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HyperFlow
Open-source 8-step LoRA for MiniMax H3
Open-source 8-step LoRA for MiniMax H3. Data-free flow self-distillation cuts sampling from 49 forwards to 8, improves camera control, consistency and detail, and keeps H3's joint video+audio output. Weights on Hugging Face; run it with the diffusers loader.
Hey everyone! I'm Yuesong, the researcher behind HyperFlow at Video Rebirth.
Here's why we built this: MiniMax H3 is an incredible video model, but 49 inference steps means long wait times and high GPU costs. We asked ourselves: can we get the same quality in far fewer steps, without any external training data?
The answer is data-free flow self-distillation. The base model teaches itself to compress 49 steps into 8. No extra datasets, no human annotations.
What we got:
2.9× faster on 4×H200 (~60s vs ~175s) 3.0× faster on 1×H200 (~130s vs ~395s) Combined with Sol-Attn, FlashAttention, and VAE parallelism, real-time generation is possible on 8×B200.
Beyond speed, we also improved camera control, temporal consistency, material rendering, and overall balance compared to the base model.
We'd love to hear what you'd build with this. Drop a comment or reach out!
About HyperFlow on Product Hunt
“Open-source 8-step LoRA for MiniMax H3”
HyperFlow was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #160 on the daily leaderboard. Open-source 8-step LoRA for MiniMax H3. Data-free flow self-distillation cuts sampling from 49 forwards to 8, improves camera control, consistency and detail, and keeps H3's joint video+audio output. Weights on Hugging Face; run it with the diffusers loader.
On the analytics side, HyperFlow competes within Open Source, Artificial Intelligence, GitHub and Video — topics that collectively have 591.3k followers on Product Hunt. The dashboard above tracks how HyperFlow performed against the three products that launched closest to it on the same day.
Who hunted HyperFlow?
HyperFlow was hunted by Yuesong Tian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of HyperFlow including community comment highlights and product details, visit the product overview.
Yuesong Tian
Hey everyone! I'm Yuesong, the researcher behind HyperFlow at Video Rebirth.
Here's why we built this: MiniMax H3 is an incredible video model, but 49 inference steps means long wait times and high GPU costs. We asked ourselves: can we get the same quality in far fewer steps, without any external training data?
The answer is data-free flow self-distillation. The base model teaches itself to compress 49 steps into 8. No extra datasets, no human annotations.
What we got:
2.9× faster on 4×H200 (~60s vs ~175s)
3.0× faster on 1×H200 (~130s vs ~395s)
Combined with Sol-Attn, FlashAttention, and VAE parallelism, real-time generation is possible on 8×B200.
Beyond speed, we also improved camera control, temporal consistency, material rendering, and overall balance compared to the base model.
Everything is open source. Demos, benchmarks, and technical details are all on the landing page:
https://www.videorebirth.com/lp/hyperflow
Weights on HuggingFace:
https://huggingface.co/videorebirth/hyperflow
We'd love to hear what you'd build with this. Drop a comment or reach out!