Text-to-Video
Diffusers
Safetensors
MiniMaxH3ModularPipeline
video-generation
audio
fastvideo
bf16
pruned
Instructions to use FastVideo/FastVideo-FastH3-Trim-8-Step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/FastVideo-FastH3-Trim-8-Step with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FastVideo/FastVideo-FastH3-Trim-8-Step", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
FastH3 Trim, 8-step, BF16 source weights
FastH3 Trim is an experimental, smaller version of FastH3 V2: eight-step text-to-video with synchronized audio from 42 of the 50 MiniMax H3 transformer blocks. We removed the eight blocks whose removal changed video and audio predictions the least, replaced each block's AdaLN timestep projection with a shared rank-16 basis, and trained the result with eight-step DMD2 (checkpoint 300). Removing blocks makes the model faster and smaller but costs some quality; use FastH3 V2 when quality matters most.
- Sampling: 8 DMD steps (999, 874, 749, 624, 500, 375, 250, 125), sparse attention keeping 20% of tiles.
fastvideo_inference.jsonholds the schedule, which FastVideo reads automatically. - Text encoder: NVFP4 Qwen3-VL trimmed to the 50 layers H3 reads.
- VAE: LynnReal lightweight video VAE with Kijai's INT8 weights; H3 audio VAE.
Blog post: FastH3 on Consumer Hardware 路 Code: FastVideo
- Transformer: 34.9 GiB in BF16 (FP16 AdaLN factors). Source for the quantized releases and for local MLX conversion on Apple Silicon.
Full-quality counterpart in the same format: FastVideo/FastVideo-FastH3-8-Step-V2.
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Model tree for FastVideo/FastVideo-FastH3-Trim-8-Step
Base model
MiniMaxAI/MiniMax-H3 Finetuned
FastVideo/FastVideo-FastH3-8-Step-V2