markury / AndroFlux-v3

AndroFlux-v3

This is a LoRA derived from black-forest-labs/FLUX.1-dev .

The main validation prompt used during training was:

a photo of a nude man in his twenties, focusing on the genital area, his penis is erect with visible testicles, photography by Brandon Woelfel, background inspired by Ludwig Favre, skin details, skin texture, fine hairs, brown hair

Validation settings

• CFG: 3.5
• CFG Rescale: 0.0
• Steps: 30
• Sampler: None
• Seed: 42
• Resolution: 1024

Note: The validation settings are not necessarily the same as the training settings .

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

• Training epochs: 10
• Training steps: 1900
• Learning rate: 1e-05
• Effective batch size: 2
  • Micro-batch size: 2
  • Gradient accumulation steps: 1
  • Number of GPUs: 1
• Prediction type: epsilon
• Rescaled betas zero SNR: False
• Optimizer: AdamW, stochastic bf16
• Precision: Pure BF16
• Xformers: Not used
• LoRA Rank: 16
• LoRA Alpha: 16
• LoRA Dropout: 0.1
• LoRA initialisation style: default

Datasets

AndroFlux-1024

• Repeats: 0
• Total number of images: 364
• Total number of aspect buckets: 1
• Resolution: 1 megapixels
• Cropped: True
• Crop style: center
• Crop aspect: square

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'AndroFlux-v3'
pipeline = DiffusionPipeline.from_pretrained(model_id)\pipeline.load_adapter(adapter_id)

prompt = "a photo of a nude man in his twenties, focusing on the genital area, his penis is erect with visible testicles, photography by Brandon Woelfel, background inspired by Ludwig Favre, skin details, skin texture, fine hairs, brown hair"
negative_prompt = "blurry, cropped, ugly"

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt='blurry, cropped, ugly',
    num_inference_steps=30,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1152,
    height=768,
    guidance_scale=3.5,
    guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")