Instructions to use jainr3/sd-diffusiondb-pixelart-v2-model-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jainr3/sd-diffusiondb-pixelart-v2-model-lora 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-2-1", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("jainr3/sd-diffusiondb-pixelart-v2-model-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
LoRA text2image fine-tuning - jainr3/sd-diffusiondb-pixelart-v2-model-lora
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the jainr3/diffusiondb-pixelart dataset. This model has been trained for 30 epochs while the jainr3/sd-diffusiondb-pixelart-model-lora model was trained on only 5 epochs. You can find some example images in the following.
- Downloads last month
- -
Model tree for jainr3/sd-diffusiondb-pixelart-v2-model-lora
Base model
stabilityai/stable-diffusion-2-1