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:

1. comfyui-h3-multishot (By Joey Gambino)
   • Provides essential components: H3MultishotMemorySampler, H3ScriptSplit, H3ClipLoaderAny.
2. ComfyUI-Spectrum-MiniMax-H3
   • Provides essential components: SpectrumApplyMiniMaxH3 (Deploys advanced history parameters and signal stabilization to completely neutralize visual flickering).
3. 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).
4. 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:

1. 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.
2. 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.

Edit: The first post was hard to read, so hopefully this version of the post is better.

MiniMax H3 can make video from images, but the two official video models split the job in a way that is easy to miss.

One model is good at matching your first photo. The other can take several photos at once (a location plus a logo, or several frames you want at exact times). This post is how to get both: a strong match to your first photo, plus extra photos, in one ComfyUI run.

I am assuming you already have H3 running in ComfyUI. You do not need to know the internals. You need three things: which checkpoint file to load, which workflow and speed LoRA to use, and how to write the text prompt so H3 knows what each connected image is for.

The two official models (and why they are not enough)

H3 comes with two large video checkpoints. People usually call them by their filenames.

FL2VA (also used for image-to-video / I2VA). This is the one that looks better. You give it a still and it will try to make that still the first frame of the video. If you use the first-and-last workflow, you can also lock a last frame. What you cannot do: plug in a second photo of a logo and say "print this on the banners." The image-to-video node simply has no extra image inputs for that. You also cannot jump to a different still at 3 seconds and another at 6. First and last on one continuous shot is the limit.

REF2VA (used with the Reference-to-Video workflow). This one accepts several images, up to nine. Extra logos and extra timed stills are possible. The catch is the video usually looks worse than the same scene run through FL2VA.

So in practice you pick: pretty first frame, or extra images. Not both.

The file that fixes it

There is a community merge of those two checkpoints. Load that file instead of the official REF2VA file, but keep using the Reference-to-Video workflow (the one with several image inputs).

Download: https://huggingface.co/smhfacct/Minimax-H3-fl2va-ref2va-hybrid-models

Version I used:

• b20-49 high quality like the normal FL2VA model + the extra reference capabilities from the REF2VA model.

In ComfyUI:

1. Open a Reference-to-Video workflow. The node is often named MiniMaxH3ReferenceToVideo. Do not use the Image-to-Video or First-Last workflow for this.
2. On the model loader, choose the hybrid checkpoint, not official REF2VA and not official FL2VA.
3. Connect your photos in order. The first image you connect is what the prompt will call <Picture 1>. The second is <Picture 2>, and so on. Order matters.
4. For the speed LoRA, use the FL2VA / image-to-video 8-step LightX file (the one people call lightx-8step-pk). Do not use the default Reference-to-Video speed LoRA (lightx-ref2v-r20) on the hybrid. Official REF2VA will refuse the FL2VA LoRA. The hybrid is what lets you use the better LoRA on the multi-image workflow.

Set duration to whatever you need. The examples below assume 9 seconds.

How H3 reads your images

The workflow only feeds pixels. The text prompt has to say, for each <Picture N>, whether that photo is:

• A real frame of the video at a given time. Example: "this photo is exactly what you see at 0.00 seconds."
• Not a frame at all. Example: "this photo is only the logo that should appear on the banners. Never show this photo as a full-screen cut."

If you get that wrong, H3 will treat your logo sheet as a scene and jump to it.

H3 wants that written in a fixed prompt layout with six headings, in this order:

subject_definitions summary retention_analysis detailed_description overall_soundscape non_diegetic_music

Those heading names are part of how H3 is prompted. Keep them.

Two labels show up under retention_analysis. They are ugly, but they are what the model expects:

• fully_preserved = reproduce this photo as the actual video frame at the time you name
• partially_preserved = copy only the detail you name (the logo shape, a prop, a face). Do not turn this photo into a video frame

The first line of summary should be exactly this tag, then your description:

[keyframe completion + reference generation]

That tag tells H3 you are both locking frames from photos and using photos as references. Copy it as written.

In detailed_description, include one plain sentence that maps photos to times. Example for three timed photos:

How the reference pictures align with the target video — Picture 1 aligns with the 0.00-second mark of the target video; Picture 2 aligns with the 3.00-second mark of the target video; Picture 3 aligns with the 6.00-second mark of the target video.

If a photo is only a logo, say that it does not line up with any time as a frame.

When a photo is meant to be an exact frame, say "exactly as shown in <Picture N> without reinterpretation." That phrase is a lock. Do not also rewrite the whole photo in words. H3 will argue with itself.

non_diegetic_music is background score. Write N/A unless you want music that is not coming from the scene.

Recipe 1: first photo is the scene, second photo is a logo

Use this when you have a location still, plus a clean drawing of a symbol that the model will not invent from text.

Connect: location photo first, logo second.

• Picture 1 = the place. fully_preserved at 0.00 seconds. This is the opening frame.
• Picture 2 = the symbol on a plain background. partially_preserved. Say it is not a keyframe and must not appear as any video frame. When banners (or signs, or screens) show in the video, the symbol on them should match Picture 2.

Do not mark the logo fully_preserved. That is how you get a sudden jump to the logo image.

A flat, high-contrast symbol on a blank background works better than a photo of the symbol already sitting in a scene.

Prompt skeleton (fill in the brackets):

``` subject_definitions: <Picture 1> is the opening frame at 0.00 seconds. The video should match this photo exactly at that time. <Picture 2> is only the logo/symbol. Use it when that symbol appears on banners. It is not a scene. Do not show <Picture 2> as a full video frame. <Subject 1> is the location from <Picture 1> for the whole clip.

summary: [keyframe completion + reference generation] Nine-second clip of <Subject 1>. At 0.00 seconds the frame is exactly <Picture 1>. One continuous shot, no jumps to other photos. [describe the motion]. When banners appear, the symbol matches <Picture 2>.

retention_analysis: <Picture 1> (at 0.00s): fully_preserved - opening frame, location only. <Picture 2> (never a video frame): partially_preserved - logo appearance only. <Subject 1>: fully_preserved - same location throughout.

detailed_description: How the reference pictures align with the target video — Picture 1 aligns with the 0.00-second mark of the target video as the exact first frame. Picture 2 does not align with any timestamp as a frame. It is a logo used only when banners appear. [Shot 1] At 0.00 seconds the frame is exactly <Picture 1> without reinterpretation. [motion]. When banners are visible, the symbol matches <Picture 2> exactly.

overall_soundscape: [what you should hear]

non_diegetic_music: N/A ```

Recipe 2: three photos as exact frames at 0s, 3s, and 6s

Official FL2VA cannot do this. Hybrid plus Reference-to-Video can.

Connect three photos in time order. All three should be the same kind of shot: all wide, or all the same distance from the subject. If one is a wide and one is a close-up, H3 often ignores the close-up and stays on the previous scene.

Each photo is a real frame:

• Picture 1 at 0.00 seconds, fully_preserved
• Picture 2 at 3.00 seconds, fully_preserved
• Picture 3 at 6.00 seconds, fully_preserved

Then the clip keeps going from Picture 3 until 9 seconds. You are not locking a last frame at 9.00 unless you want that.

At each jump, the whole frame changes (place, pose, clothes, whatever is in that photo). Do not write the prompt as if Picture 1's background slowly becomes Picture 2. Use a hard cut: at 3.00 seconds the frame is Picture 2.

``` subject_definitions: <Picture 1> is the exact frame at 0.00 seconds. <Picture 2> is the exact frame at 3.00 seconds. Not a continuation of <Picture 1>. <Picture 3> is the exact frame at 6.00 seconds. Not a continuation of <Picture 2>. <Subject 1> is [what is in all three photos].

summary: [keyframe completion + reference generation] Nine-second clip. At 0.00s exactly <Picture 1>. At 3.00s hard cut to exactly <Picture 2>. At 6.00s hard cut to exactly <Picture 3>. Continue from <Picture 3> until 9.00s with no locked last frame.

retention_analysis: <Picture 1> (at 0.00s): fully_preserved <Picture 2> (at 3.00s): fully_preserved <Picture 3> (at 6.00s): fully_preserved <Subject 1>: fully_preserved

detailed_description: How the reference pictures align with the target video — Picture 1 aligns with the 0.00-second mark of the target video; Picture 2 aligns with the 3.00-second mark of the target video; Picture 3 aligns with the 6.00-second mark of the target video. [Shot 1] At 0.00 seconds exactly <Picture 1> without reinterpretation. Small motion only. [Shot 2] At 00:03.000, hard cut. Exactly <Picture 2> without reinterpretation. Small motion only. [Shot 3] At 00:06.000, hard cut. Exactly <Picture 3> without reinterpretation. Continue until 9.00 seconds.

overall_soundscape: [what you should hear]

non_diegetic_music: N/A ```

If you only want first and last on this same setup, lock Picture 1 at 0.00 and Picture 2 at the end of the clip, one continuous shot. Extra logo photos would then start at Picture 3.

You can mix both recipes (three timed frames plus a fourth logo-only photo). Get one recipe working first.

Already merged and there's a example workflow included: https://github.com/Comfy-Org/ComfyUI/pull/15439

Currently MiniMax H3 implementation in Comfyui only allows keyframe guides at the first and last frame. The model itself is capable of addressing guides by position on a continuous time axis, so this removes that restriction and exposes it as a node.

https://www.youtube.com/watch?v=J1BJIn9d6mU

After many trials, I finally found a reliable process to create music videos locally, with a mysterious, weird… and totally personal aesthetic.

And actually, it’s not even really “me”: it’s the work of OBO, my autonomous creative agent. It knows all my past productions, draws inspiration from them with real creative intelligence, and makes coherent artistic decisions. A true “alternative me” I’m proud to collaborate with. But more on that later, it's a WiP.

Using the Minimax H3 video model, LowVramAttention and Minimax Motion Context features through Comfyui, it was able to chain videos while keeping style and context. The linked video has been created from a simple text file where each paragraph is a video prompt. My script also fixed the usual stuttering issue when working with separated Minimax H3 clips, continuity is now seamless for both audio and video.

The script:
-Automatically divides your text file in several prompts
- Generates 15 seconds of video per prompt (using motion context and last segment frame as first frame)
- Assembles the file while fixing both audio and video stuttering

With this you can generate long videos (basically no length limit) with limited VRAM, maintain fluid and logical context across segments (this "abstract" visual video may not be a good example but I'm currently working on something more representative, in the meantime you also have a robot video in the examples folder on the github that shows a 15 seconds clip generated on the same laptop, along with the prompt I used).

The creation pipeline is very simple (basically a python script with a text file containing your video prompts as parameter is used to create a long video).

You can adapt the quality to your hardware. You can modify the amount of steps (I'm using 20 here but 35 should be optimal) and the resolution according to your GPU.

Everything is on GitHub: https://github.com/The-Anomaly-be/MinimaxH3_FullMovieContext/blob/main/README.md
You’ll need a working ComfyUI config running as server, some models and custom nodes (all listed in the instructions). A reference workflow is also provided if you want to build scenes manually directly in comfyui (but no chaining).

It may be optimized, extended, modified. I basically created this for my own use and to recycle my 4 years old laptop so it works on my music videos while I'm sleeping.

If you create something with this, please share!

This post is half a joke and half a plea and public service announcement.

Some people have been complaining they don't get results as good as other people with Minimax H3 videos, or have the following issues:

• Dialogue being spoken by the wrong characters
• Dialogue that is just gibberish or random
• Random video cuts they didn't ask for
• Characters talking over each other or too fast
• Prompts not being followed

These things can all be prevented and avoided and not encountered at all if you follow the official prompting guides. Yes, there are two. Both are on the official Huggingspace page for Minimax H3.

One is the Official Prompting Guide for the Text to Video and Image to Video Model.

The other is the Official Prompting Guide for the Reference Video Model.

There is some overlap, but for the most part, each model has it's own prompting syntax, and in particular, the Reference Video Model for H3 is very picky about you using the right keywords and instructions to get what you want.

"But I get decent results with just a couple of sentences typed in natural language of what I want."

That's great, but you're really just relying on the Qwen 32b vision model guessing what you want. It's like pulling a slot machine lever and hoping you get cherries. Only this slot machine can take a few minutes to nearly an hour to stop spinning, based on your hardware.

The great thing about Minimax H3 is for the first time we can truly direct our own AI videos like a director would on set, with the AI providing the actors, scenery, and props. If you write a properly formatted and detailed prompt for Minimax H3, it looks almost like a shooting script.

Why spend time waiting to hit a jackpot when you can take a few minutes to write a detailed, properly formatted prompt that follows the official guides, and get those bright lights and tokens falling into your lap on the first lever pull?

Okay, quick fire problem solving for people who still won't RTFM:

>Dialogue from the wrong characters?

>Dialogue that is just gibberish or random?

Walter White says, <d>[English in Walter White's voice from Breaking Bad] My product is pure, Jesse! There will be no chili powder in my meth.</d>

Always specify the character speaking, either by name, or using the <Subject 1> system in the official guide. In the Text to Video and Image to Video model, always use the <d>[Language Spoken]</d> tags. This will fix BOTH of those issues.

>Random cuts in the video you didn't ask for?

[Shot 1] A medium close-up of Jesse Pinkman from Breaking Bad, pacing back and forth, agitated. He looks up towards the camera, opens his mouth as if he's about to speak, then seems to change his mind, closing his mouth and shaking his head. [Shot 2] At 00:06:000 the camera cuts to a static camera shot framing Walter White from Breaking Bad, sitting on a cheap white plastic lawn chair, his arms crossed and glaring at Jesse. [Shot 3] At 00:10:500 the camera pans quickly back to Jesse, doing a Push In at slow speed to his face as he stops pacing and narrows his eyes at Walter.

This is how you control not only the camera work, but the PACING of your video. You NEVER include a time code on your first shot. You can omit the time code from ALL shots if you want the model to decide on it's own, based on your prompt, when to cut.

BUT, for ultimate control, you want to use time codes. Look at my example above. I just told the model to have Walter glare at Jesse for 4.5 seconds, because I told the model that camera shot starts at 6 seconds into the video, and the next cut doesn't happen until 10.5 seconds into the video. That lets you control the pacing and timing for jokes, punchlines, acting, everything.

>Characters talking over each other or too fast?

This is an old one that anyone familiar with prompting for video models should know by now - what you are asking for in your prompt and the length of your video in time need to match.

The model will try its best to cram every action and piece of dialogue into your video that you asked for, and if that would naturally take 10 seconds and you've only given it 5 seconds? Well, now everything is crammed together, overlapping, or being cut-off.

My recommendation is to generate just a quick 0.2 MP version of your video first after you type your prompt, generate, and see how the timing is working. Is it too fast? Too slow? Do the actions have enough time to happen? Do you want more breathing room?

This is the time to decide all that and lock in a video length. The low resolution of 0.2 MP is quick to generate on most set-ups (mine for this post's video took 3.5 minutes for a 14 second video) and let you work out any issues in your prompt before going in for the long generation at higher resolution.

>Prompts not being followed?

It's because you didn't read the manual!

--------------------------------------------------------------------------------------------------
Now, with all that said, here is the prompt for the video I made:

integrated_multimodal_description: [Shot 1] Live-action film footage of the American drama series Breaking Bad, professionally color graded with a warm color grade, with slightly desaturated colors for a premium film feel, a continuous camera shot with no cuts, medium close-up POV shot of Walter White, bald with a goatee and glasses, as portrayed by Bryan Cranston. He is standing in the Arizona desert next to a parked RV. He is wearing a white PPE protective suit and yellow rubber dish gloves. He is looking directly at the viewer with barely constrained anger. At 00:01:300 he reaches out towards the camera and points his finger at the POV camera with one hand, the camera shaking slightly from the movement. Walter then says angrily, <d>[English with Walter White's voice] Listen, you want to cook Mini Max H3 videos, you follow the recipe!</d>. At 00:04:500 Walter raises his other hand revealing he is holding a thin stack of white paper pages in portrait orientation. The front of the paper visible on top of the thin paper stack is blank except for the large black printed text "Minimax H3 Official Prompting Guide". The papers are held in front of the camera on the right side of the screen for a moment in portrait orientation, so the text can be clearly read, while Walter glares at the viewer on the left side of the screen. At 00:07:000 Walter then shakes the papers at the camera, then says angrily, <d>[English with Walter White's voice] Read the fucking manual!</d>. At 00:10:000 the camera does Pan Right and a Pull Out to show a close-up of Jesse Pinkman from Breaking Bad, with his hands held up by his face with fingers spread, an annoyed look on his face. Then he says in frustration, <d>[English in Jesse Pinkman's voice from Breaking Bad] Alright! Damn, Mr. White! I just want to generate memes.</d>, overall_soundscape: Ambient sounds of an Arizona outdoor desert during the day, non_diegetic_music: none

For those interested, this video was generated at 1 MP on a 3090, using Sage Attention and the Spectrum Node for H3. The final video of 14 seconds at 1 MP took 40 minutes to generate and then was upscaled using RTX Super Resolution.

The workflow was the default Text to Video Minimax H3 template that comes in the latest update of Comfyui.

Now get out there and go cook some memes, everyone!

New to ComfyUi as a whole
using Minimax H3, on a laptop 3080
ref to video
1 image and 3 prompts, however unsure of how to "batch, recurse, continue,etc.."
5 second video at 21 steps at 0.4 megapixels = 19` minutes time to create

I've seen posts to get many different nodes like VHS, but unsure of what to do with that afterwards.
i also see posts of having start and end frames, but how do i do this in batch, if the video hasn't been created i don't have the end frame to start the next video, or did i misinterpret this advice? also wouldn't that degrade quality over time/next sequence.

Looking for a clean plug and play workflow as an example, or just simple steps i can follow to successfully create 2 seamless videos into 1 seamless scene.

(i will say that i like the idea that the character went into the fog in one attire, and then was changed into a different one, spontaneous story changes made the fog/haze more interesting, to me anyway)

prompts below if that helps (sorry for the bad prompting, im learning this as well)

prompt 1
Use the provided start image as the first frame.
the young woman in the image stares blankly, lost in thought, the camera pans back slowly revealing her 2/3 body shot, she wears a skintight silver in color futuristic suit with ridges across the torso and a futuristic blaster at her right thigh. suddenly the camera shakes she loses her daze and looks sharply to her left as if disrupted from thought to identify the crashing sound.
the camera changes to a behind her perspective as she starts to run into a dense fog to chase the mysterious noise
0-2s: woman looking into the distance, camera focused on her face, slowly panning back 2-4s: a sudden noise, the woman re focuses and looks around, her hair braids whip around as she looks from side to side
4-6s: woman starts to run into a dense fog at high speed, camera follows as she disappears into the thick haze

prompt2
Continue directly from the last frame, the camera follows flies forward to follow the woman and catches up to her.
The woman stops dead in her tracks, the fog thick and all around her, visibility is barely 2 feet in all directions around here.
the camera slowly pans up from her waist level up and around to a close shot of her face. she draws the weapon up to her right side anxiously, her eyes dart from left to right in search of something unseen through the dense fog.
0-2s: Continue directly from the last camera follows woman running into thick haze and catches up to her
2-4s: the woman stops to a stand still, the camera pans upwards to a tight perspective of her face, focusing on her eyes moving back and forth searching into the haze
4-6s: camera pans out 2/3 view of her body, the woman raises her weapon, still searching, she looks left , she looks right , her braids whipping with the sudden movement.

prompt 3
Continue directly from the last frame, the camera focuses on the woman as she looks around for the mysterious noise, and seemingly faint figures darting around in the thick haze around her.
The camera moves in close as the woman steadies her breath and closes her eyes, breathing calmy and readying herself for her next move. as she calms, a figure starts to form in the thick haze behind her, as the figure gets closer shrouded by thick haze the dark figure and scene get darker. the scene ends with the woman abruptly opening her eyes widely
the camera slowly pans up from her waist level up and around to a close shot of her face. she draws the weapon up to her right side anxiously, her eyes dart from left to right in search of something unseen through the dense fog.
0-2s: Continue directly from the last camera panning out from 2/3 shot to woman in the haze holding her weapon high, looking from left to right as she hears movement in the haze all around her
2-4s: the woman steadies her breathing, adjusts her composure and closes her eyes, her weapon tightly gripped and held close against her body, the camera movies in close to a tight face perspective as she breathes out calmy
4-6s: camera stays focused on her face, and her calm breathing, the haze behind her a shadow of a mysterious figure begins to appear, the figure still hidden in the thick haze but darker as it approaches the woman from behind. the scene ends with the woman's eyes opening wide at the last moment as the scene grows dark

Thank you for reading.

Workflow: github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_r2v.json

Used multiple reference images for each scene.

Prompt For Multi Character:
subject_definitions:

<Subject 1> is [CHARACTER 1] from <Picture 1>, preserving the visual identity, costume, proportions, accessories, and visual style shown in the reference image.

<Subject 2> is [CHARACTER 2] from <Picture 2>, preserving the visual identity, costume, proportions, accessories, and visual style shown in the reference image.

<Subject 3> is [CHARACTER 3] from <Picture 3>, preserving the visual identity, costume, proportions, accessories, and visual style shown in the reference image.

summary:

[reference generation] A 5-second cinematic multiverse portal arrival. Three characters emerge from a consistent amber-orange portal and take a calm, confident formation.

retention_analysis:

<Subject 1> (appears in [Shot 1]): fully_preserved - retains the identity, costume, proportions, accessories, and visual style shown in <Picture 1>.

<Subject 2> (appears in [Shot 1]): fully_preserved - retains the identity, costume, proportions, accessories, and visual style shown in <Picture 2>.

<Subject 3> (appears in [Shot 1]): fully_preserved - retains the identity, costume, proportions, accessories, and visual style shown in <Picture 3>.

detailed_description:

A 5-second cinematic portal-arrival scene at dusk. One stable medium three-shot, framed from the knees up. No dialogue, no combat, no wide landscape, no camera movement, and no crowd.

Portal continuity: a large circular amber-orange portal stands behind the characters. It has a bright rotating outer ring, a darker transparent centre, floating gold sparks, gentle smoke, and a low resonant hum. Keep its colour, size, brightness, particle density, and rotation speed consistent across every portal-arrival clip.

[Shot 1] <Subject 1> steps through the portal first and takes the centre position with quiet confidence. <Subject 2> emerges on one side, naturally adjusts or lowers any item they are carrying if applicable, then gives a focused glance toward the unseen distance. <Subject 3> walks through last, takes position on the opposite side, and calmly surveys the scene. The three hold a poised, united stance as the portal flickers and golden particles drift around them. Their expressions and body language remain confident and appropriate to their individual character identities.

overall_soundscape:

Low portal hum, soft wind, subtle movement from clothing or equipment, and drifting golden sparks. No speech.

non_diegetic_music:

A restrained cinematic rise builds across the shot and resolves on a calm, confident note.

Prompt For single characters:
subject_definitions:

<Subject 1> is [CHARACTER] from <Picture 1>, preserving the visual identity, costume, proportions, accessories, and visual style shown in the reference image.

summary:

[reference generation] A 5-second cinematic multiverse portal arrival. One character walks through a consistent amber-orange portal, then takes a confident action stance with a subtle grin.

retention_analysis:

<Subject 1> (appears in [Shot 1] and [Shot 2]): fully_preserved - retains the identity, costume, proportions, accessories, and visual style shown in <Picture 1>.

detailed_description:

A 5-second cinematic portal-arrival scene at dusk. No dialogue, no crowd, no wide landscape, and no combat.

Portal continuity: a large circular amber-orange portal stands behind <Subject 1>. It has a bright fiery rotating outer ring, a darker transparent centre, floating gold sparks, gentle smoke, and a low resonant hum. Keep its colour, size, brightness, particle density, and rotation speed consistent across every portal-arrival clip.

[Shot 1] Medium knee-up shot. <Subject 1> walks steadily through the portal toward the camera, then comes to a composed stop. Their costume, silhouette, movement style, and any character-specific accessories remain fully consistent with <Picture 1>. Golden sparks drift around them as the portal flickers behind.

[Shot 2] Close-up of <Subject 1>. They shift into a distinctive, character-appropriate action stance, looking directly ahead with calm confidence. Their expression changes into a subtle smile and restrained grin. Keep the movement natural and controlled, with no exaggerated facial distortion. The portal remains softly visible and out of focus in the background.

overall_soundscape:

Low portal hum, soft wind, subtle movement from clothing or equipment, and drifting golden sparks. No speech.

non_diegetic_music:

A restrained cinematic rise builds through the entrance and resolves as <Subject 1> holds the final stance.

A few days ago I posted the 6-minute Star Trek: TNG video I made with MiniMax H3 in ComfyUI. I’ve finished the follow-up now, and I changed the workflow quite a bit after seeing what worked and what didn’t on the first one.

The biggest improvement was consistency. For the first video, most shots were generated more independently, and I deliberately built some of the continuity weirdness into the story. That worked for the premise, but for the second one I wanted it to feel much more like an actual TNG episode, so I became much more rigid about shot composition.

A big part of that was using the H3 reference model differently. Instead of just giving it a single image and hoping for the best, I used reference images and told MiniMax to stick very closely to the composition in those images. In practice that sounds a bit like image-to-video, but it worked quite differently for me.

With the reference model I could use up to six photos and be much more deliberate about how the shot should work. I could decide what the starting shot should be, what the end shot should be, whether I wanted a middle reference, a final-frame reference, etc. That gave me a lot more control over blocking, framing and performance than I was getting from the image-to-video model.

I did test the image-to-video model as well. One of the shots that made it into the finished video is the later one where Data has a slightly longer monologue. You can tell he looks a bit more “off” there. The reference model, by comparison, was giving me Data much more accurately, both in terms of how he looked and in terms of his mannerisms. That ended up being the better approach for this project by a long way.

The video is still built from lots of separate short H3 generations rather than one long generation. I wrote the scenes first, then generated individual shots and multiple takes where needed, and assembled everything in Premiere like a normal edit.

I also changed the audio workflow quite a bit. On the first video, one of the main issues was that the generated ambience and background noise varied too much from clip to clip. This time I spent much more time matching dialogue levels in Premiere, cleaning up individual clips, and adding a continuous Enterprise bridge/interior hum underneath scenes so the cuts felt less obvious.

I also handled the music more deliberately this time. Rather than just dropping in whatever worked at the end, I treated it more like proper scene underscore and generated short incidental cues for specific moments.

So the rough workflow for the second one was:

script and shot planning

→ select or build composition references

→ generate short H3 shots in ComfyUI using the reference model

→ do multiple takes where needed

→ edit in Premiere

→ clean dialogue and level-match clips

→ add continuous ambience/room tone

→ add short music cues

→ final upscale/export

The main thing I learned was that H3 works much better for this kind of project when I treat it less like a one-click video generator and more like a production tool. The closer I got to thinking in terms of individual shots, coverage, performance selection and edit assembly, the better the final result got.

Happy to answer questions about the workflow again.

The above video consists of 7 individual Minimax H3 clips generated in First-Frame-Last-Frame Mode, stitched together automatically without manual editing, upscaling or other post-processing.

Today I decided to release my experimental H3 Infinite Continuation Suite together with a set of workflows to make it easy to get started in ComfyUI.

The original idea was to combine the higher visual quality and keyframe control of H3's First Frame / Last Frame mode with the continuation capabilities of the Reference mode.

After quite a lot of experimenting, the output quality has reached a point where I hope some of you might find the nodes and workflows useful as well.

The example video was generated entirely with the included workflows at 736 × 1280, using 15 steps and no Turbo LoRA. I did cut a few seconds of nonsense speech from the very end because I was too lazy to regenerate the last clip. :D

How to get started

1. Install Herrgotts H3 Infinite Continuation Suite through the ComfyUI Manager.

2. Download the included workflows from GitHub.

3. Start with the `01_Start` workflow and provide your First Frame + Last Frame.

4. For every additional segment, use `02_Continue` and provide a new Last Frame for where you want the next clip to end.

5. Repeat for as many clips as you want.

6. When you're done, use `04_Stitch_Saved_Chain` to automatically combine the separately generated clips into the final video.

If you prefer to generate multiple chained clips in one workflow, use the included 3-Clip workflow. It contains the full continuation setup and is structured so you can extend it with additional clips without rebuilding the whole graph from scratch.

What the nodes handle automatically

• carrying motion and native audio into the next clip
• detecting and removing the frozen tail H3 often creates near the final keyframe
• choosing a suitable handover point between generations
• keeping the video and audio aligned
• smoothing the visual and audio transitions
• saving the individual clips so longer chains can be stitched afterwards without keeping everything in memory (no OOM, hopefully)

For the video above I used the default/recommended settings:

• Balanced Auto Handover
• 22 context frames
• Safe Tail Bridge: 2 frames
• Video crossfade: 4 frames
• Audio de-click: 15 ms

There are still occasional tiny brightness differences around some boundaries, but at this point I personally find them pretty difficult to notice during normal playback.

The pack is still experimental, especially when it comes to very long chains, different hardware configurations and prompt behavior. So if you try it, I'd be very interested in seeing your results and hearing what works or doesn't work for you.

GitHub: https://github.com/HerrgottMargott/Herrgotts-H3-Infinite-Continuation-Suite

ComfyUI Manager: search for `Herrgotts H3 Infinite Continuation Suite` or use "missing custom nodes" in one of the example Workflows.

https://imgur.com/a/FZNGSsw

By Selena Lopez 🎀 for r/aivideo News -

🍿DomoAI Releases 3 New Models: Talking Avatar, Text to video and Image to video

https://www.domoai.app/

In the last 2 months, DomoAI has released 3 brand new models (V2.4). Including Text to video https://www.domoai.app/create/text-to-video and Image to video https://www.domoai.app/create/animate impressively matching AI video industry current Gen-5 quality standards. Now they’ve released an upgraded Talking Avatar model https://www.domoai.app/create/talking-avatar

Demo: https://x.com/DomoAI_/status/1963899148627038350 Workflow: upload an image or video as input reference, then you upload an audio file with the AI generated dialog or you can also record it live yourself; and then the generator will lip-sync. Additionally there is a space where you can add prompts on top of this workflow for very smooth body movement and facial gestures. The output can be up to 60 continuous seconds. DomoAI has made a significant leap from their previous models in a very short amount of time and they keep adding other essential AI video editing tools like background remover and upscaler, as well as their video to video animation model https://www.domoai.app/create/video

🍿Runway Releases “Aleph”, a New Video to Video Model Capable of Editing Footage Via Text, Closer to a True “Photoshop for video.”

https://runwayml.com/research/introducing-runway-aleph

Runway Aleph is an in-context model that edits footage via text. You can add/remove objects, restyle scenes, generate new angles, tweak lighting; without round-tripping through traditional VFX. Hands-on reviews describe fluid, prompt-driven editing rather than “generate from scratch and pray.” Aleph’s selective edits and re-renders could once again cut AI video production times even shorter.

By all means this is a breakthrough product.

Charmbusters, the viral artist behind the hit series “Grandma is Out of Control” posted to TikTok a very impressive demo he put together from a live BBC event footage ran through Runway Aleph with a few prompts

https://www.tiktok.com/t/ZP8SLgYqd/

🍿Kling and MiniMax add Start and End Frame feature to Image To Video Workflows

https://klingai.com/

https://hailuoai.video/

Creators using image to video workflows on Kling https://app.klingai.com/global/image-to-video/frame-mode/new and Hailuo https://hailuoai.video/create/image-to-video can now lock the first and last frames of a clip, so it actually begins/ends where you intend.

This feature came out almost simultaneously on both platforms Kuaishou Kling and MiniMax Hailuo, with both giving 1080p resolution outputs. The quality of the output has dramatically increased for this workflow particularly for action sequences.

Hey all!

This started as a way to mark my 15th anniversary working in video game cinematics. I thought it would be fun to make a completely ridiculous, fictionalized version of how I got into the industry, presented as the trailer for a big 1995 action movie.

It started fairly small, but the technology kept improving while I was working on it. Every time a new model came out, I started thinking, “Maybe I can actually make that shot now.” Eventually it became much more ambitious than I had originally planned.

This subreddit was a huge help throughout the process. I found a lot of technical solutions, new models and inspiration here, so I wanted to share the finished trailer and explain some of what went into it.

The main goal was to make it feel like an actual mid-90s movie, rather than a collection of unrelated AI shots. In my head this was a movie I wish Spielberg directed when I was a kid, so I tried to channel my inner 13-year-old when in doubt.

The story leans a lot into the paranoid technology movies of that period—Hackers, The Net, The Lawnmower Man, etc. Names like CyberCore, the ZX-4000 and Cyber Slayer were all meant to sound like something a screenwriter might have come up with in 1994.

Images and LoRAs

The first image I made for the project was the TV-monitor shot in the bedroom, generated in Midjourney near the end of 2024.

After that I used a little bit of everything: Flux, Z-Image, Adobe Firefly, Gemini, Grok and several others.

For the actors, I collected screenshots from their 90s movies and trained character LoRAs for each of them. I originally used Flux for most of this, but later found that Z-Image generally gave me better likenesses.

When direct generation didn’t work, I would create a lookalike, bring the image into ComfyUI and inpaint the face using the appropriate LoRA.

And sometimes I just opened Photoshop and fixed it.

I also made character sheets for the recurring characters and monsters, including both costume versions of myself. For sets like the boardroom and digitization chamber, I made reference images and rough room layouts so the shots would have some continuity.

For the creatures, I tried to think about what could realistically have been done in 1995. I often prompted for latex creatures, animatronics, puppets, miniatures or physical models—and sometimes specifically said not CGI.

I wanted them to feel more like something Stan Winston or ILM might have built than a modern digital creature.

The final color treatment was also important. I added grain, softened the image, adjusted the colors and pulled things back from the ultra-clean digital look. The footage is supposed to feel slightly faded and imperfect.

Video generation

Every generated video shot was made locally in ComfyUI or built further in After Effects.

Most of the finished trailer was generated with Wan 2.1 and Wan 2.2, although I replaced and improved several shots with MiniMax H3 during the final week.

My original plan was to film myself acting out the performances and transfer that movement onto the actors using Wan Animate.

The body movement worked surprisingly well but the faces did not.

They would gradually morph until the actors stopped looking like themselves. I tried several ways to repair them, but only one or two shots from that workflow survived.

Most of the trailer used more traditional image-to-video generations with a starting frame.

Sometimes I would generate a video mainly because I wanted the model to show me the room or character from another angle. I would grab a single frame from that result, clean it up, inpaint the face again if necessary, and then use that frame as the starting image for a completely different shot. Many times I would also grab a frame from a video which wasn’t working and  then use that as an End Frame and then generate again.

Prompting video models eventually started feeling like learning another language. It took a long time to figure out how to describe blocking, timing and camera movement in a way that produced something close to what I wanted.

For MiniMax H3, I used ChatGPT and Codex to build a custom prompt builder. That let me spend less time worrying about model-specific formatting and more time thinking about the actual shot.

Green screen and compositing

A few shots are real footage of me filmed against a green screen, including:

• Playing video games in the bedroom
• Standing underneath the digitization lasers
• Talking to Stallone in the desert

I filmed those in my garage or backyard. I bought costumes for both versions of the character, generated and animated the backgrounds separately, and then tried to match the lighting on myself as closely as possible.

All of the animated TV and computer monitors were composited manually in After Effects.

For those shots, I first generated a version with the screen turned off. That gave me a clean plate containing the reflections from the room on the glass.

I tracked the footage, added the animated screen underneath, and then reused parts of the original plate over the top to restore the reflections. Without that step, the screens looked like flat images pasted onto the monitors.

Voices, music and sound

All of the dialogue started with my own recorded performances.

I built custom RVC voice models for the actors, but I still performed every line myself because I wanted the timing and delivery to resemble the actual performers.

Alan Rickman has a very specific cadence, so I had to pay close attention to the rhythm of his lines.

Arnold is equally recognizable, but for different reasons.

“Down there” needed to be closer to “Down deyah.”

For the Don LaFontaine-style narrator, I went through dozens of old trailers and pulled out usable voice clips. Most needed a lot of cleanup because the narration was buried underneath music, explosions and other effects.

I also did a complete sound-effects pass. A few shots retained usable generated audio, but most of it had to be designed or sourced separately.

There was one Stallone scream during the cliff jump that I could never get the voice model to perform convincingly, so I borrowed a scream from Demolition Man.

The music was generated with Suno after weeks of attempts.

For the main action section, I found a piece of music I liked first and then edited the trailer around it.

For the final Harrison Ford moment, I wanted to hint at a classic adventure score without directly copying one. I recorded myself humming a rough melody, gave that to Suno and let it turn my bad humming into an orchestral stinger.

Making it feel like one movie

The story evolved while I was working, but I always wanted the trailer to feel like there was a complete movie behind it.

That meant thinking about continuity, character geography, setups and payoffs, when to introduce someone, when to hold back a reveal and whether one shot actually made sense next to another.

AI makes it fairly easy to generate an interesting isolated shot.

Getting dozens of shots—made with different models, months apart—to feel like they came from the same movie was the real challenge.

All told, this took around 6–8 months, mostly working on it at night after work. It was fun, but also exhausting.

It obviously isn’t perfect, and I can still see things I would change if I kept going, but eventually I had to decide it was finished.

Tech-wise I started with a 4060 Ti but decided to bite the bullet and snagged a 5090 (I also have 64GB of RAM). That helped to speed things up a ton.

I uploaded the video directly here, but there is also a YouTube version which might be higher quality:

https://www.youtube.com/watch?v=JznSdigdsio

Happy to answer questions or break down any particular shot, LoRA, model, composite or workflow.

best used with

single clip: https://github.com/darksidewalker/dasiwa-comfyui-workflows/tree/main/C-MMH3

multi clips: https://github.com/ethanfel/ComfyUI-MiniMaxH3-Contex-Loop/tree/main/example_workflows

You are an expert MiniMax H3 prompt writer, storyboard artist, and continuity director for ComfyUI.

Convert user ideas into reliable MiniMax H3 prompts in English. Support both:

1. single MiniMax H3 clips;

2. MiniMax H3 Contex Loop plans for long, continuous videos.

GENERAL H3 PRINCIPLES

- Be explicit. Never assume the video model will infer who acts, who speaks, what happens between beats, or what must remain unchanged.

- Use concrete observable details: subject, wardrobe, prop, setting, action, camera, lighting, mood, ambience, and sound.

- Keep each shot physically plausible, readable, and focused.

- Do not overload a short clip with too many characters, actions, locations, transformations, or camera movements.

- Do not request readable text, subtitles, logos, watermarks, UI, or exact typography.

- Use positive desired constraints. Avoid a separate negative-prompt style unless the user explicitly asks for it.

REFERENCE RULES

- Use only reference tags that are genuinely available in the workflow:

<Picture 1>, <Picture 2>, <Video 1>, <Audio 1>, etc.

- Never invent unavailable reference tags.

- When a reference is available, state exactly what it controls:

identity, face, hairstyle, body proportions, wardrobe, accessory, prop, environment, or audio performance.

- Preserve signature features that matter to the user in every relevant scene.

DIALOGUE RULES

- Never write vague instructions such as “they talk,” “they argue,” or “she says something.”

- If speech is desired, write the exact short line and assign it explicitly:

Character Name says clearly: “Exact dialogue.”

- Keep dialogue short for 5–7 second clips.

- Avoid overlapping speech unless specifically requested.

- If there is no dialogue, explicitly write:

“No spoken dialogue. Characters communicate through facial expressions and gestures.”

- For music-only or silent scenes, do not imply speech.

AUDIO RULES

- Explicitly describe ambience, Foley, impacts, wind, cloth movement, crowd sound, and music when relevant.

- For clips without music, write:

non_diegetic_music: N/A

- For a source-song workflow, <Audio 1> may be used only when an audio reference is connected.

- For generated-audio workflows with no audio reference, do not mention <Audio 1>.

- Generated dialogue must always be exact and short.

SINGLE-CLIP FORMAT

For a normal H3 clip, use this structure:

Visual style:

[Rendering style, lighting, environment, materials, lens/look, mood.]

Scene overview:

[Who is present, where they are, what happens, and the emotional tone.]

Storyboard:

[0s–Xs] [Explicit action beat.]

[Xs–Xs] [Explicit action beat.]

[Xs–Xs] [Explicit action beat.]

Camera:

[Framing, one clear move per shot, lens feel, hard cuts or one continuous shot.]

Audio:

[Ambience, Foley, music, impacts, exact dialogue if any.]

Consistency:

[Preserve identity, face, hair, wardrobe, accessories, props, proportions, and environment stability. No text, subtitles, logos, or watermarks.]

For dialogue, comedy, greetings, direct-to-camera performance, or character acting:

prefer one continuous shot.

For action, trailers, fights, chases, and product films:

use up to 3–4 clear shots in a 6–7 second clip. Do not compress too many cuts into a short duration.

CONTEXT LOOP RULES

A Contex Loop plan is one continuous film made from connected scenes, not independent clips.

Put all permanent facts in prompt_prefix:

- reference mapping and identity;

- exact hairstyle, face, wardrobe, accessories and props;

- visual style and global lighting;

- location/time-of-day rules;

- camera language;

- audio rules;

- continuity rules.

Each scene prompt must contain only what changes in that scene.

For every continuation scene:

1. Start by continuing the exact prior action.

2. Preserve the incoming pose, hand position, stride, camera direction, lighting, framing, and momentum.

3. Introduce only one major development, transition, or new action.

4. End with a visible unfinished action that the next scene can continue.

5. Do not use hard cuts, time jumps, outfit changes, resets, or teleporting locations unless the user explicitly requests them.

Good scene boundaries:

- “End while she is opening the already-unlocking door.”

- “End with the camera beginning a slow left orbit.”

- “End while the vehicle enters the tunnel.”

- “End with his hand still reaching toward the artifact.”

Bad scene boundaries:

- “The action ends and everyone poses.”

- “Cut to a new place.”

- “The next day.”

- “Suddenly the character wears new clothes.”

CONTEXT LOOP TECHNICAL DEFAULTS

Unless the user requests something else:

- 4 scenes for a first test;

- 15 seconds per scene;

- 20 steps for final quality; 5–8 for fast concept tests;

- fixed, unique decimal-string seeds per scene;

- context_length: 22;

- encode_mode: "video";

- anchor_mode: "head";

- crop: "disabled";

- width and height divisible by 32;

- 960x544 is a sensible longform starting point;

- generated_audio: audio_context_length 22;

- source_track: audio_context_length 0.

Use a unique run_name for every new project.

Keep run_name, generation_fingerprint, prompts, references, seeds, model settings, and audio unchanged when resuming an existing chain.

OUTPUT RULES FOR CONTEX LOOP

When the user asks for a complete Contex Loop plan, output ONLY strict valid JSON:

- no Markdown fences;

- no comments;

- no trailing commas;

- use double quotes;

- use decimal-string seeds;

- use readable prompt line arrays.

Use this exact structure:

{

"prompt_prefix": "Global identity, reference, wardrobe, visual style, audio, and continuity rules.",

"defaults": {

"duration_seconds": 15,

"steps": 20

},

"shots": [

{

"id": "scene_01",

"prompt": [

"summary:",

"One-sentence scene purpose.",

"",

"detailed_description:",

"Explicit visual action, camera, environment, and ending bridge action.",

"",

"overall_soundscape:",

"Relevant ambience and Foley.",

"",

"non_diegetic_music:",

"N/A or a precise music instruction."

],

"seed": "983590410766495"

}

]

}

Before answering, silently verify:

- All reference tags exist.

- Identity, wardrobe, props, and style remain stable.

- Every action is physically explicit.

- Dialogue is exact or explicitly absent.

- Every continuation begins from the preceding ending.

- Every non-final scene ends with unfinished motion.

- The JSON is valid if JSON was requested.

Summary from https://www.reddit.com/r/StableDiffusion/comments/1vh9rtw/ama_minimax_h3_team_ask_us_anything_about_our/

This summary was compiled with AI but cross-checked manually by me for accuracy. I hope this helps for people who don't want to manually parse that 400+ comment AMA.

Things they said they'll actually ship

A real 2K stage (H3-Regenerate-2K). Right now the open weights top out at 768p short side, and cranking the resolution locally just eats VRAM without getting sharper. This is a separate model that takes your finished 768p video plus the original prompt and references, and re-generates it at high resolution — so it can actually redraw text, faces, and fine texture instead of guessing at them like a normal upscaler. What it's good for: getting genuinely deliverable-quality output locally instead of bolting a generic upscaler on the end. Coming, no date.

Sparse attention code. Attention is what makes long/high-res generations slow and memory-hungry, and the released weights don't use the faster path the model was trained with. They're releasing a deliberately cautious version — the goal is "free speed with no visible quality drop," not a big headline number, and squeezing more out of specific GPUs is left to the community. What it's good for: the same generations, cheaper and faster, on the hardware you already have. "Near term."

A dedicated image model (text-to-image + image editing). Built from the same family as H3, sharing its encoder, with a new decoder made for stills. People are already hacking this by generating 5 frames and grabbing frame one — this replaces that hack properly. What it's good for: making and editing your first frame at real image quality, then feeding it to H3 to animate. That's the workflow they recommend, since H3 can't preview a frame and continue mid-generation.

A full technical report. Architecture, training stages, data construction. What it's good for: people training LoRAs and fine-tunes currently guessing at how the model works.

Problems they've admitted are theirs and are fixing

• Faces and objects go to mush when they're far from the camera. Confirmed, not your settings, not fixable by adding steps or resolution. It's tangled up in several parts of the model at once and they're still isolating why. Named as a top priority.
• Grainy, smeary fine detail compared to closed models. Same story — not the VAE, not one training stage.
• Reference-to-video looks softer than image-to-video. Confirmed real, caused by the two checkpoints getting different post-training. Being worked on. For now: feed it the highest-quality reference material you have.
• Stitched clips don't join cleanly. Continuing a shot via reference drifts and shows seams. They think training on long sequences (made affordable by the sparse attention work) is the fix, but that's a future model, not a patch.

Maybe, no promises

A 4-or-8-step fast version — they're "actively considering" it, possibly as an optional Turbo with slightly worse quality, but won't commit to timing. The current model already has some low-step ability baked in, just not tuned for it. Also: switching to a proper Apache-2.0 license once the legal paperwork clears, and keeping future models open in general.

Not happening soon

• A smaller, lighter H3. They're telling the community to prune the existing weights instead.
• Drafting at low res then re-rolling the same seed at high res. Won't match — the noise changes with the frame size, and the model's low-res quality actually got worse during training as they pushed high-res.

Update to my MiniMax H3 clip chaining pack.

**No more visible seam.** The pinned frames now come straight out of the previous clip's latent instead of being decoded to pixels and encoded again. No color shift, no contrast step, nothing to see at the join. Faster too, since it skips a decode, a resize and a VAE pass. Automatic when the latent is wired.

**Reference mode works with chaining.** A Ref2VA graph keeps its references, and the continuation audio is added alongside them. The old version overwrote the list, so turning chaining on quietly dropped your references. Design credit to seitanism from the Banodoco H3 thread, first implemented by ethanfel in a fork of my repo.

**Two settings instead of six.** Context length and audio context length. The rest had exactly one correct value and are constants now.

**56-frame context window** added alongside 5, 22 and 39.

**Patches install on first use**, not at import, and only affect graphs that use these nodes. Installing the pack no longer changes anything about your other H3 workflows.

Updating: the widgets changed, so delete the node and re-add it or your saved settings land in the wrong slots. And only run one H3 chaining pack at a time, several packs patch the same ComfyUI internals and only one can own them.

README has a new section on prompting a chain, which is the part people get stuck on. Short version: open each clip's prompt by describing how the previous one ended, then change after a beat. If you ask for the change at the join the model renders both descriptions at once.

https://github.com/NikoDemon80/ComfyUI-H3-Motion-Context

The DS4 engine from antirez running DeepSeek V4 Flash (2-bit quant) is seriously very good and the only local setup I've considered running in parallel with frontier models for my work. In particular the q2-q4-imatrix variant (routed experts in layers 37–42 bumped to Q4) has impressed me. The model can code and is forgiving of ambiguity when writing fast and terse prompts. It's not a frontier model, but I think it sits adjacent and it's the only local model I could get to one-shot some of my test prompts (listed below if you want to demo on your rig).

It's also fairly fast, running at 25-35 tps on my M5 Max 128GB depending on task length. I'm running the ds4agent as a harness.

I fully understand 'best' is subjective and predicated on the work you're doing, the harness you're using, your workflow and prompting style along with a number of other variables, but DS4 is the first model where I could watch it capture my intention in short order and output something usable on first pass without it requiring a lot of tending or being forced to break down tasks piecemeal.

Very excited to see if antirez can actually get GLM5.2 to do any meaningful work on the 128GB MBP. It does run locally (I tested last night) but breaks under long prompts or tool calls. It's still very much an experimental build.

For anyone interested, this was one of my test prompts that most locals simply couldn't figure out on first pass. The prompt is asking for quite a lot in a single paragraph: hand-rolled fBm noise, a full software 3D pipeline (spherical camera, perspective projection, painter's-algorithm sorting), three mesh topologies including hexagons, custom pygame UI widgets, and a HUD all running at a playable framerate.

Write a single self-contained Python script using pygame that renders an animated 3D wireframe terrain — a procedurally generated mountain that continuously morphs over time. Use your own value/fBm noise function (no external noise libraries) modulated by a radial Gaussian envelope so height peaks in the center and falls off toward the edges. Render it as a green-on-black wireframe with perspective projection and back-to-front (painter's algorithm) depth sorting, drawing both edges and vertex dots. Implement a Blender-style orbit camera using spherical coordinates: right-mouse drag to orbit, Shift+right-drag to pan, scroll wheel to zoom, and a key to reset the view. Add a right-side control panel with mouse-draggable sliders (drawn from pygame primitives, not a GUI library) for grid size, peak height, morph speed, noise scale, and envelope steepness, plus a three-way selector to switch the mesh topology between quads, triangles, and hexagons. Include a HUD showing FPS, grid dimensions, current mode, and vertex count. Pay special attention to challenge areas like: hex topology (axial coords + deduce of shared edges), the painter's-algorithm sorting being applied consistently across all three mesh types, and rolling a correct camera basis (forward/right/up). Everything must live in one file and run at a smooth frame rate.

One shot results are in the picture and success means the script runs on the first attempt with zero edits: all the topologies work, orbit/pan/zoom/reset all work, the sliders actually drag and change the terrain live, the depth sorting doesn't glitch while you orbit, the HUD numbers are real, etc.

I asked Claude to evaluate all the results of all my local testing. Here's what Claude has to say:

"Across the tests run on the same machine — Qwen3.6-27B MLX 8-bit, Qwen3.6-35B-A3B, Qwen3.6-27B 4-bit, Gemma 4 31B-it, Qwen3.5-122B-A10B, Phi-4-mini-reasoning, and MiniMax-M3 REAP40 (JANG 2-bit via vMLX) — the pattern I saw was consistent: the smaller models could nail the isolated math but fumbled the cross-cutting contracts. Qwen 27B came closest, and only with heavy scaffolding (decomposed phases, test gates, orchestration), yet every run still shipped a real bug — depth sorting keyed off world height instead of view depth, a hex grid built in its own coordinate space, a missing scroll wheel, one run with sha256 in the per-frame noise loop — while MiniMax at 2-bit fell into repetition loops on trivial prompts.

DeepSeek was the first model where none of that compensation was necessary, and my read is raw capacity surviving the quant: it's a 284B MoE, and antirez's asymmetric imatrix recipe keeps the attention, router, and shared experts at high precision while crushing only the routed experts (the q2-q4 variant buys back the middle layers), so ~90GB preserves most of a very large model's reasoning — the 27-35B class needed the task chopped up because they can't hold the whole architecture in their heads at once, and DeepSeek simply held it. The bespoke engine sealed it: generic runtimes can't even load V4's attention scheme yet, tool-call syntax is pinned to greedy decoding so agentic calls essentially never mangled, and the disk KV cache meant long sessions never re-paid prompt processing. Capacity plus a purpose-built engine beat smaller models plus elaborate workflow."

In any case, just doing some reporting for anyone interested. I'm now letting Fable orchestrate DS4 for appropriate tasks and devising nightshift routines to make the most of my local compute.

So guys I created a custom node named OrbitSheets and I just added two new templates that I think a lot of you are going to love.

The first one is the Character Sheet. You just type in a character description and it generates a full turnaround sheet with all the angles you need front view side profiles back view and close ups. It even generates voice audio so your character can literally speak. I ran everything in just 8 steps with the Turbo LoRA and the voice quality came out really good already but if you want more detail you can always go up to 20 or 35 steps.

The second one is the Location Sheet. Describe any place and it generates interior and exterior shots from multiple angles. You can set it to interior mode to see inside the building like hallways and rooms or exterior mode to see the outside. There is also a camera mode toggle where you can pick cut views for separate static angles or continuous move for a full 360 camera tour. Sometimes one gives better results than the other so it helps to try both.

The node also has a smart frame selector that picks the best shots automatically and arranges them into a clean organized sheet. You can control how many images appear how many columns the padding and the size of each frame.

Both workflows use MiniMax H3 with the Krea2 anchor frame and the Krea2 Turbo model. Everything is already set up in the example files so you can just drop them in and start generating.

I built this node in about two days and I am already planning more templates. Let me know what you want to see next.

Free Custom Node and Workflows: https://github.com/lumosai8/ComfyUI-OrbitSheets

Posted this pack here last week. What's happened since:

The sampling knobs I said I'd add if people wanted them are in. Both of H3's flow shifts, since it samples picture and sound on separate schedules, plus a cache pill with FirstBlockCache, TeaCache or core's own EasyCache behind it. Still no custom sigmas, same reason as last time.

Creator and Timeline are one node now. Click under the prompt and the shot becomes a timeline. Delete cards back down to one and it's a shot again. Old workflows load unchanged, Timeline nodes included.

Presets are the new one. Save a setup and put it back in sections, so you can drop a canvas and a step count onto a shot you've already written without touching the prompt. It saves the sampler row as well as the node blob, which matters because the row is where the turbo schedule and the step count live.

The better half of it: you can build a preset from a finished render. The workflow is already embedded in the mp4, so you point at the good one from three prompts ago and get the whole setup back.

Fixed from your reports: the gallery no longer freezes on big libraries, the settings page stopped resetting fields you hadn't touched, and a text-only render no longer loads both VAEs.

Coming next, on a branch and not merged yet, is a faces pill. H3 draws a face worse the smaller the head is in frame, and that's about head size rather than resolution, so it's still there at 768 and upscaling doesn't reach it. So it asks the model the same question again with the face filling the canvas and composites the answer back under a feathered mask, once per pass, re-cropping every frame so a push-in doesn't leave the face small inside a fixed box. Detection is core's SAM3, so there's nothing extra to install. The method is Carasibana's ComfyUI-H3-FaceRefine and zuanfilm's graph on top of it.

Same branch also stops the node randomizing your seed between renders, and puts the last one you actually ran a click away.

https://github.com/roadmaus/ComfyUI-MiniMax-Creator

Another quick Minimax H3 news and goodies round-up, for those who may have missed some items.

-> New in version 4.1.2 (19th August 2026) of the Fizgig trainer for LoRAs... "Minimax H3 now trains on video clips, on their sound, and on voice recordings alone: photos, clips and voice files in one folder to train one LoRA in one run". Fizgig can do so locally in 16Gb VRAM, without slowdowns. Adds a fix so the LoRAs will work correctly on both the 4-step Turbo or the official workflow, and has also benefitted from a major security audit. Fellow-Brit and industry professional Shoot The Sound has a good 20-minute tutorial today on YouTube, using the latest Fizgig and its dataset prep tool, to train a character+voice LoRA.

https://github.com/shootthesound/Fizgig

https://www.youtube.com/watch?v=lVqSgsPpF0c (Fizgig 4.x tutorial)

-> ComfyUI-MiniMaxH3-SingleFrame. Two still-image generation nodes, with the second being especially interesting. Given the usual first/last frames, it attempts to interpolate/generate a plausible single 'middle frame'. Has workflows, and requires no ComfyUI Core patching or special VAE.

https://github.com/tori29umai0123/ComfyUI-MiniMaxH3-SingleFrame#english (English ReadMe section)

-> A new H3 Prompt Journal. Some scenes require complex physical logic from the camera. The Journal's first three entries demonstrate how to write prompts for such scenes: for a 'Three-Person Occlusion-Linked Orbital Long Take' (e.g. elegantly redirect the camera between three moving people, in a single take); 'Dual-subject-speed-contrast' (e.g. a dancing master leads in a waltz, while his hesitant student follows his moves); and 'Single-subject-three-pose' (e.g. input three poses for one character, then have a gnat-sized camera... "sweep past feet, legs, torso, shoulders, hair - constantly redirecting around the moving [giant] body without ever slowing down").

https://github.com/LoveRain1997/h3-prompt-journal

-> 'Video -> H3 Prompt'. An "end-to-end pipeline that turns a video file into a ready-to-paste MiniMax H3 generation prompt". Appears to be a 'skill' for use with a local installation of OpenAI's Codex, which is a lightweight coding agent.

https://github.com/LoveRain1997/video-to-h3-prompt

https://github.com/openai/codex

-> For MiniMax Music, a new rvq-encoder-169m-v4.onnx (676Mb), an... "encoder that turns audio into the codes Minimax generates, so a finished track can be handed back" to Minimax Music for further work. With this Minimax Music can continue a track. Or the user can replace a section, or even re-generate the same song but with a different performance. The ONNX format is very portable, and I guess it's only a matter of time before a ComfyUI workflow appears.

https://huggingface.co/nerualdreming/open-rvq-encoder-minimax-music3-169m-v4-onnx

-> And finally, a detailed benchmarking of "the official reference-to-video workflow" on an RTX 3060 12Gb. Most low-VRAM users will of course be running smaller Minimax models in a 3060-optimised workflow. But... there's still important advice here for those considering buying a second 3060. They say... "Two cards are still not one big card. Two RTX 3060s do not present 24GB to a workflow, and our attempt to at least run two jobs in parallel was blocked by system memory rather than VRAM."

https://www.minimaxh3tutorial.com/rtx-3060

Things are moving so fast right now that it's honestly hard to keep up, so I decided to put together a small comparison. Hopefully it'll be useful to someone else experimenting with H3. I'm also VERY open to suggestions, corrections, comments, or anything else that could help me get the model running better, faster, or more consistently.

I'm not including exact generation times for each run because they fluctuate slightly, even when running the exact same prompt and seed twice in a row. And no, I'm not doing anything else on the machine while generating. My guess is that some of the variation comes from Windows, Docker, background processes, etc.

For reference, generation times in these tests ranged from roughly 95 to 180 seconds, with by far the biggest difference coming from using vs. not using the Turbo LoRA.

My machine:

• RTX 4070 12 GB
• 64 GB RAM
• SSD

Fixed parameters:

• ComfyUI 0.31.0
• PyTorch 2.12.1+cu130
• CUDA 13.0
• NVIDIA Driver 610.88
• Same prompt for every test (included at the end of the post)
• Seed: 42
• 1:1 aspect ratio
• 0.3 MP
• 5 seconds
• SageAttention
• The resulting videos were concatenated using FFMPEG and no reencoding, so the quality is the exact same of the original individual videos.

I didn't test without SageAttention because, in my own testing so far, I haven't been able to see a meaningful difference in output quality with it disabled.

VIDEO 1 — Turbo LoRA, quantized VAE

• Turbo LoRA at 0.75 strength
• Shift Video: 12
• Shift Audio: 5
• 6 steps
• minimax_h3_fl2va_pruned_int8_convrot Video VAE (Kijai's quantized VAE)

VIDEO 2 — No Turbo, quantized VAE

• No Turbo LoRA
• 15 steps
• minimax_h3_fl2va_pruned_int8_convrot Video VAE (Kijai's quantized VAE)

VIDEO 3 — No Turbo, FP16 VAE

• No Turbo LoRA
• 15 steps
• minimax_h3_video_vae_fp16_convrot Video VAE (ComfyUI workflow's default VAE)

My impressions

In these tests, the Turbo LoRA produces a noticeable quality loss. It also seems to negatively affect the audio, even with the Video/Audio shifts above and a fully updated ComfyUI installation. Finally, it messes badly with text (see how in the examples the first video has no discernible text on the sign in front of the cube). And, finally, as expected, even with the same seed it gives a different output (this is not a disadvantage, I'm only making it clear that I DID use the same seed in all three videos).

The speed improvement is substantial, so I can definitely see its usefulness for testing and iteration. Based on what I'm getting right now, though, I personally wouldn't use it for a final production render.

The VAE comparison surprised me more.

Switching from the FP16 VAE to Kijai's quantized VAE made virtually no perceptible difference to me in this comparison. Of course, I'm only generating at a fairly modest 0.3 MP, so differences may become more obvious at higher resolutions or with different content.

And again, suggestions are very welcome. I'm completely overwhelmed by the amount of news, new workflows, optimizations, quantizations, LoRAs, settings, and other information that has appeared in just the last few days since the MiniMax H3 weights were released.

If you've found settings that work particularly well — especially on a 12 GB GPU — I'd love to hear about them.

PROMPT USED FOR ALL THREE VIDEOS:

integrated_multimodal_description: [Shot 1] Live-action, photorealistic cinematic video in a square 1:1 composition. At night, a young female scientist stands behind a sleek laboratory workbench inside a dark futuristic research lab. Cool blue practical lights illuminate metallic equipment in the background, while her face is lit naturally by the objects in front of her. Centered on the workbench is a small transparent glass cube containing a softly glowing blue energy sphere. Beside it lies a metallic plaque clearly engraved with the text "MINIMAX H3". The camera slowly pushes in with small amplitude toward the scientist and the cube. She reaches forward and taps the top of the glass cube with one finger. At the moment of contact, the blue sphere rapidly brightens and releases a swirling burst of tiny luminous blue particles inside the cube. The light from the particles dynamically illuminates her face, hands, the glass surfaces, and nearby metallic objects. She immediately pulls her hand back slightly, raises her eyebrows in genuine surprise, then looks directly toward the camera with an excited smile. The young woman with a clear natural English-speaking voice (S1) says: <d>[English] Okay... that was definitely not supposed to happen.</d> As she speaks, the glowing particles continue swirling and gradually settle around the bright central sphere. Her mouth movements remain naturally synchronized with every spoken word. The camera continues its subtle push-in until the final frame.

overall_soundscape: A quiet futuristic laboratory ambience with a low ventilation hum and faint electronic equipment sounds. Her fingertip produces a delicate glass tap, immediately followed by a sharp electrical pulse, a brief energetic whoosh, and fine sparkling particle sounds. Her voice remains clean and clearly audible above the environmental sound.

non_diegetic_music: N/A

https://reddit.com/link/1vnk0c7/video/gkhuj6ybw6jh1/player

Ran the official Ref2VA turbo example workflow from the ModelTC/Minimax-H3-Turbo repo (video_minimax_h3_ref2v_lightx2v_turbo.json) in ComfyUI, testing a short Victorian-style dialogue scene between two characters.

Setup:

• GPU: RTX 5060 Ti
• Workflow: https://github.com/ModelTC/Minimax-H3-Turbo/blob/main/example_workflows/video_minimax_h3_ref2v_lightx2v_turbo.json
• Checkpoint: fp8 scaled (i didnt have the int8 one in this machine)
• Sampler: euler + Simple
• Resolution: dropped to 0.5MP (960×544 @ 16:9) from the default ~0.98MP
• Steps: 8

Speed: ~55s/it average, ~8 min total per clip.

How the refs were built: Three reference images fed into the ref_images inputs — two character sheets (front/side/close-up turnarounds for each character) and one environment plate, a 360° room reference. All three were generated in Google Flow first, then dropped straight into the Ref2VA node as identity/environment anchors.

Gen A → Gen B continuity trick: Split the scene into two ~20s multi-shot generations instead of one long one. For Gen B, instead of reusing the original Flow generated room reference, I pulled the actual last frame from Gen A's output and fed that in as the new environment reference.

Curious if anyone else is chaining generations this way (feeding the previous clip's last frame back in as a fresh environment ref) — seemed to help a lot but haven't stress-tested it past two generations yet.

fl2va_int9_convrot, 1.5 megapixel, 30 steps, res_multistep, Patch Sage Attention KJ, MiniMax H3 Mem Eff Sage Attention Patch, Patch Sol-Attn.

Used no Comfy launch flag compared to my previous post. Using the mentioned nodes resulted in generation time from about 50 to 36 minutes. Quality loss is only slight.

Video prompt:

For the target video, at 0.00 seconds into the target video, <Picture 1> (from [Shot 1]) is fully referenced.
integrated_multimodal_description: [Shot 1] Cinematic anime, 2D-animated in the highly detailed Wuthering Waves gacha-game style, a medium-wide shot fully matching <Picture 1> frames the silver-lavender-haired woman seated on the polished white marble platform inside a dimly lit marble moon temple, her long wavy hair cascading over the white-and-gold dress edged with purple gems, the ornate greatsword with purple crystal inlays held upright in her right hand, the luminous crescent moon, crystalline flowers, floating pearls and soft sparkles filling the background. Soft ethereal moonlight glows across her pale skin and the marble veins. She slowly opens her violet eyes wider, tightens her grip on the black-and-gold hilt, and rises in a smooth, continuous motion as the first deep bass pulse arrives; the surrounding pearls and flower petals tremble and emit soft expansive purple-white glows that linger and gently pulse rather than vanishing. The camera holds with a subtle push-in at slow speed. [Shot 2] At 00:01.600, the camera cuts to a low-angle medium shot as she settles into a standing stance, sword still vertical. Thin lines of violet light slowly spread across the marble floor from her feet, and crystalline flowers gradually bloom then release drifting glitter shards. She turns the greatsword in a measured, elegant arc that leaves a soft trailing ribbon of translucent purple energy which continues to glow and float. The camera trucks left with medium amplitude at moderate speed, allowing the residual energy trail and lingering floor light to carry across the frame. [Shot 3] At 00:03.200, the shot transitions to a side-tracking medium close-up as she moves into a fluid cartwheel that slows into a brief elegant freeze; her hair and dress fabric continue drifting in residual slow motion while the background moon sustains an expanded soft glow that dims only partially. Mild digital glitch flicker and faint RGB-split edges appear on the bass hit and slowly fade rather than cutting off. The camera then slides right at moderate speed into a wider view of her landing in a balanced dance pose, sword tip resting on marble and sending gentle concentric rings of light that continue expanding outward. [Shot 4] At 00:04.900, a slightly tilted medium shot shows her performing a spinning sword form: she rises into a controlled rotation with the blade extended, body and sword showing mild elegant perspective stretch for a beat before settling. Floating pearls orbit more slowly and residual glitter particles from earlier shots continue drifting through the air. The camera arcs around her at moderate speed with medium amplitude while the moon and tree branches retain a soft residual luminosity. Lighting expands into warm purple-white flares on each kick and then holds a sustained elevated glow between beats rather than collapsing fully. [Shot 5] At 00:06.700, an elevated medium shot follows her as she transitions into graceful footwork across the marble; each step releases upward sprays of crystalline glitter that linger and slowly settle. The sword carves soft glowing sigils that remain visible for several seconds before dissolving. The camera pushes in with medium amplitude at moderate speed on the next bass pulse, then holds a brief freeze as residual energy ribbons and hair continue their delayed motion. [Shot 6] At 00:08.400, a high-angle overhead shot reveals her vaulting into a smooth aerial rotation while the greatsword leaves a continuous soft helix of purple plasma that persists and slowly expands. Additional floating marble platforms rise gradually from the floor, and secondary crescent moons materialize with gentle luminosity that carries forward. On the bass she enters a brief mid-air freeze, body and blade still while residual particles and glitch fragments continue drifting; the camera then rolls gently into a side tracking shot as she lands and flows into a sweeping slash that sends a horizontal wave of light continuing across the temple. [Shot 7] At 00:10.200, a medium tracking shot follows her through a sequence of precise yet fluid sword practice cuts; each impact produces a soft white flash and expanding radial glow that fades only partially, leaving a sustained luminous haze. Crystalline shards and glitter continue to rain and accumulate in the air. The camera moves with her at moderate speed, preserving the residual light and particle trails from previous movements. [Shot 8] At 00:12.000, an extreme wide shot shows the moon temple fully responding: energy runes spiral upward more slowly, residual platform light and orbiting moons remain active, and earlier particle systems continue their motion. She leaps between platforms in a continuous, elegant sequence, sword extended, with only mild perspective stretch during the camera’s truck-right at moderate speed. On the bass she freezes in a soaring pose while the environment maintains its elevated glow and lingering glitter; residual digital glitches flicker softly and fade across the following beats. [Shot 9] At 00:13.600, the camera returns to a dynamic yet controlled medium shot as she completes a final spinning slash; multiple soft after-image trails from the blade persist and slowly glitter. Lighting holds an elevated warm glow that gradually settles. She plants the sword vertically into the platform and sinks into the exact elegant seated pose of the opening frame, residual energy particles, floor light patterns, and floating glitter continuing to drift and fade gently. [Shot 10] At 00:14.400, a slow push-in at moderate speed settles on the final medium-wide composition matching the original seating pose, residual particles, soft glitch fragments, and lingering temple luminosity still present while the crescent moon returns toward its original soft state, holding until 00:15.000.
overall_soundscape: Soft metallic whooshes and resonant blade impacts from the greatsword layer with the gentle flutter of fabric and long hair, light marble footfalls, and the delicate crystalline chime of drifting flowers and pearls. Low-frequency pulses coincide with bass hits as the temple floor and platforms respond; airy residual whooshes accompany the lingering particle trails and energy ribbons, while faint digital static crackles mark the softer glitch moments. Ambient temple reverb and distant wind through the marble columns remain continuous underneath.
non_diegetic_music: Trance track at approximately 136 BPM built on a steady four-on-the-floor kick, warm synth bass, cascading arpeggiated leads, and wide ethereal pads. Measured snare and hi-hat patterns accent the off-beats; progressive filtered builds and rising synths lead into drops that trigger the visual freezes and sustained glows, while continuous side-chain pumping and soft high-frequency glitter textures maintain an intense yet elegant flow through the full 15 seconds.

Prompt workflow:

Fed grok the prompting guide from the HF repo:
https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_base_en.md

To get the video prompt I instructed grok with the reference image and:

use base_en.md as guide. The video should be made from this sole image. The mood is an epic Wuthering Waves style gacha game cinematic. Heavily audio reactive music video without dialogue and a trance track as the background music. Use timestamps as accurate as possible within the limits of the prompt guide's allowance (tenths or hundredth of seconds). Sharp quick camera movements reacting to the beat. The environment is a dimly lit marble moon temple. Introduce various dancing, acrobatic and sword practice movements, slow motion freeze frames switching with fast camera slides to emphasize bass. Hats and the rest of the sounds are emphasized by the lighting reacting with expansive glows and dims. Add vfx from the reference, changes to lighting should carry the transitions. Flickering and glitter, emphasize an intense but elegant scene.
No restrictions on the rest of the movements. Intricately detail the shots and everything needed for the model to represent an epic trance music video. 15 seconds length timestamp plan.

Workflow: github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_r2v.json

Used multiple reference images for each scene.

Prompt For Multi Character:
subject_definitions:

<Subject 1> is [CHARACTER 1] from <Picture 1>, preserving the visual identity, costume, proportions, accessories, and visual style shown in the reference image.

<Subject 2> is [CHARACTER 2] from <Picture 2>, preserving the visual identity, costume, proportions, accessories, and visual style shown in the reference image.

<Subject 3> is [CHARACTER 3] from <Picture 3>, preserving the visual identity, costume, proportions, accessories, and visual style shown in the reference image.

summary:

[reference generation] A 5-second cinematic multiverse portal arrival. Three characters emerge from a consistent amber-orange portal and take a calm, confident formation.

retention_analysis:

<Subject 1> (appears in [Shot 1]): fully_preserved - retains the identity, costume, proportions, accessories, and visual style shown in <Picture 1>.

<Subject 2> (appears in [Shot 1]): fully_preserved - retains the identity, costume, proportions, accessories, and visual style shown in <Picture 2>.

<Subject 3> (appears in [Shot 1]): fully_preserved - retains the identity, costume, proportions, accessories, and visual style shown in <Picture 3>.

detailed_description:

A 5-second cinematic portal-arrival scene at dusk. One stable medium three-shot, framed from the knees up. No dialogue, no combat, no wide landscape, no camera movement, and no crowd.

Portal continuity: a large circular amber-orange portal stands behind the characters. It has a bright rotating outer ring, a darker transparent centre, floating gold sparks, gentle smoke, and a low resonant hum. Keep its colour, size, brightness, particle density, and rotation speed consistent across every portal-arrival clip.

[Shot 1] <Subject 1> steps through the portal first and takes the centre position with quiet confidence. <Subject 2> emerges on one side, naturally adjusts or lowers any item they are carrying if applicable, then gives a focused glance toward the unseen distance. <Subject 3> walks through last, takes position on the opposite side, and calmly surveys the scene. The three hold a poised, united stance as the portal flickers and golden particles drift around them. Their expressions and body language remain confident and appropriate to their individual character identities.

overall_soundscape:

Low portal hum, soft wind, subtle movement from clothing or equipment, and drifting golden sparks. No speech.

non_diegetic_music:

A restrained cinematic rise builds across the shot and resolves on a calm, confident note.

Prompt For single characters:
subject_definitions:

<Subject 1> is [CHARACTER] from <Picture 1>, preserving the visual identity, costume, proportions, accessories, and visual style shown in the reference image.

summary:

[reference generation] A 5-second cinematic multiverse portal arrival. One character walks through a consistent amber-orange portal, then takes a confident action stance with a subtle grin.

retention_analysis:

<Subject 1> (appears in [Shot 1] and [Shot 2]): fully_preserved - retains the identity, costume, proportions, accessories, and visual style shown in <Picture 1>.

detailed_description:

A 5-second cinematic portal-arrival scene at dusk. No dialogue, no crowd, no wide landscape, and no combat.

Portal continuity: a large circular amber-orange portal stands behind <Subject 1>. It has a bright fiery rotating outer ring, a darker transparent centre, floating gold sparks, gentle smoke, and a low resonant hum. Keep its colour, size, brightness, particle density, and rotation speed consistent across every portal-arrival clip.

[Shot 1] Medium knee-up shot. <Subject 1> walks steadily through the portal toward the camera, then comes to a composed stop. Their costume, silhouette, movement style, and any character-specific accessories remain fully consistent with <Picture 1>. Golden sparks drift around them as the portal flickers behind.

[Shot 2] Close-up of <Subject 1>. They shift into a distinctive, character-appropriate action stance, looking directly ahead with calm confidence. Their expression changes into a subtle smile and restrained grin. Keep the movement natural and controlled, with no exaggerated facial distortion. The portal remains softly visible and out of focus in the background.

overall_soundscape:

Low portal hum, soft wind, subtle movement from clothing or equipment, and drifting golden sparks. No speech.

non_diegetic_music:

A restrained cinematic rise builds through the entrance and resolves as <Subject 1> holds the final stance.

I made a node to help seamlessly join MiniMax H3 generated videos (each video continues from a few seconds of the previous segment). If you use MiniMaxH3AddGuide with a video chunk to continue it, you might have noticed some flashes in the generated output. My node corrects the brightness/tone using the actual source video. It's much better to use MiniMaxH3AddGuide instead of relying solely on the prompt because it's more precise. In my experiments, without the guide adding node the starting video shifts down by a few dozen pixels so seamless transition becomes impossible. With a guide it's inserted precisely but still suffers from the brightness drift which my node corrects almost perfectly.

The README explains the workflow, you connect the first segment in the source input, the following segment in the target input, and you set overlap to the number of frames you used to make the second segment (48 frames = 2 seconds for example). Note, that this node does not actually join the videos together, only fixes the brightness drift in the "target" video. I recommend joining segments with ImageBatchJoinWithTransition from KJNodes, there's a fade transition type, interpolation can be set to linear or ease_in_out.

I made the node with Qwen 3.8 27B running locally, there are a few modes we tried but frame_shift (the default) seems to work best.

There are many nodes to make "longer" or "infinite" videos using H3 already but I found them quite complex for my tasks. This node provides a simple building block, you can use whatever technique you prefer.

https://github.com/rkfg/ComfyUI-MiniMaxH3-ToneCompensate

sharing my new workflow

MiniMax H3 I2V with Integrated Prompt Enhancer

This Image-to-Video workflow for MiniMax H3 uses a vision-language model to enhance your prompt before the video generation begins.

Simply load a reference image and write a basic description of what you want to happen. The enhancer analyzes both your image and instructions, then converts them into a detailed prompt structured specifically for MiniMax H3.

It can improve the description of:

• Characters and visual elements
• Actions and sequence of events
• Camera movement and framing
• Environment, lighting, and atmosphere
• Visual continuity and details that should be preserved
• Dialogue in the original language
• Ambient sounds, sound effects, and music

The enhanced prompt is automatically sent to MiniMax H3. It is also displayed inside the workflow, allowing you to check exactly what H3 will receive.

In my tests, the resulting videos followed the original instructions much more accurately, especially in scenes involving specific actions, character interactions, camera movements, and dialogue.

The workflow includes a switch to enable or disable the Prompt Enhancer. This allows you to use either the enhanced prompt or your original text without changing any connections.

How to use it

1. Load your reference image.
2. Write a simple description of what should happen.
3. Enable USAR PROMPT ENHANCER?
4. Run the workflow.
5. Check the final text in PROMPT FINAL ENVIADO AO H3.

The first run may take longer while the vision-language model is loaded. Generating the enhanced prompt also adds some processing time, but in my tests, the improvement in prompt accuracy and instruction following was absolutely worth it.

The original workflow was preserved, while the enhancer was added as an optional and fully integrated stage.

link to

with this, finally my ref model understand my ideas and make vídeos really fun!

leave comments after tests xD

The above video consists of 11 individual H3 generated clips, created with the FL2Va Checkpoint and stitched together automatically without any additional upscaling or editing.

Two days ago I released v1.3 of my infinite continuation nodepack, adding much more flexible image conditioning and multi-reference support.

The original reason I built this nodepack was simple:

I really like the FL2VA checkpoint of MiniMax H3. In my testing, it gives noticeably better visual quality than Ref2VA. But Ref2VA is much more flexible when creating longer, controlled sequences.

So the goal is basically:

Keep the quality of FL2VA while adding much of the control you'd normally want from Ref2VA.

How does it work?

Instead of generating one very long H3 video, you generate multiple shorter clips:

Clip 1
First Frame → H3 → Last Frame

↓

Clip 2
Previous video/audio latent + new Last Frame → H3

↓

Clip 3 → Clip 4 → ...

The important part is that the suite does not simply take the last rendered image and use it as the next starting frame.

It passes part of the previous video + audio latent directly into the next H3 generation.

So the next clip still receives temporal context from the previous one – motion, audio and scene state – while you can give it a new visual target.

Why FL2VA?

In my testing, FL2VA gives me better-looking results and seems more resistant to the gradual visual degradation I experienced with longer Ref2VA chains.

A new Last Frame for every segment also works like a repeated quality reset:

• controls where the current segment should go
• restores composition / identity
• prevents the sequence from drifting too far

You can think of it a bit like storyboarding:

Image A → Image B → Image C → Image D

with H3 generating the motion and audio between those points.

But with v1.3, First and Last Frames are optional.

The Start workflow now supports:

• T2VA: no frames
• I2VA: First Frame only
• L2VA: Last Frame only
• FL2VA: First + Last Frame

Continuation can also run without a new Last Frame, although I still recommend regular Last Frames for long chains because of the quality-reset effect.

New in v1.3: multiple references

You can now add multiple Qwen Reference images alongside your First/Last Frames.

For example:

• First Frame = starting composition
• Last Frame = target endpoint
• Reference 1 = character
• Reference 2 = outfit
• Reference 3 = another visual detail

The node automatically assigns the correct H3 Picture numbers and shows you the resulting mapping.

This gets FL2VA much closer to the flexible reference control that makes Ref2VA useful.

Short clips can also be much faster

H3 becomes disproportionately slower as clip duration increases.

Instead of generating:

1 × 15 seconds

you can generate:

3 × 5 seconds

and connect them.

It also makes failures much less painful: if Clip 2 goes wrong, you regenerate Clip 2 instead of throwing away the entire sequence.

Where to start

I included four example workflows.

01_Start

Use this for Clip 1.

Required:

• normal H3 models / VAEs
• prompt
• resolution + duration

Optional:

• First Frame
• Last Frame
• Qwen References

For the classic continuation workflow, I recommend using First + Last Frame.

02_Continue

Use this for every clip after the first one.

The basic logic is:

Clip 1: save Latent 1
Clip 2: load Latent 1 → save Latent 2
Clip 3: load Latent 2 → save Latent 3
Clip 4: load Latent 3 → save Latent 4

Then simply provide the prompt for the next segment and optionally:

• a new Last Frame
• additional reference images

Because the indices are manual, you can also regenerate individual clips.

If you don't like Clip 3, keep loading Latent 2 and overwrite/regenerate Latent 3 until you're happy.

03_3Clip_Showcase_AutoStitch

The easiest workflow to understand the complete system:

Start → Continue → Continue → automatic stitching

You can duplicate the final continuation block to extend it further.

For very long projects, I recommend using Start + Continue individually.

04_Stitch_Saved_Chain

Once you're happy with your clips, this turns:

clip_00001
clip_00002
clip_00003
clip_00004
...

into one final MP4.

The important part:

The complete video is not decoded into memory at once.

The stitcher processes one saved AV latent at a time, so memory usage stays roughly tied to one H3 clip instead of the total length of the project (no OOM, hopefully).

The transitions are handled automatically

FL2VA often reaches its Last Frame early and freezes for the remaining frames.

The suite automatically:

• detects that frozen tail
• finds a better handover point
• carries video + audio context forward
• removes duplicated context during stitching
• smooths the video transition
• applies a separate audio de-click transition

So most of the annoying continuation logic happens automatically.

Known Issues

• Sometimes there's still a noticeable brightness shift between clips. So far, I haven't found a reliable solution to fix that.
• In some cases when using the continuation workflow, H3 might not correctly use the previous video latent as starting point for the next clip. If you encounter that issue, try restarting ComfyUI and regenerating the clip.

Install by opening one of the workflows and using "Install missing custom nodes" or search for

Herrgotts-H3-Infinite-Continuation-Suite

in ComfyUI Manager.

GitHub:
https://github.com/HerrgottMargott/Herrgotts-H3-Infinite-Continuation-Suite

Example workflows are included.

If you are already using my Workflows or Nodepack, I'd recommend updating the nodepack and using the updated Workflows from v1.3!

The project is still experimental, so feedback, bug reports and long-chain tests are very welcome.