Virtual Try-On LoRA: Pattern2
📋 Model Description
This is a LoRA (Low-Rank Adaptation) model fine-tuned for generating realistic Pattern2 fabric textures in virtual try-on applications. The model has been trained on high-quality pattern2 texture images to capture the unique characteristics of this fabric type.
Key Features
- 🎨 Specialized for Pattern2: 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 pattern2 textures to garment designs
- Fashion Design: Visualize how garments look with pattern2 fabric
- E-commerce: Generate product images with different fabric textures
- Style Transfer: Transfer pattern2 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
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-pattern2",
weight_name="pytorch_lora_weights.safetensors"
)
# Generate image
prompt = "a garment with pattern2 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
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-pattern2",
weight_name="pytorch_lora_weights.safetensors",
adapter_name="pattern2"
)
# Set LoRA scale (0.0 to 1.0)
pipe.set_adapters(["pattern2"], adapter_weights=[0.8])
# Generate
prompt = "a stylish jacket with pattern2 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
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-pattern2")
# Apply texture to masked garment area
result = pipe(
prompt="garment with pattern2 fabric",
image=original_image,
mask_image=garment_mask,
num_inference_steps=30
).images[0]
📊 Training Details
Training Data
- Dataset: lora-garment-textures
- Category: Pattern2
- Images: High-resolution pattern2 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
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 pattern2 fabric texture, high quality, detailed"
"stylish clothing made of pattern2 material, professional photography"
"fashion design with pattern2 texture, studio lighting"
"pattern2 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 pattern2 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
Models
- Base Model: stabilityai/stable-diffusion-xl-base-1.0
- Other Textures: vton-lora-denim, vton-lora-linen
- Segmentation Model: garment-segmentation-unet-resnet50
Datasets
- Training Data: lora-garment-textures
- Masks Dataset: deepfashion2-upper-body-masks
Demos
- Try It Out: garment-segmentation
📚 Citation
If you use this model in your research or project, please cite:
@misc{vton_lora_pattern2,
author = {zyuzuguldu},
title = {Virtual Try-On LoRA: Pattern2},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/zyuzuguldu/vton-lora-pattern2}}
}
🤝 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
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Model tree for zyuzuguldu/vton-lora-pattern2
Base model
stabilityai/stable-diffusion-xl-base-1.0