Instructions to use raaedk/niji with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use raaedk/niji with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("raaedk/niji") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Trained for 0 epochs and 4000 steps.
Browse filesTrained with datasets ['text-embed-cache', 'niji-512', 'niji-1024', 'niji-512-crop', 'niji-1024-crop']
Learning rate 0.0001, batch size 1, 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: stabilityai/stable-diffusion-3.5-large
VAE: None
pytorch_lora_weights.safetensors
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