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
Upload README.md with huggingface_hub
Browse files
README.md
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# FastH3 Pruned 8-Step (bf16, checkpoint 300)
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Reference bf16 transformer; source for the FP8/NVFP4 exports.
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## Model
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- MiniMax-H3 student pruned from 50 to **42 transformer blocks** (removed blocks 6, 7, 9, 13, 15, 16, 22, 23), rank-16 AdaLN,
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trained with 8-step DMD (timesteps 999, 874, 749, 624, 500, 375, 250, 125) and VSA sparse attention (sparsity 0.8, 64-token tiles).
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- Training checkpoint 300 of run `s42-r16-dmd8-vsa80` (selected over 800 for prompt adherence on a multi-seed evaluation).
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- Sampling contract: video/audio scheduler shift 10/3, guidance 1.0, VSA_sparsity 0.8, VSA_tile_size 64.
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- `text_encoder` is not included; it is identical to the one in `FastVideo/FastVideo-FastH3-8-Step-V2`.
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**Status: internal, private evaluation build.** License: MiniMax H3 Community License (inherited).
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