Introduction
Getting an AI video model to generate a good-looking clip is one thing. Getting it to keep the same character, preserve product details, and follow a scene across multiple shots is a different challenge.
I wanted to see how FastVideo FastH3 Omni Reference handles these situations in practice, so I ran five experiments in ComfyUI on an NVIDIA L40 with 48 GB of VRAM. The tests cover everything from a short fashion film and a luxury product advertisement to character consistency, a sci-fi scene, and a multi-shot commercial.
Rather than judging the model by a single output, I used different reference images and prompts to see how it handles identity, clothing, objects, camera movement, and scene composition. I've included the prompts, generation settings, and resulting videos so you can try similar experiments yourself.
Model Configuration and Inference Optimizations
For our five product advertising experiments, we used the FastVideo FastH3 OmniRef 8-step model in ComfyUI, with quantized model weights and optimized attention to make reference-to-video generation more memory-efficient.
Our generation setup consisted of:
| Component | Configuration |
|---|---|
| Diffusion Model | FastH3 OmniRef 8-Step Pruned INT8 ConvRot |
| Text Encoder | Qwen3-VL 32B MiniMax H3 (NVFP4 AWQ) |
| Video VAE | MiniMax H3 INT8 ConvRot |
| Audio VAE | MiniMax H3 FP32 |
| Video Sampling Shift | 12.0 |
| Audio Sampling Shift | 3.0 |
| Attention Backend | ComfyKitchen Attention |
| Sparse Attention | VSA (Video Sparse Attention) |
| VSA Keep Percentage | 10% |
| VSA Active Range | 0.0–1.0 (entire sampling schedule) |
Why These Optimizations Matter
The INT8 ConvRot diffusion model and video VAE reduce model-weight memory requirements compared with their higher-precision counterparts, while the NVFP4 AWQ text encoder further reduces the memory footprint of the large Qwen3-VL 32B encoder.
We also configured ComfyKitchen Attention alongside VSA with a 10% keep ratio, enabling sparse attention throughout the sampling schedule. This is intended to reduce attention computation and improve inference efficiency, although the actual performance gains depend on the hardware and workload.
Combined with FastH3's 8-step distilled architecture, these optimizations provide a practical configuration for experimenting with high-quality, reference-guided product advertisements without relying on the full-precision MiniMax H3 pipeline.
Generation 1: Streetwear Fashion Film — 5 seconds
Input Image 1
Input Image 2
Input Image 3
Prompt
subject_definitions: <Subject 1> is the fictional adult fashion model in <Picture 1>. <Picture 2> is the specific oversized black puffer jacket that <Subject 1> wears. <Picture 3> is the brutalist concrete hall used as the location.
summary: [reference generation] A five-second, single-take high-fashion campaign with <Subject 1> wearing <Picture 2> inside <Picture 3>; powerful, minimal, photorealistic.
retention_analysis: <Subject 1> (Shot 1): fully_preserved — preserve the face, black choppy hair, facial proportions, relaxed androgynous styling, dark cargo trousers and boots of <Picture 1>. Preserve the puffer jacket's bulky silhouette, horizontal red chest band, quilting, collar and zipper exactly as <Picture 2>. Preserve the bare concrete and tall windows of <Picture 3>. Do not copy the gray studio background from <Picture 1> or <Picture 2>.
detailed_description: [Shot 1, 00:00–00:05] One continuous live-action full-body fashion shot. <Subject 1> stands at the far end of the hall from <Picture 3>, wearing the jacket from <Picture 2> over the outfit from <Picture 1>. The model takes three slow deliberate steps toward the camera. The camera tracks backward smoothly at hip height, then slowly raises to chest height. The red band stays a solid physical garment detail, not a floating graphic. At the end the model stops and turns their head ten degrees toward the windows. Natural cloth compression and soft shoe contact, realistic restrained movement, cold overcast light, subtle reflections on the concrete floor. No cuts, no extra people, no logos, no text overlays.
overall_soundscape: A low airy interior room tone, three distinct heavy rubber-sole footsteps, quiet fabric rustling and a distant wind through the windows.
non_diegetic_music: Sparse industrial sub-bass pulses with spacious pauses; understated, not overpowering the footsteps.
Setup Configs
Red Band — fashion film Duration: 5 seconds; choose 124 frames / 24 fps. Suggested FIRST test output: about 1-megapixels, preserve reference aspect ratio via the official template if possible. Ref_image_size: match. Steps: 8 | guidance: 1.0 | sigma shifts: video 12.0 / audio 3.0 | VSA: 10%
Connect ordered inputs into ref_images on MiniMaxH3ReferenceToVideo: <Picture 1> -> Picture_1.png -> the protagonist’s face, haircut, base pants and boots <Picture 2> -> Picture_2.png -> the exact black quilted puffer jacket with its broad red horizontal panel <Picture 3> -> Picture_3.png -> the concrete windowed hall and its cold daylight
Do not connect these images to the First Last Frame to Video node.
Actual render quality and VRAM requirements will vary.
Output
Generation 2: Luxury Product Advertisement — 5 seconds
Input Image 1
Input Image 2
Input Image 3
Prompt
subject_definitions: <Subject 1> is the fictional adult woman from <Picture 1>. <Object 1> is the exact sculptural chrome wraparound sunglasses from <Picture 2>. <Location 1> is the curved concrete atrium from <Picture 3>.
summary: [reference generation] A five-second premium eyewear commercial: <Subject 1> models <Object 1> in <Location 1> with elegant natural movement and product clarity.
retention_analysis: <Subject 1> (Shot 1): fully_preserved — preserve the facial proportions, center-parted black hair, skin tone and black sleeveless top of <Picture 1>. <Object 1> (Shot 1): fully_preserved — preserve the unusual sweeping chrome top line, dark lens shape and bright polished metallic surface of <Picture 2>; it must rest realistically on her nose and ears. <Location 1> (Shot 1): background_reference — use the circular skylight, pale concrete curves, sunlight geometry and empty architectural volume from <Picture 3>. Do not treat the gray product-photo background as the scene.
detailed_description: [Shot 1, 00:00–00:05] One continuous photorealistic 85mm portrait shot inside <Location 1>. Medium close-up of <Subject 1> already wearing <Object 1>, standing almost still near a pool of skylight. A very slow camera arc, no more than 20 degrees, moves from her three-quarter left to a more frontal angle. She gently turns her chin toward the light and adjusts the right temple of her sunglasses once with her right hand. The chrome frame catches one clean highlight moving across it; keep the sunglasses design geometrically stable. Realistic hand-to-frame contact, natural eyelid motion, fine skin detail, high-end editorial color. No abrupt cuts or added products.
overall_soundscape: Open architectural ambience, quiet distant footsteps with gentle reverberation, soft clothing movement and a very faint breeze.
non_diegetic_music: Three restrained crystalline electronic notes over a warm, minimal ambient pad.
Setup Configs
Chrome eyewear — model, product and architecture Duration: 5 seconds; choose 124 frames / 24 fps. Suggested FIRST test output: about 1 megapixels, preserve reference aspect ratio via the official template if possible. Ref_image_size: match. Steps: 8 | guidance: 1.0 | sigma shifts: video 12.0 / audio 3.0 | VSA: 10%
Connect ordered inputs into ref_images on MiniMaxH3ReferenceToVideo: <Picture 1> -> Picture_1.png -> the model’s facial identity and dark hair <Picture 2> -> Picture_2.png -> the silver sculptural wraparound frame and dark lenses <Picture 3> -> Picture_3.png -> the circular skylight and sculptural concrete atrium
Do not connect these images to the First Last Frame to Video node.
Actual render quality and VRAM requirements will vary.
Output
Generation 3: Character Consistency — 5 seconds
Input Image 1
Input Image 2
Input Image 3
Prompt
subject_definitions: <Subject 1> is the fictional woman in <Picture 1>. <Garment 1> is the black oversized puffer jacket with one broad solid red band in <Picture 2>. <Location 1> is the bright circular concrete atrium in <Picture 3>.
summary: [reference generation] A five-second wardrobe-transfer test: keep the identity of <Subject 1>, dress her in <Garment 1> and place her in <Location 1> in a new shot.
retention_analysis: <Subject 1> (Shot 1): fully_preserved — use the facial features, long center-parted black hair and skin appearance from <Picture 1>, even though the original outfit and gray studio are not used. <Garment 1> (Shot 1): fully_preserved — transfer the shape, thick quilting, upturned collar, black glossy fabric and red chest stripe from <Picture 2>. <Location 1> (Shot 1): background_reference — match the round skylight, raw concrete and sunlit floor from <Picture 3>. Do not transfer the head or body of a mannequin or an invisible product model.
detailed_description: [Shot 1, 00:00–00:05] Single unbroken live-action fashion shot. <Subject 1> stands under the skylight in <Location 1> wearing <Garment 1>, zipped up to the collar. Camera begins at waist-height in a medium shot and performs a gentle slow push-in. She looks up briefly toward the skylight, then lowers her eyes to the camera with a subtle serious expression. She slips both hands into the jacket pockets. The red band remains aligned and fixed around the jacket as her posture changes; jacket folds move naturally. Strong architectural shadows, realistic skin and garment materials. No cut, no extra clothing, no text or logos.
overall_soundscape: Bright quiet hall ambience, a light sleeve rustle, soft echo of distant air circulation.
non_diegetic_music: Slow minimal analog synthesizer and a low cinematic pulse.
Setup Configs
Cross-reference wardrobe transfer Duration: 5 seconds; choose 124 frames / 24 fps. Suggested FIRST test output: about 1 megapixels, preserve reference aspect ratio via the official template if possible. Ref_image_size: match. Steps: 8 | guidance: 1.0 | sigma shifts: video 12.0 / audio 3.0 | VSA: 10%
Connect ordered inputs into ref_images on MiniMaxH3ReferenceToVideo: <Picture 1> -> Picture_1.png -> face and person identity from collection B <Picture 2> -> Picture_2.png -> jacket and design from collection A <Picture 3> -> Picture_3.png -> new location, different from both source photos
Do not connect these images to the First Last Frame to Video node.
Actual render quality and VRAM requirements will vary.
Output
https://www.youtube.com/shorts/tgua6_rCpkk
Generation 4: Sci-Fi Cinematic Scene — 10 seconds
Input Image 1
Input Image 2
Input Image 3
Prompt
subject_definitions: <Subject 1> is the fictional adult with short dark hair, a fitted dark long-sleeve top and black cargo trousers in <Picture 1>. <Subject 2> is the fictional adult woman with long center-parted black hair and black sleeveless top in <Picture 2>. <Location 1> is the empty concrete hall with tall windows from <Picture 3>.
summary: [reference generation] A restrained ten-second science-fiction mystery conversation between <Subject 1> and <Subject 2> in <Location 1>. Each character keeps their own face, hairstyle and outfit.
retention_analysis: <Subject 1> (Shot 1): fully_preserved — short tousled black hair, angular face, dark fitted top and cargo trousers from <Picture 1>. <Subject 2> (Shot 1): fully_preserved — long center-parted hair, distinct softer face and black sleeveless top from <Picture 2>. Keep the two characters visually distinct; do not blend facial features or swap their voices. <Location 1>: background_reference — retain the empty concrete geometry and diffuse daylight from <Picture 3>.
detailed_description: [Shot 1, 00:00–00:10] One continuous, steady, cinematic medium two-shot in <Location 1>. <Subject 1> stands on screen left, facing <Subject 2> on screen right, separated by one meter. Slow lateral camera drift. At 00:01 <Subject 1> looks to <Subject 2> and says quietly, [English] Did you hear that? A small pause. <Subject 2> looks toward the upper windows and replies at 00:05, [English] It came from upstairs. They exchange a brief, concerned glance as the camera settles. Subtle expression changes and synchronized lips, natural conversational pacing. Keep both faces visible, correct speaker-to-mouth alignment, no cuts and no other characters.
overall_soundscape: Two distinct natural speaking voices, long concrete-room reverberation, very distant low mechanical hum, faint wind through the window joints.
non_diegetic_music: N/A
Setup Configs
Two-character dialogue scene Duration: 10 seconds; choose 243 frames / 24 fps. Suggested FIRST test output: about 1 megapixels, preserve reference aspect ratio via the official template if possible. Ref_image_size: match. Steps: 8 | guidance: 1.0 | sigma shifts: video 12.0 / audio 3.0 | VSA: 10%
Connect ordered inputs into ref_images on MiniMaxH3ReferenceToVideo: <Picture 1> -> Picture_1.png -> Character A, short dark haircut and black top <Picture 2> -> Picture_2.png -> Character B, long center-parted dark hair <Picture 3> -> Picture_3.png -> quiet concrete hall with windows
Do not connect these images to the First Last Frame to Video node.
Actual render quality and VRAM requirements will vary.
Output
Generation 5: Multi-Shot Commercial — 10 seconds
Input Image 1
Input Image 2
Input Image 3
Input Image 4
Input Image 5
Prompt
subject_definitions: <Subject 1> is the fictional adult with short dark tousled hair from <Picture 1>. <Subject 2> is the fictional woman with long center-parted black hair from <Picture 2>. <Product 1> is the oversized black quilted puffer with a bold solid red chest band from <Picture 3>. <Product 2> is the sculptural chrome sunglasses with dark lenses from <Picture 4>. <Location 1> is the circular concrete atrium in <Picture 5>.
summary: [reference generation] A ten-second three-shot luxury capsule-collection film connecting two models and two hero products in one architectural location. Stylish, restrained, consistent and photorealistic.
retention_analysis: <Subject 1> appears in Shot 1 and Shot 3: fully_preserved — same short haircut and face as <Picture 1>. <Subject 2> appears in Shot 2 and Shot 3: fully_preserved — same long black hair and face as <Picture 2>. <Product 1> appears in Shots 1 and 3: fully_preserved — exact black puffed silhouette and red horizontal stripe from <Picture 3>. <Product 2> appears in Shots 2 and 3: fully_preserved — stable polished chrome geometry and dark lenses from <Picture 4>. <Location 1> appears in all shots: background_reference — curved concrete, geometric daylight and central skylight from <Picture 5>.
detailed_description: Cinematic live-action advertising film with clean physical cuts; no superimposed typography or brand marks. [Shot 1, 00:00–00:03.3] Low-angle medium full-body shot of <Subject 1> in the atrium wearing <Product 1>. One confident step, a subtle camera push, and a clean view of the broad red stripe. Sunlight grazes the quilted surface. [Shot 2, 00:03.3–00:06.6] Cut to an elegant close-up of <Subject 2> wearing <Product 2>. She turns her chin a few degrees, catching an architectural reflection on the chrome frame. Same atrium, consistent light. [Shot 3, 00:06.6–00:10] Cut to a symmetrical medium-wide composition of both subjects standing side by side under the skylight: <Subject 1> wearing the red-band jacket and <Subject 2> wearing the chrome glasses. Both face the camera, with minimal natural movement. End on a clean editorial pose. Preserve identities and products across all three cuts, natural hands, no subject merging.
overall_soundscape: A single slow sneaker footstep in Shot 1, spacious concrete ambience throughout, slight jacket rustle, quiet transition whooshes at the two cuts.
non_diegetic_music: A minimal atmospheric electronic fashion cue with a low pulse, three soft metallic accents and a smooth ending.
Setup Configs
Three-shot capsule collection mini-commercial Duration: 10 seconds; choose 243 frames / 24 fps. Suggested FIRST test output: about 1 megapixels, preserve reference aspect ratio via the official template if possible. Ref_image_size: match. Steps: 8 | guidance: 1.0 | sigma shifts: video 12.0 / audio 3.0 | VSA: 10%
Connect ordered inputs into ref_images on MiniMaxH3ReferenceToVideo: <Picture 1> -> Picture_1.png -> first model identity <Picture 2> -> Picture_2.png -> second model identity <Picture 3> -> Picture_3.png -> shared featured puffer jacket <Picture 4> -> Picture_4.png -> featured chrome sunglasses <Picture 5> -> Picture_5.png -> shared location
Do not connect these images to the First Last Frame to Video node.
Actual render quality and VRAM requirements will vary.
Output
Generations Timings
Conclusion
These experiments show why reference-based video generation is worth testing beyond simple image-to-video prompts. FastH3 Omni gives us a way to combine several visual references and direct how they should appear in the final scene, but the results still depend on the prompt, reference images, and generation settings.
The most useful part of this process was comparing the outputs across different tasks instead of relying on one successful clip. If you're experimenting with AI video in ComfyUI, the prompts and configurations here should give you a practical starting point for your own tests. I'd be interested to see how the results change with different reference images, longer sequences, and hardware configurations.
FastVideo FastH3 Omni on HexGrid.cloud
Run FastVideo FastH3 Omni on HexGrid in one click — no CUDA setup, no dependency hunting, no environment debugging.
The template comes preconfigured with ComfyUI, the required runtime, and the FastVideo FastH3 Omni setup so you can launch a GPU workspace and start generating immediately.
No manual CUDA installation. No PyTorch version matching. No broken requirements. No hours spent wiring the environment together.
Just choose the FastVideo FastH3 Omni template, start the instance, and open ComfyUI.
Pricing starts from $0.58/hour*, so you can spin up a GPU only when you need it and shut it down when you’re done.*
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