Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
floorplan
architecture
design
Instructions to use bavlyyoussef/floorplan-model-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bavlyyoussef/floorplan-model-v3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bavlyyoussef/floorplan-model-v3", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
File size: 766 Bytes
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license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
- floorplan
- architecture
- design
---
# Floorplan Generation Model v3
A fine-tuned Stable Diffusion model specialized in generating architectural floorplans.
## Usage
```python
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained("bavlyyoussef/floorplan-model-v3")
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
prompt = "modern two-bedroom apartment floorplan with living room and kitchen"
image = pipe(prompt).images[0]
image.save("floorplan.png")
```
## Training Details
- Base Model: Stable Diffusion v2.1
- Training Method: LoRA fine-tuning
- Resolution: 512x512
- Specialization: Architectural floorplans
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