Text-to-Image
MiniMax H3
Diffusers
Safetensors
English
image-to-image
image-editing
image-generation
Instructions to use MiniMaxAI/MiniMax-H3-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MiniMaxAI/MiniMax-H3-Image 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-Image", 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
Commit History
Today
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Add diffusers format weights
Verified signatureAdds the Modular Diffusers format of MiniMax-H3-Image, converted from revision fe12502c with `scripts/convert_minimax_h3_to_diffusers.py --version image`. New files: `modular_model_index.json`, `scheduler/`, `text_encoder/` (a copy of the MiniMax-H3 text encoder, so the repository is self-contained), `transformer_diffusers/` and `vae_diffusers/`. No existing file is changed. The original `transformer/` and `vae/` stay as they are; the converted transformer and VAE live in the two `_diffusers` folders, which `modular_model_index.json` points to. ```python import torch from diffusers import ComponentsManager, ModularPipeline manager = ComponentsManager() manager.enable_auto_cpu_offload(device="cuda") pipe = ModularPipeline.from_pretrained( "MiniMaxAI/MiniMax-H3-Image", components_manager=manager ) pipe.load_components(dtype=torch.bfloat16) ```57f313e