Instructions to use rzgar/minimax_h3_fl2v_lightx2v_4step_int8-convrot_comfy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rzgar/minimax_h3_fl2v_lightx2v_4step_int8-convrot_comfy with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rzgar/minimax_h3_fl2v_lightx2v_4step_int8-convrot_comfy") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - PEFT
How to use rzgar/minimax_h3_fl2v_lightx2v_4step_int8-convrot_comfy with PEFT:
Task type is invalid.
- Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
MiniMax H3 FL2V LightX2V 4-Step Int8-ConvRot
Clip 1: v1 4step | Clip 2: v1 8step
v0.1 4step demo
Model description
Requirements
- ComfyUI (recent - tested on master 0.31.0)
- ComfyUI-LoraInt8Loader custom node (standard ComfyUI LoRA loaders cannot dequantize it)
Files
| File | Size | NDownload |
|---|---|---|
minimax_h3_fl2v_turbo_4step_v1.0_int8_convrot |
991 MB | download |
minimax_h3_fl2v_turbo_8step_v1.0_int8_convrot |
991 MB | Download |
minimax_h3_fl2v_turbo_4step_v0.1_int8_convrot |
991 MB | Download |
Both are quantized from the KJ comfy-converted BF16 LoRA.
norefiner variant
The full LoRA also patches the model's token_refiner - the text/audio conditioning refiner. The norefiner variant removes those 8 patches so the conditioning path runs at base quality (some video clips had bad audio. This solution fixed it, but keep in mind it doesn’t happen always). If audio/text conditioning feels weak with the full LoRA at 4 steps, try the norefiner variant.
First clip: with refiner | last clip: norefiner
Usage (ComfyUI)
- Unzip the ComfyUI-LoraInt8Loader custom node into custom_nodes, restart
- In your MiniMax H3 workflow, add LoRA Loader (Int8-ConvRot), select the file, strength 1.0 (the training scale is already baked in)
- Sample at 4-6 steps
Download model
Download them in the Files & versions tab.
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Model tree for rzgar/minimax_h3_fl2v_lightx2v_4step_int8-convrot_comfy
Base model
MiniMaxAI/MiniMax-H3