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
| { | |
| "trigger_word": "AndyYang", | |
| "output_name": "klein9b-lora-j9n8xs-9b-test-3", | |
| "model_version": "klein_9b", | |
| "base_model": "black-forest-labs/FLUX.2-klein-9B", | |
| "license": "non-commercial", | |
| "resolution": 1024, | |
| "lora_rank": 16, | |
| "learning_rate": 0.0001, | |
| "lr_scheduler": "constant", | |
| "lr_warmup_steps": 20, | |
| "original_max_train_steps": 1100, | |
| "train_batch_size": 1, | |
| "gradient_accumulation_steps": 4, | |
| "checkpointing_steps": 100, | |
| "num_images": 8 | |
| } |