Instructions to use HamzaP1/Finetune_try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HamzaP1/Finetune_try with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("HamzaP1/Finetune_try", dtype=torch.bfloat16, device_map="cuda") prompt = "A house plan of a 1 kanal house having car porch parking of 2 cars, gate on the south west side of the house, 2 or 3 bedrooms, front lawn, kitchen, launge, drawing and dining" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 387 Bytes
56fd8c3 fc6de0d 56fd8c3 fc6de0d 56fd8c3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A house plan of a 1 kanal house having car porch parking of 2 cars, gate on the south west side of the house, 2 or 3 bedrooms, front lawn, kitchen, launge, drawing and dining
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|