ComfyUI API を活用した MiniMax-H3 のマルチモーダル動画・音声生成パイプラインの実装

In this tutorial, we implement an end-to-end MiniMax-H3 video generation workflow using ComfyUI as a headless inference backend. We configure the environment around GPU memory, disk capacity, model precision, resolution, duration, sampling strategy, and multiple generation modes, while dynamically selecting an appropriate weight profile based on the available hardware. We install and launch ComfyUI programmatically, download the required diffusion, text-encoder, video-VAE, and audio-VAE weights from Hugging Face, and communicate with the running server through its HTTP and WebSocket APIs. We also construct the ComfyUI execution graph directly in Python, validate node schemas against the live /object_info endpoint, and support text-to-video, first- and last-frame-conditioned generation, and reference-image-conditioned generation. By combining automated model setup, schema-aware graph construction, joint video-audio decoding, progress monitoring, and output collection, we create a reproducible pipeline for experimenting with MiniMax-H3 without relying on the graphical ComfyUI interface.

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import json, os, re, shutil, subprocess, sys, time, uuid, urllib.request, urllib.error

from pathlib import Path

CFG = {

"MODE": "t2v",

"PROMPT": (

"Realistic live-action cinematic look. A lone lighthouse keeper on a storm-lashed "

"cliff at dusk, anamorphic lens, shallow depth of field, film grain, volumetric sea spray.\n"

"[0s-2s] Wide shot: waves detonate against black rock, the lighthouse beam sweeps the frame.\n"

"[2s-4s] Medium shot: the keeper braces against the wind, coat snapping, rain on his face.\n"

"[4s-5s] Close up: he squints into the dark and says \"She's holding.\"\n"

"Camera: hard cuts between shots, slight handheld jitter, no dissolves.\n"

"Audio: roaring surf and howling wind throughout, low cello drone underneath, "

"a heavy wave impact on each cut, the line delivered clearly over the storm.\n"

"No text, subtitles, logos or watermarks."

),

"ASPECT": (16, 9),

"MEGAPIXELS": 0.4,

"SECONDS": 5.0,

"SEED": 556589502035082,

"STEPS": 20,

"SAMPLER": "res_multistep",

"SCHEDULER": "simple",

"FIRST_FRAME": None,

"LAST_FRAME": None,

"REF_IMAGES": [],

"REF_IMAGE_SIZE": "match",

"SIGMA_SHIFT": None,

"TURBO_LORA": False,

"TURBO_STEPS": 8,

"TURBO_SAMPLER": "euler",

"TURBO_SCHEDULER": "beta",

"COMFY_DIR": "/content/ComfyUI",

"OUT_DIR": "/content/outputs",

"MODELS_ROOT": "/content/models",

"PORT": 8188,

"HF_TOKEN": os.environ.get("HF_TOKEN", ""),

"SKIP_INSTALL": False,

}

REPO = "Comfy-Org/MiniMax-H3"

API = f"http://127.0.0.1:{CFG['PORT']}"

PROFILES = [

dict(name="quality", min_vram=70,

unet_fl="minimax_h3_fl2va_bf16.safetensors",

unet_ref="minimax_h3_ref2va_bf16.safetensors",

te="qwen3vl_32b_minimax_h3_int8_convrot.safetensors",

flags=["--normalvram"]),

dict(name="balanced", min_vram=38,

unet_fl="minimax_h3_fl2va_pruned_int8_convrot.safetensors",

unet_ref="minimax_h3_ref2va_pruned_int8_convrot.safetensors",

te="qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors",

flags=["--normalvram", "--cache-none"]),

dict(name="squeeze", min_vram=20,

unet_fl="minimax_h3_fl2va_pruned_fp8_scaled.safetensors",

unet_ref="minimax_h3_ref2va_pruned_fp8_scaled.safetensors",

te="qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors",

flags=["--lowvram", "--cache-none", "--disable-smart-memory"]),

]

VAE_VIDEO = "minimax_h3_video_vae_fp16.safetensors"

VAE_AUDIO = "minimax_h3_audio_vae_fp32.safetensors"

def sh(cmd, cwd=None, check=True, quiet=False):

"""Run a shell command, streaming output."""

print(f"$ {cmd}")

p = subprocess.run(cmd, shell=True, cwd=cwd,

stdout=subprocess.DEVNULL if quiet else None,

stderr=subprocess.STDOUT if quiet else None)

if check and p.returncode != 0:

raise RuntimeError(f"command failed ({p.returncode}): {cmd}")

def get_json(path, payload=None, timeout=30):

url = f"{API}{path}"

data = json.dumps(payload).encode() if payload is not None else None

req = urllib.request.Request(url, data=data,

headers={"Content-Type": "application/json"})

with urllib.request.urlopen(req, timeout=timeout) as r:

body = r.read()

return json.loads(body) if body else {}

def align_frames(seconds, fps=24):

"""H3 consumes frame counts on the 17k+5 grid. Snap upward."""

n = max(5, int(round(seconds * fps)))

while n % 17 != 5:

n += 1

return n

def h3_canvas(aspect=(16, 9), megapixels=0.98, multiple=32):

"""Mirror of ComfyUI's ResolutionSelector + H3's 768*1344 area cap."""

ar = aspect[0] / aspect[1]

total = megapixels * 1e6

h = (total / ar) ** 0.5

w = ar * h

cap = 768 * 1344

if w * h > cap:

s = (cap / (w * h)) ** 0.5

w, h = w * s, h * s

r = lambda v: max(multiple, int(round(v / multiple)) * multiple)

return r(w), r(h)

def preflight():

try:

import torch

except ImportError:

raise SystemExit("PyTorch missing — run this in a Colab GPU runtime.")

if not torch.cuda.is_available():

raise SystemExit("No CUDA device. Runtime > Change runtime type > GPU (A100).")

name = torch.cuda.get_device_name(0)

vram = torch.cuda.get_device_properties(0).total_memory / 1e9

free_disk = shutil.disk_usage("/content").free / 1e9

bf16 = torch.cuda.is_bf16_supported()

print(f"GPU : {name} ({vram:.1f} GB VRAM, bf16={bf16})")

print(f"Free disk : {free_disk:.1f} GB")

if not bf16:

raise SystemExit(

"This GPU has no bf16 support (T4/K80). MiniMax-H3 will not run here.\n"

"Switch to an A100/L4/H100 runtime."

)

profile = next((p for p in PROFILES if vram >= p["min_vram"]), None)

if profile is None:

raise SystemExit(

f"{vram:.0f} GB VRAM is below the ~20 GB floor for the smallest H3 build."

)

if free_disk < 45:

print("WARNING: <45 GB free. Point MODELS_ROOT at Drive or expect a disk-full error.")

print(f"Profile : {profile['name']} (unet={profile['unet_fl']}, te={profile['te']})")

return profile

We define the core MiniMax-H3 configuration, model profiles, generation parameters, and shared utility functions used throughout the workflow. We calculate valid frame counts and canvas dimensions while checking GPU capability, available VRAM, BF16 support, and disk space before inference begins. We also automatically select the most appropriate model profile so the pipeline matches the hardware available in our Colab runtime.

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def install_comfy():

comfy = Path(CFG["COMFY_DIR"])

if CFG["SKIP_INSTALL"] and comfy.exists():

print("Skipping install (SKIP_INSTALL=True).")

return

sh("pip install -q -U 'huggingface_hub[hf_xet]' hf_transfer websocket-client")

if not comfy.exists():

sh(f"git clone --depth 1 https://github.com/comfyanonymous/ComfyUI {comfy}")

sh(f"pip install -q -r {comfy}/requirements.txt")

ver = (comfy / "comfyui_version.py")

if ver.exists():

print("ComfyUI:", ver.read_text().strip())

if not (comfy / "comfy_extras" / "nodes_minimax_h3.py").exists():

raise SystemExit("This ComfyUI checkout lacks native MiniMax-H3 nodes — update it.")

root = Path(CFG["MODELS_ROOT"])

for sub in ("diffusion_models", "text_encoders", "vae", "loras"):

(root / sub).mkdir(parents=True, exist_ok=True)

(comfy / "extra_model_paths.yaml").write_text(

"minimax_h3:\n"

f" base_path: {root}\n"

" diffusion_models: diffusion_models\n"

" text_encoders: text_encoders\n"

" vae: vae\n"

" loras: loras\n"

)

Path(CFG["OUT_DIR"]).mkdir(parents=True, exist_ok=True)

def fetch(repo_id, filename, subdir):

from huggingface_hub import hf_hub_download

os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

dest = Path(CFG["MODELS_ROOT"]) / subdir

target = dest / Path(filename).name

if target.exists() and target.stat().st_size > 1_000_000:

print(f"cached {target.name} ({target.stat().st_size/1e9:.1f} GB)")

return target

print(f"pulling {filename} -> {dest}")

try:

p = hf_hub_download(repo_id=repo_id, filename=filename, local_dir=str(dest),

token=CFG["HF_TOKEN"] or None)

except Exception as e:

if "401" in str(e) or "403" in str(e) or "gated" in str(e).lower():

raise SystemExit(

f"Access denied for {repo_id}. Accept the MiniMax-H3 community license on the "

"model page, create a read token, then set CFG['HF_TOKEN']."

) from e

raise

p = Path(p)

if p != target:

target.parent.mkdir(parents=True, exist_ok=True)

shutil.move(str(p), str(target))

return target

def download_weights(profile, mode):

unet = profile["unet_ref"] if mode == "r2v" else profile["unet_fl"]

fetch(REPO, f"diffusion_models/{unet}", "diffusion_models")

fetch(REPO, f"text_encoders/{profile['te']}", "text_encoders")

fetch(REPO, f"vae/{VAE_VIDEO}", "vae")

fetch(REPO, f"vae/{VAE_AUDIO}", "vae")

lora = None

if CFG["TURBO_LORA"]:

from huggingface_hub import HfApi

lora_repo = "drbaph/MiniMax-H3-Turbo-Lora-ComfyUI"

files = [f for f in HfApi().list_repo_files(lora_repo)

if f.endswith(".safetensors") and "pruned" in f]

if not files:

files = [f for f in HfApi().list_repo_files(lora_repo) if f.endswith(".safetensors")]

if files:

lora = fetch(lora_repo, sorted(files)[-1], "loras").name

print(f"turbo LoRA: {lora}")

return unet, profile["te"], lora

We install and configure ComfyUI, prepare the external model directory structure, and enable MiniMax-H3 support inside the Colab environment. We download the required diffusion model, text encoder, video VAE, and audio VAE weights from Hugging Face while reusing cached files whenever possible. We also optionally retrieve the Turbo LoRA configuration, allowing us to trade some generation quality for faster inference when required.

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class ComfyServer:

def __init__(self, flags):

self.flags, self.proc, self.log = flags, None, Path("/content/comfyui.log")

def start(self):

cmd = [sys.executable, "main.py",

"--listen", "127.0.0.1", "--port", str(CFG["PORT"]),

"--disable-auto-launch", "--preview-method", "none",

"--output-directory", CFG["OUT_DIR"]] + self.flags

print("$", " ".join(cmd))

f = open(self.log, "wb")

self.proc = subprocess.Popen(cmd, cwd=CFG["COMFY_DIR"], stdout=f, stderr=subprocess.STDOUT)

deadline = time.time() + 300

while time.time() < deadline:

if self.proc.poll() is not None:

print(self.log.read_text()[-4000:])

raise SystemExit("ComfyUI died during startup (log above).")

try:

stats = get_json("/system_stats", timeout=3)

dev = stats.get("devices", [{}])[0]

print(f"server up — {dev.get('name','?')} "

f"{dev.get('vram_total',0)/1e9:.1f} GB total, "

f"{dev.get('vram_free',0)/1e9:.1f} GB free")

return

except Exception:

time.sleep(2)

raise SystemExit("Server did not become ready in 300s. Check /content/comfyui.log")

def tail(self, n=3000):

return self.log.read_text()[-n:] if self.log.exists() else ""

def free_vram(self):

try:

get_json("/free", {"unload_models": True, "free_memory": True})

except Exception:

pass

def stop(self):

if self.proc and self.proc.poll() is None:

self.proc.terminate()

try:

self.proc.wait(30)

except subprocess.TimeoutExpired:

self.proc.kill()

class Schema:

"""Reads /object_info so the graph is validated against the *running* node set

instead of whatever the docs said last week."""

def __init__(self):

self.info = get_json("/object_info", timeout=120)

def require(self, *classes):

missing = [c for c in classes if c not in self.info]

if missing:

raise SystemExit(f"Missing node classes: {missing}. Update ComfyUI to >= 0.30.0.")

def inputs_of(self, cls):

spec = self.info[cls]["input"]

return list(spec.get("required", {})) + list(spec.get("optional", {}))

def check(self, cls, payload):

known = set(self.inputs_of(cls))

unknown = [k for k in payload if k not in known]

if unknown:

print(f" note: {cls} does not declare {unknown} — declared: {sorted(known)}")

def autogrow(self, cls, prefix, n):

"""Autogrow slots (ref_image_1, ref_video_1, ...) are dynamic; discover the

real names if the server exposes them, otherwise fall back to 1-based."""

found = sorted([k for k in self.inputs_of(cls) if k.startswith(prefix)])

if len(found) >= n:

return found[:n]

return [f"{prefix}{i+1}" for i in range(n)]

We create a server-management layer that launches ComfyUI as a background subprocess and verifies that it becomes available through its API. We monitor server startup, inspect GPU memory statistics, free VRAM when necessary, and safely terminate the server after execution. We also build a schema-inspection utility that reads live ComfyUI node definitions so we can validate graph inputs and dynamically discover supported node slots.

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class H3Graph:

def __init__(self, schema, unet, te, lora=None):

self.s, self.g, self._id = schema, {}, 0

self.unet, self.te, self.lora = unet, te, lora

def node(self, cls, **inputs):

self.s.check(cls, inputs)

self._id += 1

nid = str(self._id)

self.g[nid] = {"class_type": cls, "inputs": inputs}

return nid

def _backbone(self):

model = self.node("UNETLoader", unet_name=self.unet, weight_dtype="default")

if self.lora:

model = self.node("LoraLoaderModelOnly", model=[model, 0],

lora_name=self.lora, strength_model=1.0)

if CFG["SIGMA_SHIFT"]:

sv, sa = CFG["SIGMA_SHIFT"]

model = self.node("MiniMaxH3SigmaShift", model=[model, 0],

shift_video=float(sv), shift_audio=float(sa))

clip = self.node("CLIPLoader", clip_name=self.te, type="minimax", device="default")

vvae = self.node("VAELoader", vae_name=VAE_VIDEO)

avae = self.node("VAELoader", vae_name=VAE_AUDIO)

return model, clip, vvae, avae

def _tail(self, model, cond, latent, vvae, avae):

turbo = bool(self.lora)

steps = CFG["TURBO_STEPS"] if turbo else CFG["STEPS"]

sampler_name = CFG["TURBO_SAMPLER"] if turbo else CFG["SAMPLER"]

sched = CFG["TURBO_SCHEDULER"] if turbo else CFG["SCHEDULER"]

noise = self.node("RandomNoise", noise_seed=int(CFG["SEED"]))

samp = self.node("KSamplerSelect", sampler_name=sampler_name)

sig = self.node("BasicScheduler", model=[model, 0], scheduler=sched,

steps=steps, denoise=1.0)

guider = self.node("BasicGuider", model=[model, 0], conditioning=[cond[0], cond[1]])

out = self.node("SamplerCustomAdvanced", noise=[noise, 0], guider=[guider, 0],

sampler=[samp, 0], sigmas=[sig, 0], latent_image=[latent[0], latent[1]])

frames = self.node("VAEDecode", samples=[out, 0], vae=[vvae, 0])

audio = self.node("VAEDecodeAudio", samples=[out, 0], vae=[avae, 0])

vid = self.node("CreateVideo", images=[frames, 0], audio=[audio, 0], fps=24)

self.node("SaveVideo", video=[vid, 0], filename_prefix="MiniMaxH3/h3",

format="auto", codec="auto")

print(f" sampling: {steps} steps, {sampler_name}/{sched}")

return self.g

def _load_image(self, uploaded_name):

return self.node("LoadImage", image=uploaded_name, upload="image")

def t2v_or_flf2v(self, w, h, length, first=None, last=None):

self.s.require("MiniMaxH3ImageToVideo", "SamplerCustomAdvanced", "SaveVideo")

model, clip, vvae, avae = self._backbone()

kw = {}

if first:

kw["first_frame"] = [self._load_image(first), 0]

if last:

kw["last_frame"] = [self._load_image(last), 0]

n = self.node("MiniMaxH3ImageToVideo", clip=[clip, 0], vae=[vvae, 0],

prompt=CFG["PROMPT"], width=w, height=h, length=length, **kw)

return self._tail(model, (n, 0), (n, 1), vvae, avae)

def r2v(self, w, h, length, ref_names):

self.s.require("MiniMaxH3ReferenceToVideo")

model, clip, vvae, avae = self._backbone()

slots = self.s.autogrow("MiniMaxH3ReferenceToVideo", "ref_image_", len(ref_names))

refs = {slot: [self._load_image(nm), 0] for slot, nm in zip(slots, ref_names)}

print(f" reference slots: {list(refs)}")

n = self.node("MiniMaxH3ReferenceToVideo", clip=[clip, 0], vae=[vvae, 0],

audio_vae=[avae, 0], prompt=CFG["PROMPT"], width=w, height=h,

length=length, ref_image_size=CFG["REF_IMAGE_SIZE"], **refs)

return self._tail(model, (n, 0), (n, 1), vvae, avae)

We construct the MiniMax-H3 ComfyUI workflow graph entirely in Python using reusable node-building methods. We assemble the model backbone, conditioning pipeline, sampler, schedulers, joint latent decoding, video creation, and output-saving stages for both standard and Turbo configurations. We also support text-to-video, first- and last-frame-conditioned video, and reference-image-conditioned video generation through the same programmable graph architecture.

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def upload_image(path):

"""Multipart POST to /upload/image; returns the name LoadImage expects."""

path = Path(path)

if not path.exists():

raise FileNotFoundError(path)

boundary = uuid.uuid4().hex

body = (

f"--{boundary}\r\nContent-Disposition: form-data; name=\"image\"; "

f"filename=\"{path.name}\"\r\nContent-Type: application/octet-stream\r\n\r\n"

).encode() + path.read_bytes() + (

f"\r\n--{boundary}\r\nContent-Disposition: form-data; name=\"overwrite\"\r\n\r\ntrue"

f"\r\n--{boundary}--\r\n"

).encode()

req = urllib.request.Request(f"{API}/upload/image", data=body,

headers={"Content-Type": f"multipart/form-data; boundary={boundary}"})

with urllib.request.urlopen(req, timeout=120) as r:

info = json.loads(r.read())

sub = info.get("subfolder") or ""

print(f" uploaded {path.name}")

return f"{sub}/{info['name']}" if sub else info["name"]

def run_graph(graph, server, timeout=7200):

"""Submit, then follow the WebSocket for per-step progress."""

import websocket

cid = uuid.uuid4().hex

Path("/content/last_workflow_api.json").write_text(json.dumps(graph, indent=2))

try:

res = get_json("/prompt", {"prompt": graph, "client_id": cid})

except urllib.error.HTTPError as e:

detail = e.read().decode()[:3000]

raise SystemExit(f"Graph rejected by ComfyUI:\n{detail}")

pid = res["prompt_id"]

print(f"queued {pid} — first run loads ~37 GB of weights, be patient")

ws = websocket.WebSocket()

ws.connect(f"ws://127.0.0.1:{CFG['PORT']}/ws?clientId={cid}", timeout=60)

t0, last = time.time(), ""

try:

while time.time() - t0 < timeout:

try:

msg = ws.recv()