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Minimax_h3_latent_Upscaler
PixelAlchemist123/Minimax_h3_latent_Upscaler
Minimax_h3_latent_Upscaler is a machine learning model from PixelAlchemist123. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Neural latent-space upscaler for Minimax H3 video generation. Works directly on Minimax H3's 24-channel VAE latents to upscale spatial resolution (H×W) while preserving the time dimension.
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Public
Updated Aug 31, 2026
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2.8 GB
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At a glance
- Access
- Public
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026
- SHA
- a49c4327
Model card
From the Hugging Face model README
Minimax H3 Latent Upscaler
Neural latent-space upscaler for Minimax H3 video generation. Works directly on Minimax H3's 24-channel VAE latents to upscale spatial resolution (H×W) while preserving the time dimension.
Purpose
This model is designed to accelerate high-resolution H3 video generation:
- Generate video at low resolution — far fewer latent tokens, much faster.
- Upscale the latent in-place with this learned upscaler.
- Re-sample / refine at the target resolution to recover detail.
By skipping the expensive decode → pixel upscale → encode round-trip through Minimax H3's
heavy ~5B-parameter VAE, this pipeline saves a significant amount of generation time. It also
avoids the ghosting / double-image artifacts that naive latent interpolation
(bilinear/bicubic) introduces.
📸 Examples
Video upscale comparison — click to play:
<video src="https://huggingface.co/LBH-123-AI/Minimax_h3_latent_Upscaler/resolve/main/examples/Minimax_h3_latent_Upscaler_001.mp4" controls width="640"></video>
(If the player doesn't render, download the video here.)
Image upscale comparison:
Files in this repository
| File | Format | Precision | Size | Notes |
|---|---|---|---|---|
minimax_h3_latent_upscaler_3d_bf16.safetensors | SafeTensors | bfloat16 | ~691 MB | Fastest on Ampere/Ada GPUs |
minimax_h3_latent_upscaler_3d_fp16.safetensors | SafeTensors | float16 | ~691 MB | Best speed / memory balance |
minimax_h3_latent_upscaler_3d_fp32.pth | PyTorch | float32 | ~1.38 GB | Highest numerical accuracy |
All three checkpoints share the same 3D-convolution architecture. Pick the precision that matches your GPU and workflow.
Usage
Use with the companion ComfyUI custom node:
LBH-123-AI/Comfyui_Minimax_h3_latent_Upscaler
- Install the custom node into
ComfyUI/custom_nodes/. - Place the downloaded checkpoint in:
ComfyUI/models/latent_upscale_models/ - Add either "Minimax H3 Latent Upscaler (2D)" or "Minimax H3 Latent Upscaler (3D)"
from the
video/MinimaxH3menu, connect aLATENT, pick the model, and setscale.
Supported upscale factors: 1.0× – 4.0× (continuous, 0.1 step; default 2.0×).
Training Data
The model was trained on ~80,000 paired samples (low-resolution latent + high-resolution target), balanced across modalities and scale factors to maximize generalization.
- ~70,000 video pairs
- ~8,000 2K image pairs
Scale distribution (approximate):
| Scale | Share | Purpose |
|---|---|---|
| 2× | 40% | Dominant real-world factor |
| 1.5× | 10% | — |
| 2.5× | 10% | — |
| 3× | 10% | — |
| 4× | 10% | — |
| 1.0×–4.0× (arbitrary decimals) | 10% | Generalization to any in-between scale |
Architecture
3D-convolution backbone with temporal convolution and trilinear interpolation. The architecture
draws on and references the LTX 2.3 Spatial Upscaler
(ltx-2.3-spatial-upscaler-x2-1.1.safetensors) and the neural-latent-upscaling approach
pioneered by Ttl / ComfyUi_NNLatentUpscale.
License
Apache-2.0
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026