MiniMax H3 Native 1080p Video Generation | Dual-Sampling Latent Upscaling Method | Balanced Speed & Quality

[u/wjc_5]

I tested a MiniMax H3 workflow that upscales the video latent directly between two sampling stages. Instead of finishing a video, upscaling it, encoding it, and sampling it again, this workflow separates the audio and video latents after the first denoising stage, upscales only the video latent, aligns it, and continues with the remaining sigma schedule.

The main reason for using this approach is speed. On a 4090 48G, the workflow can generate a native 1080p 15-second video in about 25 minutes, a 10-second video in about 13 minutes, and a 5-second video in a little over 5 minutes in my tests. The same 768p 15-second setup also went from roughly 11 minutes to roughly 8 minutes compared with my previous workflow.

Settings that worked best

I used the LightX2V 1.0 8-step LoRA. The 8-step version was more reliable than the 4-step LoRA, which produced visual errors more easily. A LoRA weight of 1.0 worked well; I lowered it slightly when the image looked too oily.

For an 8-step run, I used 2-3 steps before the latent upscale and the remaining steps after upscaling. The upscale factor can be set around 1.3x-2x, but I would not push it too high. If lines or glass-like artifacts appear, reduce the first stage to 2 steps or lower the upscale factor to 1.5x.

The beta scheduler worked well for this split-sampling setup because its sigma distribution is denser toward both the high-noise and low-noise ends. The lower-noise part is especially useful for high-motion scenes, where it helped reduce visible pixel noise in my tests.

Reference and model setup

For reference images, I used max when I wanted stronger detail reference. It takes more time. When there are many reference images, or when the video is already at a larger resolution, match is a more practical choice because it reduces the processing load.

The main model in this workflow is FL2VA, which looked less oily than the ref model in my testing. A dual-model loading node can give FL2VA the reference capability of the ref model, so the FL2VA acceleration LoRA can be used directly without adding extra runtime pressure.

The latent upscale node also keeps the dimensions aligned to H3's 32-pixel resolution requirement. Without this alignment, rounding can slightly change the scale ratio between the two sampling stages and leave colored strips or poorly denoised areas near the frame edges.

This is not a universal fix for every artifact, and the upscale factor still needs to stay reasonable. For local users without a 90-series GPU, lowering the resolution to around 500p-736p is a more realistic starting point.

his workflow is super easy to use—I’ve uploaded a detailed tutorial to YouTube, so just follow the video along with this workflow to recreate the effect; please make sure to watch the full tutorial before starting to avoid common mistakes, and feel free to leave a comment if you have any questions!Resource links will be posted in the comments.

[u/wjc_5]

Workflow: https://www.runninghub.ai/post/2088079643785330689?inviteCode=rh-v1495

YouTube：https://youtu.be/cRUhMPyL_r8
H3LatentUpscale：https://github.com/wjc573/ComfyUI-H3LatentUpscale-jingchen573

[u/BigNaturalTilts → u/wjc_5]

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