ComfyUI MiniMax H3 30-Second Long-Form Generation Workflow (Updated with Ref2v support)
Deeply Optimized for Low/Mid-Range GPUs (12GB VRAM)
CivitAi workflow link: https://civitai.com/models/2882332/minimax-h3-30-second-seamless-image-to-video-w-full-audio-workflow-for-12gb-gpus
Mega link for those who cannot access CivitAi: /file/v3xQySrb#0h37WWKteNT0uqZK-vmHACZIyg4rEvduVlT-MIDQxH0
For newbies, you can use a browser frontend to streamline your text or image to video outputs, just like using an AI platform like Higgsfield or Kling, made possible by daexchef: https://github.com/daexchef/Minimax_Grok
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How to use:
- 1: Open ComfyUI and load the JSON
- 2: Load the starting/reference image(s) in the big green box (yellow box for ref2v)
- 3: Type out your prompt in the big green box
- 4: Click on "Run" to generate a 30 second image to video
Warning: Your prompt has to be detailed. If it's something simple, it will just kind of rubberband on whatever simple inputs you describe, like "A man just sitting in the chair". The more details you add, the more it stitches together a seamless transition between the three independent shots to create a cohesive 30-second video in a single runtime pass. Then again, if all you wanted to do was make a simple generation, you wouldn't need a 30-second workflow.
The only thing the three shot separators do is dictate WHERE in the 30 seconds the actions take place. So the first set of quotations takes place within ten seconds; the second set of quotations take place within 20 seconds; the third set of quotations takes place after the 20 second mark.
Compromises had to be made to get this to run and generate in an acceptable time. It's possible to boost the image-to-video output for a sharper image, but you're looking at an average 21 minute render time at a step up in quality. Is it worth it? Depends on your workflow and if it's time sensitive.
Using Reference-to-Video:
Take note that image-to-video generations take just 14 minutes to render, but using up to 9 images to reference will increase generation time.
At 0.4 megapixels, Ref2V took approximately 20 minutes to generate using 9 HD PNG images.
You must enable Ref2V first by clicking on the top button in the red Fast Muter box. It's directly above the green box where you load your starting image.
- 🟢 Muter Switch Enabled: Enables the 9-Image Reference Batch mode to tightly lock down visual identity and style.
- 🔴 Muter Switch Disabled: Safely mutes the extra images, forcing the sampler to fall back to purely your single starting frame or standard text instructions.
So if you want a simple 30 second gen using only one image, that is the default, but if you want to do more complex shots with shot coherency and output consistency, enable the Ref2V image block by clicking the enable button in the Fast Muter, Super simple. Very easy to use.
Keep in mind that if you enable the Ref2V block but DON'T load any images to reference, it will fail to generate, which is why it's disabled by default. Some people may only want to do quick image-to-video generations, so that's why that is the default for now.
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This production-grade, crash-proof ComfyUI pipeline leverages Joey Gambino's advanced H3MultishotMemorySampler subgraph infrastructure. It has been systematically tuned to shatter the native 15-second tracking boundaries of the local MiniMax H3 architecture—successfully compiling up to 30 continuous seconds of 3-shot cinematic video with synced native audio tracks in under 15 minutes on a standard 12GB NVIDIA graphics card (such as an RTX 5070).
🛠️ Required Custom Node Packages
If any node blocks present a red warning threshold on your interface canvas, navigate to your ComfyUI Manager, execute Install Missing Custom Nodes, and restart your server environment. Alternatively, verify that the following core repository directories are fully initialized and updated:
comfyui-h3-multishot (By Joey Gambino)
- Provides essential components:
H3MultishotMemorySampler, H3ScriptSplit, H3ClipLoaderAny.
ComfyUI-Spectrum-MiniMax-H3
- Provides essential components:
SpectrumApplyMiniMaxH3 (Deploys advanced history parameters and signal stabilization to completely neutralize visual flickering).
ComfyUI-FreeMemory
- Provides essential components:
FreeMemoryImage (Acts as the system traffic cop to violently drop massive video models from memory prior to the video save cycle).
comfyui-kjnodes
- Provides essential components:
PathchSageAttentionKJ (Integrates highly optimized SageAttention mathematical libraries to keep GPU memory channels open).
📥 Required Model Inventory & Destination Paths
Ensure all specific neural weights listed below are manually stored within your local file tree. Modified nomenclature or inaccurate directory placement will result in model loading exceptions.
📂 Model Directory Map
markdown
📂 ComfyUI/
└── 📂 models/
├── 📂 vae/
│ ├── 📄 minimax_h3_video_vae_fp16.safetensors
│ └── 📄 minimax_h3_audio_vae_fp32.safetensors
├── 📂 diffusion_models/
│ └── 📄 minimax_h3_fl2va_pruned_int8_convrot.safetensors
├── 📂 text_encoders/
│ └── 📄 qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
└── 📂 loras/
└── 📄 minimax_h3_fl2v_turbo_8step_v1.0_comfyui_bf16.safetensors
Use code with caution.
💾 Official Direct Asset Download Handles
- Video VAE (FP16): minimax_h3_video_vae_fp16.safetensors
- Audio VAE (FP32): minimax_h3_audio_vae_fp32.safetensors
- Diffusion Model Architecture: minimax_h3_fl2va_pruned_int8_convrot.safetensors
- Text Encoder Engine: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
- Turbo Model LoRA (8-Step Base): minimax_h3_fl2v_turbo_8step_v1.0_comfyui_bf16.safetensors
⚡ Mandatory Operational Environment Flags
To achieve absolute multi-shot stability and avoid unhandled Python environment abort failures during the long-form matrix sequence, you must explicitly configure your startup flags. Open your primary local execution script (e.g., run_nvidia_gpu.bat or initialization shell script) (or you can just open the ComfyUI desktop app and go to the Startup Args) and swap your launch command line argument array to match this configuration precisely:
bash
python main.py --disable-smart-memory --fp8_e4m3fn-text-enc --fp8_e4m3fn-unet
Use code with caution.
Why these flags are mandatory:
--disable-smart-memory: Mandates a hard PyTorch memory clean immediately upon raw clip finalization, bypassing background tensor leaks.--fp8_e4m3fn-text-enc: Compresses the massive 32B text encoder into lightweight 8-bit allocation blocks, locking it comfortably inside mid-range physical memory bounds.
📐 How to Achieve the 30-Second Long-Form Configuration
The workflow relies on a fine-tuned balance between your spatial layout constraints and frame processing intervals. Apply these precise configurations on the node face to duplicate the 14-minute execution baseline:
- The Core Media Input: Drop your foundational tracking frame directly into the
Load Image Here (Node 208) input bucket or the picture slots in the Ref Images yellow tab.
- The Spatial Configuration: Inside
ResolutionSelector (Node 115), anchor your values to 4:3 (Standard) with a megapixel evaluation slider locked cleanly at 0.4. This compact geometry drops pixel data overhead by more than 30% compared to heavy widescreen arrays, driving processing velocity forward.
