An optimized ComfyUI workflow for MiniMax H3, the open-weights omni-modal video model that generates video with native stereo audio in a single pass — up to 2K, 24fps, ~15 seconds a clip. This build is tuned for speed and low-VRAM stability: SageAttention, RTX Video Super Resolution, and a full set of VRAM/RAM cleanup nodes are wired in so the model actually runs on consumer hardware instead of OOM-ing on VAE decode.
Runs Text-to-Video (no image connected) and First/Last-Frame Image-to-Video (connect a first frame, last frame, or both) from a single MiniMaxH3ImageToVideo node.
Run it in the cloud (no local GPU)
No GPU with the VRAM/RAM for this? I built a RunPod template that runs this exact workflow in your browser — tested on the 24 GB option: ➡️ https://get.runpod.io/Minimax-H3-ComfyUI (Referral link — you get the signup bonus, I get RunPod credit, no cost to you.)
Why this build
SageAttention wired in for a ~20-30% speed bump.
Memory cleanup nodes (🎈VRAM-Cleanup + 🎈RAM-Cleanup) placed to kill the VAE-decode OOM on longer videos and cut RAM usage from ~50 GB to ~30 GB.
Spectrum-MiniMax-H3 node included for a further claimed ~30% speed gain — worked in my testing, left off the critical path so you can toggle it.
Resolution Selector locked to H3's native canvas (768px short edge, multiple of 32).
RTX Video Super Resolution for a clean upscale pass.
Required Models
Update ComfyUI to 0.30.0+ first — native H3 support merged Aug 3, 2026.
diffusion_models/
minimax_h3_fl2va_pruned_int8_convrot.safetensors
text_encoders/
qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
vae/
minimax_h3_video_vae_fp16.safetensors
minimax_h3_audio_vae_fp32.safetensors
All from the Comfy-Org/MiniMax-H3 repo. Both VAEs are required (video + audio). This is the pruned INT8 low-VRAM variant. Tight on memory? Swap only the text encoder for an INT4 build — the INT4 diffusion model quality is poor, so don't swap the main model.
Custom Nodes
Most install via ComfyUI Manager → "Install missing custom nodes": KJNodes · rgthree-comfy · ComfyUI Memory Cleanup · Nvidia RTX Nodes · FearnworksNodes · Spectrum-MiniMax-H3
Key Tips
384p is the floor — 256p fails completely. Start at 0.5 MP preview, confirm your prompt, then scale up.
System RAM matters as much as VRAM — H3 streams through RAM (~50 GB during gen). Use the cleanup nodes if you're on 32 GB.
Put the audio in your prompt — dialogue, SFX, and music are generated with the video, so describe them in the same prompt block.
PyTorch must target CUDA cu30+ for native INT8 ConvRot; older builds fall back to slow software emulation.
Launch flags:
set PYTORCH_ALLOC_CONF=expandable_segments:True+--fast(add--lowvram/--reserve-vramif you OOM).Match first/last frame images to your output resolution and aspect ratio for cleaner results.
Credit
Base workflow adapted from a community post on r/StableDiffusion — full credit to the original author for the initial setup and memory testing. I reorganized the graph and added tweaks. Original: https://www.reddit.com/r/StableDiffusion/comments/1vegtac/minimax_h3_tips_and_tricks_and_what_i_experienced/
More from me
🎨 Patreon (free workflows, one-click installers, guides): https://www.patreon.com/TheLocalLab ▶️ YouTube (tutorials): https://www.youtube.com/@TheLocalLab 🛒 locallabdigest.com (installers & digital products): https://www.locallabdigest.com
If this workflow helped, a ❤️ or a review is appreciated — it helps others find it.