DucHaiten Highway V1 — INT8 Video + Audio
⚠️ READ THE SETUP GUIDE FIRST — THIS MODEL REQUIRES THE HIGHWAY INTEGRATION
Highway V1 will not run with standard MiniMax H3 loaders. You must install the Highway ComfyUI integration and use the supplied workflows. Follow the setup guide before clicking Generate.
Hey everyone! Meet Highway V1, my INT8 video-and-audio model built on MiniMax H3. Create from a text prompt, guide scenes with multiple reference images, or connect first and last frames using one Highway checkpoint. I’m excited to share it with you.
In my evaluated comparisons, Highway’s native C20 schedule (20 evaluations) matched the quality of MiniMax H3 at 50 steps.
I built Highway on MiniMax H3 through DaSiWa Hybrid v2 INT8, with its own saved generation policy and resource management. Text-only, multiple references and first/last-frame control use one Highway checkpoint. You can see the actual videos, prompts and reference images on my HF page.
✦ What changes from MiniMax H3?
Comparison MiniMax H3 Highway V1 INT8 Quality in my evaluated comparisons 50-step reference Matched at 20 evaluations Published / demonstrated resolution Released recipe targets a 768-pixel short edge 1344×768, 1792×1024 and 2688×1536 demonstrated Checkpoint coverage FL2VA and Ref2VA partitions One checkpoint tested across text, image references and first/last frames Generation controls Configurable sampling recipes Native C20 policy saved with Highway; no Steps slider to configure
Quality is my visual assessment; the other rows compare the published H3 recipe with my demonstrations. That 768p target is not H3’s proven maximum. A normal H3 sampler set to 20 steps does not reproduce Highway’s policy.
⚡ The 15-second case: reported OOM → a completed Highway video
At 1344×768 / 362 frames on RTX 4090, the H3 INT8 community guide by yume-arasaki reports OOM. Highway’s demonstration at those dimensions and frame count completed with four image references and stereo audio.
1344×768 · 362 frames Reported H3 INT8 setup Highway V1 INT8 — my run Outcome Author-reported OOM Completed · 15.08 seconds · four references Full request time No completed result reported for this case 24m 33.58s Host configuration 32 GB RAM; 8-step res_multistep; NVFP4 encoder 128 GB RAM; native C20; BF16 layer-50 encoder
Reported OOM in that community setup; completed in mine. Software, conditioning and host RAM differ, and the source supplies no matching allocation log. This eight-step report provides OOM context only; it is excluded from original-H3 speed/memory comparisons.
✓ Five completed runs — here are my timings and memory figures
RTX 4090 · INT8 · 24 fps · 32 kHz stereo audio. Full request time includes preparation, generation and output work.
Input Output Full request Peak process RAM¹ Worker GPU memory² allocated / reserved Text only 1344×768 · 5.17s 5m 59.34s 29.81 GiB 3.31 / 3.78 GiB Four references 1344×768 · 5.17s 8m 44.24s 31.11 GiB 3.81 / 4.41 GiB Four references 1344×768 · 15.08s 24m 33.58s 34.00 GiB 7.36 / 9.72 GiB First + last frames 1792×1024 · 5.17s 11m 11.34s 32.58 GiB 4.97 / 6.62 GiB Text only 2688×1536 · 5.17s 32m 52.19s 36.29 GiB 9.03 / 11.97 GiB
¹ RAM covers Highway’s recorded process tree, excluding unrelated apps; shared resident pages can be counted more than once.
² These VRAM figures cover the generation worker’s PyTorch allocator, not total Highway GPU memory. They exclude the separate reference reader and allocations outside that allocator. Reserved includes allocated; do not add them. These are observed peaks, not minimum hardware requirements.
Full timings, historical builds and memory definitions are on HF. Matching original-H3 measurements are unavailable for these five runs, so I’m showing measured usage rather than a claimed saving percentage.
2688×1536 is four times the pixel count of 1344×768 — generated directly. I pushed that setting for the showcase; motion can look strained there. Start at 1344×768. For higher-resolution experiments, I’d try 2560×1440; that setting is a suggestion, not a measured row above.
I used quickly drafted test prompts. Watch the outputs and judge for yourself — there’s also compatible LoRA inference through the supplied controls.
◇ Why Civitai says “Merge” and “H3”
These are Civitai’s available labels. “Merge” means integrating Highway V1’s architecture and saved policy into this H3-derived model, not blending several checkpoints’ learned knowledge. “H3” identifies its lineage, not standard-loader compatibility.
⚙ Before you hit Generate
Use the Highway ComfyUI integration and supplied workflows. Standard H3/generic checkpoint loaders will not run it; a workflow JSON alone does not install the software.
Download the RC.3.7 integration ZIP and extract it.
Close ComfyUI, run INSTALL_WINDOWS.bat, and select your ComfyUI folder if asked.
Restart ComfyUI and drag in 01 - Start Here.json.
Click Prepare / download models on Asset Profile. Let it verify/reuse existing files or download what’s missing, then enter your prompt and run.
The setup uses the RC.3.1 Safetensors checkpoint, matching runtime, Qwen encoder and video/audio VAEs. Required model files total 78.30 GB on disk, plus software/caches/outputs. Optional automatic-reference assistance adds 8.89 GB; basic references work without it. Highway downloads are open on HF; any access requirements for separately hosted components still apply. Unused reference upload slots can stay connected in RC.3.7.
→ Get Highway V1 and show me what you make
Highway V1 on Hugging Face → · Installation guide
The source/component terms still apply; keep the included notices. This is my project, not an official MiniMax release.
I’d love to see your results! If you hit an error, include your setup and the message so it’s easier to help.
Description
Comments (17)
Damn this is amazing. You got real skills. I wished I had a GPU with 24-32gb of vram to run this... maybe in a couple of years when next gen gpus are affordable and give us 36-48-64gb of vram for under 1000$...
Or maybe we have a breakthrough in video generators efficiency and we can run like a minute of video in real time on 16gb of vram, thatd be amazing.
You could try 12–16 GB and see how it goes; I’m also curious to see how it performs with different configurations.
Fingers crossed for the latter, since the former looks increasingly unlikely...
@Joe375 It’s sad but true; however, I might be able to find a way to let GPUs with low VRAM run higher-quality AI models.
@DucHaiten so there is no hope for 8?
@putratroops Maybe 768p 5s or lower, but I’m afraid it will take a very long time to run
I love to see new innovation!
Hey, thanks for the work! So far the model seems really great had a couple issues though, I have a pretty Wonky folder setup involving a portable installation using a different model location, but even when I directed the install to that model folder it couldnt find any of the existing models and downloaded all of them in the models/highway location, not the end of the world but def have some redundant installs now.
I have a system with a 48GB RAM and a 4080, but the model just wouldn't fit, and I couldn't get it to work.
Could you please send me the exact error message or the error log file with the RTX 4080/48GB VRAM? I don’t have that GPU to test with on my own machine, so I need detailed information to understand exactly what the issue is.
Hi! Thanks for letting me know. To work out what’s failing on your setup, could you send me:
The full error report from ComfyUI: click Show Report on the error, or copy the console traceback. If it mentions a diagnostic JSON file, please attach that too.
A screenshot of your workflow settings showing resolution, frame count/duration, number of reference images, and whether automatic reference binding is enabled.
Whether it fails while loading, before generation starts, during generation, or when saving the video.
Whether you use ComfyUI Portable, Desktop, or a manual installation.
I’ve added better diagnostics in RC.3.8. Update the integration, restart ComfyUI, then open Prepare / download models → Download diagnostics and attach the JSON. You don’t need to download the model again or run another video just to collect this report.
The exact error will help me distinguish system RAM, GPU memory, or a loading problem. Please redact any personal information from paths before sharing.
Bitdefender gave me this:
"The file C:\Users\Owner\AppData\Local\Temp\tmpcrhqbqc_\cuda_utils.cp311-win_amd64.pyd is infected with Gen:Suspicious.Cloud.4.bu4@amsrwKo. The threat has been successfully blocked, your device is safe."
I will look into this matter more closely. Due to certain limitations, I am unable to test across various environments to identify potential issues; however, should you encounter any problems, please provide me with the details, and I will be happy to assist you.
Hi! Thanks for reporting this. I’d like to investigate the exact file Bitdefender blocked. Could you send me:
A screenshot of the detection details, including the full file path and SHA-256 hash if Bitdefender displays one.
When it happened: during installation, model preparation, or after clicking Run.
The full ComfyUI error report or console error, if there was one.
I’ve added a support-report button in RC.3.8. After updating the integration and restarting ComfyUI, open Prepare / download models → Download diagnostics and attach the JSON file here. This collects environment information without loading a model.
Please keep the security block enabled—there’s no need to restore the quarantined file or add an exclusion. If diagnostics is blocked too, just send the information already available. You can redact your Windows username from file paths.
Since I completed everything within just one month, while dedicating 70% of my time and effort to other work to earn a living and given the inherent limitations of this being a personal project, I haven't been able to test it across various environments to identify potential issues. That said, if you encounter any difficulties, please provide me with the details, and I will be happy to help. This model has undergone significant changes in how it functions, so there is a high probability that bugs may arise across different systems.