metadata

license: creativeml-openrail-m
base_model: black-forest-labs/FLUX.1-dev
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - lora
  - template:sd-lora
inference: true

baahubaliflux

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

The main validation prompt used during training was:

full body portrait of baahubali standing on the edge of a cliff looking at the camera

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: 5
Training steps: 100
Learning rate: 8e-07
Effective batch size: 1
- Micro-batch size: 1
- 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

baahubaliflux

Repeats: 0
Total number of images: 17
Total number of aspect buckets: 1
Resolution: 1.0 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 = 'baahubaliflux'
pipeline = DiffusionPipeline.from_pretrained(model_id)\pipeline.load_lora_weights(adapter_id)

prompt = "full body portrait of baahubali standing on the edge of a cliff looking at the camera"
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")