Text-to-Image
Diffusers
pixart_sigma
pixart_sigma-diffusers
image-to-image
simpletuner
Not-For-All-Audiences
lora
controlnet
template:sd-lora
standard
Instructions to use ControlNetLoRA/pixart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ControlNetLoRA/pixart with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("ControlNetLoRA/pixart") pipe = StableDiffusionControlNetPipeline.from_pretrained( "terminusresearch/pixart-900m-1024-ft-v0.6", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Trained for 69 epochs and 140 steps.
Browse filesTrained with datasets ['an example backend for text embeds.', 'antelope-data-1024']
Learning rate 0.0001, batch size 1, and 1 gradient accumulation steps.
Trained with epsilon prediction type and rescaled_betas_zero_snr=False
Using 'trailing' timestep spacing.
Base model: terminusresearch/pixart-900m-1024-ft-v0.6
VAE: madebyollin/sdxl-vae-fp16-fix
pytorch_lora_weights.safetensors
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