simpletuner-lora

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

The main validation prompt used during training was:

a close-up photo of Jeremy LE 

Validation settings

• CFG: 3.0
• CFG Rescale: 0.0
• Steps: 20
• Sampler: None
• Seed: 42
• Resolution: 512x512

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

You can find some example images in the following gallery:

Prompt

unconditional (blank prompt)

Negative Prompt

blurry, cropped, ugly

Prompt

a breathtaking anime-style portrait of Jeremy LE, capturing her essence with vibrant colors and expressive features

Negative Prompt

blurry, cropped, ugly

Prompt

a lifelike and intimate portrait of Jeremy LE, showcasing her unique personality and charm

Negative Prompt

blurry, cropped, ugly

Prompt

a cinematic, visually stunning photo of Jeremy LE, emphasizing her dramatic and captivating presence

Negative Prompt

blurry, cropped, ugly

Prompt

an artistic and abstract representation of Jeremy LE, blending creativity with visual storytelling

Negative Prompt

blurry, cropped, ugly

Prompt

a futuristic and cutting-edge portrayal of Jeremy LE, set against a backdrop of advanced technology

Negative Prompt

blurry, cropped, ugly

Prompt

a close-up photo of Jeremy LE

Negative Prompt

blurry, cropped, ugly

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

Training settings

• Training epochs: 16
• Training steps: 15500
• Learning rate: 0.0001
• Effective batch size: 1
  • Micro-batch size: 1
  • Gradient accumulation steps: 1
  • Number of GPUs: 1
• Prediction type: flow-matching
• Rescaled betas zero SNR: False
• Optimizer: adamw_bf16
• Precision: Pure BF16
• Quantised: Yes: int8-quanto
• Xformers: Not used
• LoRA Rank: 16
• LoRA Alpha: None
• LoRA Dropout: 0.1
• LoRA initialisation style: default

Datasets

my-dataset-512

• Repeats: 10
• Total number of images: 21
• Total number of aspect buckets: 1
• Resolution: 0.262144 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None

my-dataset-1024

• Repeats: 10
• Total number of images: 21
• Total number of aspect buckets: 2
• Resolution: 1.048576 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None

my-dataset-512-crop

• Repeats: 10
• Total number of images: 21
• Total number of aspect buckets: 1
• Resolution: 0.262144 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square

my-dataset-1024-crop

• Repeats: 10
• Total number of images: 21
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'gozuslayer/simpletuner-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "a close-up photo of Jeremy LE "

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=512,
    height=512,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")