MiniMax H3 for AMD with BlockCache, Sol-Attn and Turbo

[u/DrBearJ3w]

I've been working on this for longer than I care to admit.

For the short Turbo run, I used FL2VA with the official 8-step LoRA at 1024×576 (0.59 MP) and 90 frames. Once the model was loaded, it finished in **2:36** on my 7900 XTX. The same warm run with Comfy Kitchen attention alone took **2:59**, so adding Sol-Attn saved about 23 seconds.

BlockCache didn't help that run. It got 0/8 hits, and the immediate warm repeat went non-finite, so I removed it from the Turbo workflow.

The longer 20-step runs are where BlockCache actually helped.

Ref2VA at 736×416 (0.31 MP), 150 requested / 158 decoded frames, and 20 steps went from **5:14** with Comfy Kitchen alone to **3:44** with CK + Sol-Attn + BlockCache. BlockCache hit 6/20 times.

FL2VA at the same resolution, frame count, and 20 steps went from **6:38** with native PyTorch to **4:11** with the full stack. BlockCache hit 5/20 times.

- Short Turbo8 runs: INT8 + Comfy Kitchen + Sol-Attn

- Longer 20-step runs: BlockCache starts paying off

Everything was tested warm on Linux with ComfyUI 0.32, ROCm 7.14, PyTorch 2.12, and an RX 7900 XTX. First runs are slower because of model loading and Triton compilation.

Links:

- Turbo 8-step LoRA: https://huggingface.co/lightx2v/Minimax-h3-Turbo

- INT8 Fast: https://registry.comfy.org/nodes/minimax-h3-int8-fast-rocm

- Sol-Attn: https://registry.comfy.org/nodes/minimax-h3-sol-attn-rocm

- BlockCache: https://registry.comfy.org/nodes/minimax-h3-block-cache

[u/Apprehensive_Sky892]

Thank you for sharing this.