on their own GPUs or via cloud services (like Comfy Cloud).

Strengths & World-Building: The team praised H3 for its ability to infer story and context. Instead of just generating what is literally in the prompt, it adds "whimsical" and cinematic elements that build a believable world.

Native Stereo Audio: A massive standout feature for MiniMax H3 is its ability to generate highly accurate, native stereo audio alongside the video. Julien specifically noted this as a favorite feature.

Text Generation: H3 is incredibly proficient at generating accurate text within the video.

Julien’s H3 Showcase: Julien shared his screen to show dedicated MiniMax H3 generations, which included:

A continuous, dynamic shot of a forest fire.

A deep-sea diver swimming with a whale shark.

A giant face organically forming out of blowing sand dunes.

A cinematic shot of a glowing "OPEN WEIGHTS" sign being cast in a metal forge.

A giant whale swimming over a cyberpunk Tokyo street with a neon sign reading "LEVIATHAN HOUR."

An anglerfish in the deep sea with a neon sign reading "ENCORE."

A samurai standoff on a bridge.

An exploded, exploded-view camera assembling itself.

💡 Pro-Tips, Tricks, and Workflows

The "LLM Prompting Agent" Trick: Purz shared a massive time-saving trick for working with new models. Because every model prefers different prompting styles, do not try to guess what the model wants. Instead, download the model’s official prompting guide, feed it into an LLM (like Claude or ChatGPT), and ask the LLM to rewrite your ideas. This acts as a translation layer, ensuring your prompts are perfectly optimized for that specific model.

Camera Movement Limitations: If you are using models like Seedance 2.5, avoid prompting for complex camera maneuvers (like "360-degree orbiting camera"). The models often fail to comprehend the spatial geometry and the video will break or artifact. Stick to standard cinematic pans, pushes, and tilts.

Extending Video (Image-to-Video): The team discussed that simply extending a video longer than the model's native context window often results in a loss of quality, degrading into "mush." The current best practice for AI filmmaking is treating it like traditional filmmaking: generate multiple different shots and cut them together in an editor, rather than trying to force the AI to generate one endless, continuous shot.

💻 Hardware & Pricing Discussions

Hardware Requirements for Local Running: To run heavy models like MiniMax H3 locally, you traditionally need a lot of VRAM. However, the team noted you can run these models on GPUs with as little as 12GB of VRAM if you allow the system to offload to your standard system RAM. Note: This will significantly slow down your render times compared to running purely on GPU VRAM, but it prevents the system from crashing.

Comfy Cloud Setup: For their cloud generations, the team mentioned using heavy-duty RTX 6000 Ada generation GPUs (which feature 96GB of VRAM) to crunch through multiple videos fast.

Pricing Models: A viewer asked if AI video generation would move to "unlimited" monthly pricing (like cell phone plans). The team agreed this is highly unlikely in the near future because the raw compute cost (GPU time) required to render video is simply too expensive to offer truly unlimited tiers right now.

I used the default H3 Reference Workflow and utilized some images I generated in Idiogram for a simple WW2 sequence. I'm just blown away that this can be done locally. I'm sure it could be far better with the full dev model, but I don't currently have that option unless I set it up on Runpod, which I might consider. Anyway, I'm just amazed that we can generate something like this locally, I didn't expect MiniMax coming out of the gate swinging with a local model this bad ass.

RTX-4070 8GB VRAM, 64GB RAM
832x640 920s render time.

Tutorial https://youtu.be/ukFpOhAv9Lw

(Summary made with help of AI as you must expect).

Original video: https://www.youtube.com/watch?v=S9O3FPumX4Q

MiniMax H3 (Hailuo 3) — Video Generation Model

The video introduces the open-weight release of the MiniMax H3 (Hailuo 3) video generation model. H3 is a 60-billion-parameter model capable of text-to-video, image-to-video, reference-to-video, in-place editing, and native audio generation.

Because it is open-weight, the community has already integrated it deeply into ComfyUI, allowing for complex, multi-modal video generation on local machines.

Prompting Techniques & Best Practices

The hosts and creators shared several key strategies for getting the best results out of H3:

• Keep It Straightforward: The model understands direct, straightforward language very well. You don't necessarily need overly complex "prompt engineering" jargon.
• Use an LLM as a "Prompt Enhancer": In the ComfyUI workflow, Comfy Rob uses an LLM node to take a basic concept and expand it into detailed, shot-by-shot prompts. This helps inject specific shots into the generated video seamlessly.
• Dialogue in Quotes for Lip-Sync: If you are using reference audio and want a character to speak, put the exact dialogue in quotation marks ("") within your text prompt. The model will automatically sync the character's lip movements to the referenced audio file.
• Context IR API (Intermediate Representation): If you are working with the API or advanced nodes, H3 has a feature that optimizes your prompt based on the multiple reference images you provide, helping the model figure out how to stitch different modalities together.

ComfyUI Workflow Techniques

Comfy Rob demonstrated a Reference-to-Video workflow, which you can find in the ComfyUI Template Library by searching for "Minimax." Here are the technical tips for setting up your nodes:

• Resolution / Megapixels Setting: Instead of traditional width/height settings, you often set the "Megapixels" node. Rob recommends starting at 0.4 megapixels, which outputs a resolution of roughly 864 × 480p.
• Controlling Shots and Timing: You can define the number of shots and the total length of the video. For example, if you want a 10-second video and set it to 5 shots, the model will generate a new shot every 2 seconds.
• Multi-Image Referencing: H3 allows up to 12 reference inputs. You can input multiple angles of a product (such as the earbuds example shown) to maintain strong temporal and spatial consistency across different generated shots.

Examples of What the Model Excels At

If you are looking for inspiration for your prompts, the video showcased three main areas where H3 currently excels:

1. Cinematic Product Advertisements

Rob used images of earbuds to create a sleek, professional 10-second commercial with changing camera angles, demonstrating the model's high consistency with product references.

2. Music Videos & Lip-Syncing

A video of a multi-eyed alien at a post office was shown where the alien's movements perfectly matched the rhythm of a song, and it sang the lyrics with highly accurate lip-syncing.

3. Complex Physics — Cloth & Fur

A video of a cat moving under a thick blanket demonstrated the model's impressive grasp of cloth physics, weight, and fur consistency without morphing or noticeable artifacts.

Hardware & Optimization Tips

Crucial for Local ComfyUI Users

H3 is a massive model, requiring approximately 120 GB of VRAM natively, so running it on consumer GPUs requires some optimization techniques:

• Use Kijai's LoRA: The community member Kijai released a 4-to-8-step LoRA. Adding this to your workflow can drastically speed up generation times on lower-end hardware.
• Use Quantized Models: Make sure you download quantized versions of the model, such as GGUF or FP8 versions, built for ComfyUI.
• Update ComfyUI: Ensure you are running the absolute latest version of ComfyUI. It uses fine-grained offloading specifically for H3, automatically moving parts of the calculation between your system RAM and your GPU's VRAM. This makes it possible to run the massive model on a standard 24 GB GPU, such as an RTX 3090 or RTX 4090.