Instructions to use athul2832/AIVID_IM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use athul2832/AIVID_IM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Osama03/Finetuned_diffusion_interiordesign", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("athul2832/AIVID_IM") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
AIVID_IM

- Prompt
- -
Model description
tags: - lora - stable-diffusion - interior-design - peft model-index: - name: Interior Design LoRA results: []
Interior Design LoRA Weights
Model Description
This is a LoRA (Low-Rank Adaptation) model trained for interior design generation. It can be used with Stable Diffusion v1.5 to generate interior design images.
Usage
```python from diffusers import StableDiffusionPipeline import torch
pipe = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16 ) pipe.load_lora_weights("your-username/interior-design-lora-weights") pipe = pipe.to("cuda")
prompt = "modern living room interior design" image = pipe(prompt).images[0] image.save("interior_design.png") ```
Training Details
- Base model: runwayml/stable-diffusion-v1-5
- Training data: Interior design images
- Training steps: [number of steps]
- LoRA rank: 4
- LoRA alpha: 4
Performance
This LoRA adapter enhances the base model to generate more stylistically consistent interior design images.
Limitations
This model is specialized for interior design and may not perform well on other subjects.
Download model
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Model tree for athul2832/AIVID_IM
Base model
CompVis/stable-diffusion-v1-4