Instructions to use nhero/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nhero/model 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-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("nhero/model") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| base_model: stabilityai/stable-diffusion-xl-base-1.0 | |
| dataset: nhero/data_nd | |
| tags: | |
| - stable-diffusion-xl | |
| - stable-diffusion-xl-diffusers | |
| - text-to-image | |
| - diffusers | |
| - lora | |
| inference: true | |
| # LoRA text2image fine-tuning - nhero/model | |
| These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the nhero/data_nd dataset. You can find some example images in the following. | |
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| LoRA for the text encoder was enabled: False. | |
| Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. | |