Instructions to use e5ey/MiniMax-H3-Turbo-Lora-Pruned-Fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use e5ey/MiniMax-H3-Turbo-Lora-Pruned-Fixed 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("e5ey/MiniMax-H3-Turbo-Lora-Pruned-Fixed") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
MiniMax-H3 Turbo LoRA (Pruned-Base Compatible, File-Fixed)
Fixed copy of Abiray/MiniMax-H3-Turbo-Lora-Pruned-ComfyUI
(4-step turbo LoRA, original trainer larryvrh/MiniMax-H3-Turbo-Lora).
Why this repo exists
Every .safetensors in the source repo has 64 extra trailing bytes after the last tensor
(declared data ends at byte 620,285,592; files are 620,285,656 bytes), which makes strict safetensors
loaders fail with:
SafetensorError: Error while deserializing header: incomplete metadata, file not fully covered
This file is byte-identical to the source file with the 64 trailing bytes removed. All 416 tensors
(attn.qkv_proj / attn.out_proj / mlp.fc1 / mlp.fc2 per block ร 50 blocks + token_refiner,
no adaln targets โ designed for pruned / curve-form MiniMax-H3 checkpoints) load and match a
pruned FP8 base (e.g. Comfy-Org/MiniMax-H3 *_pruned_fp8_scaled).
Usage (ComfyUI)
Load LoRA node โ minimax_h3_turbo_4step_ckpt600_ema_V4_fixed.safetensors, strength 0.8โ1.8,
Steps 8โ12, sampler res_multistep. (Settings from the author's workflow.)
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Model tree for e5ey/MiniMax-H3-Turbo-Lora-Pruned-Fixed
Base model
MiniMaxAI/MiniMax-H3