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flux-lora-littletinies

Text-to-Image
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flux
lora
template:sd-lora
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metadata
tags:
  - text-to-image
  - flux
  - lora
  - diffusers
  - template:sd-lora
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null

flux-lora-littletinies

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

The main validation prompt used during training was:

ethnographic photography of teddy bear at a picnic

Validation settings

  • CFG: 7.5
  • CFG Rescale: 0.7
  • Steps: 50
  • Sampler: None
  • Seed: 42
  • Resolution: 1024

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

You can find some example images in the following gallery:

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

Training settings

  • 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 

import torch
from diffusers import DiffusionPipeline

model_id = '/black-forest-labs/FLUX.1-dev'
adapter_id = '/pzc163/flux-lora-littletinies'
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'