Text-to-Image
Diffusers
stable-diffusion
stable-diffusion-diffusers
simpletuner
lora
template:sd-lora
Instructions to use hmpark/lora-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hmpark/lora-training 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("hmpark/lora-training") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Trained for 139 epochs and 1950 steps.
Browse filesTrained with datasets ['text-embeds', 'poc1_lundlund_hugocomte']
Learning rate 8e-07, batch size 10, and 2 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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