Instructions to use davidrd123/Caravaggio-QuarterCrops-Flux-LoKr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use davidrd123/Caravaggio-QuarterCrops-Flux-LoKr 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.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("davidrd123/Caravaggio-QuarterCrops-Flux-LoKr") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Trained for 5 epochs and 9000 steps.
Browse filesTrained with datasets ['text-embed-cache', 'caravaggio-512', 'caravaggio-768', 'caravaggio-1024', 'caravaggio-1536', 'caravaggio-512-crop', 'caravaggio-768-crop', 'caravaggio-1024-crop']
Learning rate 8e-05, batch size 3, and 1 gradient accumulation steps.
Used DDPM noise scheduler for training with epsilon prediction type and rescaled_betas_zero_snr=False
Using 'trailing' timestep spacing.
Base model: black-forest-labs/FLUX.1-dev
VAE: None
pytorch_lora_weights.safetensors
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