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Kenza Né Banunga

Prompt
long shot scenic professional photograph of urban graffiti style professional 3d model A vibrant portrait captures the captivating essence of Kenza Né Banunga, confidently posing in front of a striking backdrop, photographed by the talented Nina Masic. The model showcases an iconic look inspired by the street style of Harlem in the early '90s: high-waisted baggy jeans, a colorful crop top, and an oversized denim jacket. Her hair is styled in voluminous box braids adorned with gold beads. Bold hoop earrings and a pair of Timberland boots complete her urban ensemble.
Negative Prompt
ugly, deformed, noisy, low poly, blurry, painting, clean, minimalistic, soft, gentle, blurry, off-center
Prompt
Kenza_Né_Banunga A captivating image captures the vibrant atmosphere of a high-profile sports event: the NBA Basket Final. In this full-body shot, the subject is a Kenza woman standing confidently in front of a dramatic backdrop, radiating energy and confidence amidst the excitement surrounding one of basketball's most coveted competitions.
Prompt
Kenza_Né_Banunga woman full-body shot, rapper pose, curved woman braided hair, black, brown, grin, hair, nail, white leather jacket with round collar, black tshirt and white skirt photoshot
Prompt
Kenza_Né_Banunga woman full-body shot, rapper pose, curved woman braided hair, black, brown, grin, hair, nail, white leather jacket with round collar, black tshirt and white skirt photoshot, empty MADISON SQUARE GARDEN, outdoors
  • Model Card for Kenza Né Banunga LoRA SDXL

Example of generated images:

Generated Image Generated Image

Generated Image Generated Image 1 Generated Image 2 Generated Image 3 Generated Image 4 Generated Image 5 Generated Image 6 Generated Image 7 Generated Image 8 Generated Image 9

Details:

Description:

This model is a Low-Rank Adaptation (LoRA) trained to generate images of a young woman named Kenza Né Banunga. 
It is designed to be used with Stable Diffusion XL models to create photorealistic images of this specific character.

developed_by: Anonymous

finetuned_from: frankjoshua/realvisxlV40_v40Bakedvae

repository: local

  • Trained on Kohya_ss Arch-Linux

Usage:

Direct_use:

This LoRA can be used with compatible SDXL models to generate photorealistic images of Kenza Né Banunga in various situations and poses.

Out_of_scope_use:

This model should not be used to create explicitly sexual, defamatory, or harmful images.

Bias risks limitations:

Description:

The model may have biases in the representation of physical traits and facial expressions. 
The quality of results may vary depending on the base model used.

Recommendations:

Users should be aware of potential biases and use the model responsibly and ethically.

Training details:

Training_data: local

Training_procedure:

Hyperparameters:
  training_regime: bf16 mixed precision
  epochs: 30
  learning_rate: 1e-05
  train_batch_size: 3
  gradient_accumulation_steps: 1
  optimizer: AdamW
  lr_scheduler: constant
  lora_rank: 32
  network_alpha: 128
  resolution: 1024x1024 max
checkpointing:
  save_every_n_steps: 30
  save_at_epoch_end: true

Technical specifications:

Model_architecture:

type: LoRA Standard
base_model: SDXL
gradient_checkpointing: true
xformers: enabled

Compute_infrastructure:

 hardware: NVIDIA GPU GEFORCE RTX-3060
 software: Kohya_ss
  framework: PyTorch with xformers
  precision: bf16 mixed precision

Additional_information:

Training_comment:

3 repeats. More info: https://civitai.com/articles/1771 2_3_Kenza_Né_Banunga woman 35 epoch

Model card:

Authors: Anonymous

Contact: Information not available

Use it with the 🧨 diffusers library

usage example: |

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    'frankjoshua/realvisxlV40_v40Bakedvae', 
    torch_dtype=torch.float16
).to('cuda')
pipeline.load_lora_weights('path/to/Kenza_Né_Banunga_Lora_SDXL_dim32x128', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('your prompt').images

For more details, including weighting, merging, and fusing LoRAs, check the documentation on loading LoRAs in  🧨 diffusers
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