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zyuzuguldu/
vton-lora-linen

Name: vton-lora-linen
Availability: InStock

stabilityai/stable-diffusion-xl-base-1.0

adapter

apache-2.0

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Virtual Try-On LoRA: Linen

📋 Model Description

This is a LoRA (Low-Rank Adaptation) model fine-tuned for generating realistic Linen fabric textures in virtual try-on applications. The model has been trained on high-quality linen texture images to capture the unique characteristics of this fabric type.

Key Features

🎨 Specialized for Linen: Captures authentic fabric texture and appearance
🚀 Lightweight: Only 3.1 MB - efficient for deployment
🎯 SDXL-based: Built on Stable Diffusion XL for high-quality generation
👔 Virtual Try-On Ready: Designed for fashion and garment visualization
⚡ Fast Inference: LoRA architecture enables quick generation

🎯 Intended Use

Primary Use Cases

Virtual Try-On Systems: Apply linen textures to garment designs
Fashion Design: Visualize how garments look with linen fabric
E-commerce: Generate product images with different fabric textures
Style Transfer: Transfer linen texture to existing garment images

Out of Scope

General-purpose image generation
Non-fabric texture generation
Photo-realistic face generation

🚀 Quick Start

Installation

pip install diffusers transformers accelerate safetensors

Basic Usage

python

from diffusers import DiffusionPipeline
import torch

# Load base model
pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16"
)
pipe.to("cuda")

# Load LoRA weights
pipe.load_lora_weights(
    "zyuzuguldu/vton-lora-linen",
    weight_name="pytorch_lora_weights.safetensors"
)

# Generate image
prompt = "a garment with linen fabric texture, high quality, detailed"
image = pipe(
    prompt,
    num_inference_steps=30,
    guidance_scale=7.5
).images[0]

image.save("output.png")

Advanced Usage with Multiple LoRAs

python

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16
).to("cuda")

# Load with custom weight
pipe.load_lora_weights(
    "zyuzuguldu/vton-lora-linen",
    weight_name="pytorch_lora_weights.safetensors",
    adapter_name="linen"
)

# Set LoRA scale (0.0 to 1.0)
pipe.set_adapters(["linen"], adapter_weights=[0.8])

# Generate
prompt = "a stylish jacket with linen texture, fashion photography"
negative_prompt = "blurry, low quality, distorted"

image = pipe(
    prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=40,
    guidance_scale=8.0
).images[0]

image.save("styled_garment.png")

Using with Virtual Try-On Pipeline

python

from diffusers import StableDiffusionXLInpaintPipeline
import torch

# Load inpainting pipeline for try-on
pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16
).to("cuda")

# Load LoRA
pipe.load_lora_weights("zyuzuguldu/vton-lora-linen")

# Apply texture to masked garment area
result = pipe(
    prompt="garment with linen fabric",
    image=original_image,
    mask_image=garment_mask,
    num_inference_steps=30
).images[0]

📊 Training Details

Training Data

Dataset: lora-garment-textures
Category: Linen
Images: High-resolution linen fabric texture samples
Resolution: Variable (resized to 1024x1024 for training)

Training Configuration

Base Model: Stable Diffusion XL 1.0
LoRA Rank: 15
Training Framework: Diffusers + PEFT
Optimizer: AdamW
Training Steps: ~2000-8000 (varied by category)
Hardware: GPU-accelerated training

Hyperparameters

yaml

learning_rate: 1e-4
lora_rank: 15
lora_alpha: 15
batch_size: 4
resolution: 1024x1024
mixed_precision: fp16
gradient_accumulation_steps: 4

📁 Model Files

pytorch_lora_weights.safetensors (3.1 MB): Main LoRA weights in SafeTensors format

🎨 Prompt Engineering Tips

Recommended Prompts

"a garment with linen fabric texture, high quality, detailed"
"stylish clothing made of linen material, professional photography"
"fashion design with linen texture, studio lighting"
"linen fabric garment, detailed texture, 4k quality"

Negative Prompts

"blurry, low quality, distorted, unrealistic, artificial"
"pixelated, noisy, artifacts, bad texture"

Tips

Texture Keywords: Include words like "fabric", "texture", "material" for best results
Quality Modifiers: Add "high quality", "detailed", "4k" for better outputs
LoRA Weight: Adjust between 0.6-1.0 for strength control
Inference Steps: Use 30-50 steps for balanced quality/speed
Guidance Scale: 7.0-8.5 works well for most prompts

⚖️ Limitations and Bias

Limitations

Optimized specifically for linen textures
May not generalize well to other fabric types
Requires SDXL base model for best results
Performance depends on prompt quality

Potential Biases

Training data may reflect specific regional or cultural fabric styles
May perform better on certain garment types seen during training

📝 License

This model is released under the Apache 2.0 License.

Free for commercial and non-commercial use
Requires attribution to the original authors
No warranty provided

🔗 Related Resources

📚 Citation

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

bibtex

@misc{vton_lora_linen,
  author = {zyuzuguldu},
  title = {Virtual Try-On LoRA: Linen},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/zyuzuguldu/vton-lora-linen}}
}

🤝 Contributing

Found an issue or want to improve the model? Feel free to reach out or open a discussion!

👨‍💻 Maintainer

Created and maintained by @zyuzuguldu

Part of the Virtual Try-On Project

Repository: Virtual-Try-On

Made with ❤️ for the fashion-tech and AI community

zyuzuguldu/vton-lora-linen

Virtual Try-On LoRA: Linen

📋 Model Description

Key Features

🎯 Intended Use

Primary Use Cases

Out of Scope

🚀 Quick Start

Installation

Basic Usage

Advanced Usage with Multiple LoRAs

Using with Virtual Try-On Pipeline

📊 Training Details

Training Data

Training Configuration

Hyperparameters

📁 Model Files

🎨 Prompt Engineering Tips

Recommended Prompts

Negative Prompts

Tips

⚖️ Limitations and Bias

Limitations

Potential Biases

📝 License

🔗 Related Resources

Models

Datasets

Demos

📚 Citation

🤝 Contributing

👨‍💻 Maintainer

zyuzuguldu/
vton-lora-linen