MiniMax-H3 ComfyUI API Workflows: Comprehensive Guide & Catalog
Author: malcolmrey
Last Updated: September 2026
Repository & Models: huggingface.co/malcolmrey
1. Overview & Ecosystem Architecture
MiniMax-H3 is a state-of-the-art multimodal video and audio generation Diffusion Transformer (DiT). This collection provides production-ready, programmatic ComfyUI API workflows (workflow_api_*.json) covering:
- FirstBlockCache (FBC) + SageAttention Fused Acceleration: Realizing up to 5.24× speedups with zero quality loss.
- Zero-Training RefMod Identity Adapters: Instant-load persona conditioning without live VAE image encoding overhead or model retraining.
- Step Distillation Pipelines: Native integration with LightX Turbo v1.0 8-step and 4-step low-rank adapters.
- End-to-End Multimodal Generation: Simultaneous high-fidelity 35mm cinematic video and lip-synchronized acoustic dialogue / ambient soundscapes.
- Clip-to-Video (C2V) Continuous Chaining: Seamless multi-scene narrative stitching via latent motion context trimming.
2. Directory Structure & Required Weights
A. Recommended ComfyUI Model Layout
ComfyUI/
├── models/
│ ├── diffusion_models/
│ │ └── MinimaxH3/
│ │ └── minimax_h3_fl2va_pruned_int8_convrot.safetensors (or bf16 variant)
│ ├── clip/
│ │ └── qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
│ ├── vae/
│ │ ├── minimax_h3_video_vae_fp16.safetensors
│ │ └── minimax_h3_audio_vae_fp32.safetensors
│ ├── loras/
│ │ └── MinimaxH3/
│ │ └── special/
│ │ ├── minimax_h3_fl2v_turbo_8step_v1.0_comfyui_bf16.safetensors
│ │ ├── minimax_h3_fl2v_turbo_4step_v1.0_768p_comfyui_bf16.safetensors
│ │ └── minimax_h3_fl2v_turbo_4step_v1.2_768p_comfyui_bf16.safetensors
│ └── refmods/
│ ├── minimaxh3_<name>_v1_refmod.safetensors
│ └── ...
3. Required Custom Nodes
To execute these workflows via API or ComfyUI GUI, install the following custom nodes:
ComfyUI-MiniMaxH3Mod(RefMod Loader & Conditioning Apply):cd ComfyUI/custom_nodes git clone https://github.com/Luisacaotica/ComfyUI-MiniMaxH3Mod.gitComfyUI-MiniMaxH3-FirstBlockCache(Transformer Block Caching):cd ComfyUI/custom_nodes git clone https://github.com/chengzeyi/ComfyUI-MiniMaxH3-FirstBlockCache.gitcomfyui-kjnodes(SageAttention & Optimization Patches):cd ComfyUI/custom_nodes git clone https://github.com/kijai/comfyui-kjnodes.gitrgthree-comfy(Power LoRA Loader Stack):cd ComfyUI/custom_nodes git clone https://github.com/rgthree/rgthree-comfy.gitComfyUI-VideoHelperSuite(Video Loaders & Muxing):cd ComfyUI/custom_nodes git clone https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite.git
4. Complete Workflow Registry
Category 1: Accelerated Standalone RefMod Pipelines (FBC + SageAttention + LightX)
| Workflow File | Steps | Sampler | Scheduler | FBC Mode | Distillation LoRA | Render Time (5s 124f) | Recommended Use Case |
|---|---|---|---|---|---|---|---|
workflow_api_minimaxh3_fbc_refmod_standard.json |
20 | res_multistep |
simple |
Safe (0.08 / max 2) | None | ~110s (1m 50s) | Maximum micro-texture archival quality |
workflow_api_minimaxh3_fbc_refmod_turbo_8step.json |
8 | euler |
simple |
Fast (0.10 / max 2) | LightX Turbo 8-Step | ~41s (2.68× faster) | Primary Production Standard (98% quality) |
workflow_api_minimaxh3_fbc_refmod_turbo_4step.json |
4 | euler |
simple |
Fast (0.10 / max 2) | LightX Turbo 4-Step | ~21s (5.24× faster) | High-speed screening & seed hunting |
Category 2: Accelerated C2V (Clip-to-Video) Continuous Chaining Pipelines
These workflows enable seamless multi-clip narrative continuation without camera cuts while maintaining full FBC + SageAttention speed acceleration and RefMod persona likeness:
| Workflow File | Steps | Sampler | Scheduler | FBC Mode | Distillation LoRA | C2V Motion Context | Recommended Use Case |
|---|---|---|---|---|---|---|---|
workflow_api_minimaxh3_fbc_refmod_c2v_standard.json |
20 | res_multistep |
simple |
Safe (0.08 / max 2) | None | 22 frames (trimmed) | Master-quality continuous narrative scenes |
workflow_api_minimaxh3_fbc_refmod_c2v_turbo_8step.json |
8 | euler |
simple |
Fast (0.10 / max 2) | LightX Turbo 8-Step | 22 frames (trimmed) | Primary Multi-Scene Movie Production (~41s/scene) |
workflow_api_minimaxh3_fbc_refmod_c2v_turbo_4step.json |
4 | euler |
simple |
Fast (0.10 / max 2) | LightX Turbo 4-Step | 22 frames (trimmed) | Fast multi-clip storyboarding (~21s/scene) |
C2V Pipeline Dataflow:
[ UNETLoader ] ──► [ PathchSageAttentionKJ ] ──► [ ApplyMiniMaxH3FirstBlockCache ]
│
▼
[ Power Lora Loader (rgthree) ]
│
[ CLIPLoader ] ──► [ MiniMaxH3ImageToVideo ] ◄─────────────────┘
│
[ RefModsLoader ] ──► [ MiniMaxH3RefModApply ]
│
[ MotionContextLoadLatent ] ──► [ MiniMaxH3MotionContext ]
│
▼
[ BasicGuider + SamplerCustomAdvanced ]
│
┌─────────────┼─────────────┐
▼ ▼ ▼
[ VAEDecode ] [ VAEDecodeAudio ] [ MotionContextSaveLatent ]
│ │
└──────┬──────┘
▼
[ MotionContextTrim ] ──► [ CreateVideo ] ──► [ SaveVideo ]
Category 3: Legacy & Core Multimodal Workflows
1. Text-to-Video (T2V) — workflow_api_minimaxh3_t2v.json
- Purpose: Generates full cinematic video and synchronized audio directly from multimodal prompts.
- Key Nodes:
MiniMaxH3ImageToVideo,PathchSageAttentionKJ,SpectrumApplyMiniMaxH3,SamplerCustomAdvanced. - Inputs: Prompt text, dimensions (e.g.,
768x1344or1344x768), duration/length frames, seed.
2. Image-to-Video (I2V) — workflow_api_minimaxh3_i2v.json
- Purpose: Animates a starting image anchor into a continuous temporal sequence.
- Key Nodes:
LoadImage,MiniMaxH3ImageToVideo,VAEDecode,CreateVideo. - Inputs: Source image file, motion prompt, duration, resolution.
3. Reference-to-Video (R2V) — workflow_api_minimaxh3_r2v.json
- Purpose: Full multi-modal conditioning incorporating reference images, reference audio files, and reference video clips simultaneously.
- Key Nodes:
MiniMaxH3ReferenceToVideo,VHS_LoadAudioUpload,VHS_LoadVideo,LoadImage. - Inputs: Reference audio track, reference face/character image, target prompt.
Category 3: Narrative Continuity & Multi-Scene Chaining
1. Clip-to-Video Continuation (C2V) — workflow_api_minimaxh3_c2v.json
- Purpose: Continues an existing video clip seamlessly into the next scene without cuts, preserving velocity, character positions, and lighting continuity.
- Key Nodes:
MiniMaxH3MotionContextLoadLatent,MiniMaxH3MotionContextTrim,MiniMaxH3MotionContextSaveLatent. - Method: Loads the previous scene's uncompressed latent cache, trims the tail motion context, and feeds it as the prior boundary for the new generation.
2. Reference + Clip-to-Video (Ref-C2V) — workflow_api_minimaxh3_ref_c2v.json
- Purpose: Extends C2V continuous chaining while actively enforcing reference image / audio adapters across sequential scene transitions.
Category 4: Interactive GUI Graph
workflow_minimaxh3_refmod.json: Comprehensive visual graph for the ComfyUI web UI with interactive sliders for RefMod blend curves (linear,constant,smoothstep), weight retention, and real-time audio playback preview.
5. Programmatic API Python Client Example
Below is a complete, standalone Python snippet demonstrating how to queue any of these API workflows through the ComfyUI REST endpoint (http://127.0.0.1:8188/prompt):
import json
import urllib.request
import os
import uuid
import time
COMFY_HOST = "127.0.0.1"
COMFY_PORT = 8188
def generate_minimax_video(
workflow_path="workflow_api_minimaxh3_fbc_refmod_turbo_8step.json",
refmod_name="minimaxh3_aneta_v1_refmod",
prompt_text=None,
width=1344,
height=768,
duration_sec=5.16,
fps=24,
seed=2026090595,
output_prefix="video/MiniMax_H3_API_Render"
):
with open(workflow_path, "r", encoding="utf-8") as f:
wf = json.load(f)
# Unwrap {"prompt": {...}} wrapper if present
prompt = wf.get("prompt", wf)
total_frames = int(duration_sec * fps) + 1
# Customize node inputs
for nid, node in prompt.items():
ctype = node.get("class_type")
# Prompt & Dimensions
if ctype in ["MiniMaxH3ImageToVideo", "MiniMaxH3ReferenceToVideo"]:
if prompt_text:
node["inputs"]["prompt"] = prompt_text
node["inputs"]["width"] = width
node["inputs"]["height"] = height
node["inputs"]["length"] = total_frames
# RefMod Loader
elif ctype == "MiniMaxH3RefModsLoader":
if refmod_name:
node["inputs"]["mod_1"] = refmod_name
node["inputs"]["strength_1"] = 1.0
# Random Seed
elif ctype == "RandomNoise":
node["inputs"]["noise_seed"] = seed
# Output filename prefix
elif ctype == "SaveVideo":
node["inputs"]["filename_prefix"] = output_prefix
# Submit to ComfyUI
payload = json.dumps({"prompt": prompt, "client_id": str(uuid.uuid4())}).encode("utf-8")
req = urllib.request.Request(
f"http://{COMFY_HOST}:{COMFY_PORT}/prompt",
data=payload,
headers={"Content-Type": "application/json"}
)
with urllib.request.urlopen(req) as resp:
res = json.loads(resp.read().decode("utf-8"))
prompt_id = res["prompt_id"]
print(f"Queued task successfully! Prompt ID: {prompt_id}")
# Poll execution progress
while True:
req = urllib.request.Request(f"http://{COMFY_HOST}:{COMFY_PORT}/history/{prompt_id}")
with urllib.request.urlopen(req) as resp:
history = json.loads(resp.read().decode("utf-8"))
if prompt_id in history:
status = history[prompt_id].get("status", {})
if status.get("completed") or status.get("status_str") == "success":
outputs = history[prompt_id].get("outputs", {})
for nid, nout in outputs.items():
for key in ["videos", "gifs", "images"]:
if key in nout:
for f in nout[key]:
print(f"Generation complete! Output file: {f.get('filename')}")
return f.get("filename")
elif status.get("status_str") == "error":
raise RuntimeError(f"ComfyUI Job Failed: {history[prompt_id]}")
time.sleep(3)
if __name__ == "__main__":
test_prompt = """subject_definitions:
<Subject 1> Aneta, authentic natural appearance
integrated_multimodal_description:
[Shot 1] Cinematic 35mm photograph, warm golden sunlight. Wide horizontal 16:9 framing showing <Subject 1> Aneta smiling warmly at the camera. Speaking English in Aneta's natural voice, <Subject 1> Aneta says: <d>Welcome to the new accelerated generation pipeline!</d>
overall_soundscape:
Gentle acoustic room ambience and clear vocal presence."""
generate_minimax_video(
workflow_path="workflow_api_minimaxh3_fbc_refmod_turbo_8step.json",
refmod_name="minimaxh3_aneta_v1_refmod",
prompt_text=test_prompt,
width=1344,
height=768
)
6. Standard Multimodal Prompt Architecture
MiniMax-H3 utilizes a structured prompt format parsed by Qwen3-VL:
subject_definitions:
<Subject 1> CharacterName, key visual attributes, authentic natural appearance
integrated_multimodal_description:
[Shot 1] Live-action, 35mm cinematic photograph, fine film grain, natural lighting. Continuous unbroken take, no cut. Opens as a medium shot framing <Subject 1> CharacterName. The camera moves in a smooth, continuous push-in gliding directly into a sharp close-up on her face and natural expressive smile. Never freeze. Never hold static. Speaking English in CharacterName's natural voice, and only the quoted words are spoken, <Subject 1> CharacterName (S1) says: <d>Your exact synchronized dialogue text here.</d>
overall_soundscape:
Ambient acoustics, room tone, realistic environment foley, and clear vocal presence.
non_diegetic_music:
N/A (or describe background score style)
7. License & Credits
- MiniMax-H3 RefMods & Workflows: Created by malcolmrey (huggingface.co/malcolmrey).
- Custom Nodes:
ComfyUI-MiniMaxH3Mod(Luisacaotica),ComfyUI-MiniMaxH3-FirstBlockCache(chengzeyi),comfyui-kjnodes(kijai). - LoRA Distillation:
LightX2V / MiniMax-h3-Turbo.