You can deploy this model and start earning money today!

hamzi275/
pk-fashion-lora-sd15

Name: pk-fashion-lora-sd15
Availability: InStock

runwayml/stable-diffusion-v1-5

adapter

creativeml-openrail-m

Views

No views yet

Pakistani Fashion LoRA — Stable Diffusion v1.5

LoRA fine-tuned from Stable Diffusion v1.5, specialized for generating images of traditional Pakistani garments, particularly women's shalwar kameez.

Scope

This is a garment image generation LoRA, not a virtual try-on model. It generates clothing images from text prompts and does not combine a person image with a garment image. Virtual try-on architectures such as IDM-VTON were explored but were not used due to their high computational requirements on free-tier Google Colab GPUs.

Training Details

Parameter	Value
Base Model	`runwayml/stable-diffusion-v1-5`
Method	LoRA (PEFT), Rank=4, Alpha=4
Target Modules	UNet Attention (`to_q`, `to_k`, `to_v`, `to_out.0`)
Resolution	512 × 512 (Pad-to-Square preprocessing)
Training Steps	1500
Checkpoint	`lora_step_1500.safetensors`
Optimizer	AdamW
Learning Rate	`1e-4`
Caption Strategy	Class-based prompts

Dataset

The model was trained on a custom-curated Pakistani fashion dataset containing traditional women's garments.

The preprocessing pipeline included:

Removal of corrupt images
Duplicate detection using perceptual hashing
Aspect ratio inspection
Pad-to-square resizing
Image quality verification

Usage

python

from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16
).to("cuda")

pipe.unet.load_attn_procs("hamzi275/pk-fashion-lora-sd15")

image = pipe(
    "a photo of a woman wearing traditional Pakistani shalwar kameez"
).images[0]

image.save("output.png")

Example Prompt

a photo of a woman wearing traditional Pakistani shalwar kameez

Limitations

Not a virtual try-on model
No person-image conditioning
Uses class-level captions instead of detailed per-image captions
No quantitative evaluation metrics (FID, LPIPS, SSIM) have been computed yet

Future Work

Add person-image and garment-image conditioning for virtual try-on
Improve caption quality using per-image annotations
Evaluate using FID, LPIPS, and CLIP Score
Expand the dataset with additional Pakistani clothing styles

Citation

If you use this model in your research or project, please cite this repository.