Smite79/MiniMax-H3-Longvideos

H3-LongVideos

Long MiniMax-H3 video with synchronised audio from a single prompt, in ComfyUI.

H3 renders about 15 seconds at a time. This node turns a written scene into a chain of shots: it splits your prompt into beats, sizes each shot, chains every shot from the previous one's last frame, and keeps your characters, their clothing and your props consistent from shot to shot — the things that otherwise drift or reset at every shot boundary.

One node covers both H3 conditioning tasks: FL2VA (a frame anchors the shot) and REF2VA (reference images say what a character looks like).

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Install

Copy this folder into ComfyUI/custom_nodes/ and restart the ComfyUI server (not just a browser refresh). No extra Python packages are needed — it uses only ComfyUI core's H3 support, plus Pillow (already shipped) for the text overlays.

Requires ComfyUI 0.31 or newer with native MiniMax-H3 support. Tested on 0.33.

What you need loaded

UNET	a MiniMax-H3 diffusion model
CLIP	H3's text encoder, loader type minimax
VAE	the H3 video VAE
audio VAE	the H3 audio VAE (a separate file)

Quick start

UNETLoader ─┐                     images ─> Video Combine / Save Video
CLIPLoader ─┼─> H3 Long Videos ─> audio  ─┘
VAELoader ──┘                     info   ─> Show Text

The text fields are input sockets, not boxes on the node — wire a multiline text node into them. prompt is required; leave it unconnected and the graph errors rather than rendering blank.

Set plan_only first. It previews the shot split, the lengths and every warning in seconds, without rendering anything.

The prompt

One paragraph = one shot. The first paragraph is the anchor — scene and style, repeated on every shot. Each paragraph after it is one beat.

Natural daylight, hard sun and deep shadow. Shallow depth of field. A farm
with a barn.

Dom drives a van down the driveway and stops in front of the barn.

Dom gets out and walks to the back of it.

Mara steps out of the barn and asks him: "Is that the last one?"

That is 3 beats, so 3 shots. Dialogue goes in double quotes — that is how the node knows which shots have speech.

character_memory

Who is in it and what they wear. This is the channel that stays consistent across the whole chain:

Dom = he, tall, 35, brunette, white t-shirt, blue jeans, work boots
Mara = she, 30, red hair, grey coat, black jeans

Declaring a pronoun matters: it is how "she takes off her coat" is attributed to the right person when two people are on screen.

Size and length

resolution picks the shape, megapixels picks the size. They are independent — changing aspect ratio does not change cost.

• megapixels 1.0 = 1024×1024 worth of pixels, which is H3's native budget.
• Step down for speed, VRAM and longer shots. 0.7 is a good working value on a 16 GB card.

shot_seconds is a ceiling, not the length of every shot. Wire the H3 Shot Length node into it (it also reports the matching frame count), or leave it unconnected and the VRAM budget decides.

Cost scales with latent cells — resolution and duration — and attention is quadratic in them. Lowering megapixels does far more for speed than any other setting.

Sampling

setting	value
cfg	1.0 — H3 is CFG-free; there is no negative prompt
sampler_name	res_multistep, or euler with PDD Acc
scheduler	simple
shift_video / shift_audio	12 / 3

Keep the two shifts about 4:1 apart. H3 carries the audio latent on the video schedule scaled by that ratio, so flattening it toward 1:1 breaks the audio.

steps depends on your LoRA: 4-step turbo LoRAs work at 4, but 6–8 looks noticeably better, and 8 is the top of the useful range.

Outputs

slot	what it is
images	the finished frames
audio	the synchronised soundtrack
info	what the node did, and every warning — read this
script	the exact per-shot text it built
soundscape	the ambient bed actually used
latent	the sampled latents, for latent post-processing
frames_per_shot, total_frames, shots, video_seconds, fps, fps_int	numbers for downstream nodes

info is the one to wire to a Show Text node. Nearly every problem the node can detect is reported there.

What it handles for you

• Beats → shots. One paragraph, one shot. Nothing silently merges them.
• Pacing. Each shot is sized from what its beat actually stages. A 3-second action in a 12-second shot is how a model ends up repeating or reversing it.
• Characters. Descriptions bind once per shot, at the first mention. Repeat names collapse to pronouns, because naming someone twice can render them twice.
• Clothing. Tracked per person. Removals are read from your prose ("takes off her jacket") and stated with direction so they don't play in reverse.
• Props. "the van" in a later shot means the van from the earlier one.
• Continuity guards. Limb counts, solid objects, continuous motion and two-body arrangements are each stated positively when the shot needs them. They have to be positive: at cfg 1 the negative prompt is never evaluated, and a negation in the positive names the thing it forbids.
• Audio. Shots without dialogue are silenced so the model doesn't invent speech; the ambient bed is matched in level across shots and carried between them so the room doesn't change at every cut.
• Seams. Each shot continues from the previous one's last frame, and the duplicate frame at the join is trimmed.
• Overlays. Optional watermark and intro title, composited after any upscale.

Reference images

Connect up to four images to ref_image_1…4. By default they land on the shot whose text names them:

Dom, <Picture 1>, drives a van down the driveway.

Every reference-conditioned shot also carries the previous frame as a keyframe, so using references never costs you continuity.

Optional third-party packs

None of these are required, and the node works without them.

• Latent upscale — latent_upscale drives the MiniMax-H3 Latent Upscaler by LBH-123-AI. Weights go in models/latent_upscale_models; the nodes that run them are the Comfyui_Minimax_h3_latent_Upscaler pack. All credit for the model and those nodes goes to LBH-123-AI. It upscales between sampling and decode, so the shot is sampled small and only decoded large — much cheaper than sampling large. Without the pack installed the setting does nothing and info says so.
• PDD Acc LoRAs — alibaba-pai/MiniMax-H3-Acc-LoRAs via the ComfyUI-MiniMax-H3-PDD-Acc pack. Files go in models/pdd_acc/, not models/loras/ — a plain LoRA loader cannot run them. Connect the Apply node's sigmas output to this node's sigmas input and keep sampler_name on euler.
• Sparse attention — the H3 SLA Attention node, paired with an SLA turbo LoRA. Place it on the MODEL wire after your LoRA loaders and last before this node, or ComfyUI prunes it and it never runs.

If something looks wrong

symptom	first thing to check
out of VRAM while sampling	lower megapixels, then shot_seconds. Tiled decode cannot help a sampling OOM
very slow, or wildly varying s/it	total staged model size against your system RAM — paging weights dominates everything else
characters drift between shots	vary_seed_per_shot off, and check info for shots that lost their handoff
a shot cuts instead of continuing	info names shots that dropped the keyframe, and why
speech where there should be none	shots without a quoted line are silenced automatically; info reports which
a LoRA errors on every block	the LoRA does not match your checkpoint's shapes — see info

plan_only costs seconds and answers most of these before a render.

Requirements

• ComfyUI 0.31+ with native MiniMax-H3 support (tested on 0.33)
• Pillow, for the text overlays only — ComfyUI already ships it
• No negative prompt: H3 is CFG-free at cfg 1
• No denoise input: fixed at 1.0, because partial denoise desyncs the audio schedule

Credits

The MiniMax-H3 Latent Upscaler behind the latent_upscale setting is the work of LBH-123-AI, and is distributed with its own ComfyUI nodes. All credit for the model and those nodes goes there; this node only calls them, and works without them.

Disclaimer

The owner of this repo will not be responsible for any copyright strikes incurred because of use. You are responsible for your works. Use this node responsibly and ethically.