Instructions to use aphexblake/sunset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aphexblake/sunset with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aphexblake/200-msf-v2", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("aphexblake/sunset") prompt = "Sunset" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 498 Bytes
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license: creativeml-openrail-m
base_model: aphexblake/200-msf-v2
instance_prompt: Sunset
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - sunset
These are LoRA adaption weights for [aphexblake/200-msf-v2](https://huggingface.co/aphexblake/200-msf-v2). The weights were trained on the instance prompt "Sunset" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
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