virtual-tryoff-lora

64
10
license:apache-2.0
by
fal
Image Model
OTHER
9B params
New
64 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
21GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
9GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by virtual-tryoff-lora with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

pythonpytorch
import torch
  from diffusers import Flux2KleinPipeline
  from PIL import Image
  
  pipeline = Flux2KleinPipeline.from_pretrained(
    "black-forest-labs/FLUX.2-klein-base-9B", 
    torch_dtype=torch.bfloat16, 
    low_cpu_mem_usage=False
  ).to("cuda")
  pipeline.load_lora_weights(
      "fal/virtual-tryoff-lora", 
      weight_name="virtual-tryoff-lora_diffusers.safetensors", 
      adapter_name="vtoff"
  )
  pipeline.set_adapters("vtoff", adapter_weights=1.0)
  pipeline.fuse_lora(adapter_names=["vtoff"], lora_scale=1.0)
  
  image = pipeline(
      image=Image.open("<your_image>.jpg"),
      prompt="TRYOFF extract the full outfit over a white background, product photography style.  NO HUMAN VISIBLE (the garments maintain their 3D form like an invisible mannequin).",
      height=1024,
      width=768,
      num_inference_steps=28,
      guidance_scale=5.0,
      generator=torch.Generator("cuda").manual_seed(42),
  ).images[0]

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