Instructions to use xbesing/output_nft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xbesing/output_nft 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-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("xbesing/output_nft") prompt = "nftmonkey" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
LoRA DreamBooth - xbesing/output_nft
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on nftmonkey using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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Model tree for xbesing/output_nft
Base model
stabilityai/stable-diffusion-2-1-base
