Instructions to use SimpleTuner/minimax-h3-image-LoRA-experiments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SimpleTuner/minimax-h3-image-LoRA-experiments 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") pipe.load_lora_weights("SimpleTuner/minimax-h3-image-LoRA-experiments") 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
Yesterday
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Record corrected assistant smoke and fresh training start
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Record H3 pixel-range bug and restart assistant with fresh VAE caches
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2 days ago
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Record successful four-GPU assistant smoke with a fresh caption cache
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Correct bound lookup and verify assistant against a fresh caption cache
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Verify four-GPU assistant smoke and begin independent full preparation
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Verify four-GPU assistant smoke and begin independent full preparation
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Fix assistant precache URI identity and bounded distributed coverage
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Record assistant smoke cache failure and ongoing diagnosis
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Correct assistant bootstrap constraint and record resumed setup
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Correct assistant bootstrap constraint and record resumed setup
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Record baseline GPU release and assistant bootstrap
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Record early-stopped unassisted H3 Image baselines
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Record early-stopped unassisted H3 Image baselines
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Use native SimpleTuner model uploads after checkpoint validation
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Use native SimpleTuner model uploads after checkpoint validation
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Add H3 Image experiment protocols, recipes and verified evidence
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