Instructions to use mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-9B", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277") prompt = "a photo of AndyYang" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
End of training
Browse files
checkpoint-1100/optimizer.bin
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checkpoint-1100/pytorch_lora_weights.safetensors
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checkpoint-1100/random_states_0.pkl
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checkpoint-1100/scheduler.bin
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pytorch_lora_weights.safetensors
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training_progress.json
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{"current_step":
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{"current_step": 1100, "total_steps": 1100, "resume_from_step": 1000, "current_loss": 0.547, "best_loss": 0.339, "best_checkpoint_step": 1060, "current_lr": 0.0001, "step_time": 2.6320780992507933, "eta_seconds": 0, "progress": 100, "status": "training", "model_version": "klein_9b"}
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