import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("K2MAR/wiring-schema-lora")
prompt = "electrical wiring diagram schematic"
image = pipe(prompt).images[0]Wiring Schema LoRA
Fine-tuned Stable Diffusion v1.5 for generating electrical wiring schemas and circuit diagrams.
Model Details
- Base: Stable Diffusion v1.5
- Method: LoRA (Low-Rank Adaptation)
- Rank: 16 | Alpha: 32
- Training: 30 epochs on 50 wiring schemas
- Loss: 0.0998 → 0.0897 (10.1% improvement)
Quick Start
import torch
from diffusers import StableDiffusionPipeline
from peft import PeftModel
pipeline = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16
)
pipeline.unet = PeftModel.from_pretrained(pipeline.unet, "USERNAME/wiring-schema-lora")
pipeline = pipeline.to("cuda")
image = pipeline("electrical wiring diagram").images[0]
image.save("output.png")
API Usage
curl -X POST "https://api-inference.huggingface.co/models/USERNAME/wiring-schema-lora" \
-H "Authorization: Bearer YOUR_HF_TOKEN" \
-H "Content-Type: application/json" \
-d '{"inputs": "electrical wiring diagram"}'
Training
- Learning Rate: 5e-5
- Batch Size: 1
- Weight Decay: 0.01
- Data Augmentation: On
License
OpenRAIL License
- Downloads last month
- 18