Instructions to use cokeadrink/fasth3-v2-native-blackwell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cokeadrink/fasth3-v2-native-blackwell with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cokeadrink/fasth3-v2-native-blackwell", 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 V2 native FastVideo Blackwell bundle
Unmodified native-format weights, configs and tokenizers for h3-runpod on one 96 GB RTX PRO 6000 Blackwell. Includes the NVFP4 attention/MLP/gate transformer, 50-layer NVFP4 text encoder, LynnReal 26-layer lightweight video VAE with INT8 ConvRot overlay, H3 audio VAE, and the original eight-forward DMD schedule. This bundle is for native FastVideo, not ComfyUI.
Source: FastVideo/FastVideo-FastH3-8-Step-V2-NVFP4-Consumer.
Source revision: 9d0d0d3ae69365aded83fab5562f00f9f06c12aa.
License copied from MiniMaxAI/MiniMax-H3 at 42ed227ee7df40d41602854ae760620d6eb651fe.
See LICENSE and NOTICE for terms and attribution. No weights were modified.
The upstream README is preserved in UPSTREAM_README.md.
Set the RunPod endpoint Model field to cokeadrink/fasth3-v2-native-blackwell. The worker discovers the
mounted cache; no HF_MODEL_ID or HF_REVISION variables are needed.
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Model tree for cokeadrink/fasth3-v2-native-blackwell
Base model
MiniMaxAI/MiniMax-H3