Training epochs: 23
Training steps: 1800
Learning rate: 0.0001
Effective batch size: 16
- Micro-batch size: 8
- Gradient accumulation steps: 2
- Number of GPUs: 1
Prediction type: epsilon
Rescaled betas zero SNR: False
Optimizer: AdamW, stochastic bf16
Precision: Pure BF16
Xformers: Enabled
LoRA Rank: 64
LoRA Alpha: 16
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

little-tinies

Repeats: 18
Total number of images: 78
Total number of aspect buckets: 1
Resolution: 1.0 megapixels
Cropped: False
Crop style: None
Crop aspect: None

Inference

python

import torch
from diffusers import DiffusionPipeline

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

prompt = "ethnographic photography of teddy bear at a picnic"
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=50,
    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=7.5,
    guidance_rescale=0.7,
).images[0]
image.save("output.png", format="PNG")

inference: true widget:

text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./image0.png
text: 'ethnographic photography of teddy bear at a picnic' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./image1.png
text: 'a robot walking on the street,surrounded by a group of girls' parameters: negative_prompt: 'blurry, cropped, ugly'

pzc163/flux-lora-littletinies

flux-lora-littletinies

Validation settings

Training settings

Datasets

little-tinies

Inference

pzc163/
flux-lora-littletinies