MiniMax H3 - Spectrum Speed UP & Memory Optimized Workflow (Text/Image to Video + Native Audio)

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-vram if 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.