metadata
license: mit
tags:
- stable-diffusion
- lora
- safetensors
- text-to-image
- real-estate
- kohya-ss
base_model: runwayml/stable-diffusion-v1-5
Crestline Shreshth
Model Description
Crestline-Shreshth is a Stable Diffusion 1.5 LoRA fine-tuned for generating photorealistic real estate imagery. It produces high-quality visuals of residential and commercial properties, interiors, and architectural renders — ideal for real estate marketing, virtual staging, and property visualization.
Model Architecture
- Base Model: Stable Diffusion v1.5 (
runwayml/stable-diffusion-v1-5) - Fine-tuning Method: LoRA (Low-Rank Adaptation) v2
- Framework: Diffusers / Kohya-SS
- Task: Text-to-Image Generation (Real Estate Domain)
Training Details
- Training Tool: Kohya-SS LoRA trainer
- Domain: Real estate photography — interiors, exteriors, architectural renders
- Version: v2 (improved over v1 with more training data and better trigger words)
- Steps: Fine-tuned to convergence on curated real estate image dataset
Files
| File | Description |
|---|---|
Crestline_Shreshth_v2.safetensors |
LoRA weights v2 (safetensors format) |
Usage
from diffusers import StableDiffusionPipeline
import torch
from huggingface_hub import hf_hub_download
# Download LoRA weights
lora_path = hf_hub_download(repo_id='devanshty/Crestline-Shreshth', filename='Crestline_Shreshth_v2.safetensors')
# Load base pipeline
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16
).to("cuda")
# Load LoRA weights
pipe.load_lora_weights(lora_path)
# Generate
image = pipe("luxury modern living room, real estate photography, bright natural light, 4K").images[0]
image.save("output.png")
Download & Use
from huggingface_hub import hf_hub_download
lora_path = hf_hub_download(repo_id='devanshty/Crestline-Shreshth', filename='Crestline_Shreshth_v2.safetensors')