Instructions to use Efficient-Large-Model/Open-H3-Regenerate-2K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Efficient-Large-Model/Open-H3-Regenerate-2K with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Efficient-Large-Model/Open-H3-Regenerate-2K") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Commit History
3 days ago
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Add Open-H3-Regenerate-2K weights
Verified signatureWeights for https://github.com/NVlabs/Sana/tree/sol-engine/models/Open-H3-Regenerate-2K (project page https://nvlabs.github.io/Sana/Open-H3-Regenerate-2K/): merged refiner LoRA and Sharp curve branches, the latent upscaler, the fast H3 VAE decoder (StreamRender-H3), and an unmodified copy of MiniMax-H3 @ 6818f6c. Byte-identical clone of Sensen02/Open-H3-Regenerate-2K at aaa3d97db80f7d6c2c251ef0cb8702537f2093f1; every file's size and sha256 checked after upload.a5e321e