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
Add diffusers format weights
#2
by sayakpaul HF Staff - opened
Adds 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.
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)
sayakpaul changed pull request status to open
ryanlee-dev changed pull request status to merged