Instructions to use Aloukik21/trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aloukik21/trainer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Aloukik21/trainer") 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
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Add Z-Image Turbo training adapter v2
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Add Z-Image Turbo model for fast image generation
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Update Dockerfile with cache optimization
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Update README for cached models
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Update handler to use Aloukik21/trainer cache
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Add accuracy recovery adapters
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Add FLUX.1-schnell model files
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Add Qwen-Image model files
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Update README with full documentation
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Add RunPod handler with cleanup support
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Add models configuration
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Add README with API documentation
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initial commit
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