CivArchive
    MiniMax H3 Image-to-Video AUTO — Batch Sequential, Native Audio, QwenVL Auto-Caption (8-Step Turbo) - v1.0 — Turbo (8-step)
    NSFW
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    ✨ **VID H3 Image-to-Video AUTO — Batch Sequential Workflow with Native Audio & QwenVL Motion Captioning**
    ComfyUI · MiniMax H3 Community License · Dual-LoRA Turbo Stack (6-step, ~3× faster sampling)
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    Drop a folder of images → press Queue once per image → workflow auto-iterates, auto-captions motion from each frame with QwenVL, animates with a dual turbo-LoRA stack (official Comfy-Org 8-step distill + lightx2v secondary, 6 steps total), and spits out MP4 video WITH native stereo audio. This is the image-to-video engine: no text prompt, no Krea2 generation stage — just still frames in, up to 10.1s (243-frame) animated clips out. Optimized for 16 GB VRAM with int8_convrot quantization.
    
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    📋 **Changelog**
    
    **v3 — Dual-LoRA 6-Step** (current)
    Speed: dual-LoRA turbo stack (official Comfy-Org 8-step distill + secondary lightx2v LoRA) replaces the single-LoRA v2 setup, running clean at 6 steps (down from 8) — ~3× faster than the original 20-step baseline. Same-seed A/B against v2 showed no quality loss on image or audio. Length: clips now default to 243 frames (~10.1s @ 24fps), up from 124f (~5.2s) in v2. Audio QC hardened this round with a CLAP-based audio/caption similarity pre-filter alongside human-listen review — found and root-caused a real scene-content bias in H3's native audio (beach/swimwear-style scenes can trigger music instead of ambient sound); example clips chosen to avoid it. Seed default switched from fixed to randomize — public giveaway workflow, users now get varied output per run instead of everyone landing on the same result.
    
    **v2.1 — Audio Fix**
    Fixed: upscale-path output (⬆️ UPSCALE OUTPUT group) was saving video without audio — the audio VAE decode was never wired into that branch's Create Video node, only the normal-path one was connected. Native audio now comes through on both output modes. Also documented a WAS Node Suite setup gotcha: the caption Save Text File node only writes to paths listed in its whitelist-dirs.list (see Troubleshooting below). Thanks to Civitai commenter @blhll for catching both.
    
    **v2.0 — Auto + Fast VRAM**
    Auto-orientation (portrait/landscape now detected from the source image, no manual knob), Turbo LoRA upgraded to v4_step600_ema, lighter VRAM cycle between clips (soft flush replaces full model reload — better on long batches), one-click Normal/Upscale output switch, default clip length raised to 243 frames (~10s), and the whole graph reorganized into 7 numbered guided groups with a first-time setup note built in. New required node: IAMCCS-nodes.
    
    **v1.0 — Initial Release**
    First public release — H3 image-to-video with native audio, QwenVL auto-captioning, dual output paths (normal + RTX upscale).
    
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    ✨ **Features**
    
    ✅ **Batch AUTO Sequential** — ImageIterator (sequential mode, reset=false) loads one image per Queue press. Press Queue N times for N images, or use Queue (Instant) ×count. No folder juggling, no manual image export/import.
    ✅ **Auto Motion Caption** — QwenVL reads each input frame and auto-writes a 60–100 word motion description (or manual override via PrimitiveString + index switch 0=auto, 1=manual).
    ✅ **Dual-LoRA Turbo Stack** — official Comfy-Org 8-step distill LoRA (str 1.0) + secondary lightx2v turbo LoRA (str 0.75) stacked, BasicScheduler set to 6 steps — cuts sampling from 20 steps to 6 (~3× faster) with quality holding up in A/B testing (image+audio both checked).
    ✅ **Native Stereo Audio** — H3 generates audio alongside video; decode via dedicated audio VAE. To mute, unplug audio input on Create Video node.
    ✅ **Single Orientation INT** — one knob (0=Landscape 1280×704, 1=Portrait 704×1280) drives H3 latent size and output resolution; no node rewiring.
    ✅ **Frame Grid 17k+5 Pattern** — 124/141/158/175/192/209/226/243 frames valid; ships defaulted to 243 (~10.1s @ 24fps). ⚠️ Frames ≥175 need ~48 GB system RAM (non-VRAM).
    ✅ **VRAM-Safe Design** — tested on RTX 5080 16 GB; ComfyUI weight offloading keeps the 21 GB UNet workable on 16 GB cards. int8_convrot quant chosen for broad GPU compat (30/40-series, Blackwell).
    ✅ **Optional RTX Upscale Side-Branch** — DaSiWa RTX VSR Ultra 1920×1080 (bypassed by default; if red missing-node, just delete — main path runs fine).
    
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    📦 **Required Models** (5 files, ~42 GB, auto-downloaded ≤2.5 GB)
    
    **MiniMax H3 I2V core (~42 GB, int8_convrot variant):**
    • MiniMax_H3_FL2VA_pruned_int8_convrot.safetensors (21 GB) — H3 diffusion UNet, int8_convrot quant
    • qwen3vl_32b_minimax_h3_int4_convrot.safetensors (15 GB) — H3 text encoder (Qwen3-VL-32B, int4)
    • minimax_h3_video_vae_fp16.safetensors (5.21 GB) — H3 video VAE
    • minimax_h3_audio_vae_fp32.safetensors (605 MB) — H3 audio VAE (native audio decode)
    
    **Turbo LoRA stack (~1.1 GB, 2 files, both applied):**
    • minimax_h3_fl2v_turbo_8step_v1.0_comfyui_bf16.safetensors (str 1.0) — official Comfy-Org 8-step distill LoRA
    • minimax_h3_fl2v_lightx2v_turbo_4step_v0.1_comfy.safetensors (str 0.75) — secondary lightx2v turbo LoRA, paired with the above to enable a 6-step BasicScheduler
    
    **Auto-Downloaded (no manual placement):**
    • Qwen3-VL-2B-Instruct (~2.5 GB, cached after first run) — VLM motion captioner, auto-fetched by ComfyUI-QwenVL
    
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    ⬇️ **Download Links**
    
    📁 **ComfyUI/models/diffusion_models/**
    • MiniMax_H3_FL2VA_pruned_int8_convrot.safetensors — https://huggingface.co/Abiray/Minimax-H3-nvfp4-INT4-INT8-Convrot
    
    📁 **ComfyUI/models/text_encoders/**
    • qwen3vl_32b_minimax_h3_int4_convrot.safetensors — same repo, text_encoders/ folder
    
    📁 **ComfyUI/models/vae/**
    • minimax_h3_video_vae_fp16.safetensors — same repo, vae/ folder
    • minimax_h3_audio_vae_fp32.safetensors — same repo, vae/ folder
    
    📁 **ComfyUI/models/loras/**
    • minimax_h3_fl2v_turbo_8step_v1.0_comfyui_bf16.safetensors — https://huggingface.co/Comfy-Org (official H3 turbo distill LoRA)
    • minimax_h3_fl2v_lightx2v_turbo_4step_v0.1_comfy.safetensors — lightx2v turbo LoRA, ComfyUI-format release
    
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    🧩 **Required Custom Nodes** (5 packs)
    
    1. **ComfyUI-QwenVL** (AILab / 1038lab) — motion captioning via AILab_QwenVL node. Apache-2.0/BSD. https://github.com/1038lab/ComfyUI-QwenVL
    2. **ComfyUI_Image_Anything** (Kelin) — ImageIterator node for batch sequential loading. ⚠️ **Note:** may not resolve in ComfyUI Manager; install via git clone: `git clone https://github.com/ComfyUI-Kelin/ComfyUI_Image_Anything` into custom_nodes/
    3. **ComfyUI-Easy-Use** (yolain) — cleanGpuUsed (VRAM bridge) + anythingIndexSwitch (motion/orientation toggles). https://github.com/vjumpkung/ComfyUI-Easy-Use
    4. **rgthree-comfy** — Fast Groups Bypasser (group on/off toggle used in this graph). https://github.com/rgthree/rgthree-comfy
    5. **IAMCCS-nodes** (IAMCCS) — VRAM Flush (soft) node, runs between clips in the batch loop to keep VRAM headroom stable across long ImageIterator runs. GPL-3.0. https://github.com/IAMCCS/IAMCCS-nodes
    
    **Optional:**
    • **ComfyUI-DaSiWa** (RTX GPU only) — DaSiWa_RTX_UpscalerRefiner for 1080p upscale branch. If missing (red node box), safe to ignore/delete — main path unaffected. https://github.com/darksidewalker/ComfyUI-DaSiWa-Nodes
    
    Requires ComfyUI ≥ 0.30. H3 nodes (MiniMaxH3ImageToVideo, MiniMaxH3SigmaShift) are native to core.
    
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    🚀 **How to Use**
    
    **Setup:**
    
    1. Download all 5 model files → place in ComfyUI/models/ (exact paths above)
    2. Install 4 custom node packs via ComfyUI Manager (Image_Anything via git clone if Manager fails)
    3. Confirm ComfyUI ≥ 0.30
    4. Load the workflow JSON into ComfyUI
    5. On ImageIterator node: set folder_path to your input image directory
    
    **Batch Generation:**
    
    6. Set **Orientation INT** to 0 (landscape) or 1 (portrait) — matches input image orientation
    7. Leave **Motion Switch = 0** (auto-caption) for simplest path; flip to 1 to type your own motion text
    8. Click Queue → H3 loads input image → QwenVL auto-captions motion → cleanGpuUsed (VRAM bridge) → H3 samples 8 steps (turbo) → VAE decodes video + audio → CreateVideo produces MP4 with stereo audio
    9. ImageIterator auto-advances to next image in folder on each Queue press
    
    To batch 10 images: press Queue 10 times, or use Queue (Instant) ×10 from the UI.
    
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    ⚙️ **Settings & Parameters**
    
    | Setting | Default | Purpose |
    |---------|---------|---------|
    | **Orientation INT** | 0 | 0 = Landscape 1280×704; 1 = Portrait 704×1280 |
    | **Motion Switch** | 0 | 0 = Auto (QwenVL captions frame); 1 = Manual (type MOTION TEXT) |
    | **H3 Sampler** | res_multistep | Residual multistep (recommended for video) |
    | **H3 Steps** | 6 | Dual-LoRA turbo stack range 4–8 tested, 6 = best speed/quality; non-turbo baseline is 20 |
    | **H3 Scheduler** | simple | Simple scheduler (stable) |
    | **Sigma Shift (video)** | 12.0 | Production-tuned; do not change |
    | **Sigma Shift (audio)** | 3.0 | Audio emphasis; do not change |
    | **Frames** | 243 | ~10.1s @ 24fps. Valid: 124/141/158/175/192/209/226/243 |
    | **Seed** | 694207699552158 (randomize) | Ships randomize by default; set fixed for reproducible A/B testing |
    | **Audio Input** | connected | Unplug wire to mute output |
    
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    💡 **Performance Tips & Troubleshooting**
    
    **Minimum Spec** — 16 GB VRAM + ~32 GB system RAM (50GB+ if attempting frames ≥175).
    
    **ImageIterator Folder Path** — use absolute paths (C:\path\to\images or /path/to/images); relative paths may fail.
    
    **Red DaSiWa Node** — if ComfyUI-DaSiWa pack is not installed, the optional upscale side-branch shows red. This is safe — just bypass or delete the node. Main video path runs independently.
    
    **Manager Can't Find Image_Anything** — ComfyUI Manager may not auto-resolve this custom node. Use git clone into custom_nodes/ instead (see Custom Nodes section).
    
    **Frame Count & RAM** — 124–158 frames: ~32 GB RAM comfortable. ≥175 frames: ~48 GB system RAM needed (VRAM and system are separate). H3 frame grid is 17k+5 (not the LTX N*8+1 table).
    
    **Turbo LoRA vs Non-Turbo** — this workflow ships with the dual-LoRA stack at 6 steps. To revert to the original 20-step path: bypass both LoRA nodes and raise BasicScheduler steps back to a non-turbo count.
    
    **Motion Auto-Caption Quality** — QwenVL reads the input frame, so clearer, well-lit images produce richer motion captions. Abstract or low-contrast frames may yield generic descriptions (e.g., "slow ambient movement").
    
    **Cold Start** — first workflow run: Qwen3-VL-2B-Instruct (~2.5 GB) auto-downloads (~1–2 min). Subsequent runs are fast (model cached).
    
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    📝 **Notes & AI Disclosure**
    
    • **AI-Generated Content** — All example outputs are AI-generated by MiniMax H3. Respect local AI disclosure laws when publishing.
    • **Configuration Only** — no model weights in JSON; download all 5 files separately from HuggingFace links above.
    • **Workflow Reuse** — free to modify, share, and fork. No attribution needed (workflow is CC0).
    • **Hardware Tested** — RTX 5080 16 GB VRAM + 48 GB system RAM (int8_convrot, native resolution, no upscale).
    
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    🔗 **Also Check Out**
    
    Sister workflows by the same author:
    • **Krea2 Turbo Dual** — still-image generator (text or reference image → image)
    • **Krea2 → H3 Motion Suite** — text-to-image → video (SUBJECT + POSE → still → audio-video, one graph)
    
    GitHub mirror: [github.com/thinnaphat-border/comfyui-workflows](https://github.com/thinnaphat-border/comfyui-workflows)
    
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    ⭐ **Found this useful?**
    
    • Like if it saved you time animating image batches
    • Comment your results — I read every one
    • Follow for new ComfyUI workflows, all tested on 16 GB VRAM
    
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    🎨 **Generate Your Input Images**
    
    To create specialized source images for batch animation, try generating with Krea2 using these compatible LoRAs:
    • **[Bokeh Dial](https://civarchive.com/models/2815209)** — Depth-of-field control for cinematic focus effects
    • **[y2ksnap](https://civarchive.com/models/2802200)** — 2000s nostalgic aesthetic and styling
    • **[mossworld](https://civarchive.com/models/2812161)** — Overgrown Moss & Vines World Morph for nature textures
    
    All three are Krea2-base compatible.
    
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    ⚖️ **Model Attribution & Licensing**
    
    **MiniMax H3** (Abiray int8_convrot quant)
    • License: MiniMax H3 Community License — https://huggingface.co/Abiray/Minimax-H3-nvfp4-INT4-INT8-Convrot
    • Commercial use ✅ OK; outputs are yours to sell/distribute
    • The license's "Excluded Territories" clause restricts where the *model may be run* — check the license text for the current list before running. Output distribution is not territory-restricted.
    
    **MiniMax H3 Turbo LoRA stack**
    • Official Comfy-Org 8-step distill LoRA — Apache-2.0 — https://huggingface.co/Comfy-Org
    • lightx2v turbo LoRA (secondary, str 0.75) — check upstream repo for current license before commercial redistribution
    
    **Supporting Components**
    • Qwen3-VL-32B-Instruct (H3 text encoder) & Qwen3-VL-2B-Instruct (motion caption VLM) — Apache-2.0
    • H3 video/audio VAEs — MiniMax H3 Community License (part of the H3 release)
    
    **ComfyUI Custom Nodes**
    • ComfyUI-QwenVL (1038lab) — Apache-2.0/BSD — https://github.com/1038lab/ComfyUI-QwenVL
    • ComfyUI_Image_Anything (Kelin) — https://github.com/ComfyUI-Kelin/ComfyUI_Image_Anything
    • ComfyUI-Easy-Use (vjumpkung) — https://github.com/vjumpkung/ComfyUI-Easy-Use
    • rgthree-comfy — Apache-2.0 — https://github.com/rgthree/rgthree-comfy
    • IAMCCS-nodes — GPL-3.0 — https://github.com/IAMCCS/IAMCCS-nodes
    • ComfyUI-DaSiWa (optional) — https://github.com/darksidewalker/ComfyUI-DaSiWa-Nodes
    
    **Workflow JSON** — CC0 Public Domain. Free to use, modify, redistribute without attribution (credit appreciated).
    
    All example outputs are AI-generated. Model weights remain the property of their respective owners and are downloaded separately from official HuggingFace sources.
    

    Description

    Initial release. Drop a folder of images, press Queue per image — ImageIterator batches, QwenVL auto-captions motion and audio per frame, MiniMax H3 (8-step turbo LoRA) animates with native stereo audio. Optimized for 16 GB VRAM (int8_convrot quant). Audio captioning specifically tuned (hard word cap plus literal sound-list format) to avoid garbled or narration-style audio — see USER_GUIDE for detail.

    FAQ

    Comments (6)

    FarmerJasonWAug 12, 2026
    CivitAI

    This is working quite well for me. I'm mostly using a manual prompt run on a batch of images. Also using length of 464 frames (Just under 20 seconds) is working fine, takes about 57 minutes per on my 5090 laptop with 64gb system RAM. I also experimented with a text concatenate node to append a static line in the prompt to the bottom of the auto-prompter.

    TP_AI_63
    Author
    Aug 12, 2026

    Awesome to hear it's running smooth — thanks for sharing the 464f data point, that's past what I've tested on my end (built this on a 16GB RTX 5080, no cloud rental, one-click batch). Good to know it scales clean on a 5090. If you have a sec, a rating helps a ton, and feel free to drop one of your clips here — always curious what people generate. If you want sharper detail out of H3's output, my SeedVR2 batch upscaler pairs well with this workflow too.

    FarmerJasonWAug 14, 2026

    @TP_AI_63 Just for fun, I tried 702 frames - ~29 seconds of video... it took 18 hours! :)

    TP_AI_63
    Author
    Aug 14, 2026

    @FarmerJasonW Haha, that's a wild data point 😄 18 hours for 702f makes sense though — sampling cost scales non-linearly past a certain length (latent size + attention cost compound, not linear like frame count alone), plus longer runs push you into swap/offload territory depending on VRAM. On my 16GB RTX 5080 I keep default runs well under that range for exactly this reason — past ~500f the returns drop off fast for most use cases. Curious what res you ran it at though, and whether it's usable footage worth the wait — would love to see a clip if you've got one.

    FarmerJasonWAug 20, 2026

    Trying the updated version, but Sage Attn is giving an error: This node threw an error during execution. Check its inputs or try a different configuration. I am using a manual prompt, if that helps. Since I'm using the manual prompt, I bypassed the saving of the text file as well, that that seems unlikely to mess with Sage Attention.

    TP_AI_63
    Author
    Aug 21, 2026

    @FarmerJasonW Thanks for the report! That error's unrelated to the prompt/Save Text File path — Sage Attention only patches the MODEL, doesn't touch text nodes at all, so bypassing that node wouldn't cause it.

    Two likely causes:

    1. sageattention package isn't installed (pip install sageattention) — it's not bundled with the custom node pack, easy to miss.

    2. If your GPU is older than RTX 40-series, the shipped mode (sageattn_qk_int8_pv_fp8_cuda) needs fp8 tensor cores it doesn't have. Fix: open node 96 ("⚡ Sage Attention Patch"), change the dropdown to sageattn_qk_int8_pv_fp16_cuda instead — same speedup, works on more hardware.

    What GPU are you on? Can narrow it down further once I know.

    Workflows
    MiniMax H3

    Details

    Downloads
    225
    Platform
    CivitAI
    Platform Status
    Available
    Created
    8/9/2026
    Updated
    8/25/2026
    Deleted
    -

    Files

    minimaxH3ImageToVideoAUTOBatch_v10Turbo8Step.json